作品說明

隨疫情升溫，醫療資源嚴重不足的問題日益嚴重，例如：病床數不足、醫療照護比率上升，加上傳統點滴裝置主要依賴醫護人員的經驗判斷，一旦疏於控制，將造成心臟疾病患者誘發心肌梗塞或心臟衰竭，或容易造成脫水、膿血症及組織水腫等問題。為了有效解決此問題並提升病患照護品質，本企劃將針對靜脈注射(輸液)這項常見的醫療處置，透過物聯網與機器學習技術，設計一套具有以下三項功能的AIoT 智慧點滴系統：1. 智慧遠端監控：能夠在行動裝置上即時查看患者生理數據及控制提醒。2. AI心肌缺血判讀：透過機器學習偵測是否有心肌梗塞前兆發生並送出異常提醒。3. 精準量測輸液速度及控制：透過 3D 列印設計控速裝置，並以遠端調控所需點滴速度。透過本系統，期望能舒緩醫療人力不足，建立未來遠距照護合適的環境，並透過物聯網及機器學習等科技，輔助日後逐漸嚴苛的醫療環境。經實驗測試，本系統在機器學習模型判斷方面，高達 97%準確率。 With the rise of the epidemic, the serious shortage of medical resources is becoming increasingly serious, such as the shortage of hospital beds and the high nurse to patient ratio, coupled with the fact that the traditional drip device relies on the experienced judgment of medical staff. Improper drip control may cause dehydration, sepsis and tissue edema, etc. Patients with heart disease may suffer from myocardial infarction or heart failure if the infusion rate is too fast. In response to the above issues and improving the quality of care for patients. This research uses IoT and machine learning to design a smart infusion system with the following three functions for intravenous injection, a common medical treatment.1. Smart Remote Transmission Monitoring: A web page that enables real-time viewing of patient physiological data on mobile devices.2. Artificial intelligence Myocardial Ischemia judgment technology: Use machine learning to recommend infusion rates and send alerts to inform the nursing staff of physical abnormalities in patients. 3. Device to measure and control the infusion rate: It can remotely adjust the desired IV drip rate. The objective of this study is to enable the system to alleviate the shortage of medical manpower and establish a suitable environment for telecare. Our team uses technologies such as the Internet of Things and machine learning to assist in the increasingly demanding medical environment of the future. The experiment results show that the proposed system achieves up to 97% accuracy in determining the disease by machine learning model.