Rich Caruana, Radu S Niculescu, R Bharat Rao, Cynthia Simms
{"title":"亚人群评估的机器学习:评估不同医师实践的剖腹产率。","authors":"Rich Caruana, Radu S Niculescu, R Bharat Rao, Cynthia Simms","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>We apply machine learning to the problem of subpopulation assessment for Caesarian Section. In subpopulation assessment, we are interested in making predictions not for a single patient, but for groups of patients. Typically, in any large population, different subpopulations will have different \"outcome\" rates. In our example, the C-section rate of a population of 22,176 expectant mothers is 16.8%; yet, the 17 physician groups that serve this population have vastly different group C-section rates, ranging from 11% to 23%. The ultimate goal of subpopulation assessment is to determine if these variations in the observed rates can be attributed to (a) variations in intrinsic risk of the patient sub-populations (i.e. some groups contain more \"high-risk C-section\" patients), or (b) differences in physician practice (i.e. some groups do more C-sections). Our results indicate that although there is some variation in intrinsic risk, there is also much variation in physician practice.</p>","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244521/pdf/procamiasymp00001-0167.pdf","citationCount":"0","resultStr":"{\"title\":\"Machine learning for sub-population assessment: evaluating the C-section rate of different physician practices.\",\"authors\":\"Rich Caruana, Radu S Niculescu, R Bharat Rao, Cynthia Simms\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We apply machine learning to the problem of subpopulation assessment for Caesarian Section. In subpopulation assessment, we are interested in making predictions not for a single patient, but for groups of patients. Typically, in any large population, different subpopulations will have different \\\"outcome\\\" rates. In our example, the C-section rate of a population of 22,176 expectant mothers is 16.8%; yet, the 17 physician groups that serve this population have vastly different group C-section rates, ranging from 11% to 23%. The ultimate goal of subpopulation assessment is to determine if these variations in the observed rates can be attributed to (a) variations in intrinsic risk of the patient sub-populations (i.e. some groups contain more \\\"high-risk C-section\\\" patients), or (b) differences in physician practice (i.e. some groups do more C-sections). Our results indicate that although there is some variation in intrinsic risk, there is also much variation in physician practice.</p>\",\"PeriodicalId\":79712,\"journal\":{\"name\":\"Proceedings. AMIA Symposium\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244521/pdf/procamiasymp00001-0167.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. AMIA Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"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. AMIA Symposium","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine learning for sub-population assessment: evaluating the C-section rate of different physician practices.
We apply machine learning to the problem of subpopulation assessment for Caesarian Section. In subpopulation assessment, we are interested in making predictions not for a single patient, but for groups of patients. Typically, in any large population, different subpopulations will have different "outcome" rates. In our example, the C-section rate of a population of 22,176 expectant mothers is 16.8%; yet, the 17 physician groups that serve this population have vastly different group C-section rates, ranging from 11% to 23%. The ultimate goal of subpopulation assessment is to determine if these variations in the observed rates can be attributed to (a) variations in intrinsic risk of the patient sub-populations (i.e. some groups contain more "high-risk C-section" patients), or (b) differences in physician practice (i.e. some groups do more C-sections). Our results indicate that although there is some variation in intrinsic risk, there is also much variation in physician practice.