Published on in Vol 18, No 12 (2016): December

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

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

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
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  441. Karabacak M, Margetis K. Precision medicine for traumatic cervical spinal cord injuries: accessible and interpretable machine learning models to predict individualized in-hospital outcomes. The Spine Journal 2023;23(12):1750 View
  442. Lei M, Wu B, Zhang Z, Qin Y, Cao X, Cao Y, Liu B, Su X, Liu Y. A Web-Based Calculator to Predict Early Death Among Patients With Bone Metastasis Using Machine Learning Techniques: Development and Validation Study. Journal of Medical Internet Research 2023;25:e47590 View
  443. Thapa I, Lee R, Fernandez Vina M, Zhang B, Ahmed H, Shin A, Bambos N, Rosenthal D, Scheinker D. Examining the feasibility of data-driven decision support for the virtual crossmatch for solid organ transplantation: A single center study. Transplantation Reports 2023;8(3):100144 View
  444. Jia M, Wu Y, Xiang C, Fang Y. Predicting Alzheimer’s Disease with Interpretable Machine Learning. Dementia and Geriatric Cognitive Disorders 2023;52(4):249 View
  445. Elmahdy M, Sebro R. Sex, ethnicity, and race data are often unreported in artificial intelligence and machine learning studies in medicine. Intelligence-Based Medicine 2023;8:100113 View
  446. Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior K, Poirrier J, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Review of Pharmacoeconomics & Outcomes Research 2024;24(1):63 View
  447. Karabacak M, Jagtiani P, Carrasquilla A, Shrivastava R, Margetis K. Advancing personalized prognosis in atypical and anaplastic meningiomas through interpretable machine learning models. Journal of Neuro-Oncology 2023;164(3):671 View
  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 2023 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 2023 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 2023 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 2023 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 2023 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 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
  498. Ghazi L, Farhat K, Hoenig M, Durant T, El-Khoury J. Biomarkers vs Machines: The Race to Predict Acute Kidney Injury. Clinical Chemistry 2024 View
  499. Farrag A, Kamel A, El‐Baraky I. Opportunities and challenges for the application of artificial intelligence paradigms into the management of endemic viral infections: The example of Chronic Hepatitis C Virus. Reviews in Medical Virology 2024;34(2) View
  500. Eken A, Nassehi F, Eroğul O. Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review. Reviews in the Neurosciences 2024;0(0) View
  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 2024:1 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 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