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For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
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Related Experiment Video

Updated: Jun 22, 2025

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Treatment Discontinuation Prediction in Patients With Diabetes Using a Ranking Model: Machine Learning Model

Hisashi Kurasawa1, Kayo Waki2, Akihiro Chiba1,3

  • 1Nippon Telegraph and Telephone Corporation, Tokyo, Japan.

JMIR Bioinformatics and Biotechnology
|June 27, 2024
PubMed
Summary
This summary is machine-generated.

A new machine-learned model predicts diabetes treatment discontinuation risk by calculating a time-to-discontinuation score. This helps prioritize patients for interventions, improving diabetes care and preventing missed appointments.

Keywords:
EHRalgorithmbig datadiabeteselectronic health recordmachine learningmachine-learned ranking modelpredictionrankingtreatment discontinuation

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Area of Science:

  • Medical Informatics
  • Diabetes Care
  • Machine Learning

Background:

  • Treatment discontinuation (TD) is a significant issue in diabetes management, impacting patient outcomes.
  • Existing binary classification models struggle to accurately assess TD risk in patients with irregular appointment schedules.
  • This limitation hinders effective prioritization of patients for crucial intervention support.

Purpose of the Study:

  • To develop a novel machine-learned prediction model for diabetes treatment discontinuation.
  • The model aims to output a time-to-discontinuation risk score for personalized patient prioritization.
  • To enhance intervention strategies by accurately identifying patients at high risk of TD.

Main Methods:

  • Utilized electronic medical records from 7551 diabetes patients at the University of Tokyo Hospital (2004-2016).
  • Developed a machine-learned ranking model to calculate a TD risk score, defined by missed appointments.
  • Evaluated model performance using C-index, AUC-ROC, and AUC-PR, with internal validation.

Main Results:

  • The TD risk score achieved a C-index of 0.749, AUC-ROC of 0.758, and AUC-PR of 0.713.
  • Calibration plots demonstrated a strong correlation between observed and predicted probabilities.
  • The model effectively ranks patients based on their individual TD risk.

Conclusions:

  • A robust TD risk score was developed for diabetes patients using machine learning and EHR data.
  • Integration into medical records can identify high-risk patients, aiding proactive diabetes care.
  • This approach offers a valuable tool for preventing treatment discontinuation and improving patient management.