This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2388) Machine Learning (1370) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1481) Intensive Care Unit (ICU) (101)

Published on in Vol 22, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23128, first published .
Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation

Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation

Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation

Authors of this article:

Pan Pan1 Author Orcid Image ;   Yichao Li2 Author Orcid Image ;   Yongjiu Xiao3 Author Orcid Image ;   Bingchao Han4 Author Orcid Image ;   Longxiang Su5 Author Orcid Image ;   Mingliang Su2 Author Orcid Image ;   Yansheng Li2 Author Orcid Image ;   Siqi Zhang2 Author Orcid Image ;   Dapeng Jiang2 Author Orcid Image ;   Xia Chen2 Author Orcid Image ;   Fuquan Zhou2 Author Orcid Image ;   Ling Ma3 Author Orcid Image ;   Pengtao Bao6 Author Orcid Image ;   Lixin Xie6 Author Orcid Image
Article Authors Cited by (80) Tweetations (2) Metrics

Journals

  1. Jamshidi E, Asgary A, Tavakoli N, Zali A, Dastan F, Daaee A, Badakhshan M, Esmaily H, Jamaldini S, Safari S, Bastanhagh E, Maher A, Babajani A, Mehrazi M, Sendani Kashi M, Jamshidi M, Sendani M, Rahi S, Mansouri N. Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning. Frontiers in Artificial Intelligence 2021;4 View
  2. Taylor E, Marson E, Elhadi M, Macleod K, Yu Y, Davids R, Boden R, Overmeyer R, Ramakrishnan R, Thomson D, Coetzee J, Biccard B. Factors associated with mortality in patients with COVID‐19 admitted to intensive care: a systematic review and meta‐analysis. Anaesthesia 2021;76(9):1224 View
  3. Ye J, Hua M, Zhu F. Machine Learning Algorithms are Superior to Conventional Regression Models in Predicting Risk Stratification of COVID-19 Patients. Risk Management and Healthcare Policy 2021;Volume 14:3159 View
  4. Abdulaal A, Patel A, Al-Hindawi A, Charani E, Alqahtani S, Davies G, Mughal N, Moore L. Clinical Utility and Functionality of an Artificial Intelligence–Based App to Predict Mortality in COVID-19: Mixed Methods Analysis. JMIR Formative Research 2021;5(7):e27992 View
  5. Sankaranarayanan S, Balan J, Walsh J, Wu Y, Minnich S, Piazza A, Osborne C, Oliver G, Lesko J, Bates K, Khezeli K, Block D, DiGuardo M, Kreuter J, O’Horo J, Kalantari J, Klee E, Salama M, Kipp B, Morice W, Jenkinson G. COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation. Journal of Medical Internet Research 2021;23(9):e30157 View
  6. Asteris P, Kokoris S, Gavriilaki E, Tsoukalas M, Houpas P, Paneta M, Koutzas A, Argyropoulos T, Alkayem N, Armaghani D, Bardhan A, Cavaleri L, Cao M, Mansouri I, Mohammed A, Samui P, Gerber G, Boumpas D, Tsantes A, Terpos E, Dimopoulos M. Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices. Clinical Immunology 2023;246:109218 View
  7. Murri R, Lenkowicz J, Masciocchi C, Iacomini C, Fantoni M, Damiani A, Marchetti A, Sergi P, Arcuri G, Cesario A, Patarnello S, Antonelli M, Bellantone R, Bernabei R, Boccia S, Calabresi P, Cambieri A, Cauda R, Colosimo C, Crea F, De Maria R, De Stefano V, Franceschi F, Gasbarrini A, Parolini O, Richeldi L, Sanguinetti M, Urbani A, Zega M, Scambia G, Valentini V, Armuzzi A, Barba M, Baroni S, Bellesi S, Bentivoglio A, Biasucci L, Biscetti F, Candelli M, Capalbo G, Cattani P, Chiusolo P, Cingolani A, Corbo G, Covino M, Cozzolino A, D’Alfonso M, De Angelis G, De Pascale G, Frisullo G, Gabrielli M, Gambassi G, Garcovich M, Gremese E, Grieco D, Iaconelli A, Iorio R, Landi F, Larici A, Liuzzo G, Maviglia R, Miele L, Montalto M, Natale L, Nicolotti N, Ojetti V, Pompili M, Posteraro B, Rapaccini G, Rinaldi R, Rossi E, Santoliquido A, Sica S, Tamburrini E, Teofili L, Testa A, Tosoni A, Trani C, Varone F, Verme L. A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19. Scientific Reports 2021;11(1) View
