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

JMIR Theme Issue: COVID-19 Special Issue (2391) Machine Learning (1385) Secondary Use of Clinical Data for Research and Surveillance (320) Infectious Diseases (non-STD/STI) (1485) Outbreak and Pandemic Preparedness and Management (1675) Surveillance Systems (428) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1482) Intensive Care Unit (ICU) (101)

Published on in Vol 22, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21439, first published .
Clinical Predictive Models for COVID-19: Systematic Study

Clinical Predictive Models for COVID-19: Systematic Study

Clinical Predictive Models for COVID-19: Systematic Study

Authors of this article:

Patrick Schwab1 Author Orcid Image ;   August DuMont Schütte2 Author Orcid Image ;   Benedikt Dietz2 Author Orcid Image ;   Stefan Bauer3 Author Orcid Image
Article Authors Cited by (62) Tweetations (3) Metrics

Journals

  1. Zhang R, Tie X, Qi Z, Bevins N, Zhang C, Griner D, Song T, Nadig J, Schiebler M, Garrett J, Li K, Reeder S, Chen G. Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence. Radiology 2021;298(2):E88 View
  2. Vaid A, Somani S, Russak A, De Freitas J, Chaudhry F, Paranjpe I, Johnson K, Lee S, Miotto R, Richter F, Zhao S, Beckmann N, Naik N, Kia A, Timsina P, Lala A, Paranjpe M, Golden E, Danieletto M, Singh M, Meyer D, O'Reilly P, Huckins L, Kovatch P, Finkelstein J, Freeman R, Argulian E, Kasarskis A, Percha B, Aberg J, Bagiella E, Horowitz C, Murphy B, Nestler E, Schadt E, Cho J, Cordon-Cardo C, Fuster V, Charney D, Reich D, Bottinger E, Levin M, Narula J, Fayad Z, Just A, Charney A, Nadkarni G, Glicksberg B. Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation. Journal of Medical Internet Research 2020;22(11):e24018 View
  3. Dai W, Ke P, Li Z, Zhuang Q, Huang W, Wang Y, Xiong Y, Huang X. Establishing Classifiers With Clinical Laboratory Indicators to Distinguish COVID-19 From Community-Acquired Pneumonia: Retrospective Cohort Study. Journal of Medical Internet Research 2021;23(2):e23390 View
  4. 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
  5. Feki I, Ammar S, Kessentini Y, Muhammad K. Federated learning for COVID-19 screening from Chest X-ray images. Applied Soft Computing 2021;106:107330 View
  6. Göreke V, Sarı V, Kockanat S. A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings. Applied Soft Computing 2021;106:107329 View
  7. Tretter F, Wolkenhauer O, Meyer-Hermann M, Dietrich J, Green S, Marcum J, Weckwerth W. The Quest for System-Theoretical Medicine in the COVID-19 Era. Frontiers in Medicine 2021;8 View
  8. Kline J, Camargo C, Courtney D, Kabrhel C, Nordenholz K, Aufderheide T, Baugh J, Beiser D, Bennett C, Bledsoe J, Castillo E, Chisolm-Straker M, Goldberg E, House H, House S, Jang T, Lim S, Madsen T, McCarthy D, Meltzer A, Moore S, Newgard C, Pagenhardt J, Pettit K, Pulia M, Puskarich M, Southerland L, Sparks S, Turner-Lawrence D, Vrablik M, Wang A, Weekes A, Westafer L, Wilburn J, Kalendar R. Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021. PLOS ONE 2021;16(3):e0248438 View
  9. Chee M, Ong M, Siddiqui F, Zhang Z, Lim S, Ho A, Liu N. Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review. International Journal of Environmental Research and Public Health 2021;18(9):4749 View
  10. 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
  11. Halasz G, Sperti M, Villani M, Michelucci U, Agostoni P, Biagi A, Rossi L, Botti A, Mari C, Maccarini M, Pura F, Roveda L, Nardecchia A, Mottola E, Nolli M, Salvioni E, Mapelli M, Deriu M, Piga D, Piepoli M. A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score. Journal of Medical Internet Research 2021;23(5):e29058 View
  12. TOKMAK M, KÜÇÜKSİLLE E. DERİN SİNİR AĞLARI VE YENİDEN ÖRNEKLEME METOTLARI İLE RUTİN KAN TESTLERİNE DAYALI COVID-19 TESPİTİ. Konya Journal of Engineering Sciences 2021;9(2):522 View
  13. Gök E, Olgun M. SMOTE-NC and gradient boosting imputation based random forest classifier for predicting severity level of covid-19 patients with blood samples. Neural Computing and Applications 2021;33(22):15693 View
  14. Nevel A, Kline J. Inter‐rater reliability and prospective validation of a clinical prediction rule for SARS‐CoV‐2 infection. Academic Emergency Medicine 2021;28(7):761 View
