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Enhancing Type 2 Diabetes Treatment Decisions With Interpretable Machine Learning Models for Predicting Hemoglobin A1c Changes: Machine Learning Model Development.

Hisashi KurasawaKayo WakiTomohisa SekiAkihiro ChibaAkinori FujinoKatsuyoshi HayashiEri NakaharaTsuneyuki HagaTakashi NoguchiKazuhiko Ohe
Published in: JMIR AI (2024)
The proposed model accurately predicts poor glycemic control for patients with T2D receiving usual care, including patients receiving usual-care treatment intensifications, allowing physicians to identify cases warranting extraordinary treatment intensifications. If used by a nonspecialist, the model's indication of likely future poor glycemic control may warrant a referral to a specialist. Future efforts could incorporate diverse and large-scale clinical data for improved accuracy.
Keyphrases
  • glycemic control
  • type diabetes
  • machine learning
  • healthcare
  • palliative care
  • primary care
  • blood glucose
  • big data
  • insulin resistance
  • weight loss
  • metabolic syndrome
  • combination therapy