Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:329-332. doi: 10.1109/EMBC48229.2022.9870889.
Glucose prediction is used in diabetes self-management as it allows to take suitable actions for proper glycemic regulation of the patient. The aim of this work is the short-term personalized glucose prediction in patients with Type 1 diabetes mellitus (T1DM). In this scope, we compared two different models, an autoregressive moving average (ARMA) model and a long short-term memory (LSTM) model for different prediction horizons. The comparison of two models was performed using the evaluation metrics of root mean square error (RMSE) and mean absolute error (MAE). The models were trained and tested in 29 real patients. The results shown that the LSTM model had better performance than ARMA with RMSE 3.13, 6.41 and 8.81 mg/dL and MAE 1.98, 5.06 and 6.47 mg/dL for 5-, 15- and 30-minutes prediction horizon.
血糖预测在糖尿病自我管理中被广泛应用，因为它可以帮助患者采取适当的措施来控制血糖。本研究的目的是对 1 型糖尿病（T1DM）患者进行短期个性化血糖预测。在这个范围内，我们比较了两种不同的模型，自回归移动平均（ARMA）模型和长短时记忆（LSTM）模型，用于不同的预测范围。通过均方根误差（RMSE）和平均绝对误差（MAE）评估指标对两种模型进行了比较。模型在 29 名真实患者中进行了训练和测试。结果表明，LSTM 模型的性能优于 ARMA 模型，在 5 分钟、15 分钟和 30 分钟的预测范围内，RMSE 分别为 3.13、6.41 和 8.81mg/dL，MAE 分别为 1.98、5.06 和 6.47mg/dL。
