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

JMIR Theme Issue: COVID-19 Special Issue (2388) Decision Support for Health Professionals (1127) Electronic Health Records (961) Machine Learning (1370) Theme Issue: Medical Informatics and COVID-19 (103) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1481)

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

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24018, first published .
Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation

Authors of this article:

Akhil Vaid1 Author Orcid Image ;   Sulaiman Somani1 Author Orcid Image ;   Adam J Russak1, 2 Author Orcid Image ;   Jessica K De Freitas1, 3 Author Orcid Image ;   Fayzan F Chaudhry1, 3 Author Orcid Image ;   Ishan Paranjpe1 Author Orcid Image ;   Kipp W Johnson3 Author Orcid Image ;   Samuel J Lee1 Author Orcid Image ;   Riccardo Miotto1, 3 Author Orcid Image ;   Felix Richter1, 3 Author Orcid Image ;   Shan Zhao1, 4 Author Orcid Image ;   Noam D Beckmann3 Author Orcid Image ;   Nidhi Naik1 Author Orcid Image ;   Arash Kia5, 6 Author Orcid Image ;   Prem Timsina5, 6 Author Orcid Image ;   Anuradha Lala5, 7 Author Orcid Image ;   Manish Paranjpe8 Author Orcid Image ;   Eddye Golden1 Author Orcid Image ;   Matteo Danieletto1 Author Orcid Image ;   Manbir Singh1 Author Orcid Image ;   Dara Meyer3 Author Orcid Image ;   Paul F O'Reilly3, 9, 10 Author Orcid Image ;   Laura Huckins3, 9, 10 Author Orcid Image ;   Patricia Kovatch11 Author Orcid Image ;   Joseph Finkelstein5 Author Orcid Image ;   Robert M. Freeman5, 6 Author Orcid Image ;   Edgar Argulian12, 13 Author Orcid Image ;   Andrew Kasarskis3, 5, 14, 15 Author Orcid Image ;   Bethany Percha2 Author Orcid Image ;   Judith A Aberg2, 16 Author Orcid Image ;   Emilia Bagiella6, 7 Author Orcid Image ;   Carol R Horowitz2, 5 Author Orcid Image ;   Barbara Murphy2 Author Orcid Image ;   Eric J Nestler17, 18 Author Orcid Image ;   Eric E Schadt3, 14 Author Orcid Image ;   Judy H Cho19 Author Orcid Image ;   Carlos Cordon-Cardo20 Author Orcid Image ;   Valentin Fuster7, 12, 13 Author Orcid Image ;   Dennis S Charney21 Author Orcid Image ;   David L Reich4 Author Orcid Image ;   Erwin P Bottinger1, 22 Author Orcid Image ;   Matthew A Levin3, 4 Author Orcid Image ;   Jagat Narula12, 13 Author Orcid Image ;   Zahi A Fayad23, 24 Author Orcid Image ;   Allan C Just25 Author Orcid Image ;   Alexander W Charney3, 9, 10 Author Orcid Image ;   Girish N Nadkarni1, 2, 19 Author Orcid Image ;   Benjamin S Glicksberg1, 3 Author Orcid Image
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Journals

  1. Ho T, Park J, Kim T, Park B, Lee J, Kim J, Kim K, Choi S, Kim Y, Lim J, Choi S. Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study. JMIR Medical Informatics 2021;9(1):e24973 View
  2. Fubini P, Suppan L. Prehospital Diagnosis of Shortness of Breath Caused by Profound Metformin-Associated Metabolic Acidosis. Healthcare 2021;9(1):74 View
  3. Sang S, Sun R, Coquet J, Carmichael H, Seto T, Hernandez-Boussard T. Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study. Journal of Medical Internet Research 2021;23(2):e23026 View
  4. El-Solh A, Lawson Y, Carter M, El-Solh D, Mergenhagen K, Lazzeri C. Comparison of in-hospital mortality risk prediction models from COVID-19. PLOS ONE 2020;15(12):e0244629 View
  5. Ikemura K, Bellin E, Yagi Y, Billett H, Saada M, Simone K, Stahl L, Szymanski J, Goldstein D, Reyes Gil M. Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study. Journal of Medical Internet Research 2021;23(2):e23458 View
  6. Wanyan T, Vaid A, De Freitas J, Somani S, Miotto R, Nadkarni G, Azad A, Ding Y, Glicksberg B. Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit. IEEE Transactions on Big Data 2021;7(1):38 View
  7. Viana dos Santos Santana Í, CM da Silveira A, Sobrinho Á, Chaves e Silva L, Dias da Silva L, Santos D, Gurjão E, Perkusich A. Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach. Journal of Medical Internet Research 2021;23(4):e27293 View
  8. Chung H, Ko H, Kang W, Kim K, Lee H, Park C, Song H, Choi T, Seo J, Lee J. Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation. Journal of Medical Internet Research 2021;23(4):e27060 View
