Development of Deep Learning Models for Predicting In-Hospital Mortality Using an Administrative Claims Database: Retrospective Cohort Study.

ABSTRACT:

Background

Administrative claims databases have been used widely in studies because they have large sample sizes and are easily available. However, studies using administrative databases lack information on disease severity, so a risk adjustment method needs to be developed.

Objective

We aimed to develop and validate deep learning-based prediction models for in-hospital mortality of acute care patients.

Methods

The main model was developed using only administrative claims data (age, sex, diagnoses, and procedures on the day of admission). We also constructed disease-specific models for acute myocardial infarction, heart failure, stroke, and pneumonia using common severity indices for these diseases. Using the Japanese Diagnosis Procedure Combination data from July 201

SUBMITTER: Matsui H 

PROVIDER: S-EPMC8881780 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature