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Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation

Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation

Xie et al [29] developed the interpretable machine learning–based Auto Score framework and used it to derive the score for emergency risk prediction to estimate the probability of mortality during an inpatient stay [30]. By leveraging large-scale electronic health records (EHRs) and machine learning, we intend to create an innovative, dynamic, and interpretable System for Emergency Risk Triage (SERT) for risk stratification in the ED.

Nan Liu, Feng Xie, Fahad Javaid Siddiqui, Andrew Fu Wah Ho, Bibhas Chakraborty, Gayathri Devi Nadarajan, Kenneth Boon Kiat Tan, Marcus Eng Hock Ong

JMIR Res Protoc 2022;11(3):e34201

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