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

  1. Pham D, Tran B, Nguyen S, Alahakoon D. Swarm-Based Machine Learning Algorithm for Building Interpretable Classifiers. IEEE Access 2020;8:228136 View
  2. Liu L, Zhao Q, Wu J, Luo Y, Dong H, Chen Z, Gui R, Wang Y. Machine Learning for the Prediction of Red Blood Cell Transfusion in Patients During or After Liver Transplantation Surgery. Frontiers in Medicine 2021;8 View
  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
  5. Liu N, Wnent J, Lee J, Ning Y, Ho A, Siddiqui F, Lim S, Chia M, Tiah L, Mao D, Gräsner J, Ong M, Gan H, Cheah S, Ng W, Tay W, Leong B, Nadarajan G, Doctor N, Tham L, Arulanandam S. Validation of the CaRdiac Arrest Survival Score (CRASS) for predicting good neurological outcome after out-of-hospital cardiac arrest in an Asian emergency medical service system. Resuscitation 2022;176:42 View
  6. Ning Y, Li S, Ong M, Xie F, Chakraborty B, Ting D, Liu N, Lu H. A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study. PLOS Digital Health 2022;1(6):e0000062 View
  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
  8. Liu N, Liu M, Chen X, Ning Y, Lee J, Siddiqui F, Saffari S, Ho A, Shin S, Ma M, Tanaka H, Ong M. Development and validation of an interpretable prehospital return of spontaneous circulation (P-ROSC) score for patients with out-of-hospital cardiac arrest using machine learning: A retrospective study. eClinicalMedicine 2022;48:101422 View
  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
  10. Wong X, Ang Y, Li K, Chin Y, Lam S, Tan K, Chua M, Ong M, Liu N, Pourghaderi A, Ho A. Development and validation of the SARICA score to predict survival after return of spontaneous circulation in out of hospital cardiac arrest using an interpretable machine learning framework. Resuscitation 2022;170:126 View
  11. Liu X, DuMontier C, Hu P, Liu C, Yeung W, Mao Z, Ho V, Thoral P, Kuo P, Hu J, Li D, Cao D, Mark R, Zhou F, Zhang Z, Celi L, Lipsitz L. Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome: An International Multicenter Retrospective Study. The Journals of Gerontology: Series A 2022 View
  12. Liu N, Chee M, Foo M, Pong J, Guo D, Koh Z, Ho A, Niu C, Chong S, Ong M, Crivellari M. Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department. PLOS ONE 2021;16(8):e0249868 View
  13. Pai K, Su S, Chan M, Wu C, Chao W. Explainable machine learning approach to predict extubation in critically ill ventilated patients: a retrospective study in central Taiwan. BMC Anesthesiology 2022;22(1) View
  14. Liao W, J Derijks H, A Blencke A, de Vries E, van Seyen M, J van Marum R. Dual autoencoders modeling of electronic health records for adverse drug event preventability prediction. Intelligence-Based Medicine 2022;6:100077 View
  15. Xie F, Ning Y, Yuan H, Goldstein B, Ong M, Liu N, Chakraborty B. AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. Journal of Biomedical Informatics 2022;125:103959 View
  16. Ning Y, Ong M, Chakraborty B, Goldstein B, Ting D, Vaughan R, Liu N. Shapley variable importance cloud for interpretable machine learning. Patterns 2022;3(4):100452 View
  17. Testa A, Versaci F, Biondi-Zoccai G. Individualised prognosis in out-of-hospital cardiac arrest: The case for P-ROSC in Asian people. eClinicalMedicine 2022;48:101446 View
  18. Yu J, Heo S, Xie F, Liu N, Yoon S, Chang H, Kim T, Lee S, Hock Ong M, Ng Y, Do shin S, Kajino K, Cha W. Development and Asian-wide validation of the Grade for Interpretable Field Triage (GIFT) for predicting mortality in pre-hospital patients using the Pan-Asian Trauma Outcomes Study (PATOS). The Lancet Regional Health - Western Pacific 2023;34:100733 View
  19. Rudin C, Chen C, Chen Z, Huang H, Semenova L, Zhong C. Interpretable machine learning: Fundamental principles and 10 grand challenges. Statistics Surveys 2022;16(none) View
