{"title":"使用交互式自然语言处理的临床文本分析","authors":"Gaurav Trivedi","doi":"10.1145/2732158.2732162","DOIUrl":null,"url":null,"abstract":"Natural Language Processing (NLP) systems are typically developed by informaticists skilled in machine learning techniques that are unfamiliar to end-users. Although NLP has been widely used in extracting information from clinical text, current systems generally do not provide any provisions for incorporating feedback and revising models based on input from domain experts. The goal of this research is to close this gap by building highly-usable tools suitable for the analysis of free text reports.","PeriodicalId":177570,"journal":{"name":"Proceedings of the 20th International Conference on Intelligent User Interfaces Companion","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Clinical Text Analysis Using Interactive Natural Language Processing\",\"authors\":\"Gaurav Trivedi\",\"doi\":\"10.1145/2732158.2732162\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Natural Language Processing (NLP) systems are typically developed by informaticists skilled in machine learning techniques that are unfamiliar to end-users. Although NLP has been widely used in extracting information from clinical text, current systems generally do not provide any provisions for incorporating feedback and revising models based on input from domain experts. The goal of this research is to close this gap by building highly-usable tools suitable for the analysis of free text reports.\",\"PeriodicalId\":177570,\"journal\":{\"name\":\"Proceedings of the 20th International Conference on Intelligent User Interfaces Companion\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-03-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 20th International Conference on Intelligent User Interfaces Companion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2732158.2732162\",\"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 20th International Conference on Intelligent User Interfaces Companion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2732158.2732162","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Clinical Text Analysis Using Interactive Natural Language Processing
Natural Language Processing (NLP) systems are typically developed by informaticists skilled in machine learning techniques that are unfamiliar to end-users. Although NLP has been widely used in extracting information from clinical text, current systems generally do not provide any provisions for incorporating feedback and revising models based on input from domain experts. The goal of this research is to close this gap by building highly-usable tools suitable for the analysis of free text reports.