{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T07:38:45Z","timestamp":1789717125640,"version":"4.0.1"},"reference-count":34,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2021,5,2]],"date-time":"2021-05-02T00:00:00Z","timestamp":1619913600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Translational Sci"],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad\u2010spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two\u2010center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diagnostic and prognostic capabilities of a machine\u2010learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine\u2010learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), 30\u2010day mortality, and 3\u2010day inpatient re\u2010admission both in our entire testing cohort and various subpopulations. The area under the receiver operating curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared with patients with sepsis but without shock (\n                    <jats:italic>p<\/jats:italic>\n                    \u00a0&lt;\u00a00.0001). Scores for patients with infection and organ dysfunction were higher compared with those without either condition (\n                    <jats:italic>p<\/jats:italic>\n                    \u00a0&lt;\u00a00.0001). Stratification based on predicted scores of the patients into low, medium, and high\u2010risk groups showed significant differences in LOS (\n                    <jats:italic>p<\/jats:italic>\n                    \u00a0&lt;\u00a00.0001), 30\u2010day mortality (\n                    <jats:italic>p<\/jats:italic>\n                    \u00a0&lt;\u00a00.0001), and 30\u2010day inpatient readmission (\n                    <jats:italic>p<\/jats:italic>\n                    \u00a0&lt;\u00a00.0001). In conclusion, a machine\u2010learning algorithm based on electronic medical record (EMR) data and three nonroutinely measured biomarkers demonstrated good diagnostic and prognostic capability at the time of initial blood culture.\n                  <\/jats:p>","DOI":"10.1111\/cts.13030","type":"journal-article","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T02:33:34Z","timestamp":1617158014000},"page":"1578-1589","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Diagnostic and prognostic capabilities of a biomarker and EMR\u2010based machine learning algorithm for sepsis"],"prefix":"10.1111","volume":"14","author":[{"given":"Ishan","family":"Taneja","sequence":"first","affiliation":[{"name":"Prenosis Inc. 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