Published on in Vol 8, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21798, first published .
AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records

AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records

AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records

Journals

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  3. Lu Z, Zhang J, Hong J, Wu J, Liu Y, Xiao W, Hua T, Yang M. Development of a Nomogram to Predict 28-Day Mortality of Patients With Sepsis-Induced Coagulopathy: An Analysis of the MIMIC-III Database. Frontiers in Medicine 2021;8 View
  4. Lin M, Li C, Lin P, Wang J, Chan M, Wu C, Chao W. Explainable Machine Learning to Predict Successful Weaning Among Patients Requiring Prolonged Mechanical Ventilation: A Retrospective Cohort Study in Central Taiwan. Frontiers in Medicine 2021;8 View
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  7. Saffari S, Ning Y, Xie F, Chakraborty B, Volovici V, Vaughan R, Ong M, Liu N. AutoScore-Ordinal: an interpretable machine learning framework for generating scoring models for ordinal outcomes. BMC Medical Research Methodology 2022;22(1) View
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  9. Shu T, Huang J, Deng J, Chen H, Zhang Y, Duan M, Wang Y, Hu X, Liu X. Development and assessment of scoring model for ICU stay and mortality prediction after emergency admissions in ischemic heart disease: a retrospective study of MIMIC-IV databases. Internal and Emergency Medicine 2023;18(2):487 View
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  25. Yu J, Xie F, Nan L, Yoon S, Ong M, Ng Y, Cha W. An external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department. Scientific Reports 2022;12(1) View
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  30. Petersen K, Lipton R, Grober E, Davatzikos C, Sperling R, Ezzati A. Predicting Amyloid Positivity in Cognitively Unimpaired Older Adults. Neurology 2022;98(24) View
  31. Tan H, Liu N, Tan C, Sia A, Sng B. Developing the BreakThrough Pain Risk Score: an interpretable machine-learning-based risk score to predict breakthrough pain with labour epidural analgesia. Canadian Journal of Anesthesia/Journal canadien d'anesthésie 2022;69(10):1315 View
  32. Xie F, Zhou J, Lee J, Tan M, Li S, Rajnthern L, Chee M, Chakraborty B, Wong A, Dagan A, Ong M, Gao F, Liu N. Benchmarking emergency department prediction models with machine learning and public electronic health records. Scientific Data 2022;9(1) View
  33. Rajendram M, Zarisfi F, Xie F, Shahidah N, Pek P, Yeo J, Tan B, Ma M, Do Shin S, Tanaka H, Ong M, Liu N, Ho A. External validation of the Survival After ROSC in Cardiac Arrest (SARICA) score for predicting survival after return of spontaneous circulation using multinational pan-asian cohorts. Frontiers in Medicine 2022;9 View
  34. Xie F, Liu N, Yan L, Ning Y, Lim K, Gong C, Kwan Y, Ho A, Low L, Chakraborty B, Ong M. Development and validation of an interpretable machine learning scoring tool for estimating time to emergency readmissions. eClinicalMedicine 2022;45:101315 View
  35. Xie F, Yuan H, Ning Y, Ong M, Feng M, Hsu W, Chakraborty B, Liu N. Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. Journal of Biomedical Informatics 2022;126:103980 View
  36. Han L, Wang X, Cai T. Identifying surrogate markers in real‐world comparative effectiveness research. Statistics in Medicine 2022;41(26):5290 View
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  39. Lee S, Yu J, Kim Y, Kim M, Lew H. Application of an Interpretable Machine Learning for Estimating Severity of Graves’ Orbitopathy Based on Initial Finding. Journal of Clinical Medicine 2023;12(7):2640 View
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  42. Chong S, Niu C, Ong G, Piragasam R, Khoo Z, Koh Z, Guo D, Lee J, Ong M, Liu N. Febrile infants risk score at triage (FIRST) for the early identification of serious bacterial infections. Scientific Reports 2023;13(1) View
  43. Zahid S, Agrawal A, Salman F, Khan M, Ullah W, Teebi A, Khan S, Sulaiman S, Balla S. Development and Validation of a Machine Learning Risk-Prediction Model for 30-Day Readmission for Heart Failure Following Transcatheter Aortic Valve Replacement (TAVR-HF Score). Current Problems in Cardiology 2024;49(2):102143 View
  44. Li S, Ning Y, Ong M, Chakraborty B, Hong C, Xie F, Yuan H, Liu M, Buckland D, Chen Y, Liu N. FedScore: A privacy-preserving framework for federated scoring system development. Journal of Biomedical Informatics 2023;146:104485 View
  45. Jiang Z, Zhang G, Xia J, Lv S. Development and Validation Nomogram for Predicting the Survival of Patients with Thrombocytopenia in Intensive Care Units. Risk Management and Healthcare Policy 2023;Volume 16:1287 View
  46. Jeon J, Yu J, Song Y, Jung W, Lee K, Lee J, Huh W, Cha W, Jang H. Prediction tool for renal adaptation after living kidney donation using interpretable machine learning. Frontiers in Medicine 2023;10 View
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Books/Policy Documents

  1. Awotunde J, Imoize A, Adeniyi A, Abiodun K, Ayo E, Kavitha K, Ajamu G, Ogundokun R. Explainable Machine Learning for Multimedia Based Healthcare Applications. View
  2. Franklin J, Powers H, Erickson J, McCusker J, McGuinness D, Bennett K. Knowledge Graphs and Semantic Web. View
  3. Liu S, Chen H. Trustworthy Artificial Intelligence for Healthcare. View