Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Feb 13, 2021
Date Accepted: Jan 2, 2022
Date Submitted to PubMed: Jan 8, 2022
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Development of deep learning models for predicting in-hospital mortality using an administrative claims database: Nationwide retrospective cohort study.
ABSTRACT
Background:
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, or pneumonia using common severity indices for these diseases. Using the Japanese Diagnosis Procedure Combination data from July 2010 to March 2017, we identified 46,665,933 inpatients and divided them into derivation and validation cohorts in a ratio of 95:5. The main model was developed using a 9-layer deep neural network with four hidden dense layers that had 1000 nodes and were fully connected to adjacent layers. We evaluated model discrimination ability by an area under the receiver operating characteristics curve and calibration ability by calibration plot.
Results:
Among the eligible patients, 2,005,035 (4.3%) died. Discrimination and calibration of the models were satisfactory. The AUC of the main model in the validation cohort was 0.954 (95% confidential interval 0.9537–0.9547). The main model had higher discrimination ability than the disease-specific models.
Conclusions:
Our deep learning-based model using diagnoses and procedures produced valid predictions of in-house mortality.
Citation
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