Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

Journals

1. Thomas Homescu A. Leveraging Big Data for Personalized Treatment of Anxiety and Depression: Review and Possible Future Directions. SSRN Electronic Journal 2018 View
2. Park S, Kim Y, Lee J, Yoo S, Kim C. Ethical challenges regarding artificial intelligence in medicine from the perspective of scientific editing and peer review. Science Editing 2019;6(2):91 View
3. Kim D, Jang H, Kim K, Shin Y, Park S. Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers. Korean Journal of Radiology 2019;20(3):405 View
4. Khan O, Badhiwala J, Wilson J, Jiang F, Martin A, Fehlings M. Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions. Neurospine 2019;16(4):678 View
5. Coleman B, Fodeh S, Lisi A, Goulet J, Corcoran K, Bathulapalli H, Brandt C. Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization. Chiropractic & Manual Therapies 2020;28(1) View
6. Tandon N, Tandon R. Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype. Schizophrenia Research 2019;214:70 View
7. Mathis M, Engoren M, Joo H, Maile M, Aaronson K, Burns M, Sjoding M, Douville N, Janda A, Hu Y, Najarian K, Kheterpal S. Early Detection of Heart Failure With Reduced Ejection Fraction Using Perioperative Data Among Noncardiac Surgical Patients: A Machine-Learning Approach. Anesthesia & Analgesia 2020;130(5):1188 View
8. Tandon N, Tandon R. Machine learning in psychiatry- standards and guidelines. Asian Journal of Psychiatry 2019;44:A1 View
9. Karhade A, Shah A, Bono C, Ferrone M, Nelson S, Schoenfeld A, Harris M, Schwab J. Development of machine learning algorithms for prediction of mortality in spinal epidural abscess. The Spine Journal 2019;19(12):1950 View
10. Bhambhvani H, Zamora A, Shkolyar E, Prado K, Greenberg D, Kasman A, Liao J, Shah S, Srinivas S, Skinner E, Shah J. Development of robust artificial neural networks for prediction of 5-year survival in bladder cancer. Urologic Oncology: Seminars and Original Investigations 2021;39(3):193.e7 View
11. Calanna P, Lauriola M, Saggino A, Tommasi M, Furlan S. Using a supervised machine learning algorithm for detecting faking good in a personality self‐report. International Journal of Selection and Assessment 2020;28(2):176 View
12. Mongan J, Moy L, Kahn C. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiology: Artificial Intelligence 2020;2(2):e200029 View
13. Kim D, Jang H, Ko Y, Son J, Kim P, Kim S, Lim J, Park S, Hong J. Inconsistency in the use of the term “validation” in studies reporting the performance of deep learning algorithms in providing diagnosis from medical imaging. PLOS ONE 2020;15(9):e0238908 View
14. Verrusio W, Renzi A, Dellepiane U, Renzi S, Zaccone M, Gueli N, Cacciafesta M. A new tool for the evaluation of the rehabilitation outcomes in older persons: a machine learning model to predict functional status 1 year ahead. European Geriatric Medicine 2018;9(5):651 View
15. Molina-García D, Vera-Ramírez L, Pérez-Beteta J, Arana E, Pérez-García V. Prognostic models based on imaging findings in glioblastoma: Human versus Machine. Scientific Reports 2019;9(1) View
16. Kim H, Lee S, Lee S, Hong S, Kang H, Kim N. Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone. JMIR mHealth and uHealth 2019;7(10):e14149 View
17. Lonsdale H, Jalali A, Ahumada L, Matava C. Machine Learning and Artificial Intelligence in Pediatric Research: Current State, Future Prospects, and Examples in Perioperative and Critical Care. The Journal of Pediatrics 2020;221:S3 View
18. Panchagnula U, Shanmugam M, Rao B. Digital future in perioperative medicine: Are we there yet?. Journal of Anaesthesiology Clinical Pharmacology 2019;35(3):292 View
19. Behrend M, Basáñez M, Hamley J, Porco T, Stolk W, Walker M, de Vlas S, Blanton J. Modelling for policy: The five principles of the Neglected Tropical Diseases Modelling Consortium. PLOS Neglected Tropical Diseases 2020;14(4):e0008033 View
20. Parisi L, RaviChandran N, Manaog M. A novel hybrid algorithm for aiding prediction of prognosis in patients with hepatitis. Neural Computing and Applications 2020;32(8):3839 View
21. Liu X, Faes L, Kale A, Wagner S, Fu D, Bruynseels A, Mahendiran T, Moraes G, Shamdas M, Kern C, Ledsam J, Schmid M, Balaskas K, Topol E, Bachmann L, Keane P, Denniston A. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet Digital Health 2019;1(6):e271 View
22. Buchlak Q, Esmaili N, Leveque J, Bennett C, Piccardi M, Farrokhi F. Ethical thinking machines in surgery and the requirement for clinical leadership. The American Journal of Surgery 2020;220(5):1372 View
23. Bey R, Goussault R, Grolleau F, Benchoufi M, Porcher R. Fold-stratified cross-validation for unbiased and privacy-preserving federated learning. Journal of the American Medical Informatics Association 2020;27(8):1244 View
24. De la Garza-Salazar F, Romero-Ibarguengoitia M, Rodriguez-Diaz E, Azpiri-Lopez J, González-Cantu A, Ab Rahman N. Improvement of electrocardiographic diagnostic accuracy of left ventricular hypertrophy using a Machine Learning approach. PLOS ONE 2020;15(5):e0232657 View
25. Panwar S, Joshi S, Gupta A, Agarwal P. Automated Epilepsy Diagnosis Using EEG With Test Set Evaluation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2019;27(6):1106 View
26. Dihge L, Ohlsson M, Edén P, Bendahl P, Rydén L. Artificial neural network models to predict nodal status in clinically node-negative breast cancer. BMC Cancer 2019;19(1) View
27. Klimuntowski M, Alam M, Singh G, Howlader M. Electrochemical Sensing of Cannabinoids in Biofluids: A Noninvasive Tool for Drug Detection. ACS Sensors 2020;5(3):620 View
28. Triantafyllidis A, Tsanas A. Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature. Journal of Medical Internet Research 2019;21(4):e12286 View
29. Burns M, Mathis M, Vandervest J, Tan X, Lu B, Colquhoun D, Shah N, Kheterpal S, Saager L. Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning. Anesthesiology 2020;132(4):738 View
30. Karhade A, Cha T, Fogel H, Hershman S, Tobert D, Schoenfeld A, Bono C, Schwab J. Predicting prolonged opioid prescriptions in opioid-naïve lumbar spine surgery patients. The Spine Journal 2020;20(6):888 View
31. Hatib F, Jian Z, Buddi S, Lee C, Settels J, Sibert K, Rinehart J, Cannesson M. Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis. Anesthesiology 2018;129(4):663 View
32. Cherifa M, Blet A, Chambaz A, Gayat E, Resche-Rigon M, Pirracchio R. Prediction of an Acute Hypotensive Episode During an ICU Hospitalization With a Super Learner Machine-Learning Algorithm. Anesthesia & Analgesia 2020;130(5):1157 View
33. Zhao R, Zhang W, Zhou L, Chen Y. Building a predictive model for successful vaginal delivery in nulliparas with term cephalic singleton pregnancies using decision tree analysis. Journal of Obstetrics and Gynaecology Research 2019;45(8):1536 View
34. Schultebraucks K, Qian M, Abu-Amara D, Dean K, Laska E, Siegel C, Gautam A, Guffanti G, Hammamieh R, Misganaw B, Mellon S, Wolkowitz O, Blessing E, Etkin A, Ressler K, Doyle F, Jett M, Marmar C. Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors. Molecular Psychiatry 2021;26(9):5011 View
35. Higaki A, Uetani T, Ikeda S, Yamaguchi O. Co-authorship network analysis in cardiovascular research utilizing machine learning (2009–2019). International Journal of Medical Informatics 2020;143:104274 View
36. Kakarmath S, Golas S, Felsted J, Kvedar J, Jethwani K, Agboola S. Validating a Machine Learning Algorithm to Predict 30-Day Re-Admissions in Patients With Heart Failure: Protocol for a Prospective Cohort Study. JMIR Research Protocols 2018;7(9):e176 View
37. Kendale S, Kulkarni P, Rosenberg A, Wang J. Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension. Anesthesiology 2018;129(4):675 View
38. Tosado J, Zdilar L, Elhalawani H, Elgohari B, Vock D, Marai G, Fuller C, Mohamed A, Canahuate G. Clustering of Largely Right-Censored Oropharyngeal Head and Neck Cancer Patients for Discriminative Groupings to Improve Outcome Prediction. Scientific Reports 2020;10(1) View
39. Bracher-Smith M, Crawford K, Escott-Price V. Machine learning for genetic prediction of psychiatric disorders: a systematic review. Molecular Psychiatry 2021;26(1):70 View
