Objective: The objective of this study was to evaluate the incremental predictive power of
electronic medical record (EMR) data, relative to the information available in more easily …

The availability of data from electronic health records facilitates the development and
evaluation of risk-prediction models, but estimation of prediction accuracy could be limited …

ABSTRACT OBJECTIVES: Electronic health record (EHR) data have become increasingly
available and may help inform clinical prediction. However, predicting hospitalizations …

Background There is increasing interest in using prediction models to identify patients at risk
of readmission or death after hospital discharge, but existing models have significant …

Objective To develop a predictive model for real-time predictions of length of stay, mortality,
and readmission for hospitalized patients using electronic health records (EHRs). Materials …

Objective To provide focused evaluation of predictive modeling of electronic medical record
(EMR) data to predict 30 day hospital readmission. Design Systematic review. Data source …

Background Predicting death in a cohort of clinically diverse, multicondition hospitalized
patients is difficult. Prognostic models that use electronic medical record (EMR) data to …

Background: It is unclear whether machine learning methods yield more accurate electronic
health record (EHR) prediction models compared with traditional regression methods …

Predicting a patient's hospital length of stay (LoS) can help manage staffing. In this paper,
we explore LoS prediction for a large group of patients admitted non-electively. We use …

Background Numerous predictive models in the literature stratify patients by risk of mortality
and readmission. Few prediction models have been developed to optimize impact while …