  9. Domínguez-Olmedo J, Gragera-Martínez Á, Mata J, Pachón Álvarez V. Machine Learning Applied to Clinical Laboratory Data in Spain for COVID-19 Outcome Prediction: Model Development and Validation. Journal of Medical Internet Research 2021;23(4):e26211 View
  10. Incerti D, Rizzo S, Li X, Lindsay L, Yau V, Keebler D, Chia J, Tsai L. Prognostic model to identify and quantify risk factors for mortality among hospitalised patients with COVID-19 in the USA. BMJ Open 2021;11(4):e047121 View
  11. Borycki E, Kushniruk A, Kletke R, Vimarlund V, Senathirajah Y, Quintana Y. Enhancing Safety During a Pandemic Using Virtual Care Remote Monitoring Technologies and UML Modeling. Yearbook of Medical Informatics 2021;30(01):264 View
  12. Tezza F, Lorenzoni G, Azzolina D, Barbar S, Leone L, Gregori D. Predicting in-Hospital Mortality of Patients with COVID-19 Using Machine Learning Techniques. Journal of Personalized Medicine 2021;11(5):343 View
  13. Patrício A, Costa R, Henriques R. Predictability of COVID-19 Hospitalizations, Intensive Care Unit Admissions, and Respiratory Assistance in Portugal: Longitudinal Cohort Study. Journal of Medical Internet Research 2021;23(4):e26075 View
  14. Islam M, Poly T, Alsinglawi B, Lin M, Hsu M, Li Y. A State-of-the-Art Survey on Artificial Intelligence to Fight COVID-19. Journal of Clinical Medicine 2021;10(9):1961 View
  15. Adamidi E, Mitsis K, Nikita K. Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review. Computational and Structural Biotechnology Journal 2021;19:2833 View
  16. Karthikeyan A, Garg A, Vinod P, Priyakumar U. Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction. Frontiers in Public Health 2021;9 View
  17. Mamidi T, Tran-Nguyen T, Melvin R, Worthey E. Development of An Individualized Risk Prediction Model for COVID-19 Using Electronic Health Record Data. Frontiers in Big Data 2021;4 View
  18. Saria S, Schulam P, Yeh B, Burke D, Mooney S, Fong C, Sunshine J, Long D, O’Reilly-Shah V. Development and Validation of ARC, a Model for Anticipating Acute Respiratory Failure in Coronavirus Disease 2019 Patients. Critical Care Explorations 2021;3(6):e0441 View
  19. Syrowatka A, Kuznetsova M, Alsubai A, Beckman A, Bain P, Craig K, Hu J, Jackson G, Rhee K, Bates D. Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases. npj Digital Medicine 2021;4(1) View
  20. Khan I, Aslam N, Aljabri M, Aljameel S, Kamaleldin M, Alshamrani F, Chrouf S. Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients. International Journal of Environmental Research and Public Health 2021;18(12):6429 View
  21. Luo J, Zhang Z, Fu Y, Rao F. Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms. Results in Physics 2021;27:104462 View
  22. 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
  23. Roder J, Maguire L, Georgantas R, Roder H. Explaining multivariate molecular diagnostic tests via Shapley values. BMC Medical Informatics and Decision Making 2021;21(1) View
  24. Lin J, Chien T, Wang L, Chou W. An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission. Medicine 2021;100(28):e26532 View
  25. Werfel S, Jakob C, Borgmann S, Schneider J, Spinner C, Schons M, Hower M, Wille K, Haselberger M, Heuzeroth H, Rüthrich M, Dolff S, Kessel J, Heemann U, Vehreschild J, Rieg S, Schmaderer C. Development and validation of a simplified risk score for the prediction of critical COVID‐19 illness in newly diagnosed patients. Journal of Medical Virology 2021;93(12):6703 View
  26. 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
  27. Li R, Jin X, Ren J, Deng G, Li J, Gao Y, Zhang J, Du L, Liu J, Liu X, Wang X, Wang G, Zeng X. Relationship of Admission Serum Anion Gap and Prognosis of Critically Ill Patients: A Large Multicenter Cohort Study. Disease Markers 2022;2022:1 View
  28. Portuondo-Jiménez J, Barrio I, España P, García J, Villanueva A, Gascón M, Rodríguez L, Larrea N, García-Gutierrez S, Quintana J. Clinical prediction rules for adverse evolution in patients with COVID-19 by the Omicron variant. International Journal of Medical Informatics 2023;173:105039 View
  29. Tariq A, Celi L, Newsome J, Purkayastha S, Bhatia N, Trivedi H, Gichoya J, Banerjee I. Patient-specific COVID-19 resource utilization prediction using fusion AI model. npj Digital Medicine 2021;4(1) View
  30. Calabuig J, Jiménez-Fernández E, Sánchez-Pérez E, Manzanares S. Modeling Hospital Resource Management during the COVID-19 Pandemic: An Experimental Validation. Econometrics 2021;9(4):38 View
