臺灣國際科展

Development of an Android Application for Triage Prediction in Hospital Emergency Departments

科展類別
臺灣國際科展作品
屆次
2022年
科別
電腦科學與資訊工程
學校名稱
Philippine Science High School–Main Campus
指導老師
Donna Salve C. Hipolito
作者
Clyde Ambroz S. Acyatan;Lucas Sebastian F. Khan;Uriel Nathan D. Orpilla
關鍵字
triage、machine learning、Android

摘要或動機

Triage is the process by which nurses manage hospital emergency departments by assigning patients varying degrees of urgency. While triage algorithms such as the Emergency Severity Index (ESI) have been standardized worldwide, many of them are highly inconsistent, which could endanger the lives of thousands of patients. One way to improve on nurses’ accuracy is to use machine learning models (ML), which can learn from past data to make predictions. We tested six ML models: random forest, XGBoost, logistic regression, support vector machines, k-nearest neighbors, and multilayer perceptron. These models were tasked with predicting whether a patient would be admitted to the intensive care unit (ICU), another unit in the hospital, or be discharged. After training on data from more than 30,000 patients and testing using 10-fold cross-validation, we found that all six models outperformed ESI. Of the six, the random forest model achieved the highest average accuracy in predicting both ICU admission (81% vs. 69% using ESI; p<0.001) and hospitalization (75% vs. 57%; p<0.001). These models were then added to an Android application, which would accept patient data, predict their triage, and then add them to a priority-ordered waiting list. This approach may offer significant advantages over conventional triage: mainly, it has a higher accuracy than nurses and returns predictions instantaneously. It could also stand-in for triage nurses entirely in disasters, where medical personnel must deal with a large influx of patients in a short amount of time.


「為配合國家發展委員會「推動ODF-CNS15251為政府為文件標準格式實施計畫」,以及 提供使用者有文書軟體選擇的權利,本館檔案下載部分文件將公布ODF開放文件格式, 免費開源軟體可至LibreOffice 下載安裝使用,或依貴慣用的軟體開啟文件。」

檔案名稱 檔案大小 格式
190042.pdf 924 KB Adobe Reader(Pdf)檔案