Shao Jian, Pan Ying, Kou Wei-Bin, Feng Huyi, Zhao Yu, Zhou Kaixin, Zhong Shao
Guangzhou Laboratory, Guangzhou, China.
Department of Endocrinology, Kunshan Hospital Affiliated to Jiangsu University, Kunshan, China.
JMIR Med Inform. 2024 May 24;12:e56909. doi: 10.2196/56909.
Predicting hypoglycemia while maintaining a low false alarm rate is a challenge for the wide adoption of continuous glucose monitoring (CGM) devices in diabetes management. One small study suggested that a deep learning model based on the long short-term memory (LSTM) network had better performance in hypoglycemia prediction than traditional machine learning algorithms in European patients with type 1 diabetes. However, given that many well-recognized deep learning models perform poorly outside the training setting, it remains unclear whether the LSTM model could be generalized to different populations or patients with other diabetes subtypes.
The aim of this study was to validate LSTM hypoglycemia prediction models in more diverse populations and across a wide spectrum of patients with different subtypes of diabetes.
We assembled two large data sets of patients with type 1 and type 2 diabetes. The primary data set including CGM data from 192 Chinese patients with diabetes was used to develop the LSTM, support vector machine (SVM), and random forest (RF) models for hypoglycemia prediction with a prediction horizon of 30 minutes. Hypoglycemia was categorized into mild (glucose=54-70 mg/dL) and severe (glucose<54 mg/dL) levels. The validation data set of 427 patients of European-American ancestry in the United States was used to validate the models and examine their generalizations. The predictive performance of the models was evaluated according to the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).
For the difficult-to-predict mild hypoglycemia events, the LSTM model consistently achieved AUC values greater than 97% in the primary data set, with a less than 3% AUC reduction in the validation data set, indicating that the model was robust and generalizable across populations. AUC values above 93% were also achieved when the LSTM model was applied to both type 1 and type 2 diabetes in the validation data set, further strengthening the generalizability of the model. Under different satisfactory levels of sensitivity for mild and severe hypoglycemia prediction, the LSTM model achieved higher specificity than the SVM and RF models, thereby reducing false alarms.
Our results demonstrate that the LSTM model is robust for hypoglycemia prediction and is generalizable across populations or diabetes subtypes. Given its additional advantage of false-alarm reduction, the LSTM model is a strong candidate to be widely implemented in future CGM devices for hypoglycemia prediction.
在糖尿病管理中，在保持低误报率的同时预测低血糖是连续血糖监测（CGM）设备广泛应用面临的一项挑战。一项小型研究表明，在欧洲1型糖尿病患者中，基于长短期记忆（LSTM）网络的深度学习模型在低血糖预测方面比传统机器学习算法表现更好。然而，鉴于许多公认的深度学习模型在训练环境之外表现不佳，LSTM模型是否能推广到不同人群或其他糖尿病亚型的患者仍不清楚。
本研究旨在验证LSTM低血糖预测模型在更多样化人群以及不同亚型糖尿病患者中的有效性。
我们收集了1型和2型糖尿病患者的两个大型数据集。主要数据集包括来自192名中国糖尿病患者的CGM数据，用于开发预测低血糖的LSTM、支持向量机（SVM）和随机森林（RF）模型，预测时间范围为30分钟。低血糖分为轻度（血糖=54 - 70mg/dL）和重度（血糖<54mg/dL）水平。美国427名欧美血统患者的验证数据集用于验证模型并检验其泛化能力。根据敏感性、特异性和受试者工作特征曲线下面积（AUC）评估模型的预测性能。
对于难以预测的轻度低血糖事件，LSTM模型在主要数据集中始终实现AUC值大于97%，在验证数据集中AUC降低不到3%，表明该模型具有稳健性且可在不同人群中推广。当LSTM模型应用于验证数据集中的1型和2型糖尿病时，AUC值也达到了93%以上，进一步加强了该模型的泛化能力。在轻度和重度低血糖预测的不同满意敏感性水平下，LSTM模型比SVM和RF模型具有更高的特异性，从而减少了误报。
我们的结果表明，LSTM模型在低血糖预测方面具有稳健性，并且可在不同人群或糖尿病亚型中推广。鉴于其在减少误报方面的额外优势，LSTM模型是未来用于低血糖预测的CGM设备中广泛应用的有力候选者。
