Prediction of complications in diabetes mellitus using machine learning models with transplanted topic model features.

ABSTRACT: Purpose: This study aims to predict the progression of Diabetes Mellitus (DM) from the clinical notes through machine learning based on latent Dirichlet allocation (LDA) topic modeling. Particularly, 174,427 clinical notes of DM patients were collected from the electronic medical record (EMR) system of the Seoul National University Hospital outpatient clinic. Method: We developed a model to predict the development of DM complications. Topics developed by the topic model were exploited as the key feature of our machine-learning model. The proposed model generalized a correlation between topic structures and complications. Results: The model provided acceptable predictive performance for all four types of complications (diabetic retinopathy, diabetic nephropathy, nonalco

SUBMITTER: Han BC 

PROVIDER: S-EPMC10769946 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature