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Patient Readmission

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A Novel Model for Predicting Rehospitalization Risk Incorporating Physical Function, Cognitive Status, and Psychosocial Support Using Natural Language Processing.

Medical care
BACKGROUND: With the increasing focus on reducing hospital readmissions in the United States, numerous readmissions risk prediction models have been proposed, mostly developed through analyses of structured data fields in electronic medical records a...

Prediction of 30-Day All-Cause Readmissions in Patients Hospitalized for Heart Failure: Comparison of Machine Learning and Other Statistical Approaches.

JAMA cardiology
IMPORTANCE: Several attempts have been made at developing models to predict 30-day readmissions in patients with heart failure, but none have sufficient discriminatory capacity for clinical use. Machine-learning (ML) algorithms represent a novel appr...

PREDICTIVE MODELING OF HOSPITAL READMISSION RATES USING ELECTRONIC MEDICAL RECORD-WIDE MACHINE LEARNING: A CASE-STUDY USING MOUNT SINAI HEART FAILURE COHORT.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Reduction of preventable hospital readmissions that result from chronic or acute conditions like stroke, heart failure, myocardial infarction and pneumonia remains a significant challenge for improving the outcomes and decreasing the cost of healthca...

A clinical perspective on the relevance of research domain criteria in electronic health records.

The American journal of psychiatry
OBJECTIVE: The limitations of the DSM nosology for capturing dimensionality and overlap in psychiatric syndromes, and its poor correspondence to underlying neurobiology, have been well established. The Research Domain Criteria (RDoC), a proposed dime...