Do deep learning techniques perform better than traditional techniques in predicting 30-day readmissions in heart failure patients?
Patients with heart failure (retrospective electronic medical records data)
Deep learning techniques for EMR-based prediction model
Other traditional techniques
Prediction of 30-day readmissions
Deep learning models outperform traditional techniques in predicting 30-day readmissions for heart failure patients using EMR data, potentially enabling targeted interventions.
Deep learning techniques performed better than other traditional techniques in developing this EMR-based prediction model for 30-day readmissions in heart failure patients. Such models can be used to identify heart failure patients with impending hospitalization, enabling care teams to target interventions at their most high-risk patients and improving overall clinical outcomes.
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Sara Golas
Massachusetts General Hospital
Takuma Shibahara
Mie University
Stephen Agboola
Boston University
SHILAP Revista de lepidopterología
BMC Medical Informatics and Decision Making
Harvard University
Massachusetts General Hospital
Hitachi (Japan)
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Golas et al. (Thu,) studied this question.
synapsesocial.com/papers/69d56ce675589c71d767ce73 — DOI: https://doi.org/10.1186/s12911-018-0620-z