발간년도 : [2021]
| 논문정보 |
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| 논문명(한글) |
[Vol.16, No.4] Real-time Healthcare Platform System Predicating in High Risk Disease Management Using Deep Learning |
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| 논문투고자 |
Byoung-Chan Jeon, Sue-Ah Kim |
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| 논문내용 |
In recent years, diabetic patients attach CGM (Continuous Glucose Monitoring) devices to continuously monitor the patient's blood sugar level. Attached to the patient's body in the form of a patch, it provides blood glucose levels and trends in capillaries every 5 minutes, while simultaneously providing hyperglycemia and hypoglycemia alarm functions for continuous blood glucose monitoring through CGM. Recently, research on blood sugar prediction and treatment methods by applying such continuous blood sugar data to deep learning is being actively studied. In this paper, a deep learning-based real-time, high-risk prediction healthcare platform for diabetic high-risk patients for quick action and efficient management is designed and implemented. Blood glucose data of patients is collected through the CGM(Continuous Glucose Monitoring) device and the collected data is delivered to the deep learning predictive model stored in the platform using the IoT gateway and MQTT broker. The deep learning prediction model receives the transmitted blood glucose level data and learns from FFNN(Feed Forward Neural Network) to predict the blood glucose level after PH 5, 15, 30 and 45 minutes. If the prediction results are in a high-risk state, push notification is sent to the healthcare worker's device. Also, blood glucose data is saved in a repository and healthcare workers can retrieve patient's historical blood glucose data. In the future, the neural network used in the deep learning prediction model will be replaced with RNN or LSTM, which is a neural network more suitable for time series data such as blood sugar data. |
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