  31. Li Y, Wang L, Law J, Murali T, Pandey G, Lengauer T. Integrating multimodal data through interpretable heterogeneous ensembles. Bioinformatics Advances 2022;2(1) View
  32. Meystre S, Heider P, Kim Y, Davis M, Obeid J, Madory J, Alekseyenko A. Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing. Journal of the American Medical Informatics Association 2021;29(1):12 View
  33. Excoffier J, Salaün-Penquer N, Ortala M, Raphaël-Rousseau M, Chouaid C, Jung C. Analysis of COVID-19 inpatients in France during first lockdown of 2020 using explainability methods. Medical & Biological Engineering & Computing 2022;60(6):1647 View
  34. Roland T, Böck C, Tschoellitsch T, Maletzky A, Hochreiter S, Meier J, Klambauer G. Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests. Journal of Medical Systems 2022;46(5) View
  35. Demko I, Korchagin E, Cherkashin O, Gordeeva N, Anikin D, Anikina D. Possibilities of information systems for prediction of outcomes of new coronavirus infection COVID-19. Meditsinskiy sovet = Medical Council 2022;(4):42 View
  36. Verspoor K. The Evolution of Clinical Knowledge During COVID-19: Towards a Global Learning Health System. Yearbook of Medical Informatics 2021;30(01):176 View
  37. Cheng J, Sollee J, Hsieh C, Yue H, Vandal N, Shanahan J, Choi J, Tran T, Halsey K, Iheanacho F, Warren J, Ahmed A, Eickhoff C, Feldman M, Mortani Barbosa E, Kamel I, Lin C, Yi T, Healey T, Zhang P, Wu J, Atalay M, Bai H, Jiao Z, Wang J. COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data. European Radiology 2022;32(7):4446 View
  38. Bakin E, Stanevich O, Chmelevsky M, Belash V, Belash A, Savateeva G, Bokinova V, Arsentieva N, Sayenko L, Korobenkov E, Lioznov D, Totolian A, Polushin Y, Kulikov A. A Novel Approach for COVID-19 Patient Condition Tracking: From Instant Prediction to Regular Monitoring. Frontiers in Medicine 2021;8 View
  39. 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
  40. Moradi H, Bunnell H, Price B, Khodaverdi M, Vest M, Porterfield J, Anzalone A, Santangelo S, Kimble W, Harper J, Hillegass W, Hodder S, Chatterjee B. Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach. PLOS ONE 2023;18(3):e0282587 View
  41. Gong H, Wang M, Zhang H, Elahe M, Jin M. An Explainable AI Approach for the Rapid Diagnosis of COVID-19 Using Ensemble Learning Algorithms. Frontiers in Public Health 2022;10 View
  42. Douthit B, Walden R, Cato K, Coviak C, Cruz C, D'Agostino F, Forbes T, Gao G, Kapetanovic T, Lee M, Pruinelli L, Schultz M, Wieben A, Jeffery A. Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature. Applied Clinical Informatics 2022;13(01):161 View
  43. Bae J, Kapse S, Singh G, Gattu R, Ali S, Shah N, Marshall C, Pierce J, Phatak T, Gupta A, Green J, Madan N, Prasanna P. Predicting Mechanical Ventilation and Mortality in COVID-19 Using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study. Diagnostics 2021;11(10):1812 View
  44. Kuno T, Sahashi Y, Kawahito S, Takahashi M, Iwagami M, Egorova N. Prediction of in‐hospital mortality with machine learning for COVID‐19 patients treated with steroid and remdesivir. Journal of Medical Virology 2022;94(3):958 View
  45. AlJame M, Imtiaz A, Ahmad I, Mohammed A. Deep forest model for diagnosing COVID-19 from routine blood tests. Scientific Reports 2021;11(1) View
  46. Famiglini L, Campagner A, Carobene A, Cabitza F. A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patients. Medical & Biological Engineering & Computing 2022 View
  47. Kas’janenko K, Kozlov K, Zhdanov K, Lapikov I, Belikov V. SARS-CoV-2 severity prediction in young adults using artificial intelligence. Journal Infectology 2022;14(5):14 View
  48. Campbell T, Wilson M, Roder H, MaWhinney S, Georgantas R, Maguire L, Roder J, Erlandson K. Predicting prognosis in COVID-19 patients using machine learning and readily available clinical data. International Journal of Medical Informatics 2021;155:104594 View
  49. Jung C, Excoffier J, Raphaël-Rousseau M, Salaün-Penquer N, Ortala M, Chouaid C, Singh K. Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis. PLOS ONE 2022;17(2):e0263266 View
