Wael Salloum, Greg P. Finley, Erik Edwards, Mark Miller, David Suendermann-Oeft
{"title":"Automated Preamble Detection in Dictated Medical Reports","authors":"Wael Salloum, Greg P. Finley, Erik Edwards, Mark Miller, David Suendermann-Oeft","doi":"10.18653/v1/W17-2336","DOIUrl":null,"url":null,"abstract":"Dictated medical reports very often feature a preamble containing metainformation about the report such as patient and physician names, location and name of the clinic, date of procedure, and so on. In the medical transcription process, the preamble is usually omitted from the final report, as it contains information already available in the electronic medical record. We present a method which is able to automatically identify preambles in medical dictations. The method makes use of stateof-the-art NLP techniques including word embeddings and Bi-LSTMs and achieves preamble detection performance superior to humans.","PeriodicalId":200974,"journal":{"name":"Workshop on Biomedical Natural Language Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on Biomedical Natural Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/W17-2336","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Dictated medical reports very often feature a preamble containing metainformation about the report such as patient and physician names, location and name of the clinic, date of procedure, and so on. In the medical transcription process, the preamble is usually omitted from the final report, as it contains information already available in the electronic medical record. We present a method which is able to automatically identify preambles in medical dictations. The method makes use of stateof-the-art NLP techniques including word embeddings and Bi-LSTMs and achieves preamble detection performance superior to humans.