  15. Martinez-Velazquez R, Tobón V. D, Sanchez A, El Saddik A, Petriu E. A Machine Learning Approach as an Aid for Early COVID-19 Detection. Sensors 2021;21(12):4202 View
  16. Szklanna P, Altaie H, Comer S, Cullivan S, Kelliher S, Weiss L, Curran J, Dowling E, O'Reilly K, Cotter A, Marsh B, Gaine S, Power N, Lennon Á, McCullagh B, Ní Áinle F, Kevane B, Maguire P. Routine Hematological Parameters May Be Predictors of COVID-19 Severity. Frontiers in Medicine 2021;8 View
  17. 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
  18. Tolmachev I, Kaverina I, Vrazhnov D, Starikov I, Starikova E, Kostuchenko E. Application of Artificial Intelligence Methods Depending on the Tasks Solved during COVID-19 Pandemic. COVID 2022;2(10):1341 View
  19. Hernández-Pereira E, Fontenla-Romero O, Bolón-Canedo V, Cancela-Barizo B, Guijarro-Berdiñas B, Alonso-Betanzos A. Machine learning techniques to predict different levels of hospital care of CoVid-19. Applied Intelligence 2022;52(6):6413 View
  20. Wang J, Zhou X, Hou Z, Xu X, Zhao Y, Chen S, Zhang J, Shao L, Yan R, Wang M, Ge M, Hao T, Tu Y, Huang H. Homogeneous ensemble models for predicting infection levels and mortality of COVID-19 patients: Evidence from China. DIGITAL HEALTH 2022;8:205520762211336 View
  21. Gosnell J, Finn M, Marckini D, Molla A, Sowinski H. Identifying Predictors of Psychological Problems Among Adolescents With Congenital Heart Disease for Referral to Psychological Care: A Pilot Study. CJC Pediatric and Congenital Heart Disease 2023;2(1):3 View
  22. 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
  23. 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
  24. ARSLAN H, ER O. A Comparative Study on COVID-19 Prediction Using Deep Learning and Machine Learning Algorithms: A Case Study on Performance Analysis. Sakarya University Journal of Computer and Information Sciences 2022;5(1):71 View
  25. Yaman S, Karakaya B, Erol Y. A novel normalization algorithm to facilitate pre-assessment of Covid-19 disease by improving accuracy of CNN and its FPGA implementation. Evolving Systems 2023;14(4):581 View
  26. Dairi A, Harrou F, Sun Y. Deep Generative Learning-Based 1-SVM Detectors for Unsupervised COVID-19 Infection Detection Using Blood Tests. IEEE Transactions on Instrumentation and Measurement 2022;71:1 View
  27. Xiong Y, Ma Y, Ruan L, Li D, Lu C, Huang L. Comparing different machine learning techniques for predicting COVID-19 severity. Infectious Diseases of Poverty 2022;11(1) View
  28. Çubukçu H, Topcu D, Bayraktar N, Gülşen M, Sarı N, Arslan A. Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests. American Journal of Clinical Pathology 2022;157(5):758 View
  29. 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
  30. Wibowo P, Fatichah C. Pruning-based oversampling technique with smoothed bootstrap resampling for imbalanced clinical dataset of Covid-19. Journal of King Saud University - Computer and Information Sciences 2022;34(9):7830 View
  31. Jha R, Bhattacharjee V, Mustafi A, Sahana S. Improved disease diagnosis system for COVID-19 with data refactoring and handling methods. Frontiers in Psychology 2022;13 View
  32. Park Y, Park S, Lee M. Digital Health Care Industry Ecosystem: Network Analysis. Journal of Medical Internet Research 2022;24(8):e37622 View
  33. Marfe G, Perna S, Shukla A. Effectiveness of COVID‑19 vaccines and their challenges (Review). Experimental and Therapeutic Medicine 2021;22(6) View
  34. Domínguez-Olmedo J, Gragera-Martínez Á, Mata J, Pachón V. Age-Stratified Analysis of COVID-19 Outcome Using Machine Learning Predictive Models. Healthcare 2022;10(10):2027 View
  35. Jordan E, Shin D, Leekha S, Azarm S. Optimization in the Context of COVID-19 Prediction and Control: A Literature Review. IEEE Access 2021;9:130072 View
  36. Phuong J, Hyland S, Mooney S, Long D, Takeda K, Vavilala M, O’Hara K, Khubchandani J. Sociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types. PLOS ONE 2021;16(10):e0258339 View
  37. McRae A, Hohl C, Rosychuk R, Vatanpour S, Ghaderi G, Archambault P, Brooks S, Cheng I, Davis P, Hayward J, Lang E, Ohle R, Rowe B, Welsford M, Yadav K, Morrison L, Perry J. CCEDRRN COVID-19 Infection Score (CCIS): development and validation in a Canadian cohort of a clinical risk score to predict SARS-CoV-2 infection in patients presenting to the emergency department with suspected COVID-19. BMJ Open 2021;11(12):e055832 View