40. Wu G, Woodruff H, Chatterjee A, Lambin P. Reply to “COVID-19 prediction models should adhere to methodological and reporting standards”. European Respiratory Journal 2020;56(3):2002918 View
41. Doupe P, Faghmous J, Basu S. Machine Learning for Health Services Researchers. Value in Health 2019;22(7):808 View
42. Spence J, Mazer C. The Future Directions of Research in Cardiac Anesthesiology. Anesthesiology Clinics 2019;37(4):801 View
43. Kopitar L, Kocbek P, Cilar L, Sheikh A, Stiglic G. Early detection of type 2 diabetes mellitus using machine learning-based prediction models. Scientific Reports 2020;10(1) View
44. Chancellor S, De Choudhury M. Methods in predictive techniques for mental health status on social media: a critical review. npj Digital Medicine 2020;3(1) View
45. de Keijzer I, Vos J, Scheeren T. Hypotension Prediction Index: from proof-of-concept to proof-of-feasibility. Journal of Clinical Monitoring and Computing 2020;34(6):1135 View
46. Mathis M, Kheterpal S, Najarian K. Artificial Intelligence for Anesthesia: What the Practicing Clinician Needs to Know. Anesthesiology 2018;129(4):619 View
47. Koprowski R, Foster K. Machine learning and medicine: book review and commentary. BioMedical Engineering OnLine 2018;17(1) View
48. Grados D, García S, Schrevens E. Assessing the potato yield gap in the Peruvian Central Andes. Agricultural Systems 2020;181:102817 View
49. Curchoe C, Bormann C. Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018. Journal of Assisted Reproduction and Genetics 2019;36(4):591 View
50. Danielsen A, Fenger M, Østergaard S, Nielbo K, Mors O. Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data. Acta Psychiatrica Scandinavica 2019;140(2):147 View
51. Ming C, Viassolo V, Probst-Hensch N, Chappuis P, Dinov I, Katapodi M. Letter to the editor: Response to Giardiello D, Antoniou AC, Mariani L, Easton DF, Steyerberg EW. Breast Cancer Research 2020;22(1) View
52. Chi T, Zhu H, Zhang M. Risk factors associated with nonsteroidal anti-inflammatory drugs (NSAIDs)-induced gastrointestinal bleeding resulting on people over 60 years old in Beijing. Medicine 2018;97(18):e0665 View
53. Karhade A, Ogink P, Thio Q, Cha T, Gormley W, Hershman S, Smith T, Mao J, Schoenfeld A, Bono C, Schwab J. Development of machine learning algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation. The Spine Journal 2019;19(11):1764 View
54. Lee C, Hofer I, Gabel E, Baldi P, Cannesson M. Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality. Anesthesiology 2018;129(4):649 View
55. Rahimian F, Salimi-Khorshidi G, Payberah A, Tran J, Ayala Solares R, Raimondi F, Nazarzadeh M, Canoy D, Rahimi K, Sheikh A. Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records. PLOS Medicine 2018;15(11):e1002695 View
56. Zhang X, Bellolio M, Medrano-Gracia P, Werys K, Yang S, Mahajan P. Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department. BMC Medical Informatics and Decision Making 2019;19(1) View
57. Jethanandani A, Lin T, Volpe S, Elhalawani H, Mohamed A, Yang P, Fuller C. Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review. Frontiers in Oncology 2018;8 View
58. Smith M, Dietrich B, Bai E, Bockholt H. Vocal pattern detection of depression among older adults. International Journal of Mental Health Nursing 2020;29(3):440 View
59. Shirole U, Joshi M, Bagul P. Cardiac, diabetic and normal subjects classification using decision tree and result confirmation through orthostatic stress index. Informatics in Medicine Unlocked 2019;17:100252 View
60. Zhang B, Yu K, Ning Z, Wang K, Dong Y, Liu X, Liu S, Wang J, Zhu C, Yu Q, Duan Y, Lv S, Zhang X, Chen Y, Wang X, Shen J, Peng J, Chen Q, Zhang Y, Zhang X, Zhang S. Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study. Bone 2020;140:115561 View
61. Hofer I, Lee C, Gabel E, Baldi P, Cannesson M. Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set. npj Digital Medicine 2020;3(1) View
62. Sapir-Pichhadze R, Kaplan B. Seeing the Forest for the Trees: Random Forest Models for Predicting Survival in Kidney Transplant Recipients. Transplantation 2020;104(5):905 View
63. Schultebraucks K, Galatzer‐Levy I. Machine Learning for Prediction of Posttraumatic Stress and Resilience Following Trauma: An Overview of Basic Concepts and Recent Advances. Journal of Traumatic Stress 2019;32(2):215 View
64. op den Buijs J, Simons M, Golas S, Fischer N, Felsted J, Schertzer L, Agboola S, Kvedar J, Jethwani K. Predictive Modeling of 30-Day Emergency Hospital Transport of Patients Using a Personal Emergency Response System: Prognostic Retrospective Study. JMIR Medical Informatics 2018;6(4):e49 View
65. Speiser J, Callahan K, Houston D, Fanning J, Gill T, Guralnik J, Newman A, Pahor M, Rejeski W, Miller M, Melzer D. Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults. The Journals of Gerontology: Series A 2021;76(4):647 View
66. Neubauer N, Liu L. Development and validation of a conceptual model and strategy adoption guidelines for persons with dementia at risk of getting lost. Dementia 2021;20(2):534 View
67. Groezinger M, Huppert D, Strobl R, Grill E. Development and validation of a classification algorithm to diagnose and differentiate spontaneous episodic vertigo syndromes: results from the DizzyReg patient registry. Journal of Neurology 2020;267(S1):160 View
68. Ershoff B, Lee C, Wray C, Agopian V, Urban G, Baldi P, Cannesson M. Training and Validation of Deep Neural Networks for the Prediction of 90-Day Post-Liver Transplant Mortality Using UNOS Registry Data. Transplantation Proceedings 2020;52(1):246 View
69. Karhade A, Ogink P, Thio Q, Broekman M, Cha T, Hershman S, Mao J, Peul W, Schoenfeld A, Bono C, Schwab J. Machine learning for prediction of sustained opioid prescription after anterior cervical discectomy and fusion. The Spine Journal 2019;19(6):976 View
70. Park S, Do K, Choi J, Sim J, Yang D, Eo H, Woo H, Lee J, Jung S, Oh J. Principles for evaluating the clinical implementation of novel digital healthcare devices. Journal of the Korean Medical Association 2018;61(12):765 View
71. Wei W, Wang K, Liu Z, Tian K, Wang L, Du J, Ma J, Wang S, Li L, Zhao R, Cui L, Wu Z, Tian J. Radiomic signature: A novel magnetic resonance imaging-based prognostic biomarker in patients with skull base chordoma. Radiotherapy and Oncology 2019;141:239 View
72. Anderson A, Grazal C, Balazs G, Potter B, Dickens J, Forsberg J. Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?. Clinical Orthopaedics & Related Research 2020;478(7):00 View
73. Bahl M. Artificial Intelligence: A Primer for Breast Imaging Radiologists. Journal of Breast Imaging 2020;2(4):304 View
74. Thomsen K, Iversen L, Titlestad T, Winther O. Systematic review of machine learning for diagnosis and prognosis in dermatology. Journal of Dermatological Treatment 2020;31(5):496 View
75. Christodoulou E, Ma J, Collins G, Steyerberg E, Verbakel J, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. Journal of Clinical Epidemiology 2019;110:12 View
76. Karhade A, Thio Q, Ogink P, Bono C, Ferrone M, Oh K, Saylor P, Schoenfeld A, Shin J, Harris M, Schwab J. Predicting 90-Day and 1-Year Mortality in Spinal Metastatic Disease: Development and Internal Validation. Neurosurgery 2019;85(4):E671 View
77. Moon S, Hwang J, Kana R, Torous J, Kim J. Accuracy of Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder: Systematic Review and Meta-Analysis of Brain Magnetic Resonance Imaging Studies. JMIR Mental Health 2019;6(12):e14108 View
78. Futoma J, Simons M, Panch T, Doshi-Velez F, Celi L. The myth of generalisability in clinical research and machine learning in health care. The Lancet Digital Health 2020;2(9):e489 View
79. Young C, Luo W, Gastin P, Tran J, Dwyer D. The relationship between match performance indicators and outcome in Australian Football. Journal of Science and Medicine in Sport 2019;22(4):467 View
80. Park S, Kressel H. Connecting Technological Innovation in Artificial Intelligence to Real-world Medical Practice through Rigorous Clinical Validation: What Peer-reviewed Medical Journals Could Do. Journal of Korean Medical Science 2018;33(22) View
81. Saugel B, Kouz K, Hoppe P, Maheshwari K, Scheeren T. Predicting hypotension in perioperative and intensive care medicine. Best Practice & Research Clinical Anaesthesiology 2019;33(2):189 View