  8. Liu Y, Gao K, Deng H, Ling T, Lin J, Yu X, Bo X, Zhou J, Gao L, Wang P, Hu J, Zhang J, Tong Z, Liu Y, Shi Y, Ke L, Gao Y, Li W. A time-incorporated SOFA score-based machine learning model for predicting mortality in critically ill patients: A multicenter, real-world study. International Journal of Medical Informatics 2022;163:104776 View
  9. Safaei N, Safaei B, Seyedekrami S, Talafidaryani M, Masoud A, Wang S, Li Q, Moqri M, Chen R. E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database. PLOS ONE 2022;17(5):e0262895 View
  10. Smit J, Krijthe J, Endeman H, Tintu A, de Rijke Y, Gommers D, Cremer O, Bosman R, Rigter S, Wils E, Frenzel T, Dongelmans D, De Jong R, Peters M, Kamps M, Ramnarain D, Nowitzky R, Nooteboom F, De Ruijter W, Urlings-Strop L, Smit E, Mehagnoul-Schipper D, Dormans T, De Jager C, Hendriks S, Achterberg S, Oostdijk E, Reidinga A, Festen-Spanjer B, Brunnekreef G, Cornet A, Van den Tempel W, Boelens A, Koetsier P, Lens J, Faber H, karakus A, Entjes R, De Jong P, Rettig T, Arbous M, Lalisang R, Tonutti M, De Bruin D, Elbers P, Van Bommel J, Reinders M. Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study. Intelligence-Based Medicine 2022;6:100071 View
  11. Jung C, Mamandipoor B, Fjølner J, Bruno R, Wernly B, Artigas A, Bollen Pinto B, Schefold J, Wolff G, Kelm M, Beil M, Sviri S, van Heerden P, Szczeklik W, Czuczwar M, Elhadi M, Joannidis M, Oeyen S, Zafeiridis T, Marsh B, Andersen F, Moreno R, Cecconi M, Leaver S, De Lange D, Guidet B, Flaatten H, Osmani V. Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation. JMIR Medical Informatics 2022;10(3):e32949 View
  12. Zou Y, Shi Y, Sun F, Liu J, Guo Y, Zhang H, Lu X, Gong Y, Xia S. Extreme gradient boosting model to assess risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual prediction using SHapley Additive exPlanations. Computer Methods and Programs in Biomedicine 2022;225:107038 View
  13. Shi Y, Zou Y, Liu J, Wang Y, Chen Y, Sun F, Yang Z, Cui G, Zhu X, Cui X, Liu F. Ultrasound-based radiomics XGBoost model to assess the risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual application of SHAP. Frontiers in Oncology 2022;12 View
  14. Moulaei K, Shanbehzadeh M, Mohammadi-Taghiabad Z, Kazemi-Arpanahi H. Comparing machine learning algorithms for predicting COVID-19 mortality. BMC Medical Informatics and Decision Making 2022;22(1) View
  15. Cañada-Soriano M, Bovaira M, García-Vitoria C, Salvador-Palmer R, Cibrián Ortiz de Anda R, Moratal D, Priego-Quesada J. Application of machine learning algorithms in thermal images for an automatic classification of lumbar sympathetic blocks. Journal of Thermal Biology 2023;113:103523 View
  16. Kuo K, Talley P, Chang C. The accuracy of machine learning approaches using non-image data for the prediction of COVID-19: A meta-analysis. International Journal of Medical Informatics 2022;164:104791 View
  17. Kim J, Blaum C, Ferris R, Arcila‐Mesa M, Do H, Pulgarin C, Dolle J, Scherer J, Kalyanaraman Marcello R, Zhong J. Factors associated with hospital admission and severe outcomes for older patients with COVID‐19. Journal of the American Geriatrics Society 2022;70(7):1906 View