  38. Muñoz-Organero M, Queipo-Álvarez P. Deep Spatiotemporal Model for COVID-19 Forecasting. Sensors 2022;22(9):3519 View
  39. Alharbi A, Abdur Rahman M. Review of Recent Technologies for Tackling COVID-19. SN Computer Science 2021;2(6) View
  40. KAPUCU V, TURHAN S, PIÇAKÇIEFE M, DOĞU E. COVID19PREDICTOR: WEB-BASED INTERFACE TO DEVELOP MACHINE LEARNING MODELS FOR DIAGNOSIS OF COVID-19 BASED ON CLINICAL DATA AND ROUTINE TESTS. Karya Journal of Health Science 2022;3(3):216 View
  41. ARSLAN H. COVID-19 Hastalarının Mortalitesini Tahmin Etmek için Torbalama ve Arttırma Yöntemleri. DÜMF Mühendislik Dergisi 2022 View
  42. Ortiz-Barrios M, Arias-Fonseca S, Ishizaka A, Barbati M, Avendaño-Collante B, Navarro-Jiménez E. Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study. Journal of Business Research 2023;160:113806 View
  43. Kim Chi D, Van Lang T, Nguyen T. Clinical data-driven approach to identifying COVID-19 and influenza from a gradient-boosting model. Cogent Engineering 2023;10(1) View
  44. Levy T, Makhnevich A, Barish M, Zanos T, Cohen S. The association of clinically relevant variables with chest radiograph lung disease burden quantified in real-time by radiologists upon initial presentation in individuals hospitalized with COVID-19. Clinical Imaging 2023;101:56 View
  45. 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
  46. Zhu J, Galhotra S, Sabri N, Salimi B. Consistent Range Approximation for Fair Predictive Modeling. Proceedings of the VLDB Endowment 2023;16(11):2925 View
  47. Wan G, Wu X, Zhang X, Sun H, Yu X. Development of a novel machine learning model based on laboratory and imaging indices to predict acute cardiac injury in cancer patients with COVID-19 infection: a retrospective observational study. Journal of Cancer Research and Clinical Oncology 2023;149(19):17039 View
  48. Zhunissova U, Dzierżak R, Omiotek Z, Lytvynenko V. A Novel COVID-19 Diagnosis Approach Utilizing a Comprehensive Set of Diagnostic Information (CSDI). Journal of Clinical Medicine 2023;12(21):6912 View
  49. Butt M, Tariq N, Ashraf M, Alsagri H, Moqurrab S, Alhakbani H, Alduraywish Y. A Fog-Based Privacy-Preserving Federated Learning System for Smart Healthcare Applications. Electronics 2023;12(19):4074 View
  50. Mudumbai S, Gabriel R, Howell S, Tan J, Freundlich R, O’Reilly-Shah V, Kendale S, Poterack K, Rothman B. Public Health Informatics and the Perioperative Physician: Looking to the Future. Anesthesia & Analgesia 2024;138(2):253 View
  51. Malik H, Anees T. Federated learning with deep convolutional neural networks for the detection of multiple chest diseases using chest x-rays. Multimedia Tools and Applications 2024;83(23):63017 View
  52. Feriz A, Jalali M, Forghani Y. Enhancing CNN‐LSTM neural networks using jellyfish search algorithm for pandemic modeling. Concurrency and Computation: Practice and Experience 2024;36(17) View
  53. El Gannour O, Hamida S, Cherradi B, Raihani A. Enhancing early detection of COVID-19 with machine learning and blood test results. Multimedia Tools and Applications 2024 View
  54. Puttegowda K, DS S, Mallu S, CP V, Ravi V, BC S. Automatic COVID-19 Prediction with Comprehensible Machine Learning Models. The Open Public Health Journal 2024;17(1) View
  55. Villanueva I, Conesa D, Català M, López Cano C, Perramon-Malavez A, Molinuevo D, de Rioja V, López D, Alonso S, Cardona P, Montañola-Sales C, Prats C, Alvarez-Lacalle E. Country-report pattern corrections of new cases allow accurate 2-week predictions of COVID-19 evolution with the Gompertz model. Scientific Reports 2024;14(1) View
  56. Meyer L, Stead S, Salge T, Antons D. Artificial intelligence in acute care: A systematic review, conceptual synthesis, and research agenda. Technological Forecasting and Social Change 2024;206:123568 View

Books/Policy Documents

  1. Durgadevi V, Arunagiri B, Dhanapal V, Seeniraj Y, Thirugnanam S. Ambient Intelligence in Health Care. View
  2. Gupta R. Technologies, Artificial Intelligence and the Future of Learning Post-COVID-19. View
  3. Saggu J, Bansal A. Proceedings of Data Analytics and Management. View