82. Ortiz A, Costa C, Silva R, Biazevic M, Michel-Crosato E. Sex estimation: Anatomical references on panoramic radiographs using Machine Learning. Forensic Imaging 2020;20:200356 View
83. Weenk M, van Goor H, Frietman B, Engelen L, van Laarhoven C, Smit J, Bredie S, van de Belt T. Continuous Monitoring of Vital Signs Using Wearable Devices on the General Ward: Pilot Study. JMIR mHealth and uHealth 2017;5(7):e91 View
84. Yusuf M, Atal I, Li J, Smith P, Ravaud P, Fergie M, Callaghan M, Selfe J. Reporting quality of studies using machine learning models for medical diagnosis: a systematic review. BMJ Open 2020;10(3):e034568 View
85. Khan O, Badhiwala J, Grasso G, Fehlings M. Use of Machine Learning and Artificial Intelligence to Drive Personalized Medicine Approaches for Spine Care. World Neurosurgery 2020;140:512 View
86. Pickhardt P, Graffy P, Zea R, Lee S, Liu J, Sandfort V, Summers R. Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study. The Lancet Digital Health 2020;2(4):e192 View
87. Unberath P, Prokosch H, Gründner J, Erpenbeck M, Maier C, Christoph J. EHR-Independent Predictive Decision Support Architecture Based on OMOP. Applied Clinical Informatics 2020;11(03):399 View
88. Weenk M, Bredie S, Koeneman M, Hesselink G, van Goor H, van de Belt T. Continuous Monitoring of Vital Signs in the General Ward Using Wearable Devices: Randomized Controlled Trial. Journal of Medical Internet Research 2020;22(6):e15471 View
89. Sullivan S, Hewner S, Chandola V, Westra B. Mortality Risk in Homebound Older Adults Predicted From Routinely Collected Nursing Data. Nursing Research 2019;68(2):156 View
90. Khan O, Badhiwala J, Witiw C, Wilson J, Fehlings M. Machine learning algorithms for prediction of health-related quality-of-life after surgery for mild degenerative cervical myelopathy. The Spine Journal 2021;21(10):1659 View
91. Montagnon E, Cerny M, Cadrin-Chênevert A, Hamilton V, Derennes T, Ilinca A, Vandenbroucke-Menu F, Turcotte S, Kadoury S, Tang A. Deep learning workflow in radiology: a primer. Insights into Imaging 2020;11(1) View
92. Salagre E, Dodd S, Aedo A, Rosa A, Amoretti S, Pinzon J, Reinares M, Berk M, Kapczinski F, Vieta E, Grande I. Toward Precision Psychiatry in Bipolar Disorder: Staging 2.0. Frontiers in Psychiatry 2018;9 View
93. Sheyn D, Ju M, Zhang S, Anyaeche C, Hijaz A, Mangel J, Mahajan S, Conroy B, El-Nashar S, Ray S. Development and Validation of a Machine Learning Algorithm for Predicting Response to Anticholinergic Medications for Overactive Bladder Syndrome. Obstetrics & Gynecology 2019;134(5):946 View
94. Karhade A, Ogink P, Thio Q, Broekman M, Cha T, Gormley W, Hershman S, Peul W, Bono C, Schwab J. Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders. Neurosurgical Focus 2018;45(5):E6 View
95. Ordovas K, Seo Y. Artificial Intelligence Pipeline for Risk Prediction in Cardiovascular Imaging. Circulation: Cardiovascular Imaging 2020;13(2) View
96. Fritz B, Cui Z, Zhang M, He Y, Chen Y, Kronzer A, Ben Abdallah A, King C, Avidan M. Deep-learning model for predicting 30-day postoperative mortality. British Journal of Anaesthesia 2019;123(5):688 View
97. Flechet M, Falini S, Bonetti C, Güiza F, Schetz M, Van den Berghe G, Meyfroidt G. Machine learning versus physicians’ prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor. Critical Care 2019;23(1) View
98. Roth J, Radevski G, Marzolini C, Rauch A, Günthard H, Kouyos R, Fux C, Scherrer A, Calmy A, Cavassini M, Kahlert C, Bernasconi E, Bogojeska J, Battegay M. Cohort-Derived Machine Learning Models for Individual Prediction of Chronic Kidney Disease in People Living With Human Immunodeficiency Virus: A Prospective Multicenter Cohort Study. The Journal of Infectious Diseases 2021;224(7):1198 View
99. López Seguí F, Ander Egg Aguilar R, de Maeztu G, García-Altés A, García Cuyàs F, Walsh S, Sagarra Castro M, Vidal-Alaball J. Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning. International Journal of Environmental Research and Public Health 2020;17(3):1093 View
100. Sufriyana H, Wu Y, Su E. Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia. EBioMedicine 2020;54:102710 View
101. Weenk M, Koeneman M, van de Belt T, Engelen L, van Goor H, Bredie S. Wireless and continuous monitoring of vital signs in patients at the general ward. Resuscitation 2019;136:47 View
102. Hendrickx L, Sobol G, Langerhuizen D, Bulstra A, Hreha J, Sprague S, Sirkin M, Ring D, Kerkhoffs G, Jaarsma R, Doornberg J. A Machine Learning Algorithm to Predict the Probability of (Occult) Posterior Malleolar Fractures Associated With Tibial Shaft Fractures to Guide “Malleolus First” Fixation. Journal of Orthopaedic Trauma 2020;34(3):131 View
103. Zhong J, Hu Y, Si L, Jia G, Xing Y, Zhang H, Yao W. A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation. European Radiology 2021;31(3):1526 View
104. Fatima N, Zheng H, Massaad E, Hadzipasic M, Shankar G, Shin J. Development and Validation of Machine Learning Algorithms for Predicting Adverse Events After Surgery for Lumbar Degenerative Spondylolisthesis. World Neurosurgery 2020;140:627 View
105. Sufriyana H, Wu Y, Su E. Prediction of Preeclampsia and Intrauterine Growth Restriction: Development of Machine Learning Models on a Prospective Cohort. JMIR Medical Informatics 2020;8(5):e15411 View
106. Fransquet P, Ryan J. Micro RNA as a potential blood-based epigenetic biomarker for Alzheimer's disease. Clinical Biochemistry 2018;58:5 View
107. Farran B, AlWotayan R, Alkandari H, Al-Abdulrazzaq D, Channanath A, Thanaraj T. Use of Non-invasive Parameters and Machine-Learning Algorithms for Predicting Future Risk of Type 2 Diabetes: A Retrospective Cohort Study of Health Data From Kuwait. Frontiers in Endocrinology 2019;10 View
108. Skrede O, De Raedt S, Kleppe A, Hveem T, Liestøl K, Maddison J, Askautrud H, Pradhan M, Nesheim J, Albregtsen F, Farstad I, Domingo E, Church D, Nesbakken A, Shepherd N, Tomlinson I, Kerr R, Novelli M, Kerr D, Danielsen H. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. The Lancet 2020;395(10221):350 View
109. Schultebraucks K, Shalev A, Michopoulos V, Grudzen C, Shin S, Stevens J, Maples-Keller J, Jovanovic T, Bonanno G, Rothbaum B, Marmar C, Nemeroff C, Ressler K, Galatzer-Levy I. A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor. Nature Medicine 2020;26(7):1084 View
110. Silva K, Lee W, Forbes A, Demmer R, Barton C, Enticott J. Use and performance of machine learning models for type 2 diabetes prediction in community settings: A systematic review and meta-analysis. International Journal of Medical Informatics 2020;143:104268 View
111. Zarinabad N, Meeus E, Manias K, Foster K, Peet A. Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis. JMIR Medical Informatics 2018;6(2):e30 View
112. Thomsen K, Christensen A, Iversen L, Lomholt H, Winther O. Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases. Frontiers in Medicine 2020;7 View
113. El Naqa I, Ruan D, Valdes G, Dekker A, McNutt T, Ge Y, Wu Q, Oh J, Thor M, Smith W, Rao A, Fuller C, Xiao Y, Manion F, Schipper M, Mayo C, Moran J, Ten Haken R. Machine learning and modeling: Data, validation, communication challenges. Medical Physics 2018;45(10) View
114. Carson N, Mullin B, Sanchez M, Lu F, Yang K, Menezes M, Cook B, Fiorini N. Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records. PLOS ONE 2019;14(2):e0211116 View
115. Karhade A, Schwab J, Bedair H. Development of Machine Learning Algorithms for Prediction of Sustained Postoperative Opioid Prescriptions After Total Hip Arthroplasty. The Journal of Arthroplasty 2019;34(10):2272 View
116. de Barros A, Silva A, Zibordi M, Spagnolo J, Corrêa R, Belli C, de Camargo M. Equine simplified acute physiology score: Personalised medicine for the equine emergency patient. Veterinary Record 2021;189(5) View
117. Sufriyana H, Husnayain A, Chen Y, Kuo C, Singh O, Yeh T, Wu Y, Su E. Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis. JMIR Medical Informatics 2020;8(11):e16503 View
118. Feng C, Zhou S, Qu Y, Wang Q, Bao S, Li Y, Yang T, Si W. Overview of Artificial Intelligence Applications in Chinese Medicine Therapy. Evidence-Based Complementary and Alternative Medicine 2021;2021:1 View
119. Lu Y, Khazi Z, Agarwalla A, Forsythe B, Taunton M. Development of a Machine Learning Algorithm to Predict Nonroutine Discharge Following Unicompartmental Knee Arthroplasty. The Journal of Arthroplasty 2021;36(5):1568 View
