{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T09:24:02Z","timestamp":1790673842690,"version":"4.1.0"},"reference-count":0,"publisher":"Georg Thieme Verlag KG","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>\n          Background\u2003Despite advances in natural language processing (NLP), extracting information from clinical text is expensive. Interactive tools that are capable of easing the construction, review, and revision of NLP models can reduce this cost and improve the utility of clinical reports for clinical and secondary use.<\/jats:p><jats:p>\n          Objectives\u2003We present the design and implementation of an interactive NLP tool for identifying incidental findings in radiology reports, along with a user study evaluating the performance and usability of the tool.<\/jats:p><jats:p>\n          Methods\u2003Expert reviewers provided gold standard annotations for 130 patient encounters (694 reports) at sentence, section, and report levels. We performed a user study with 15 physicians to evaluate the accuracy and usability of our tool. Participants reviewed encounters split into intervention (with predictions) and control conditions (no predictions). We measured changes in model performance, the time spent, and the number of user actions needed. The System Usability Scale (SUS) and an open-ended questionnaire were used to assess usability.<\/jats:p><jats:p>\n          Results\u2003Starting from bootstrapped models trained on 6 patient encounters, we observed an average increase in F1 score from 0.31 to 0.75 for reports, from 0.32 to 0.68 for sections, and from 0.22 to 0.60 for sentences on a held-out test data set, over an hour-long study session. We found that tool helped significantly reduce the time spent in reviewing encounters (134.30 vs. 148.44\u2009seconds in intervention and control, respectively), while maintaining overall quality of labels as measured against the gold standard. The tool was well received by the study participants with a very good overall SUS score of 78.67.<\/jats:p><jats:p>\n          Conclusion\u2003The user study demonstrated successful use of the tool by physicians for identifying incidental findings. These results support the viability of adopting interactive NLP tools in clinical care settings for a wider range of clinical applications.<\/jats:p>","DOI":"10.1055\/s-0039-1695791","type":"journal-article","created":{"date-parts":[[2019,9,5]],"date-time":"2019-09-05T07:42:18Z","timestamp":1567669338000},"page":"655-669","source":"Crossref","is-referenced-by-count":23,"title":["Interactive NLP in Clinical Care: Identifying Incidental Findings in Radiology Reports"],"prefix":"10.1055","volume":"10","author":[{"given":"Gaurav","family":"Trivedi","sequence":"additional","affiliation":[{"name":"Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esmaeel R.","family":"Dadashzadeh","sequence":"additional","affiliation":[{"name":"Department of Surgery and Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert M.","family":"Handzel","sequence":"additional","affiliation":[{"name":"Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wendy W.","family":"Chapman","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shyam","family":"Visweswaran","sequence":"additional","affiliation":[{"name":"Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania, United States"},{"name":"Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harry","family":"Hochheiser","sequence":"additional","affiliation":[{"name":"Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania, United States"},{"name":"Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"194","published-online":{"date-parts":[[2019,9,4]]},"container-title":["Applied Clinical Informatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/s-0039-1695791.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,5]],"date-time":"2019-09-05T07:42:18Z","timestamp":1567669338000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/s-0039-1695791"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2019,8,7]]},"published-print":{"date-parts":[[2019,8]]}},"URL":"https:\/\/doi.org\/10.1055\/s-0039-1695791","relation":{},"ISSN":["1869-0327"],"issn-type":[{"value":"1869-0327","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8]]}}}