  50. Lin F, Goebel B, Lee B, Lu Y, Baskaran L, Yoon Y, Maliakal G, Gianni U, Bax A, Sengupta P, Slomka P, Dey D, Rozanski A, Han D, Berman D, Budoff M, Miedema M, Nasir K, Rumberger J, Whelton S, Blaha M, Shaw L. Mortality impact of low CAC density predominantly occurs in early atherosclerosis: explainable ML in the CAC consortium. Journal of Cardiovascular Computed Tomography 2023;17(1):28 View
  51. Baikpour M, Carlos A, Morasse R, Gissel H, Perez-Gutierrez V, Nino J, Amaya-Suarez J, Ali F, Toledano T, Arampulikan J, Gold M, Venugopal U, Pillai A, Omonuwa K, Menon V. Role of a Chest X-ray Severity Score in a Multivariable Predictive Model for Mortality in Patients with COVID-19: A Single-Center, Retrospective Study. Journal of Clinical Medicine 2022;11(8):2157 View
  52. Xiong Z, Zhang W, Liu S, Liu K, Wang J, Qin P, Liu Y, Jiang Q. The combination of CD138/MUM1 dual‐staining and artificial intelligence for plasma cell counting in the diagnosis of chronic endometritis. American Journal of Reproductive Immunology 2023;89(3) View
  53. Heilbroner S, Few R, Neilan T, Mueller J, Chalwa J, Charest F, Suryadevara S, Kratt C, Gomez-Caminero A, Dreyfus B. Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach. Journal for ImmunoTherapy of Cancer 2021;9(10):e002545 View
  54. Barnwal A, Cho H, Hocking T. Survival Regression with Accelerated Failure Time Model in XGBoost. Journal of Computational and Graphical Statistics 2022;31(4):1292 View
  55. Segall R, Sankarasubbu V. Survey of Recent Applications of Artificial Intelligence for Detection and Analysis of COVID-19 and Other Infectious Diseases. International Journal of Artificial Intelligence and Machine Learning 2022;12(2):1 View
  56. Iyengar A, Cohen W, Han J, Helmers M, Kelly J, Patrick W, Moss N, Molina E, Sheikh F, Houston B, Tedford R, Shore S, Vorovich E, Hsich E, Bensitel A, Alexander K, Chaudhry S, Vidula H, Kilic A, Genuardi M, Birati E, Atluri P. Effects of Body Mass Index on Presentation and Outcomes of COVID-19 among Heart Transplant and Left Ventricular Assist Device Patients: A Multi-Institutional Study. ASAIO Journal 2023;69(1):43 View
  57. Ovcharenko E, Kutikhin A, Gruzdeva O, Kuzmina A, Slesareva T, Brusina E, Kudasheva S, Bondarenko T, Kuzmenko S, Osyaev N, Ivannikova N, Vavin G, Moses V, Danilov V, Komossky E, Klyshnikov K. Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study. Journal of Cardiovascular Development and Disease 2023;10(2):39 View
  58. Sîrbu A, Barbieri G, Faita F, Ferragina P, Gargani L, Ghiadoni L, Priami C. Early outcome detection for COVID-19 patients. Scientific Reports 2021;11(1) View
  59. Rai N, Kaushik N, Kumar D, Raj C, Ali A. Mortality prediction of COVID-19 patients using soft voting classifier. International Journal of Cognitive Computing in Engineering 2022;3:172 View
  60. Dubowski K, Braganza G, Bozack A, Colicino E, DeFelice N, McGuinn L, Maru D, Lee A. COVID-19 subphenotypes at hospital admission are associated with mortality: a cross-sectional study. Annals of Medicine 2023;55(1):12 View
  61. Syed A, Khan T, Alromema N. A Hybrid Feature Selection Approach to Screen a Novel Set of Blood Biomarkers for Early COVID-19 Mortality Prediction. Diagnostics 2022;12(7):1604 View
  62. Hasan M, Bath P, Marincowitz C, Sutton L, Pilbery R, Hopfgartner F, Mazumdar S, Campbell R, Stone T, Thomas B, Bell F, Turner J, Biggs K, Petrie J, Goodacre S. Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and comparison of machine learning and deep learning methods. Computers in Biology and Medicine 2022;151:106024 View
  63. Barough S, Safavi-Naini S, Siavoshi F, Tamimi A, Ilkhani S, Akbari S, Ezzati S, Hatamabadi H, Pourhoseingholi M. Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features. Scientific Reports 2023;13(1) View
  64. Ramón A, Torres A, Milara J, Cascón J, Blasco P, Mateo J. eXtreme Gradient Boosting-based method to classify patients with COVID-19. Journal of Investigative Medicine 2022;70(7):1472 View
  65. Wanyan T, Honarvar H, Jaladanki S, Zang C, Naik N, Somani S, De Freitas J, Paranjpe I, Vaid A, Zhang J, Miotto R, Wang Z, Nadkarni G, Zitnik M, Azad A, Wang F, Ding Y, Glicksberg B. Contrastive learning improves critical event prediction in COVID-19 patients. Patterns 2021;2(12):100389 View
  66. Wong K, Xiang Y, Yin L, So H. Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach. JMIR Public Health and Surveillance 2021;7(9):e29544 View