120. ZhuParris A, Kruizinga M, Gent M, Dessing E, Exadaktylos V, Doll R, Stuurman F, Driessen G, Cohen A. Development and Technical Validation of a Smartphone-Based Cry Detection Algorithm. Frontiers in Pediatrics 2021;9 View
121. Zhao J, Zhang W, Fan C, Zhang J, Yuan F, Liu S, Li F, Song B. Development and validation of preoperative magnetic resonance imaging-based survival predictive nomograms for patients with perihilar cholangiocarcinoma after radical resection: A pilot study. European Journal of Radiology 2021;138:109631 View
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123. Lu Y, Forlenza E, Wilbur R, Lavoie-Gagne O, Fu M, Yanke A, Cole B, Verma N, Forsythe B. Machine-learning model successfully predicts patients at risk for prolonged postoperative opioid use following elective knee arthroscopy. Knee Surgery, Sports Traumatology, Arthroscopy 2022;30(3):762 View
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448. Chen Y, Tozer D, Liu W, Peake E, Markus H. Prediction of response to thrombolysis in acute stroke using neural network analysis of CT perfusion imaging. European Stroke Journal 2023;8(3):629 View
449. Johnson Q, Jabal M, Arguello A, Lu Y, Jurgensmeier K, Levy B, Camp C, Krych A. Machine learning can accurately predict risk factors for all‐cause reoperation after ACLR: creating a clinical tool to improve patient counseling and outcomes. Knee Surgery, Sports Traumatology, Arthroscopy 2023;31(10):4099 View
450. Sasaki S, Katsuki M, Kawahara J, Yamagishi C, Koh A, Kawamura S, Kashiwagi K, Ikeda T, Goto T, Kaneko K, Wada N, Yamagishi F. Developing an Artificial Intelligence-Based Pediatric and Adolescent Migraine Diagnostic Model. Cureus 2023 View
451. Ahmed R, Al Shehhi A, Hassan B, Werghi N, Seghier M. An appraisal of the performance of AI tools for chronic stroke lesion segmentation. Computers in Biology and Medicine 2023;164:107302 View
452. Zhang Y, Li X, Liu Y, Li A, Yang X, Tang X. A Multilabel Text Classifier of Cancer Literature at the Publication Level: Methods Study of Medical Text Classification. JMIR Medical Informatics 2023;11:e44892 View
453. Ma M, Wan X, Chen Y, Lu Z, Guo D, Kong H, Pan B, Zhang H, Chen D, Xu D, Sun D, Lang H, Zhou C, Li T, Cao C. A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study. Journal of Translational Medicine 2023;21(1) View
454. Shaikh H, Botros M, Ramirez G, Thirukumaran C, Ricciardi B, Myers T. Comparable performance of machine learning algorithms in predicting readmission and complications following total joint arthroplasty with external validation. Arthroplasty 2023;5(1) View
455. Nikolova-Simons M, Keldermann R, Peters Y, Compagner W, Montenij L, de Jong Y, Bouwman R. Predictive analytics for cardio-thoracic surgery duration as a stepstone towards data-driven capacity management. npj Digital Medicine 2023;6(1) View
456. Oikonomou E, Khera R. Machine learning in precision diabetes care and cardiovascular risk prediction. Cardiovascular Diabetology 2023;22(1) View
457. Oeding J, Lu Y, Pareek A, Marigi E, Okoroha K, Barlow J, Camp C, Sanchez-Sotelo J. Understanding risk for early dislocation resulting in reoperation within 90 days of reverse total shoulder arthroplasty: extreme rare event detection through cost-sensitive machine learning. Journal of Shoulder and Elbow Surgery 2023;32(9):e437 View
458. Karabacak M, Margetis K. Development of personalized machine learning-based prediction models for short-term postoperative outcomes in patients undergoing cervical laminoplasty. European Spine Journal 2023;32(11):3857 View
459. Gonzalez R, Nejat P, Saha A, Campbell C, Norgan A, Lokker C. Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review. Journal of Pathology Informatics 2024;15:100348 View
460. Schönnagel L, Caffard T, Vu-Han T, Zhu J, Nathoo I, Finos K, Camino-Willhuber G, Tani S, Guven A, Haffer H, Muellner M, Arzani A, Chiapparelli E, Amoroso K, Shue J, Duculan R, Pumberger M, Zippelius T, Sama A, Cammisa F, Girardi F, Mancuso C, Hughes A. Predicting postoperative outcomes in lumbar spinal fusion: development of a machine learning model. The Spine Journal 2024;24(2):239 View
461. Canahuate G, Wentzel A, Mohamed A, van Dijk L, Vock D, Elgohari B, Elhalawani H, Fuller C, Marai G. Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas. Oral Oncology 2023;144:106460 View
462. Taeidi E, Ranjbar A, Montazeri F, Mehrnoush V, Darsareh F. Machine Learning-Based Approach to Predict Intrauterine Growth Restriction. Cureus 2023 View
463. Kendale S, Bishara A, Burns M, Solomon S, Corriere M, Mathis M. Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study. JMIR AI 2023;2:e44909 View
464. Bae K, Jeon Y, Hwangbo Y, Yoo C, Han N, Feng M. Data-Efficient Computational Pathology Platform for Faster and Cheaper Breast Cancer Subtype Identifications: Development of a Deep Learning Model. JMIR Cancer 2023;9:e45547 View
465. Trinh V, Zhang S, Kovoor J, Gupta A, Chan W, Gilbert T, Bacchi S. The use of natural language processing in detecting and predicting falls within the healthcare setting: a systematic review. International Journal for Quality in Health Care 2023;35(4) View
466. Liu J, Xiao H, Fan J, Hu W, Yang Y, Dong P, Xing L, Cai J. An overview of artificial intelligence in medical physics and radiation oncology. Journal of the National Cancer Center 2023;3(3):211 View
467. Katsuki M, Matsumori Y, Kawamura S, Kashiwagi K, Koh A, Tachikawa S, Yamagishi F. Developing an artificial intelligence–based diagnostic model of headaches from a dataset of clinic patients' records. Headache: The Journal of Head and Face Pain 2023;63(8):1097 View
468. Zhang J, Ma G, Peng S, Hou J, Xu R, Luo L, Hu J, Yao N, Wang J, Huang X. Risk Factors and Predictive Models for Peripherally Inserted Central Catheter Unplanned Extubation in Patients With Cancer: Prospective, Machine Learning Study. Journal of Medical Internet Research 2023;25:e49016 View
469. van der Weegen W, Warren T, Das D, Agricola R, Timmers T, Siebelt M. Operative or Nonoperative Treatment is Predicted Accurately for Patients Who Have Hip Complaints Consulting an Orthopedic Surgeon Using Machine Learning Algorithms Trained With Prehospital Acquired History-Taking Data. The Journal of Arthroplasty 2024;39(5):1173 View
470. Jia T, Xu K, Bai Y, Lv M, Shan L, Li W, Zhang X, Li Z, Wang Z, Zhao X, Li M, Zhang Y. Machine-learning predictions for acute kidney injuries after coronary artery bypass grafting: a real-life muticenter retrospective cohort study. BMC Medical Informatics and Decision Making 2023;23(1) View
471. Schönnagel L, Tani S, Vu-Han T, Zhu J, Camino-Willhuber G, Dodo Y, Caffard T, Chiapparelli E, Oezel L, Shue J, Zelenty W, Lebl D, Cammisa F, Girardi F, Sokunbi G, Hughes A, Sama A. Predicting conversion of ambulatory ACDF patients to inpatient: a machine learning approach. The Spine Journal 2024;24(4):563 View
472. Zhu G, Yuan A, Yu D, Zha A, Wu H, Guillot G. Machine learning to predict mortality for aneurysmal subarachnoid hemorrhage (aSAH) using a large nationwide EHR database. PLOS Digital Health 2023;2(12):e0000400 View
473. Laferrière-Langlois P, Imrie F, Geraldo M, Wingert T, Lahrichi N, van der Schaar M, Cannesson M. Novel Preoperative Risk Stratification Using Digital Phenotyping Applying a Scalable Machine-Learning Approach. Anesthesia & Analgesia 2023 View
474. Karabacak M, Jagtiani P, Shrivastava R, Margetis K. Personalized Prognosis with Machine Learning Models for Predicting In-Hospital Outcomes Following Intracranial Meningioma Resections. World Neurosurgery 2024;182:e210 View
475. Tessler I, Primov-Fever A, Soffer S, Anteby R, Gecel N, Livneh N, Alon E, Zimlichman E, Klang E. Deep learning in voice analysis for diagnosing vocal cord pathologies: a systematic review. European Archives of Oto-Rhino-Laryngology 2024;281(2):863 View
476. Karabacak M, Jagtiani P, Panov F, Margetis K. Predicting 30-Day Non-Seizure Outcomes Following Temporal Lobectomy with Personalized Machine Learning Models. World Neurosurgery 2024;183:e59 View
477. Çalışkan M, Tazaki K. AI/ML advances in non-small cell lung cancer biomarker discovery. Frontiers in Oncology 2023;13 View
