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Development and validation of a distributed representation model of Japanese high-dimensional administrative claims data for clinical epidemiology studies
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Development and validation of a distributed representation model of Japanese high-dimensional administrative claims data for clinical epidemiology studies
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Development and validation of a distributed representation model of Japanese high-dimensional administrative claims data for clinical epidemiology studies
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Journal Article
Development and validation of a distributed representation model of Japanese high-dimensional administrative claims data for clinical epidemiology studies
2025
Overview
Background
Unmeasured confounders pose challenges when observational data are analysed in comparative effectiveness studies. Integrating high-dimensional administrative claims data may help adjust for unmeasured confounders. We determined whether distributed representations can compress high-dimensional administrative claims data to adjust for unmeasured confounders.
Method
Using the Japanese Diagnosis Procedure Combination (DPC) database from 1291 hospitals (between April 2018 and March 2020), we applied the word2vec algorithm to create distributed representations for all medical codes. We focused on patients with heart failure (HF) and simulated four risk-adjustment models: 1, no adjustment; 2, adjusting for previously reported confounders; 3, adjusting for the sum of distributed representation weights of administrative claims data on the day of hospitalisation (novel method); and 4, a combination of models 2 and 3. We re-evaluated a previous study on the effect of early rehabilitation in patients with HF and compared these risk-adjustment methods (models 1–4).
Results
Distributed representations were generated from the data of 15 998 963 in-patients, and 319 581 HF patients were identified. In the simulation study, Model 3 reduced the impact of unmeasured confounders and achieved better covariate balances than Model 1. Model 4 showed no increase in bias compared with the true model (Model 2) and was used as a reference model in the real-world application. When applied to a previous study, models 3 and 4 showed similar results.
Conclusion
Distributed representation can compress detailed administrative claims data and adjust for unmeasured confounders in comparative effectiveness studies.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
/ Administrative Claims, Healthcare - statistics & numerical data
/ Aged
/ Causal Inference and Observational Data
/ Confounding Factors, Epidemiologic
/ Databases, Factual - statistics & numerical data
/ Female
/ Heart Failure - epidemiology
/ Heart Failure - rehabilitation
/ High-dimensional propensity score
/ Hospital information systems
/ Hospitalization - statistics & numerical data
/ Humans
/ Information storage and retrieval systems
/ Insurance Claim Review - statistics & numerical data
/ Male
/ Medicine
/ Methods
/ Patients
/ Risk Adjustment - statistics & numerical data
/ Statistical Theory and Methods
/ Statistics for Life Sciences
/ Theory of Medicine/Bioethics
/ Word2vec
