{"title":"比较机器学习模型在健康结果预测中的讨论","authors":"Janusz Wojtusiak, Negin Asadzadehzanjani","doi":"10.5220/0010916600003123","DOIUrl":null,"url":null,"abstract":": This position paper argues the need for more details than simple statistical accuracy measures when comparing machine learning models constructed for patient outcome prediction. First, statistical accuracy measures are briefly discussed, including AROC, APRC, predictive accuracy, precision, recall, and their variants. Then, model correlation plots are introduced that compare outputs from two models. Finally, a more detailed analysis of inputs to the models is presented. The discussions are illustrated with two classification problems in predicting patient mortality and high utilization of medical services.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":"14 1","pages":"711-718"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Discussion on Comparing Machine Learning Models for Health Outcome Prediction\",\"authors\":\"Janusz Wojtusiak, Negin Asadzadehzanjani\",\"doi\":\"10.5220/0010916600003123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\": This position paper argues the need for more details than simple statistical accuracy measures when comparing machine learning models constructed for patient outcome prediction. First, statistical accuracy measures are briefly discussed, including AROC, APRC, predictive accuracy, precision, recall, and their variants. Then, model correlation plots are introduced that compare outputs from two models. Finally, a more detailed analysis of inputs to the models is presented. The discussions are illustrated with two classification problems in predicting patient mortality and high utilization of medical services.\",\"PeriodicalId\":20676,\"journal\":{\"name\":\"Proceedings of the International Conference on Health Informatics and Medical Application Technology\",\"volume\":\"14 1\",\"pages\":\"711-718\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the International Conference on Health Informatics and Medical Application Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5220/0010916600003123\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0010916600003123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Discussion on Comparing Machine Learning Models for Health Outcome Prediction
: This position paper argues the need for more details than simple statistical accuracy measures when comparing machine learning models constructed for patient outcome prediction. First, statistical accuracy measures are briefly discussed, including AROC, APRC, predictive accuracy, precision, recall, and their variants. Then, model correlation plots are introduced that compare outputs from two models. Finally, a more detailed analysis of inputs to the models is presented. The discussions are illustrated with two classification problems in predicting patient mortality and high utilization of medical services.