Crescenzo Diomaiuta, Maria Mercorella, Mario Ciampi, G. Pietro
{"title":"一种新的病人问题摘要自动提取系统","authors":"Crescenzo Diomaiuta, Maria Mercorella, Mario Ciampi, G. Pietro","doi":"10.1109/ISCC.2017.8024526","DOIUrl":null,"url":null,"abstract":"Clinical summarization means the collection and synthesis of a patient's significant data, undertaken in order to support health-care providers in the process of patient care. Considering that medical information comes from multiple sources, a system for the automatic generation of problem lists could prove to be very effective in terms of saving time in the analysis of large amounts of medical data. In this paper, we propose a system able to acquire and present relevant references to medical disorders from a patient's history, producing a subject-oriented summary. The implemented system relies on an NLP pipeline, for the extraction of relevant medical entities contained in narrative health records, and on several queries, necessary for the scanning of structured documents. The tool aggregates any medical problems, performed procedures, and prescribed medications, providing the healthcare practitioner with a visual summary of the patient's data.","PeriodicalId":106141,"journal":{"name":"2017 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A novel system for the automatic extraction of a patient problem summary\",\"authors\":\"Crescenzo Diomaiuta, Maria Mercorella, Mario Ciampi, G. Pietro\",\"doi\":\"10.1109/ISCC.2017.8024526\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Clinical summarization means the collection and synthesis of a patient's significant data, undertaken in order to support health-care providers in the process of patient care. Considering that medical information comes from multiple sources, a system for the automatic generation of problem lists could prove to be very effective in terms of saving time in the analysis of large amounts of medical data. In this paper, we propose a system able to acquire and present relevant references to medical disorders from a patient's history, producing a subject-oriented summary. The implemented system relies on an NLP pipeline, for the extraction of relevant medical entities contained in narrative health records, and on several queries, necessary for the scanning of structured documents. The tool aggregates any medical problems, performed procedures, and prescribed medications, providing the healthcare practitioner with a visual summary of the patient's data.\",\"PeriodicalId\":106141,\"journal\":{\"name\":\"2017 IEEE Symposium on Computers and Communications (ISCC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Symposium on Computers and Communications (ISCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCC.2017.8024526\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Symposium on Computers and Communications (ISCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCC.2017.8024526","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A novel system for the automatic extraction of a patient problem summary
Clinical summarization means the collection and synthesis of a patient's significant data, undertaken in order to support health-care providers in the process of patient care. Considering that medical information comes from multiple sources, a system for the automatic generation of problem lists could prove to be very effective in terms of saving time in the analysis of large amounts of medical data. In this paper, we propose a system able to acquire and present relevant references to medical disorders from a patient's history, producing a subject-oriented summary. The implemented system relies on an NLP pipeline, for the extraction of relevant medical entities contained in narrative health records, and on several queries, necessary for the scanning of structured documents. The tool aggregates any medical problems, performed procedures, and prescribed medications, providing the healthcare practitioner with a visual summary of the patient's data.