  18. Hong W, Zhou X, Jin S, Lu Y, Pan J, Lin Q, Yang S, Xu T, Basharat Z, Zippi M, Fiorino S, Tsukanov V, Stock S, Grottesi A, Chen Q, Pan J. A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile. Frontiers in Cellular and Infection Microbiology 2022;12 View
  19. Shen Z, Zhang S, Jiao Y, Shi Y, Zhang H, Wang F, Wang L, Zhu T, Miao Y, Sang W, Cai G, Huaihai Lymphoma W, Zhang Z. LASSO Model Better Predicted the Prognosis of DLBCL than Random Forest Model: A Retrospective Multicenter Analysis of HHLWG. Journal of Oncology 2022;2022:1 View
  20. Wynants L, Van Calster B, Collins G, Riley R, Heinze G, Schuit E, Albu E, Arshi B, Bellou V, Bonten M, Dahly D, Damen J, Debray T, de Jong V, De Vos M, Dhiman P, Ensor J, Gao S, Haller M, Harhay M, Henckaerts L, Heus P, Hoogland J, Hudda M, Jenniskens K, Kammer M, Kreuzberger N, Lohmann A, Levis B, Luijken K, Ma J, Martin G, McLernon D, Navarro C, Reitsma J, Sergeant J, Shi C, Skoetz N, Smits L, Snell K, Sperrin M, Spijker R, Steyerberg E, Takada T, Tzoulaki I, van Kuijk S, van Bussel B, van der Horst I, Reeve K, van Royen F, Verbakel J, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons K, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020:m1328 View
  21. Alkaabi S, Alnuaimi A, Alharbi M, Amari M, Ganapathy R, Iqbal I, Nauman J, Oulhaj A. A clinical risk score to predict in-hospital mortality in critically ill patients with COVID-19: a retrospective cohort study. BMJ Open 2021;11(8):e048770 View
  22. Wang J, Wang Y, Zeng Y, Huang D. Feature selection approaches identify potential plasma metabolites in postmenopausal osteoporosis patients. Metabolomics 2022;18(11) View
  23. Tabatabaie M, Sarrami A, Didehdar M, Tasorian B, Shafaat O, Sotoudeh H. Accuracy of Machine Learning Models to Predict Mortality in COVID-19 Infection Using the Clinical and Laboratory Data at the Time of Admission. Cureus 2021 View
  24. Vázquez-Jiménez A, Avila-Ponce De León U, Matadamas-Guzman M, Muciño-Olmos E, Martínez-López Y, Escobedo-Tapia T, Resendis-Antonio O. On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers. Frontiers in Immunology 2021;12 View
  25. Wu M, Zhao Y, Dong X, Jin Y, Cheng S, Zhang N, Xu S, Gu S, Wu Y, Yang J, Yao L, Wang Y. Artificial intelligence-based preoperative prediction system for diagnosis and prognosis in epithelial ovarian cancer: A multicenter study. Frontiers in Oncology 2022;12 View
  26. Ahsan M, Uddin M, Ali M, Islam M, Farjana M, Sakib A, Momin K, Luna S. Deep transfer learning approaches for Monkeypox disease diagnosis. Expert Systems with Applications 2023;216:119483 View
  27. Maestre-Muñiz M, Arias Á, Lucendo A. Predicting In-Hospital Mortality in Severe COVID-19: A Systematic Review and External Validation of Clinical Prediction Rules. Biomedicines 2022;10(10):2414 View
  28. Khadem H, Nemat H, Elliott J, Benaissa M. Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay. Sensors 2022;22(22):8757 View
  29. Hong W, Lu Y, Zhou X, Jin S, Pan J, Lin Q, Yang S, Basharat Z, Zippi M, Goyal H. Usefulness of Random Forest Algorithm in Predicting Severe Acute Pancreatitis. Frontiers in Cellular and Infection Microbiology 2022;12 View
  30. Caires Silveira E, Mattos Pretti S, Santos B, Santos Corrêa C, Madureira Silva L, Freire de Melo F. Prediction of hospital mortality in intensive care unit patients from clinical and laboratory data: A machine learning approach. World Journal of Critical Care Medicine 2022;11(5):317 View