  20. Mumoli N, Cei F, Vecchiè A. A risk score for patients with ischemic heart disease in the emergency department. Internal and Emergency Medicine 2023;18(3):849 View
  21. Dey S, Chakraborty P, Kwon B, Dhurandhar A, Ghalwash M, Suarez Saiz F, Ng K, Sow D, Varshney K, Meyer P. Human-centered explainability for life sciences, healthcare, and medical informatics. Patterns 2022;3(5):100493 View
  22. Peng T, Liu L, Liu F, Ding L, Liu J, Zhou H, Liu C. Machine learning-based infection prediction model for newly diagnosed multiple myeloma patients. Frontiers in Neuroinformatics 2023;16 View
  23. Chang H, Cha W. Artificial intelligence decision points in an emergency department. Clinical and Experimental Emergency Medicine 2022;9(3):165 View
  24. Ang Y, Li S, Ong M, Xie F, Teo S, Choong L, Koniman R, Chakraborty B, Ho A, Liu N. Development and validation of an interpretable clinical score for early identification of acute kidney injury at the emergency department. Scientific Reports 2022;12(1) View
  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
  26. Liu N, Xie F, Siddiqui F, Ho A, Chakraborty B, Nadarajan G, Tan K, Ong M. Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation. JMIR Research Protocols 2022;11(3):e34201 View
  27. Kaplan A, Tipnis U, Beckham J, Kimbrel N, Oslin D, McMahon B. Continuous-time probabilistic models for longitudinal electronic health records. Journal of Biomedical Informatics 2022;130:104084 View
  28. Xie F, Ong M, Liew J, Tan K, Ho A, Nadarajan G, Low L, Kwan Y, Goldstein B, Matchar D, Chakraborty B, Liu N. Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions. JAMA Network Open 2021;4(8):e2118467 View
  29. Yuan H, Xie F, Ong M, Ning Y, Chee M, Saffari S, Abdullah H, Goldstein B, Chakraborty B, Liu N. AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. Journal of Biomedical Informatics 2022;129:104072 View
  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
  37. Kwok S, Wang G, Sohel F, Kashani K, Zhu Y, Wang Z, Antpack E, Khandelwal K, Pagali S, Nanda S, Abdalrhim A, Sharma U, Bhagra S, Dugani S, Takahashi P, Murad M, Yousufuddin M. An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems. Respiratory Research 2023;24(1) View
  38. Chen Y, Clayton E, Novak L, Anders S, Malin B. Human-Centered Design to Address Biases in Artificial Intelligence. Journal of Medical Internet Research 2023;25:e43251 View
  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
  40. Xie F, Ning Y, Liu M, Li S, Saffari S, Yuan H, Volovici V, Ting D, Goldstein B, Ong M, Vaughan R, Chakraborty B, Liu N. A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes. STAR Protocols 2023;4(2):102302 View
  41. Piliuk K, Tomforde S. Artificial intelligence in emergency medicine. A systematic literature review. International Journal of Medical Informatics 2023;180:105274 View
  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
  47. Okada Y, Ning Y, Ong M. Explainable artificial intelligence in emergency medicine: an overview. Clinical and Experimental Emergency Medicine 2023;10(4):354 View
  48. Miranda O, Fan P, Qi X, Wang H, Brannock M, Kosten T, Ryan N, Kirisci L, Wang L. DeepBiomarker2: Prediction of Alcohol and Substance Use Disorder Risk in Post-Traumatic Stress Disorder Patients Using Electronic Medical Records and Multiple Social Determinants of Health. Journal of Personalized Medicine 2024;14(1):94 View
  49. Leung E, Lee A, Liu Y, Hung C, Fan N, Ching S, Yee H, He Y, Xu R, Tsang H, Guan J. Impact of Environment on Pain among the Working Poor: Making Use of Random Forest-Based Stratification Tool to Study the Socioecology of Pain Interference. International Journal of Environmental Research and Public Health 2024;21(2):179 View

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