{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T11:48:12Z","timestamp":1778759292983,"version":"3.51.4"},"reference-count":17,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,8,29]],"date-time":"2021-08-29T00:00:00Z","timestamp":1630195200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>The Epic electronic health record (EHR) is a commonly used EHR in the United States. This EHR contain large semi-structured \u201cflowsheet\u201d fields. Flowsheet fields lack a well-defined data dictionary and are unique to each site. We evaluated a simple free-text-like method to extract these data. As a use case, we demonstrate this method in predicting mortality during emergency department (ED) triage. We retrieved demographic and clinical data for ED visits from the Epic EHR (1\/2014\u201312\/2018). Data included structured, semi-structured flowsheet records and free-text notes. The study outcome was in-hospital death within 48 h. Most of the data were coded using a free-text-like Bag-of-Words (BoW) approach. Two machine-learning models were trained: gradient boosting and logistic regression. Term frequency-inverse document frequency was employed in the logistic regression model (LR-tf-idf). An ensemble of LR-tf-idf and gradient boosting was evaluated. Models were trained on years 2014\u20132017 and tested on year 2018. Among 412,859 visits, the 48-h mortality rate was 0.2%. LR-tf-idf showed AUC 0.98 (95% CI: 0.98\u20130.99). Gradient boosting showed AUC 0.97 (95% CI: 0.96\u20130.99). An ensemble of both showed AUC 0.99 (95% CI: 0.98\u20130.99). In conclusion, a free-text-like approach can be useful for extracting knowledge from large amounts of complex semi-structured EHR data.<\/jats:p>","DOI":"10.3390\/bdcc5030040","type":"journal-article","created":{"date-parts":[[2021,8,29]],"date-time":"2021-08-29T21:45:16Z","timestamp":1630273516000},"page":"40","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Simple Free-Text-like Method for Extracting Semi-Structured Data from Electronic Health Records: Exemplified in Prediction of In-Hospital Mortality"],"prefix":"10.3390","volume":"5","author":[{"given":"Eyal","family":"Klang","sequence":"first","affiliation":[{"name":"Chaim Sheba Medical Center, Department of Diagnostic Imaging, Affiliated to Tel-Aviv University, Tel Aviv-Yafo 52621, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6013-2684","authenticated-orcid":false,"given":"Matthew A.","family":"Levin","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shelly","family":"Soffer","sequence":"additional","affiliation":[{"name":"Internal Medicine B, Assuta Medical Center, Ben-Gurion University of the Negev, Be\u2019er Sheva 7747629, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4058-5050","authenticated-orcid":false,"given":"Alexis","family":"Zebrowski","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4515-8090","authenticated-orcid":false,"given":"Benjamin S.","family":"Glicksberg","sequence":"additional","affiliation":[{"name":"Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY 10065, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brendan G.","family":"Carr","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jolion","family":"Mcgreevy","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David L.","family":"Reich","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4946-6533","authenticated-orcid":false,"given":"Robert","family":"Freeman","sequence":"additional","affiliation":[{"name":"Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1056\/NEJMp1606181","article-title":"Predicting the Future-Big Data, Machine Learning, and Clinical Medicine","volume":"375","author":"Obermeyer","year":"2016","journal-title":"N. 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