  67. Chu K, Alharahsheh B, Garg N, Guha P. Evaluating risk stratification scoring systems to predict mortality in patients with COVID-19. BMJ Health & Care Informatics 2021;28(1):e100389 View
  68. Sokologorskiy S, Ovechkin A, Khapov I, Politov M, Bulanova E. Risk Factors of Severe Disease and Methods for Clinical Outcome Prediction in Patients with COVID-19 (Review). General Reanimatology 2022;18(1):31 View
  69. Schaefer J, Riley J, Li M, Cheney‐Peters D, Venkataraman C, Li C, Smaltz C, Bradley C, Lee C, Fitzpatrick D, Ney D, Zaret D, Chalikonda D, Mairose J, Chauhan K, Szot M, Jones R, Bashir‐Hamidu R, Mitsuhashi S, Kubey A. Comparing reliability of ICD‐10‐based COVID‐19 comorbidity data to manual chart review, a retrospective cross‐sectional study. Journal of Medical Virology 2022;94(4):1550 View
  70. Cotton D, Liu L, Vinson D, Ballard D, Sax D, Hofmann E, Lin J, Durant E, Kene M, Casey S, Ghiya M, Shan J, Bouvet S, McLachlan I, Rauchwerger A, Mark D, Reed M. Clinical characteristics of COVID‐19 patients evaluated in the emergency department: A retrospective cohort study of 801 cases. Journal of the American College of Emergency Physicians Open 2021;2(4) View
  71. González-Cebrián A, Borràs-Ferrís J, Ordovás-Baines J, Hermenegildo-Caudevilla M, Climente-Marti M, Tarazona S, Vitale R, Palací-López D, Sierra-Sánchez J, Saez de la Fuente J, Ferrer A, Camps J. Machine-learning-derived predictive score for early estimation of COVID-19 mortality risk in hospitalized patients. PLOS ONE 2022;17(9):e0274171 View
  72. Sievering A, Wohlmuth P, Geßler N, Gunawardene M, Herrlinger K, Bein B, Arnold D, Bergmann M, Nowak L, Gloeckner C, Koch I, Bachmann M, Herborn C, Stang A. Comparison of machine learning methods with logistic regression analysis in creating predictive models for risk of critical in-hospital events in COVID-19 patients on hospital admission. BMC Medical Informatics and Decision Making 2022;22(1) View
  73. Fang Z, Yang S, Lv C, An S, Wu W. Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study. BMJ Open 2022;12(7):e056685 View
  74. Jha P, Sahu M, Bisoy S, Sain M. Application of Model-Based Software Testing in the Health Care Domain. Electronics 2022;11(13):2062 View
  75. Xu Y, Trivedi A, Becker N, Blazes M, Ferres J, Lee A, Conrad Liles W, Bhatraju P. Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19. Scientific Reports 2022;12(1) View
  76. Linden T, Hanses F, Domingo-Fernández D, DeLong L, Kodamullil A, Schneider J, Vehreschild M, Lanznaster J, Ruethrich M, Borgmann S, Hower M, Wille K, Feldt T, Rieg S, Hertenstein B, Wyen C, Roemmele C, Vehreschild J, Jakob C, Stecher M, Kuzikov M, Zaliani A, Fröhlich H. Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases. Artificial Intelligence in the Life Sciences 2021;1:100020 View
  77. López M, Fernández‐Castro M, Martín‐Gil B, Muñoz‐Moreno M, Jiménez J. Auditing completion of nursing records as an outcome indicator for identifying patients at risk of developing pressure ulcers, falling, and social vulnerability: An observational study. Journal of Nursing Management 2022;30(4):1061 View
  78. Jia L, Wei Z, Zhang H, Wang J, Jia R, Zhou M, Li X, Zhang H, Chen X, Yu Z, Wang Z, Li X, Li T, Liu X, Liu P, Chen W, Li J, He K. An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19. Scientific Reports 2021;11(1) View
  79. Lau K, Ng K, Kwok K, Tsia K, Sin C, Lam C, Vardhanabhuti V. An Unsupervised Machine Learning Clustering and Prediction of Differential Clinical Phenotypes of COVID-19 Patients Based on Blood Tests—A Hong Kong Population Study. Frontiers in Medicine 2022;8 View
  80. Baik S, Lee M, Hong K, Park D. Development of Machine-Learning Model to Predict COVID-19 Mortality: Application of Ensemble Model and Regarding Feature Impacts. Diagnostics 2022;12(6):1464 View
  81. 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
  82. Dixon B, Holmes J. Managing Pandemics with Health Informatics. Yearbook of Medical Informatics 2021;30(01):069 View
  83. 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
  84. Doyle R. Machine Learning–Based Prediction of COVID-19 Mortality With Limited Attributes to Expedite Patient Prognosis and Triage: Retrospective Observational Study. JMIRx Med 2021;2(4):e29392 View
