Kidney diseases (Basel, Switzerland) · 2025
Predicting Chronic Kidney Disease in Type 2 Diabetes Using Natural Language Processing on Healthcare Data.
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Navarro-González Juan F, Pérez de Isla Leopoldo, Cánovas Molina Gloria, Brito-Sanfiel Miguel Ángel, Barajas Galindo David Emilio, Cuellar Olmedo Luís Ángel, Mauricio Dídac, Povedano Santiago Tofé, Balsa Barro José Antonio, Rubio Almanza Matilde, Aparicio Sánchez José Juan, Sequera Mutiozabal Miren, Pimentel Belén, Domínguez Ana Pérez, Arias-Cabrales Carlos, Fanjul Víctor, Blanco-Carrasco Antonio Jesús, Merino Torres Juan Francisco
Аннотация
INTRODUCTION: Persons with type 2 diabetes mellitus (T2DM) attending hospitals frequently experience major complications. We assessed the potential use of unstructured free-text data extracted from electronic health records (EHRs) using natural language processing (NLP) and machine learning (ML) to develop a predictive model for chronic kidney disease (CKD) in T2DM.
METHODS: This multicenter retrospective study included data from eight Spanish hospitals (2013-2018), extracted using NLP and ML techniques (EHRead®) based on SNOMED CT terminology. From a cohort of individuals with T2DM, we identified those with and without CKD at inclusion. Among individuals without CKD, we trained and validated a 2-year predictive model for CKD development. The model showing the best balance between performance and clinical interpretability was selected for integration into a web-based tool to support early detection and risk stratification.
RESULTS: Of 588,786 individuals with T2DM, 316,597 were included for model development (training: 291,429 [92.1%]; validation: 25,168 [7.9%]; CKD incidence: 15.4% and 18.4%, respectively). A high proportion of missing data was observed in key clinical variables. Among models evaluated, logistic regression achieved the best performance (receiver operating characteristic area under the curve 0.72) using 27 predictors. Both a reduced 10-predictor model and a clinically refined 8-predictor model showed comparable performance to the full model in training and validation cohorts. The clinically refined model was selected for implementation in the web-based tool.
CONCLUSION: Unstructured EHR data enabled the development of a predictive model for 2-year CKD risk in persons with T2DM. Improving EHR data completeness remains essential to enhance future predictive modeling.
METHODS: This multicenter retrospective study included data from eight Spanish hospitals (2013-2018), extracted using NLP and ML techniques (EHRead®) based on SNOMED CT terminology. From a cohort of individuals with T2DM, we identified those with and without CKD at inclusion. Among individuals without CKD, we trained and validated a 2-year predictive model for CKD development. The model showing the best balance between performance and clinical interpretability was selected for integration into a web-based tool to support early detection and risk stratification.
RESULTS: Of 588,786 individuals with T2DM, 316,597 were included for model development (training: 291,429 [92.1%]; validation: 25,168 [7.9%]; CKD incidence: 15.4% and 18.4%, respectively). A high proportion of missing data was observed in key clinical variables. Among models evaluated, logistic regression achieved the best performance (receiver operating characteristic area under the curve 0.72) using 27 predictors. Both a reduced 10-predictor model and a clinically refined 8-predictor model showed comparable performance to the full model in training and validation cohorts. The clinically refined model was selected for implementation in the web-based tool.
CONCLUSION: Unstructured EHR data enabled the development of a predictive model for 2-year CKD risk in persons with T2DM. Improving EHR data completeness remains essential to enhance future predictive modeling.