  31. SAĞLAM E, ÖZBEK ŞEBİN S, KOÇMAN S, BALABAN C, YÜCELSEN C, ATAY A. High Mean Platelet Volume and Mean Platelet Volume/Platelet Ratio Predict Mortality for COVID-19 Patients in Intensive Care Unit. New Trends in Medicine Sciences 2022;3(2):75 View
  32. Nopour R, Erfannia L, Mehrabi N, Mashoufi M, Mahdavi A, Shanbehzadeh M. Comparison of Two Statistical Models for Predicting Mortality in COVID-19 Patients in Iran. Shiraz E-Medical Journal 2022;23(6) View
  33. Zheng Y, Guo Z, Zhang Y, Shang J, Yu L, Fu P, Liu Y, Li X, Wang H, Ren L, Zhang W, Hou H, Tan X, Wang W. Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine. EPMA Journal 2022;13(2):285 View
  34. Nopour R, Shanbezadeh M, Kazemi-Arpanahi H. Predicting intubation risk among COVID-19 hospitalized patients using artificial neural networks. Journal of Education and Health Promotion 2023;12(1):16 View
  35. Giuste F, Shi W, Zhu Y, Naren T, Isgut M, Sha Y, Tong L, Gupte M, Wang M. Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review. IEEE Reviews in Biomedical Engineering 2023;16:5 View
  36. Zhang G, Shi Y, Yin P, Liu F, Fang Y, Li X, Zhang Q, Zhang Z. A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn and SHAP. Frontiers in Oncology 2022;12 View
  37. Bottino F, Tagliente E, Pasquini L, Napoli A, Lucignani M, Figà-Talamanca L, Napolitano A. COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal. Journal of Personalized Medicine 2021;11(9):893 View
  38. Kieninger M, Sinning A, Vadász T, Gruber M, Gronwald W, Zeman F, Lunz D, Dienemann T, Schmid S, Graf B, Lubnow M, Müller T, Holzmann T, Salzberger B, Kieninger B, Zivkovic A. Lower blood pH as a strong prognostic factor for fatal outcomes in critically ill COVID-19 patients at an intensive care unit: A multivariable analysis. PLOS ONE 2021;16(9):e0258018 View
  39. Shanbehzadeh M, Haghiri H, Afrash M, Amraei M, Erfannia L, Kazemi-Arpanahi H. Comparison of Machine Learning Tools for the Prediction of ICU Admission in COVID-19 Hospitalized Patients. Shiraz E-Medical Journal 2022;23(5) View
  40. Zarei J, Jamshidnezhad A, Haddadzadeh Shoushtari M, Mohammad Hadianfard A, Cheraghi M, Sheikhtaheri A, Ieracitano C. Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations. Journal of Healthcare Engineering 2022;2022:1 View
  41. Cao Z, Qiu Z, Tang F, Liang S, Wang Y, Long H, Chen C, Zhang B, Zhang C, Wang Y, Tang K, Tang J, Chen J, Yang C, Xu Y, Yang Y, Xiao S, Tian D, Jiang G, Du X. Drivers and forecasts of multiple waves of the coronavirus disease 2019 pandemic: A systematic analysis based on an interpretable machine learning framework. Transboundary and Emerging Diseases 2022;69(5) View
  42. Lee J, Ahn J, Chung M, Jeong Y, Kim J, Lim J, Kim J, Kim Y, Lee J, Kim E. Development and Validation of a Multimodal-Based Prognosis and Intervention Prediction Model for COVID-19 Patients in a Multicenter Cohort. Sensors 2022;22(13):5007 View
  43. Föll S, Lison A, Maritsch M, Klingberg K, Lehmann V, Züger T, Srivastava D, Jegerlehner S, Feuerriegel S, Fleisch E, Exadaktylos A, Wortmann F. A Scalable Risk-Scoring System Based on Consumer-Grade Wearables for Inpatients With COVID-19: Statistical Analysis and Model Development. JMIR Formative Research 2022;6(6):e35717 View
  44. Li J, Liu S, Hu Y, Zhu L, Mao Y, Liu J. Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study. Journal of Medical Internet Research 2022;24(8):e38082 View
