{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T18:59:59Z","timestamp":1778180399743,"version":"3.51.4"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2019,4,20]],"date-time":"2019-04-20T00:00:00Z","timestamp":1555718400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01AI124255"],"award-info":[{"award-number":["U01AI124255"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Clostridium (Clostridioides) difficile infection (CDI) is a health care\u2013associated infection that can lead to serious complications. Potential complications include intensive care unit (ICU) admission, development of toxic megacolon, need for colectomy, and death. However, identifying the patients most likely to develop complicated CDI is challenging. To this end, we explored the utility of a machine learning (ML) approach for patient risk stratification for complications using electronic health record (EHR) data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We considered adult patients diagnosed with CDI between October 2010 and January 2013 at the University of Michigan hospitals. Cases were labeled complicated if the infection resulted in ICU admission, colectomy, or 30-day mortality. Leveraging EHR data, we trained a model to predict subsequent complications on each of the 3 days after diagnosis. We compared our EHR-based model to one based on a small set of manually curated features. We evaluated model performance using a held-out data set in terms of the area under the receiver operating characteristic curve (AUROC).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Of 1118 cases of CDI, 8% became complicated. On the day of diagnosis, the model achieved an AUROC of 0.69 (95% confidence interval [CI], 0.55\u20130.83). Using data extracted 2 days after CDI diagnosis, performance increased (AUROC, 0.90; 95% CI, 0.83\u20130.95), outperforming a model based on a curated set of features (AUROC, 0.84; 95% CI, 0.75\u20130.91).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Using EHR data, we can accurately stratify CDI cases according to their risk of developing complications. Such an approach could be used to guide future clinical studies investigating interventions that could prevent or mitigate complicated CDI.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ofid\/ofz186","type":"journal-article","created":{"date-parts":[[2019,4,20]],"date-time":"2019-04-20T05:53:02Z","timestamp":1555739582000},"source":"Crossref","is-referenced-by-count":51,"title":["Using Machine Learning and the Electronic Health Record to Predict Complicated Clostridium difficile Infection"],"prefix":"10.1093","volume":"6","author":[{"given":"Benjamin Y","family":"Li","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeeheh","family":"Oh","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincent B","family":"Young","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine\/Division of Infectious Diseases, University of Michigan, Ann Arbor, Michigan"},{"name":"Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krishna","family":"Rao","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine\/Division of Infectious Diseases, University of Michigan, Ann Arbor, Michigan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jenna","family":"Wiens","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,4,20]]},"reference":[{"key":"2020081003565443900_CIT0001","volume-title":"Centers for Disease Control and Prevention. 2015 Annual Report for the Emerging Infections Program for Clostridium difficile Infection. 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