  85. Rosario B, Zhang A, Patel M, Rajmane A, Xie N, Weeraratne D, Alterovitz G. Characterizing Thrombotic Complication Risk Factors Associated With COVID-19 via Heterogeneous Patient Data: Retrospective Observational Study. Journal of Medical Internet Research 2022;24(10):e35860 View
  86. Shafiekhani S, Namdar P, Rafiei S. A COVID-19 forecasting system for hospital needs using ANFIS and LSTM models: A graphical user interface unit. DIGITAL HEALTH 2022;8:205520762210850 View
  87. Michelucci U, Sperti M, Piga D, Venturini F, Deriu M. A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification. Algorithms 2021;14(11):301 View
  88. Prosepe I, Groenwold R, Knevel R, Pajouheshnia R, van Geloven N. The Disconnect Between Development and Intended Use of Clinical Prediction Models for Covid-19: A Systematic Review and Real-World Data Illustration. Frontiers in Epidemiology 2022;2 View
  89. Clark-Boucher D, Boss J, Salvatore M, Smith J, Fritsche L, Mukherjee B, Kardeş S. Assessing the added value of linking electronic health records to improve the prediction of self-reported COVID-19 testing and diagnosis. PLOS ONE 2022;17(7):e0269017 View
  90. Kocadagli O, Baygul A, Gokmen N, Incir S, Aktan C. Clinical prognosis evaluation of COVID-19 patients: An interpretable hybrid machine learning approach. Current Research in Translational Medicine 2022;70(1):103319 View
  91. Moslehi S, Mahjub H, Farhadian M, Soltanian A, Mamani M. Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran. BMC Medical Research Methodology 2022;22(1) View
  92. Hasan A, Levene M, Weston D, Fromson R, Koslover N, Levene T. Monitoring COVID-19 on Social Media: Development of an End-to-End Natural Language Processing Pipeline Using a Novel Triage and Diagnosis Approach. Journal of Medical Internet Research 2022;24(2):e30397 View
  93. Shade J, Doshi A, Sung E, Popescu D, Minhas A, Gilotra N, Aronis K, Hays A, Trayanova N. Real-Time Prediction of Mortality, Cardiac Arrest, and Thromboembolic Complications in Hospitalized Patients With COVID-19. JACC: Advances 2022;1(2):100043 View
  94. Han X, Yu Z, Zhuo Y, Zhao B, Ren Y, Lamm L, Xue X, Feng J, Marr C, Shan F, Peng T, Zhang X. The value of longitudinal clinical data and paired CT scans in predicting the deterioration of COVID-19 revealed by an artificial intelligence system. iScience 2022;25(5):104227 View
  95. Chen M, Tan X, Padman R. A Machine Learning Approach to Support Urgent Stroke Triage Using Administrative Data and Social Determinants of Health at Hospital Presentation: Retrospective Study. Journal of Medical Internet Research 2023;25:e36477 View
  96. Abbas A, O’Byrne C, Fu D, Moraes G, Balaskas K, Struyven R, Beqiri S, Wagner S, Korot E, Keane P. Evaluating an automated machine learning model that predicts visual acuity outcomes in patients with neovascular age-related macular degeneration. Graefe's Archive for Clinical and Experimental Ophthalmology 2022 View
  97. Saadatmand S, Salimifard K, Mohammadi R, Kuiper A, Marzban M, Farhadi A. Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients. Annals of Operations Research 2023;328(1):1043 View
  98. Zhang Q, Huang A, Shao L, Wu P, Heidari A, Cai Z, Liang G, Chen H, Alotaibi F, Mafarja M, Ouyang J. A machine learning framework for identifying influenza pneumonia from bacterial pneumonia for medical decision making. Journal of Computational Science 2022;65:101871 View
  99. Zhou Y, Ge Y, Yang X, Cai Q, Ding Y, Hu L, Lu G. Prevalence and Outcomes of Pancreatic Enzymes Elevation in Patients With COVID-19: A Meta-Analysis and Systematic Review. Frontiers in Public Health 2022;10 View
  100. Gusev A, Vladzimirskiy A, Gavrilenko G. Methodical approach and recommendations for scientific description of creation and validation of machine learning model. Medical Technologies. Assessment and Choice 2022;(3):12 View
  101. Rahman T, Al-Ishaq F, Al-Mohannadi F, Mubarak R, Al-Hitmi M, Islam K, Khandakar A, Hssain A, Al-Madeed S, Zughaier S, Chowdhury M. Mortality Prediction Utilizing Blood Biomarkers to Predict the Severity of COVID-19 Using Machine Learning Technique. Diagnostics 2021;11(9):1582 View
  102. Han X, Yu Z, Zhuo Y, Zhao B, Ren Y, Lamm L, Xue X, Feng J, Marr C, Shan F, Peng T, Zhang X. The Value of Longitudinal Clinical Data and Paired CT Scans in Predicting the Deterioration of COVID-19 Revealed by an Artificial Intelligence System. SSRN Electronic Journal 2021 View