478. Liu J, Chen J, Dong Y, Lou Y, Tian Y, Sun H, Jin Y, Li J, Qiu Y. Clinical Timing-Sequence Warning Models for Serious Bacterial Infections in Adults Based on Machine Learning: Retrospective Study. Journal of Medical Internet Research 2023;25:e45515 View
479. Karabacak M, Jagtiani P, Margetis K. The Predictive Abilities of Machine Learning Algorithms in Patients with Thoracolumbar Spinal Cord Injuries. World Neurosurgery 2024;182:e67 View
480. Biswas S, McMenemy L, Sarkar V, MacArthur J, Snowdon E, Tetlow C, George K. Natural language processing for the automated detection of intra-operative elements in lumbar spine surgery. Frontiers in Surgery 2023;10 View
481. Bednorz A, Mak J, Jylhävä J, Religa D. Use of Electronic Medical Records (EMR) in Gerontology: Benefits, Considerations and a Promising Future. Clinical Interventions in Aging 2023;Volume 18:2171 View
482. Cho H, She J, De Marchi D, El-Zaatari H, Barnes E, Kahkoska A, Kosorok M, Virkud A. Machine Learning and Health Science Research: Tutorial. Journal of Medical Internet Research 2024;26:e50890 View
483. Thiele K, Musi R, Prohaska T, Irrgeher J, Michelic S. AI assisted steel cleanness evaluation: Predicting the morphology of La-traced non-metallic inclusions using backscattered-electron images. Journal of Materials Research and Technology 2024;28:2247 View
484. Fong N, Feng J, Hubbard A, Dang L, Pirracchio R. IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study. Critical Care Explorations 2023;6(1):e1024 View
485. Guermazi A, Omoumi P, Tordjman M, Fritz J, Kijowski R, Regnard N, Carrino J, Kahn C, Knoll F, Rueckert D, Roemer F, Hayashi D. How AI May Transform Musculoskeletal Imaging. Radiology 2024;310(1) View
486. Zhang P, Wu L, Zou T, Zou Z, Tu J, Gong R, Kuang J. Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study. JMIR Formative Research 2024;8:e48487 View
487. Katsuki M, Matsumori Y, Ichihara T, Yamada Y, Kawamura S, Kashiwagi K, Koh A, Goto T, Kaneko K, Wada N, Yamagishi F. Treatment patterns and characteristics of headache in patients in Japan: A retrospective cross-sectional and longitudinal analysis of health insurance claims data. Cephalalgia 2024;44(1) View
488. Leaning I, Ikani N, Savage H, Leow A, Beckmann C, Ruhé H, Marquand A. From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression. Neuroscience & Biobehavioral Reviews 2024;158:105541 View
489. Kawashima A, Furukawa T, Imaizumi T, Morohashi A, Hara M, Yamada S, Hama M, Kawaguchi A, Sato K. Predictive Models for Palliative Care Needs of Advanced Cancer Patients Receiving Chemotherapy. Journal of Pain and Symptom Management 2024;67(4):306 View
490. Zhu J, Wu Y, Lin S, Duan S, Wang X, Fang Y. Identifying and predicting physical limitation and cognitive decline trajectory group of older adults in China: A data-driven machine learning analysis. Journal of Affective Disorders 2024;350:590 View
491. Baek S, Jeong Y, Kim Y, Kim J, Kim J, Kim E, Lim J, Kim J, Kim Z, Kim K, Chung M. Development and Validation of a Robust and Interpretable Early Triaging Support System for Patients Hospitalized With COVID-19: Predictive Algorithm Modeling and Interpretation Study. Journal of Medical Internet Research 2024;26:e52134 View
492. Kocak B, Akinci D’Antonoli T, Mercaldo N, Alberich-Bayarri A, Baessler B, Ambrosini I, Andreychenko A, Bakas S, Beets-Tan R, Bressem K, Buvat I, Cannella R, Cappellini L, Cavallo A, Chepelev L, Chu L, Demircioglu A, deSouza N, Dietzel M, Fanni S, Fedorov A, Fournier L, Giannini V, Girometti R, Groot Lipman K, Kalarakis G, Kelly B, Klontzas M, Koh D, Kotter E, Lee H, Maas M, Marti-Bonmati L, Müller H, Obuchowski N, Orlhac F, Papanikolaou N, Petrash E, Pfaehler E, Pinto dos Santos D, Ponsiglione A, Sabater S, Sardanelli F, Seeböck P, Sijtsema N, Stanzione A, Traverso A, Ugga L, Vallières M, van Dijk L, van Griethuysen J, van Hamersvelt R, van Ooijen P, Vernuccio F, Wang A, Williams S, Witowski J, Zhang Z, Zwanenburg A, Cuocolo R. METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII. Insights into Imaging 2024;15(1) View
493. O'Connor S, Vercell A, Wong D, Yorke J, Fallatah F, Cave L, Anny Chen L. The application and use of artificial intelligence in cancer nursing: A systematic review. European Journal of Oncology Nursing 2024;68:102510 View
494. Lin S, Lu W, Wang T, Wang Y, Leng X, Chi L, Jin P, Bian J. Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database. Renal Failure 2024;46(1) View
495. Kleinstreuer N, Hartung T. Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*. Archives of Toxicology 2024;98(3):735 View
496. Liu J, Shen W, Qin Q, Li J, Li X, Liu M, Hu W, Wu Y, Huang F. Prediction of positive pulmonary nodules based on machine learning algorithm combined with central carbon metabolism data. Journal of Cancer Research and Clinical Oncology 2024;150(2) View
497. Cai Y, Cai Y, Tang L, Wang Y, Gong M, Jing T, Li H, Li-Ling J, Hu W, Yin Z, Gong D, Zhang G. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Medicine 2024;22(1) View
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501. Singh H, Nim D, Randhawa A, Ahluwalia S. Integrating clinical pharmacology and artificial intelligence: potential benefits, challenges, and role of clinical pharmacologists. Expert Review of Clinical Pharmacology 2024;17(4):381 View
502. Karabacak M, Schupper A, Carr M, Bhimani A, Steinberger J, Margetis K. Development and internal validation of machine learning models for personalized survival predictions in spinal cord glioma patients. The Spine Journal 2024;24(6):1065 View
503. Choo S, Sartori D, Lee S, Yang H, Syed-Abdul S. Data-Driven Identification of Factors That Influence the Quality of Adverse Event Reports: 15-Year Interpretable Machine Learning and Time-Series Analyses of VigiBase and QUEST. JMIR Medical Informatics 2024;12:e49643 View
504. Hornung A, Rudisill S, McCormick J, Streepy J, Harkin W, Bryson N, Simcock X, Garrigues G. Preoperative factors predict prolonged length of stay, serious adverse complications, and readmission following operative intervention of proximal humerus fractures: a machine learning analysis of a national database. JSES International 2024;8(4):699 View
505. Yoon H, Kim H, Kim Y, Lee H, Kim B, Oh H, Park H, Lee H. Multicentre validation of a machine learning model for predicting respiratory failure after noncardiac surgery. British Journal of Anaesthesia 2024;132(6):1304 View
506. Ritter D, Denard P, Raiss P, Wijdicks C, Bachmaier S. Preoperative 3-dimensional computed tomography bone density measures provide objective bone quality classifications for stemless anatomic total shoulder arthroplasty. Journal of Shoulder and Elbow Surgery 2024;33(7):1503 View
507. Legouis D, Rinaldi A, Malpetti D, Arnoux G, Verissimo T, Faivre A, Mangili F, Rinaldi A, Ruinelli L, Pugin J, Moll S, Clivio L, Bolis M, de Seigneux S, Azzimonti L, Cippà P. A transfer learning framework to elucidate the clinical relevance of altered proximal tubule cell states in kidney disease. iScience 2024;27(3):109271 View
508. Salybekov A, Wolfien M, Hahn W, Hidaka S, Kobayashi S. Artificial Intelligence Reporting Guidelines’ Adherence in Nephrology for Improved Research and Clinical Outcomes. Biomedicines 2024;12(3):606 View
509. Oosterhoff J, de Hond A, Peters R, van Steenbergen L, Sorel J, Zijlstra W, Poolman R, Ring D, Jutte P, Kerkhoffs G, Putter H, Steyerberg E, Doornberg J. Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty. Clinical Orthopaedics & Related Research 2024;482(8):1472 View
510. Wang J, Tozzi F, Ashraf Ganjouei A, Romero-Hernandez F, Feng J, Calthorpe L, Castro M, Davis G, Withers J, Zhou C, Chaudhary Z, Adam M, Berrevoet F, Alseidi A, Rashidian N. Machine learning improves prediction of postoperative outcomes after gastrointestinal surgery: a systematic review and meta-analysis. Journal of Gastrointestinal Surgery 2024;28(6):956 View
511. Saluja S, Trivedi M, Saha A. Deep CNNs for glioma grading on conventional MRIs: Performance analysis, challenges, and future directions. Mathematical Biosciences and Engineering 2024;21(4):5250 View
512. Yang M, Chen H, Hu W, Mischi M, Shan C, Li J, Long X, Liu C. Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study. Journal of Medical Internet Research 2024;26:e50369 View