  45. Andrade E, Pinheiro P, Barros A, Nunes L, Pinheiro L, Pinheiro P, Holanda Filho R. Towards Machine Learning Algorithms in Predicting the Clinical Evolution of Patients Diagnosed with COVID-19. Applied Sciences 2022;12(18):8939 View
  46. Khounraz F, Khodadoost M, Gholamzadeh S, Pourhamidi R, Baniasadi T, Jafarbigloo A, Mohammadi G, Ahmadi M, Ayyoubzadeh S. Prognosis of COVID‐19 patients using lab tests: A data mining approach. Health Science Reports 2023;6(1) View
  47. Kurban L, AlDhaheri S, Elkkari A, Khashkhusha R, AlEissaee S, AlZaabi A, Ismail M, Bakoush O. Predicting Severe Disease and Critical Illness on Initial Diagnosis of COVID-19: Simple Triage Tools. Frontiers in Medicine 2022;9 View
  48. Khadem H, Nemat H, Eissa M, Elliott J, Benaissa M. COVID-19 mortality risk assessments for individuals with and without diabetes mellitus: Machine learning models integrated with interpretation framework. Computers in Biology and Medicine 2022;144:105361 View
  49. Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Design of an artificial neural network to predict mortality among COVID-19 patients. Informatics in Medicine Unlocked 2022;31:100983 View
  50. An C, Yang H, Yu X, Han Z, Cheng Z, Liu F, Dou J, Li B, Li Y, Li Y, Yu J, Liang P. A Machine Learning Model Based on Health Records for Predicting Recurrence After Microwave Ablation of Hepatocellular Carcinoma. Journal of Hepatocellular Carcinoma 2022;Volume 9:671 View
  51. Tian J, Yan J, Han G, Du Y, Hu X, He Z, Han Q, Zhang Y. Machine learning prognosis model based on patient-reported outcomes for chronic heart failure patients after discharge. Health and Quality of Life Outcomes 2023;21(1) View
  52. Li F, Chen A, Li Z, Gu L, Pan Q, Wang P, Fan Y, Feng J. Machine learning-based prediction of cerebral hemorrhage in patients with hemodialysis: A multicenter, retrospective study. Frontiers in Neurology 2023;14 View
  53. Zakariaee S, Abdi A, Naderi N, Babashahi M. Prognostic significance of chest CT severity score in mortality prediction of COVID-19 patients, a machine learning study. Egyptian Journal of Radiology and Nuclear Medicine 2023;54(1) View
  54. Mano L, Torres A, Morales A, Cruz C, Cardoso F, Alves S, Faria C, Lanzillotti R, Cerceau R, da Costa R, Figueiredo K, Werneck V. Machine Learning Applied to COVID-19: A Review of the Initial Pandemic Period. International Journal of Computational Intelligence Systems 2023;16(1) View
  55. 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
  56. Giuste F, He L, Lais P, Shi W, Zhu Y, Hornback A, Tsai C, Isgut M, Anderson B, Wang M. Early and fair COVID-19 outcome risk assessment using robust feature selection. Scientific Reports 2023;13(1) View
  57. Ma F, He C, Yang H, Hu Z, Mao H, Fan C, Qi Y, Zhang J, Xu B. Interpretable machine-learning model for Predicting the Convalescent COVID-19 patients with pulmonary diffusing capacity impairment. BMC Medical Informatics and Decision Making 2023;23(1) View
  58. Yan P, Huang S, Li Y, Chen T, Li X, Zhang Y, Wu H, Xu J, Xie G, Xie L, Mo G. A New Risk Model based on the Machine Learning Approach for Prediction of Mortality in the Respiratory Intensive Care Unit. Current Pharmaceutical Biotechnology 2023;24(13):1673 View
  59. Yao C, Huangqi C, Huang A. Lesson Learned from COVID-19 Retrospective Study: An Entropy-Based Clinical-Interpretable Scorecard for Mortality Risk Control at ICU Admission. Tsinghua Science and Technology 2024;29(1):34 View
