Khalilnejad Arash, Sun Ruo-Ting, Kompala Tejaswi, Painter Stefanie, James Roberta, Wang Yajuan
Teladoc Health, Purchase, NY, United States.
JMIR Form Res. 2024 Apr 26;8:e54373. doi: 10.2196/54373.
The growth in the capabilities of telehealth have made it possible to identify individuals with a higher risk of uncontrolled diabetes and provide them with targeted support and resources to help them manage their condition. Thus, predictive modeling has emerged as a valuable tool for the advancement of diabetes management.
This study aimed to conceptualize and develop a novel machine learning (ML) approach to proactively identify participants enrolled in a remote diabetes monitoring program (RDMP) who were at risk of uncontrolled diabetes at 12 months in the program.
Registry data from the Livongo for Diabetes RDMP were used to design separate dynamic predictive ML models to predict participant outcomes at each monthly checkpoint of the participants' program journey (month-n models) from the first day of onboarding (month-0 model) up to the 11th month (month-11 model). A participant's program journey began upon onboarding into the RDMP and monitoring their own blood glucose (BG) levels through the RDMP-provided BG meter. Each participant passed through 12 predicative models through their first year enrolled in the RDMP. Four categories of participant attributes (ie, survey data, BG data, medication fills, and health signals) were used for feature construction. The models were trained using the light gradient boosting machine and underwent hyperparameter tuning. The performance of the models was evaluated using standard metrics, including precision, recall, specificity, the area under the curve, the F-score, and accuracy.
The ML models exhibited strong performance, accurately identifying observable at-risk participants, with recall ranging from 70% to 94% and precision from 40% to 88% across the 12-month program journey. Unobservable at-risk participants also showed promising performance, with recall ranging from 61% to 82% and precision from 42% to 61%. Overall, model performance improved as participants progressed through their program journey, demonstrating the importance of engagement data in predicting long-term clinical outcomes.
This study explored the Livongo for Diabetes RDMP participants' temporal and static attributes, identification of diabetes management patterns and characteristics, and their relationship to predict diabetes management outcomes. Proactive targeting ML models accurately identified participants at risk of uncontrolled diabetes with a high level of precision that was generalizable through future years within the RDMP. The ability to identify participants who are at risk at various time points throughout the program journey allows for personalized interventions to improve outcomes. This approach offers significant advancements in the feasibility of large-scale implementation in remote monitoring programs and can help prevent uncontrolled glycemic levels and diabetes-related complications. Future research should include the impact of significant changes that can affect a participant's diabetes management.
远程医疗能力的提升使得识别糖尿病控制不佳风险较高的个体成为可能，并为他们提供有针对性的支持和资源以帮助他们管理病情。因此，预测建模已成为糖尿病管理进步的一项有价值的工具。
本研究旨在构思并开发一种新颖的机器学习（ML）方法，以主动识别参加远程糖尿病监测项目（RDMP）且在项目12个月时有糖尿病控制不佳风险的参与者。
来自Livongo for Diabetes RDMP的注册数据用于设计单独的动态预测ML模型，以预测参与者项目旅程中每个月度检查点（第n个月模型）的参与者结果，从注册第一天（第0个月模型）到第11个月（第11个月模型）。参与者的项目旅程从加入RDMP并通过RDMP提供的血糖仪监测自己的血糖（BG）水平开始。每个参与者在参加RDMP的第一年中要经过12个预测模型。四类参与者属性（即调查数据、BG数据、药物填充和健康信号）用于特征构建。模型使用轻梯度提升机进行训练并进行超参数调整。使用标准指标评估模型性能，包括精确率、召回率、特异性、曲线下面积、F分数和准确率。
ML模型表现出强大的性能，准确识别出可观察到的有风险参与者，在12个月的项目旅程中召回率从70%到94%，精确率从40%到88%。不可观察到的有风险参与者也表现出良好的性能，召回率从61%到82%，精确率从42%到61%。总体而言，随着参与者在项目旅程中的进展，模型性能有所提高，表明参与数据在预测长期临床结果中的重要性。
本研究探索了Livongo for Diabetes RDMP参与者的时间和静态属性、糖尿病管理模式和特征的识别及其与预测糖尿病管理结果之间的关系。主动靶向ML模型以高精度准确识别出有糖尿病控制不佳风险的参与者，这种高精度在RDMP未来几年中具有可推广性。在项目旅程的各个时间点识别有风险的参与者的能力允许进行个性化干预以改善结果。这种方法在远程监测项目大规模实施的可行性方面取得了重大进展，并有助于预防血糖水平失控和糖尿病相关并发症。未来的研究应包括可能影响参与者糖尿病管理的重大变化的影响。
