Development and Implementation of Patient-Level Prediction Models of End-Stage Renal Disease for Type 2 Diabetes Patients Using Fast Healthcare Interoperability Resources

Authors: San Wang (4), Jieun Han (2), Se Young Jung (2), Tae Jung Oh (1), Sen Yao (4), Sanghee Lim (4), Hee Hwang (3), Ho-Young Lee (3), Keehyuck Lee (3)

1. Department of Internal Medicine, Seoul National University College of Medicine and Seoul National University Bundang Hospital, 82 Gumi-ro, Bundang-gu, Seongnam, 13620, Republic of Korea
2. Department of Family Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
3. Department of Digital Healthcare, Seoul National University Bundang Hospital, 172 Dolma-ro, Bundang-gu, Seongnam, 13620, Republic of Korea
4. Enolink, Cambridge MA

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Abstract:

This study aimed to develop a model to predict the 5-year risk of developing end-stage renal disease (ESRD) in patients with type 2 diabetes mellitus (T2DM) using machine learning (ML). It also aimed to implement the developed algorithms into electronic medical records (EMR) system using Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR). The final dataset used for modeling included 19,159 patients. The medical data were engineered to generate various types of features that were input into the various ML classifiers. The classifier with the best performance was XgBoost, with an area under the receiver operator characteristics curve of 0.95 and area under the precision recall curve of 0.79 using three-fold cross-validation. The algorithm was implemented in the EMR system using HL7 FHIR through an ML-dedicated server that preprocessed unstructured data and trained updated data. Our prediction model performed better than traditional prediction models of ESRD in T2DM patients.

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