  60. Chadaga K, Prabhu S, Sampathila N, Chadaga R. Severity prediction in COVID-19 patients using clinical markers and explainable artificial intelligence: A stacked ensemble machine learning approach. Intelligent Decision Technologies 2023;17(4):959 View
  61. Kim J, An S, Hong S. Exploring the factors that influence academic stress among elementary school students using a LASSO penalty regression model. Asia Pacific Education Review 2023 View
  62. Xiang X, Yu H, Wang Y, Wang G. Stable local interpretable model-agnostic explanations based on a variational autoencoder. Applied Intelligence 2023;53(23):28226 View
  63. Sharma N, Mohanty S, Mahato S, Pattanaik C. A novel dataset and local interpretable model-agnostic explanations (LIME) for monkeypox prediction. Intelligent Decision Technologies 2023;17(4):1297 View
  64. Zakariaee S, Naderi N, Ebrahimi M, Kazemi-Arpanahi H. Comparing machine learning algorithms to predict COVID‑19 mortality using a dataset including chest computed tomography severity score data. Scientific Reports 2023;13(1) View
  65. Sun S, Wang L, Lin J, Sun Y, Ma C. An effective prediction model based on XGBoost for the 12-month recurrence of AF patients after RFA. BMC Cardiovascular Disorders 2023;23(1) View
  66. Liontos A, Biros D, Matzaras R, Tsarapatsani K, Kolios N, Zarachi A, Tatsis K, Pappa C, Nasiou M, Pargana E, Tsiakas I, Lymperatou D, Filippas-Ntekouan S, Athanasiou L, Samanidou V, Konstantopoulou R, Vagias I, Panteli A, Milionis H, Christaki E. Inflammation and Venous Thromboembolism in Hospitalized Patients with COVID-19. Diagnostics 2023;13(22):3477 View
  67. Zhuang Z, Qi Y, Yao Y, Yu Y. A predictive model for disease severity among COVID-19 elderly patients based on IgG subtypes and machine learning. Frontiers in Immunology 2023;14 View
  68. Budiarto A, Tsang K, Wilson A, Sheikh A, Shah S. Machine Learning–Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review. JMIR AI 2023;2:e46717 View
  69. Viderman D, Kotov A, Popov M, Abdildin Y. Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review. International Journal of Medical Informatics 2024;182:105308 View
  70. Asteris P, Gandomi A, Armaghani D, Kokoris S, Papandreadi A, Roumelioti A, Papanikolaou S, Tsoukalas M, Triantafyllidis L, Koutras E, Bardhan A, Mohammed A, Naderpour H, Paudel S, Samui P, Ntanasis-Stathopoulos I, Dimopoulos M, Terpos E. Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm. European Journal of Internal Medicine 2024 View
  71. Badiola-Zabala G, Lopez-Guede J, Estevez J, Graña M. Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022. Electronics 2024;13(6):1005 View
  72. Bate S, Stokes V, Greenlee H, Goh K, Whiting G, Kitchen G, Martin G, Parker A, Wilson A. External Validation of Prognostic Models in Critical Care: A Cautionary Tale From COVID-19 Pneumonitis. Critical Care Explorations 2024;6(4):e1067 View
  73. Yue G. Screening of lung cancer serum biomarkers based on Boruta-shap and RFC-RFECV algorithms. Journal of Proteomics 2024;301:105180 View
  74. 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
  75. Alie M, Negesse Y, Kindie K, Merawi D. Machine learning algorithms for predicting COVID-19 mortality in Ethiopia. BMC Public Health 2024;24(1) View

Books/Policy Documents

  1. Uphade D, Muley A. Techno-societal 2022. View
  2. Petch J, Tabja Bortesi J, Nelson W, Di S, Mamdani M. Artificial Intelligence for Medicine. View