  103. Emami H, Rabiei R, Sohrabei S, Atashi A. Predicting the mortality of patients with Covid‐19: A machine learning approach. Health Science Reports 2023;6(4) View
  104. Hasan M, Bath P, Marincowitz C, Sutton L, Pilbery R, Hopfgartner F, Mazumdar S, Campbell R, Stone T, Benjamin T, Bell F, Turner J, Biggs K, Petrie J, Goodacre S. Pre-Hospital Prediction of Adverse Outcomes in Patients with Suspected COVID-19: Development, Application and Comparison of Machine Learning and Deep Learning Methods. SSRN Electronic Journal 2022 View
  105. Yangchen T, Koraishy F, Xu C, Hou W, Rohatgi R, Wang Y. Initial mean arterial blood pressure (MABP) measurement is a risk factor for mortality in hypertensive COVID-19 positive hospitalized patients. PLOS ONE 2023;18(3):e0283331 View
  106. Banoei M, Rafiepoor H, Zendehdel K, Seyyedsalehi M, Nahvijou A, Allameh F, Amanpour S. Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study. Frontiers in Medicine 2023;10 View
  107. Natanov D, Avihai B, McDonnell E, Lee E, Cook B, Altomare N, Ko T, Chaia A, Munoz C, Ouellette S, Nyalakonda S, Cederbaum V, Parikh P, Blaser M, Moscona A. Predicting COVID-19 prognosis in hospitalized patients based on early status. mBio 2023;14(5) View
  108. Dipaola F, Gatti M, Giaj Levra A, Menè R, Shiffer D, Faccincani R, Raouf Z, Secchi A, Rovere Querini P, Voza A, Badalamenti S, Solbiati M, Costantino G, Savevski V, Furlan R. Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study. Scientific Reports 2023;13(1) View
  109. Shakibfar S, Nyberg F, Li H, Zhao J, Nordeng H, Sandve G, Pavlovic M, Hajiebrahimi M, Andersen M, Sessa M. Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review. Frontiers in Public Health 2023;11 View
  110. Casillas N, Ramón A, Torres A, Blasco P, Mateo J. Predictive Model for Mortality in Severe COVID-19 Patients across the Six Pandemic Waves. Viruses 2023;15(11):2184 View
  111. Catalano M, Bortolotto C, Nicora G, Achilli M, Consonni A, Ruongo L, Callea G, Lo Tito A, Biasibetti C, Donatelli A, Cutti S, Comotto F, Stella G, Corsico A, Perlini S, Bellazzi R, Bruno R, Filippi A, Preda L. Performance of an AI algorithm during the different phases of the COVID pandemics: what can we learn from the AI and vice versa.. European Journal of Radiology Open 2023;11:100497 View
  112. 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
  113. Zheng F, Chen B, Zhang L, Chen H, Zang Y, Chen X, Li Y. Radiogenomic Analysis of Vascular Endothelial Growth Factor in Patients With Glioblastoma. Journal of Computer Assisted Tomography 2023;47(6):967 View
  114. Verzellesi L, Botti A, Bertolini M, Trojani V, Carlini G, Nitrosi A, Monelli F, Besutti G, Castellani G, Remondini D, Milanese G, Croci S, Sverzellati N, Salvarani C, Iori M. Machine and Deep Learning Algorithms for COVID-19 Mortality Prediction Using Clinical and Radiomic Features. Electronics 2023;12(18):3878 View
  115. 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
  116. Xin Y, Li H, Zhou Y, Yang Q, Mu W, Xiao H, Zhuo Z, Liu H, Wang H, Qu X, Wang C, Liu H, Yu K. The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis. BMC Medical Informatics and Decision Making 2023;23(1) View
  117. Kinoshita F, Takenaka T, Yamashita T, Matsumoto K, Oku Y, Ono Y, Wakasu S, Haratake N, Tagawa T, Nakashima N, Mori M. Development of artificial intelligence prognostic model for surgically resected non-small cell lung cancer. Scientific Reports 2023;13(1) View
  118. Nuutinen M, Aaltonen M, Edgren J, Häsä J, Lahelma M, Haavisto I. A Machine Learning Approach to Characterizing Long-Term Care Clients Affected by COVID-19 Restrictions - The Case Study of Hospitalization. SSRN Electronic Journal 2023 View
  119. Chen R, Chen J, Yang S, Luo S, Xiao Z, Lu L, Liang B, Liu S, Shi H, Xu J. Prediction of prognosis in COVID-19 patients using machine learning: A systematic review and meta-analysis. International Journal of Medical Informatics 2023;177:105151 View
  120. Abbasi Habashi S, Koyuncu M, Alizadehsani R. A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques. Diagnostics 2023;13(10):1749 View
