Iain J Marshall, Joël Kuiper, Edward Banner, Byron C Wallace
{"title":"自动化生物医学证据合成:RobotReviewer。","authors":"Iain J Marshall, Joël Kuiper, Edward Banner, Byron C Wallace","doi":"10.18653/v1/P17-4002","DOIUrl":null,"url":null,"abstract":"<p><p>We present <i>RobotReviewer</i>, an open-source web-based system that uses machine learning and NLP to semi-automate biomedical evidence synthesis, to aid the practice of Evidence-Based Medicine. RobotReviewer processes full-text journal articles (PDFs) describing randomized controlled trials (RCTs). It appraises the reliability of RCTs and extracts text describing key trial characteristics (e.g., descriptions of the population) using novel NLP methods. RobotReviewer then automatically generates a report synthesising this information. Our goal is for RobotReviewer to automatically extract and synthesise the full-range of structured data needed to inform evidence-based practice.</p>","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2017 ","pages":"7-12"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5662138/pdf/nihms880383.pdf","citationCount":"54","resultStr":"{\"title\":\"Automating Biomedical Evidence Synthesis: RobotReviewer.\",\"authors\":\"Iain J Marshall, Joël Kuiper, Edward Banner, Byron C Wallace\",\"doi\":\"10.18653/v1/P17-4002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We present <i>RobotReviewer</i>, an open-source web-based system that uses machine learning and NLP to semi-automate biomedical evidence synthesis, to aid the practice of Evidence-Based Medicine. RobotReviewer processes full-text journal articles (PDFs) describing randomized controlled trials (RCTs). It appraises the reliability of RCTs and extracts text describing key trial characteristics (e.g., descriptions of the population) using novel NLP methods. RobotReviewer then automatically generates a report synthesising this information. Our goal is for RobotReviewer to automatically extract and synthesise the full-range of structured data needed to inform evidence-based practice.</p>\",\"PeriodicalId\":74541,\"journal\":{\"name\":\"Proceedings of the conference. Association for Computational Linguistics. Meeting\",\"volume\":\"2017 \",\"pages\":\"7-12\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5662138/pdf/nihms880383.pdf\",\"citationCount\":\"54\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the conference. Association for Computational Linguistics. Meeting\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/P17-4002\",\"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 conference. Association for Computational Linguistics. Meeting","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/P17-4002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We present RobotReviewer, an open-source web-based system that uses machine learning and NLP to semi-automate biomedical evidence synthesis, to aid the practice of Evidence-Based Medicine. RobotReviewer processes full-text journal articles (PDFs) describing randomized controlled trials (RCTs). It appraises the reliability of RCTs and extracts text describing key trial characteristics (e.g., descriptions of the population) using novel NLP methods. RobotReviewer then automatically generates a report synthesising this information. Our goal is for RobotReviewer to automatically extract and synthesise the full-range of structured data needed to inform evidence-based practice.