{"title":"基于机器学习的疾病分析和个性化治疗的系统文献综述","authors":"Ricardo Buettner, Florian Klenk, M. Ebert","doi":"10.1109/COMPSAC48688.2020.00-15","DOIUrl":null,"url":null,"abstract":"To analyze the state of the art of machine learning-based disease profiling and personalized treatments, we review the relevant literature included in top peer-reviewed journals and evaluate the coverage according to the ICD-11 framework. We identify advantages, but also research needs and limitations within the ICD-11 disease categories to foster the adaptation of these new E-health technologies.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A Systematic Literature Review of Machine Learning-Based Disease Profiling and Personalized Treatment\",\"authors\":\"Ricardo Buettner, Florian Klenk, M. Ebert\",\"doi\":\"10.1109/COMPSAC48688.2020.00-15\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To analyze the state of the art of machine learning-based disease profiling and personalized treatments, we review the relevant literature included in top peer-reviewed journals and evaluate the coverage according to the ICD-11 framework. We identify advantages, but also research needs and limitations within the ICD-11 disease categories to foster the adaptation of these new E-health technologies.\",\"PeriodicalId\":430098,\"journal\":{\"name\":\"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COMPSAC48688.2020.00-15\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMPSAC48688.2020.00-15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Systematic Literature Review of Machine Learning-Based Disease Profiling and Personalized Treatment
To analyze the state of the art of machine learning-based disease profiling and personalized treatments, we review the relevant literature included in top peer-reviewed journals and evaluate the coverage according to the ICD-11 framework. We identify advantages, but also research needs and limitations within the ICD-11 disease categories to foster the adaptation of these new E-health technologies.
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