JMIR Publications

ABSTRACT

Background:

Type 2 diabetes (T2D) is a significant global health challenge. Physicians need to assess whether future glycemic control will be poor on the current trajectory of usual care and usual-care treatment intensifications so that they can consider taking extra treatment measures to prevent poor outcomes. Predicting poor glycemic control from trends in HbA1c levels is difficult due to the influence of seasonal fluctuations and other factors.

Objective:

We sought to develop a model that accurately predicts poor glycemic control among T2D patients receiving usual care.

Methods:

Our machine learning model predicts poor glycemic control (HbA1c ≥ 8%) using the Transformer architecture, incorporating an attention mechanism to process irregularly spaced HbA1c time-series and quantify temporal relationships of past HbA1c levels at each time point. We assessed the model using HbA1c levels from 7652 T2D patients seeing specialist physicians at the University of Tokyo Hospital. The training data includes instances of poor glycemic control occurring during usual care with usual-care treatment intensifications. We compared prediction accuracy, assessed with the area under the receiver operating characteristic curve, area under the precision-recall curve, and accuracy rate, to that of LightGBM.

Results:

The means (95% confidence limits) of the area under the receiver operating characteristic curve, area under the precision-recall curve, and accuracy rate of the proposed model were 0.925 (0.923, 0.928), 0.864 (0.852, 0.875), and 0.864 (0.86, 0.869), respectively. The proposed model achieved high prediction accuracy comparable to or surpassing LightGBM's performance. The model prioritized the most recent HbA1c levels for predictions. Older HbA1c levels in patients with poor glycemic control were slightly more influential in predictions compared to patients with good glycemic control.

Conclusions:

The proposed model accurately predicts poor glycemic control for T2D patients receiving usual care, including patients receiving usual-care treatment intensifications, allowing physicians to identify cases warranting extraordinary treatment intensifications. If used by a non-specialist, 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.