513. Wu Y, Xiang C, Wang Z, Fang Y. Interpretable prediction models for disability in older adults with hypertension: the Chinese Longitudinal Healthy Longevity and Happy Family Study. Psychogeriatrics 2024;24(3):645 View
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515. Verdonck M, Carvalho H, Fuchs-Buder T, Brull S, Poelaert J. Machine learning based analysis and detection of trend outliers for electromyographic neuromuscular monitoring. Journal of Clinical Monitoring and Computing 2024;38(5):1163 View
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518. Yurick S, Ray S, El-Nashar S, Brennand E, Kim-Fine S, Sanaee M, Regan S, Geoffrion R, Occhino J, Hijaz A, Sheyn D. Prediction of Postoperative Urinary Tract Infection Following Benign Gynecologic Surgery. International Urogynecology Journal 2024;35(5):1035 View
519. Zhang W, Wang J, Xie F, Wang X, Dong S, Luo N, Li F, Li Y. Development and validation of machine learning models to predict frailty risk for elderly. Journal of Advanced Nursing 2024;80(12):5064 View
520. Xiao Y, Chen Y, Huang R, Jiang F, Zhou J, Yang T. Interpretable machine learning in predicting drug-induced liver injury among tuberculosis patients: model development and validation study. BMC Medical Research Methodology 2024;24(1) View
521. Colangelo G, Ribo M, Montiel E, Dominguez D, Olivé-Gadea M, Muchada M, Garcia-Tornel Á, Requena M, Pagola J, Juega J, Rodriguez-Luna D, Rodriguez-Villatoro N, Rizzo F, Taborda B, Molina C, Rubiera M. PRERISK: A Personalized, Artificial Intelligence–Based and Statistically–Based Stroke Recurrence Predictor for Recurrent Stroke. Stroke 2024;55(5):1200 View
522. Mateussi N, Rogers M, Grimsley E, Read M, Parikh R, Pietrobon R, Kuo P. Clinical Applications of Machine Learning. Annals of Surgery Open 2024;5(2):e423 View
523. Danelakis A, Langseth H, Nachev P, Nelson A, Bjørk M, Matharu M, Tronvik E, May A, Stubberud A. What predicts citation counts and translational impact in headache research? A machine learning analysis. Cephalalgia 2024;44(5) View
524. Li M, Han S, Liang F, Hu C, Zhang B, Hou Q, Zhao S. Machine Learning for Predicting Risk and Prognosis of Acute Kidney Disease in Critically Ill Elderly Patients During Hospitalization: Internet-Based and Interpretable Model Study. Journal of Medical Internet Research 2024;26:e51354 View
525. Gutierrez-Naranjo J, Moreira A, Valero-Moreno E, Bullock T, Ogden L, Zelle B. ­A machine learning model to predict surgical site infection after surgery of lower extremity fractures. International Orthopaedics 2024;48(7):1887 View
526. Agarwalla A, Lu Y, Reinholz A, Marigi E, Liu J, Sanchez-Sotelo J. Identifying clinically meaningful subgroups following open reduction and internal fixation for proximal humerus fractures: a risk stratification analysis for mortality and 30-day complications using machine learning. JSES International 2024;8(5):932 View
527. Shojaee-Mend H, Velayati F, Tayefi B, Babaee E. Prediction of Diabetes Using Data Mining and Machine Learning Algorithms: A Cross-Sectional Study. Healthcare Informatics Research 2024;30(1):73 View
528. Fan G, Liu H, Yang S, Luo L, Pang M, Liu B, Zhang L, Han L, Rong L, Liao X. Early Prognostication of Critical Patients With Spinal Cord Injury. Spine 2024;49(11):754 View
529. Holcroft S, Karangwa I, Little F, Behoor J, Bazirete O. Predictive Modelling of Postpartum Haemorrhage Using Early Risk Factors: A Comparative Analysis of Statistical and Machine Learning Models. International Journal of Environmental Research and Public Health 2024;21(5):600 View
530. Cui X, Zheng X, Lu Y. Prediction Model for Cognitive Impairment among Disabled Older Adults: A Development and Validation Study. Healthcare 2024;12(10):1028 View
531. Jandy K, Weichbroth P. A machine learning approach to classifying New York Heart Association (NYHA) heart failure. Scientific Reports 2024;14(1) View
532. Karabacak M, Bhimani A, Schupper A, Carr M, Steinberger J, Margetis K. Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion. BMC Musculoskeletal Disorders 2024;25(1) View
533. Tiruneh S, Vu T, Rolnik D, Teede H, Enticott J. Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review. Current Hypertension Reports 2024;26(7):309 View
534. Bulashevska A, Nacsa Z, Lang F, Braun M, Machyna M, Diken M, Childs L, König R. Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy. Frontiers in Immunology 2024;15 View
535. Santhanam P, Dinparastisaleh R, Popuri K, Faisal Beg M, Chen Cardenas S, Hamrahian A. Fully-automated CT derived body composition analysis reveals sarcopenia in functioning adrenocortical carcinomas. Scientific Reports 2024;14(1) View
536. Wang Q, Liang T, Li Y, Liu X. Machine Learning for Prediction of Non-Small Cell Lung Cancer Based on Inflammatory and Nutritional Indicators in Adults: A Cross-Sectional Study. Cancer Management and Research 2024;Volume 16:527 View
537. Noda R, Ichikawa D, Shibagaki Y. Machine learning-based diagnostic prediction of IgA nephropathy: model development and validation study. Scientific Reports 2024;14(1) View
538. Zemariam A, Adisu M, Habesse A, Abate B, Bizuayehu M, Wondie W, Alamaw A, Ngusie H. Employing advanced supervised machine learning approaches for predicting micronutrient intake status among children aged 6–23 months in Ethiopia. Frontiers in Nutrition 2024;11 View
539. Zhao R, Wang G, Li F, Wang J, Zhang Y, Li D, Liu S, Li J, Song J, Wei F, Wang C. Developing Machine Learning–Based Predictive Models for Hallux Valgus Recurrence Based on Measurements From Radiographs. Foot & Ankle International 2024;45(9):1000 View
540. Zheng Y, Zhao A, Yang Y, Wang L, Hu Y, Luo R, Wu Y. Real-World Survival Comparisons Between Radiotherapy and Surgery for Metachronous Second Primary Lung Cancer and Predictions of Lung Cancer–Specific Outcomes Using Machine Learning: Population-Based Study. JMIR Cancer 2024;10:e53354 View
541. Zhai Y, Lan D, Lv S, Mo L. Interpretability-based machine learning for predicting the risk of death from pulmonary inflammation in Chinese intensive care unit patients. Frontiers in Medicine 2024;11 View
542. Chen Y, Rivier C, Mora S, Torres Lopez V, Payabvash S, Sheth K, Harloff A, Falcone G, Rosand J, Mayerhofer E, Anderson C. Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan. European Stroke Journal 2024 View
543. Herrera C, Gimenes F, Herrera J, Cavalli R. Development of Automated Triggers in Ambulatory Settings in Brazil: Protocol for a Machine Learning–Based Design Thinking Study. JMIR Research Protocols 2024;13:e55466 View
544. Malik S, Tenorio B, Moond V, Dahiya D, Vora R, Dbouk N. Systematic review of machine learning models in predicting the risk of bleed/grade of esophageal varices in patients with liver cirrhosis: A comprehensive methodological analysis. Journal of Gastroenterology and Hepatology 2024;39(10):2043 View
545. Kaur K. Artificial intelligence in robo dentistry: A double-edged sword. Journal of Oral Medicine, Oral Surgery, Oral Pathology and Oral Radiology 2024;10(2):88 View
546. Zhai X, Chen M. Accelerated Design for Perovskite-Oxide-Based Photocatalysts Using Machine Learning Techniques. Materials 2024;17(12):3026 View
547. Tariq R, Malik S, Redij R, Arunachalam S, Faubion W, Khanna S. Machine Learning-Based Prediction Models for Clostridioides difficile Infection: A Systematic Review. Clinical and Translational Gastroenterology 2024;15(6):e1 View
548. Okada M, Katsuki M, Shimazu T, Takeshima T, Mitsufuji T, Ito Y, Ohbayashi K, Imai N, Miyahara J, Matsumori Y, Nakazato Y, Fujita K, Hoshino E, Yamamoto T. Preliminary External Validation Results of the Artificial Intelligence-Based Headache Diagnostic Model: A Multicenter Prospective Observational Study. Life 2024;14(6):744 View
549. Zhu Y, Deng X, Zhang X, Tian L, Cui C, Lei F, Xu G, Li H, Liu L, Ma H. Predicting distant metastasis in nasopharyngeal carcinoma using gradient boosting tree model based on detailed magnetic resonance imaging reports. World Journal of Radiology 2024;16(6):203 View
550. Ghasemi A, Hashtarkhani S, Schwartz D, Shaban‐Nejad A. Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review. Cancer Innovation 2024;3(5) View
551. Tahhan Z, Hatem G, Abouelmaty A, Rafei Z, Awada S. Design and validation of an artificial intelligence-powered instrument for the assessment of migraine risk in university students in Lebanon. Computers in Human Behavior Reports 2024;15:100453 View
