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Predicting patient outcomes from electronic health records

Published on:

31 October 2023

Primary Category:

Machine Learning

Paper Authors:

Junu Kim,

Chaeeun Shim,

Bosco Seong Kyu Yang,

Chami Im,

Sung Yoon Lim,

Han-Gil Jeong,

Edward Choi


Key Details

Proposes REMed, a new deep learning model for EHR prediction tasks

REMed can handle unlimited input records with no feature selection needed

Demonstrates strong performance across 27 prediction tasks and 2 datasets

Shows REMed's event selections align with clinical knowledge

Minimizes bottlenecks of manual input curation and selection

AI generated summary

Predicting patient outcomes from electronic health records

This paper proposes a new deep learning model called REMed that can process virtually unlimited electronic health records to make predictions about patient outcomes like mortality and length of stay. REMed eliminates the need for manual feature selection or limiting the observation window, which are common bottlenecks. It works by retrieving the most relevant events from a patient's history and using those to make accurate predictions across many clinical tasks.

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