  121. Casas-Rojo J, Ventura P, Antón Santos J, de Latierro A, Arévalo-Lorido J, Mauri M, Rubio-Rivas M, González-Vega R, Giner-Galvañ V, Otero Perpiñá B, Fonseca-Aizpuru E, Muiño A, Del Corral-Beamonte E, Gómez-Huelgas R, Arnalich-Fernández F, Llorente Barrio M, Sancha-Lloret A, Rábago Lorite I, Loureiro-Amigo J, Pintos-Martínez S, García-Sardón E, Montaño-Martínez A, Rojano-Rivero M, Ramos-Rincón J, López-Escobar A. Improving prediction of COVID-19 mortality using machine learning in the Spanish SEMI-COVID-19 registry. Internal and Emergency Medicine 2023;18(6):1711 View
  122. Carvantes-Barrera A, Díaz-González L, Rosales-Rivera M, Chávez-Almazán L. Risk Factors Associated with COVID-19 Lethality: A Machine Learning Approach Using Mexico Database. Journal of Medical Systems 2023;47(1) View
  123. Mohammedain S, Badran S, Elzouki A, Salim H, Chalaby A, Siddiqui M, Hussein Y, Rahim H, Thalib L, Alam M, Al-Badriyeh D, Al-Maadeed S, Doi S. Validation of a Risk Prediction Model for COVID-19: The PERIL Prospective Cohort Study. Future Virology 2023;18(15):991 View
  124. Huang S, Chaisson L, Galanter W, Jalali A, Menchaca M, Parde N, Rodríguez-Fernández J, Trotter A, Kochendorfer K. Lessons learned: Development of COVID-19 clinical staging models at a large urban research institution. Journal of Clinical and Translational Science 2023;7(1) View
  125. Talko A, Nevzorova V, Ermolitskaya M, Bondareva Z. The possibilities of data mining methods for assessing the outcomes of COVID-19 in patients with diseases of the blood system. Bulletin Physiology and Pathology of Respiration 2023;(88):50 View
  126. Xie F, Beukelman T, Sun D, Yun H, Curtis J. Identifying inpatient mortality in MarketScan claims data using machine learning. Pharmacoepidemiology and Drug Safety 2023;32(11):1299 View
  127. Shakibfar S, Zhao J, Li H, Nordeng H, Lupattelli A, Pavlovic M, Sandve G, Nyberg F, Wettermark B, Hajiebrahimi M, Andersen M, Sessa M. Machine learning-driven development of a disease risk score for COVID-19 hospitalization and mortality: a Swedish and Norwegian register-based study. Frontiers in Public Health 2023;11 View
  128. Panç K, Hürsoy N, Başaran M, Yazici M, Kaba E, Nalbant E, Gündoğdu H, Gürün E. Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets. Cureus 2023 View
  129. Pan C, Luo H, Cheung G, Zhou H, Cheng R, Cullum S, Wu C. Identifying Frailty in Older Adults Receiving Home Care Assessment Using Machine Learning: Longitudinal Observational Study on the Role of Classifier, Feature Selection, and Sample Size. JMIR AI 2024;3:e44185 View
  130. Xing J, Li C, Wu P, Cai X, Ouyang J. Optimized fuzzy K-nearest neighbor approach for accurate lung cancer prediction based on radial endobronchial ultrasonography. Computers in Biology and Medicine 2024;171:108038 View
  131. Yin M, Xu C, Zhu J, Xue Y, Zhou Y, He Y, Lin J, Liu L, Gao J, Liu X, Shen D, Fu C. Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images. BMC Medical Imaging 2024;24(1) View
  132. 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
  133. Ramón A, Bas A, Herrero S, Blasco P, Suárez M, Mateo J. Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach. Journal of Clinical Medicine 2024;13(7):1837 View
  134. 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
  135. Rajwa B, Naved M, Adibuzzaman M, Grama A, Khan B, Dundar M, Rochet J, Guillot G. Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality. PLOS Digital Health 2024;3(4):e0000327 View
  136. Singh K, Kaur N, Prabhu A. Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review. Current Topics in Medicinal Chemistry 2024;24(8):737 View
  137. Hussain S, Songhua X, Aslam M, Hussain F, Ali I. Optimal Prognostic Accuracy: Machine Learning Approaches for COVID-19 Prognosis with Biomarkers and Demographic Information. New Generation Computing 2024 View
  138. Zou W, Yao X, Chen Y, Li X, Huang J, Zhang Y, Yu L, Xie B. An elastic net regression model for predicting the risk of ICU admission and death for hospitalized patients with COVID-19. Scientific Reports 2024;14(1) View
  139. RAO A, JAIN R, SINGH M, GARG R. Predictive Interpretable Analytics Models for Forecasting Healthcare Costs Using Open Healthcare Data. Healthcare Analytics 2024:100351 View

Books/Policy Documents

  1. Gupta R. Technologies, Artificial Intelligence and the Future of Learning Post-COVID-19. View
  2. Segall R. Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning. View
  3. Munnangi A, Sekaran R, Raveendran A, Ramachandran M. How COVID-19 is Accelerating the Digital Revolution. View