552. Cho Y, Yoon M, Kim J, Lee J, Oh I, Lee C, Kang S, Choi D. Artificial Intelligence–Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study. Journal of Medical Internet Research 2024;26:e52139 View
553. Yu D, Kane M, Koay E, Wistuba I, Hobbs B. Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC. Scientific Reports 2024;14(1) View
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555. Pawuś D, Porażko T, Paszkiel S. Automation and Decision Support in the Area of Nephrology Using Numerical Algorithms, Artificial Intelligence, and Expert Approach: Review of the Current State of Knowledge. IEEE Access 2024;12:86043 View
556. Karabacak M, Jagtiani P, Di L, Shah A, Komotar R, Margetis K. Advancing precision prognostication in neuro-oncology: Machine learning models for data-driven personalized survival predictions in IDH-wildtype glioblastoma. Neuro-Oncology Advances 2024;6(1) View
557. Zhao S, Zhou H, He M. Considerations regarding a prediction model of surgical site infection after gastrointestinal surgery. International Journal of Surgery 2024 View
558. Vieta E, Salagre E, Grande I, Carvalho A, Fernandes B, Berk M, Birmaher B, Tohen M, Suppes T. Early Intervention in Bipolar Disorder. American Journal of Psychiatry 2018;175(5):411 View
559. Cao S, Yang S, Chen B, Chen X, Fu X, Tang S. Establishing a differential diagnosis model between primary membranous nephropathy and non-primary membranous nephropathy by machine learning algorithms. Renal Failure 2024;46(2) View
560. Endo Y, Tsilimigras D, Munir M, Woldesenbet S, Guglielmi A, Ratti F, Marques H, Cauchy F, Lam V, Poultsides G, Kitago M, Alexandrescu S, Popescu I, Martel G, Gleisner A, Hugh T, Aldrighetti L, Shen F, Endo I, Pawlik T. Machine learning models including preoperative and postoperative albumin-bilirubin score: short-term outcomes among patients with hepatocellular carcinoma. HPB 2024;26(11):1369 View
561. Cai Y, Gong D, Tang L, Cai Y, Li H, Jing T, Gong M, Hu W, Zhang Z, Zhang X, Zhang G. Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions. Journal of Medical Internet Research 2024;26:e47645 View
562. Li Q, Li P, Chen J, Ren R, Ren N, Xia Y. Machine Learning for Predicting Stillbirth: A Systematic Review. Reproductive Sciences 2024 View
563. Boubekri A, Murphy M, Scheidt M, Shivdasani K, Anderson J, Garbis N, Salazar D. Artificial Intelligence Machine Learning Algorithms Versus Standard Linear Demographic Analysis in Predicting Implant Size of Anatomic and Reverse Total Shoulder Arthroplasty. JAAOS: Global Research and Reviews 2024;8(8) View
564. Vachon J, Kerckhoffs J, Buteau S, Smargiassi A. Do machine learning methods improve prediction of ambient air pollutants with high spatial contrast? A systematic review. Environmental Research 2024;262:119751 View
565. Stubberud A, Langseth H, Nachev P, Matharu M, Tronvik E. Artificial intelligence and headache. Cephalalgia 2024;44(8) View
566. Hornung A, Rudisill S, Smith S, Streepy J, Simcock X. Can Machine Learning Identify Patients Who are Appropriate for Outpatient Open Reduction and Internal Fixation of Distal Radius Fractures?. Journal of Hand Surgery Global Online 2024;6(6):808 View
567. Kong F, Zou Y, Li Z, Deng Y. Advances in Portable and Wearable Acoustic Sensing Devices for Human Health Monitoring. Sensors 2024;24(16):5354 View
568. Iwagami M, Inokuchi R, Kawakami E, Yamada T, Goto A, Kuno T, Hashimoto Y, Michihata N, Goto T, Shinozaki T, Sun Y, Taniguchi Y, Komiyama J, Uda K, Abe T, Tamiya N, Penzel T. Comparison of machine-learning and logistic regression models for prediction of 30-day unplanned readmission in electronic health records: A development and validation study. PLOS Digital Health 2024;3(8):e0000578 View
569. Szumilas D, Ochmann A, Zięba K, Bartoszewicz B, Kubrak A, Makuch S, Agrawal S, Mazur G, Chudek J. Evaluation of AI-Driven LabTest Checker for Diagnostic Accuracy and Safety: Prospective Cohort Study. JMIR Medical Informatics 2024;12:e57162 View
570. Zhang T, Ye Z, Cai J, Chen J, Zheng T, Xu J, Zhao J. Ensemble Algorithm for Risk Prediction of Clinical Failure After Anterior Cruciate Ligament Reconstruction. Orthopaedic Journal of Sports Medicine 2024;12(8) View
571. Ojha T, Patel A, Sivapragasam K, Sharma R, Vosoughi T, Skidmore B, Pinto A, Hosseini B. Exploring Machine Learning Applications in Pediatric Asthma Management: Scoping Review. JMIR AI 2024;3:e57983 View
572. Oeding J, Boos A, Kalk J, Sorenson D, Verhooven F, Moatshe G, Camp C. Pitch-Tracking Metrics as a Predictor of Future Shoulder and Elbow Injuries in Major League Baseball Pitchers: A Machine-Learning and Game-Theory Based Analysis. Orthopaedic Journal of Sports Medicine 2024;12(8) View
573. Karabacak M, Schupper A, Carr M, Margetis K. A machine learning-based approach for individualized prediction of short-term outcomes after anterior cervical corpectomy. Asian Spine Journal 2024;18(4):541 View
574. Lisik D, Milani G, Salisu M, Özuygur Ermis S, Goksör E, Basna R, Wennergren G, Kankaanranta H, Nwaru B. Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a systematic review. BMJ Open 2024;14(8):e080263 View
575. Cai Z, Sun Q, Li C, Xu J, Jiang B. Machine-learning-based prediction by stacking ensemble strategy for surgical outcomes in patients with degenerative cervical myelopathy. Journal of Orthopaedic Surgery and Research 2024;19(1) View
576. Werneburg G, Werneburg E, Goldman H, Slopnick E, Roberts L, Vasavada S. External validation demonstrates machine learning models outperform human experts in prediction of objective and patient-reported overactive bladder treatment outcomes. Urology 2024 View
577. Kim Y, Seo W, Lee S, Koo J, Kim G, Song H, Lee M. Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study. Journal of Medical Internet Research 2024;26:e62890 View
578. Marigi E, Oeding J, Nieboer M, Marigi I, Wahlig B, Barlow J, Sanchez-Sotelo J, Sperling J. The relationship between design-based lateralization, humeral bearing design, polyethylene angle, and patient-related factors on surgical complications after reverse shoulder arthroplasty: a machine learning analysis. Journal of Shoulder and Elbow Surgery 2024 View
579. Turrisi R, Verri A, Barla A. Deep learning-based Alzheimer's disease detection: reproducibility and the effect of modeling choices. Frontiers in Computational Neuroscience 2024;18 View
580. Stahl D. New horizons in prediction modelling using machine learning in older people’s healthcare research. Age and Ageing 2024;53(9) View
581. Cata J, Soni B, Bhavsar S, Pillai P, Rypinski T, Deva A, Siewerdsen J, Soliz J. Forecasting intraoperative hypotension during hepatobiliary surgery. Journal of Clinical Monitoring and Computing 2024 View
582. Benlaharche K, Benlaharche H. Machine learning for HELLP syndrome prediction: algorithms, case study and challenges. STUDIES IN ENGINEERING AND EXACT SCIENCES 2024;5(2):e8237 View
583. van Spanning S, Verweij L, Hendrickx L, Allaart L, Athwal G, Lafosse T, Lafosse L, Doornberg J, Oosterhoff J, van den Bekerom M, Buijze G. Methodology and development of a machine learning probability calculator: Data heterogeneity limits ability to predict recurrence after arthroscopic Bankart repair. Knee Surgery, Sports Traumatology, Arthroscopy 2024 View
584. Zhou Z, Wang D, Sun J, Zhu M, Teng L. A Machine Learning–Based Prediction Model for the Probability of Fall Risk Among Chinese Community-Dwelling Older Adults. CIN: Computers, Informatics, Nursing 2024;42(12):913 View
585. Marzano L. Predicting the resolution of hypertension following adrenalectomy in primary aldosteronism: Controversies and unresolved issues a narrative review. Langenbeck's Archives of Surgery 2024;409(1) View
586. Araújo D, de Macedo A, Veloso A, Alpoim P, Gomes K, Carvalho M, Dusse L. Complete blood count as a biomarker for preeclampsia with severe features diagnosis: a machine learning approach. BMC Pregnancy and Childbirth 2024;24(1) View
587. Koh R, Ribeiro M, Jabban L, Fang B, Nesovic K, Bayat S, Metcalfe B. A Scoping Review of Machine Learning Applied to Peripheral Nerve Interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2024;32:3689 View
588. Noda R, Ichikawa D, Shibagaki Y. Machine learning-based diagnostic prediction of minimal change disease: model development study. Scientific Reports 2024;14(1) View
589. O’Dowling A, Rodriguez B, Gallagher T, Thorpe S. Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis. Computational and Structural Biotechnology Journal 2024;24:661 View
590. Ngusie H, Enyew E, Walle A, Tilahun Assaye B, Kasaye M, Tesfa G, Zemariam A. Employing machine learning techniques for prediction of micronutrient supplementation status during pregnancy in East African Countries. Scientific Reports 2024;14(1) View
591. Feng M, Meng F, Jia Y, Wang Y, Ji G, Gao C, Luo J. Exploration of Risk Factors for Cardiovascular Disease in Patients with Rheumatoid Arthritis: A Retrospective Study. Inflammation 2024 View
592. Ahamed Fayaz S, Babu L, Paridayal L, Vasantha M, Paramasivam P, Sundarakumar K, Ponnuraja C, Subbian S. Machine learning algorithms to predict treatment success for patients with pulmonary tuberculosis. PLOS ONE 2024;19(10):e0309151 View
593. Jo E, Yoo H, Kim J, Kim Y, Song S, Joo H. Fine-Tuned Bidirectional Encoder Representations From Transformers Versus ChatGPT for Text-Based Outpatient Department Recommendation: Comparative Study. JMIR Formative Research 2024;8:e47814 View
594. Salman L. Transforming orthopedics: A glimpse into the future with artificial intelligence. Journal of Musculoskeletal Surgery and Research 2024;0:1 View
595. Seghier M. Image Segmentation Evaluation With the Dice Index: Methodological Issues. International Journal of Imaging Systems and Technology 2024;34(6) View
596. Song Y, Huang H, Ma J, Xing R, Song Y, Li L, Zhou J, Ou C. Early prediction of sepsis in emergency department patients using various methods and scoring systems. Nursing in Critical Care 2024 View
597. Moler-Zapata S, Hutchings A, Grieve R, Hinchliffe R, Smart N, Moonesinghe S, Bellingan G, Vohra R, Moug S, O’Neill S. An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions. Medical Decision Making 2024;44(8):944 View
598. Lu Y, Till S, Labott J, Reinholz A, Hevesi M, Krych A, Camp C, Okoroha K. Graft Failure and Contralateral ACL Injuries After Primary ACL Reconstruction: An Analysis of Risk Factors Using Interpretable Machine Learning. Orthopaedic Journal of Sports Medicine 2024;12(10) View
599. Dholariya S, Dutta S, Sonagra A, Kaliya M, Singh R, Parchwani D, Motiani A. Unveiling the utility of artificial intelligence for prediction, diagnosis, and progression of diabetic kidney disease: an evidence-based systematic review and meta-analysis. Current Medical Research and Opinion 2024;40(12):2025 View
600. Song W, Frakes D, Dasi L. Active Machine Learning for Pre-procedural Prediction of Time-Varying Boundary Condition After Fontan Procedure Using Generative Adversarial Networks. Annals of Biomedical Engineering 2024 View
601. Chung W, Yoon J, Yoon D, Kim S, Kim Y, Park J, Kang Y. Development and Validation of Deep Learning–Based Infectivity Prediction in Pulmonary Tuberculosis Through Chest Radiography: Retrospective Study. Journal of Medical Internet Research 2024;26:e58413 View
602. Do W, Shin S, Lim J, Yoon T, Chun Y. Predicting the Reparability of Rotator Cuff Tears: Machine Learning and Comparison With Previous Scoring Systems. The American Journal of Sports Medicine 2024;52(14):3512 View
603. Clark S, Hartwell E, Choi D, Krystal J, Messing R, Ferguson L. Next‐generation biomarkers for alcohol consumption and alcohol use disorder diagnosis, prognosis, and treatment: A critical review. Alcohol, Clinical and Experimental Research 2024 View
604. Hong M, Kang R, Yang J, Rhee S, Lee H, Kim Y, Lee K, Kim H, Lee Y, Youn T, Kim S, Ahn Y. Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study. Journal of Medical Internet Research 2024;26:e65994 View
605. Ritter D, Denard P, Raiss P, Wijdicks C, Werner B, Bedi A, Müller P, Bachmaier S. Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty. Journal of Shoulder and Elbow Surgery 2024 View
606. Alfardan A, Alharbi R, Alrammaal W, Alharbi F, Almotairi M, Almutairi M, Almutairi N, Hazazi M, Alanazi M, Alharbi F. The role of artificial intelligence in predicting disease outbreaks: A multidisciplinary approach. International journal of health sciences 2024;8(S1):1556 View
607. Kumar D, Haag D, Blechert J, Niebauer J, Smeddinck J. Investigating Feature Selection for Physical Activity Prediction Based on Ecological Momentary Assessments: Towards Tailoring the Timing of Behaviour Change Support Messages (Preprint). JMIR mHealth and uHealth 2024 View
608. Karabacak M, Jagtiani P, Schupper A, Carr M, Steinberger J, Margetis K. Machine Learning for Individualized Risk Estimation in Anterior Lumbar Interbody Fusion. Neurosurgery Practice 2024;5(3) View
609. Karabacak M, Margetis K. Machine Learning–Driven Prognostication in Traumatic Subdural Hematoma: Development of a Predictive Web Application. Neurosurgery Practice 2024;5(1) View
610. Mai H, Lu Y, Fu Y, Luo T, Li X, Zhang Y, Liu Z, Zhang Y, Zhou S, Chen C. Identification of a Susceptible and High-Risk Population for Postoperative Systemic Inflammatory Response Syndrome in Older Adults: Machine Learning–Based Predictive Model. Journal of Medical Internet Research 2024;26:e57486 View
611. Koole D, Shen O, Lans A, de Groot T, Verlaan J, Schwab J. Development of Machine Learning Algorithms for Identifying Patients With Limited Health Literacy. Journal of Evaluation in Clinical Practice 2025;31(1) View
612. Yang Y, Bo Z, Wang J, Chen B, Su Q, Lian Y, Guo Y, Yang J, Zheng C, Wang J, Zeng H, Zhou J, Chen Y, Chen G, Wang Y. Machine learning based on alcohol drinking-gut microbiota-liver axis in predicting the occurrence of early-stage hepatocellular carcinoma. BMC Cancer 2024;24(1) View
613. Tanaka C, Tagami T, Kitano S, Nakayama F, Sakamoto T, Kaneko J, Kuno M. Developing a simple field triage scheme compatible with regional trauma situations and medical systems using machine learning: A nationwide trauma database study. Hong Kong Journal of Emergency Medicine 2024 View
614. Todd E, Orr R, Gamage E, West E, Jabeen T, McGuinness A, George V, Phuong-Nguyen K, Voglsanger L, Jennings L, Angwenyi L, Taylor S, Khosravi A, Jacka F, Dawson S. Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review. Computers in Biology and Medicine 2025;185:109521 View
615. Chrysafi P, Lam B, Carton S, Patell R. From Code to Clots: Applying Machine Learning to Clinical Aspects of Venous Thromboembolism Prevention, Diagnosis, and Management. Hämostaseologie 2024;44(06):429 View
616. Shen L, Jin Y, Pan A, Wang K, Ye R, Lin Y, Anwar S, Xia W, Zhou M, Guo X. Machine learning-based predictive models for perioperative major adverse cardiovascular events in patients with stable coronary artery disease undergoing noncardiac surgery. Computer Methods and Programs in Biomedicine 2025;260:108561 View

Books/Policy Documents

1. F.I. Osman A. Artificial Intelligence - Applications in Medicine and Biology. View
2. Dankers F, Traverso A, Wee L, van Kuijk S. Fundamentals of Clinical Data Science. View
3. Allen B, Gish R, Dreyer K. Artificial Intelligence in Medical Imaging. View
4. Haymond S, Julian R, Gill E, Master S. Biochemical and Molecular Basis of Pediatric Disease. View
5. Cychnerski J, Dziubich T. New Trends in Database and Information Systems. View
6. Schwarzerova J, Kostoval A, Bajger A, Jakubikova L, Pierides I, Popelinsky L, Sedlar K, Weckwerth W. Information Technology in Biomedicine. View
7. Dee E, Yu R, Celi L, Nehal U. Artificial Intelligence in Medicine. View
8. Kalpana , Srivastava A, Jha S. Predictive Modeling in Biomedical Data Mining and Analysis. View
9. Dee E, Yu R, Celi L, Nehal U. Artificial Intelligence in Medicine. View
10. Lopez-Ramos L. Intelligent Technologies and Applications. View
11. Urbanowicz R, Zhang R, Cui Y, Suri P. Genetic Programming Theory and Practice XIX. View
12. McMahon J, Craig A, Cameron I. Service-Oriented Computing – ICSOC 2023 Workshops. View
13. Glavaški M, Velicki L. In Silico Clinical Trials for Cardiovascular Disease. View
14. Ong Y, Kee S, Chai K, Lim T, Tan C. Advances in Intelligent Healthcare Delivery and Management. View
15. McFarland E, Łukasiewicz P, Goldfarb S. Artificial Intelligence in Orthopaedic Surgery Made Easy. View