{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:42:25Z","timestamp":1785404545238,"version":"3.56.0"},"reference-count":36,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T00:00:00Z","timestamp":1770940800000},"content-version":"unspecified","delay-in-days":43,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Acta Neuropsychiatr."],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"S0924270826100635_as1\">\n                    <jats:title>Objectives:<\/jats:title>\n                    <jats:p>Electroconvulsive therapy (ECT) is an effective treatment of severe manifestations of mental illness. Since delay in initiation of ECT can have detrimental effects, prediction of the need for ECT could improve outcomes via more timely treatment initiation. Therefore, this study aimed to predict the need for ECT following admission to a psychiatric hospital.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0924270826100635_as2\">\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>\n                      This study was based on electronic health record (EHR) data from routine clinical practice. Adult patients admitted to a hospital within the Psychiatric Services of the Central Denmark Region between January 2013 and November 2021 were included in the study. The outcome was initiation of ECT &gt;7 days (to not include patients admitted for planned ECT) and \u226467 days after admission. The data was randomly split into an 85% training set and a 15% test set. On the 7\n                      <jats:sup>th<\/jats:sup>\n                      day of the inpatient stay, machine learning models (extreme gradient boosting (XGBoost)) were trained to predict initiation of ECT and subsequently tested on the test set.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0924270826100635_as3\">\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The cohort consisted of 41,610 patients with 164,961 admissions. In the held out test set, the trained model predicted ECT initiation with an area under the receiver operating characteristic curve of 0.94, 47% sensitivity, 98% specificity, positive predictive value (PPV) of 24% and negative predictive value (NPV) of 99%. The top predictors were the highest suicide assessment score and mean Br\u00f8set violence checklist score in the preceding three months.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0924270826100635_as4\">\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>EHR data from routine clinical practice may be used to predict need for ECT. This may lead to more timely treatment initiation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1017\/neu.2026.10063","type":"journal-article","created":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T02:35:57Z","timestamp":1770950157000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":1,"title":["Predicting the need for electroconvulsive therapy via machine learning trained on electronic health record data"],"prefix":"10.1017","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1113-4779","authenticated-orcid":false,"given":"Lasse","family":"Hansen","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01aj84f44","id-type":"ROR","asserted-by":"publisher"}],"name":"Aarhus University"},{"id":[{"id":"https:\/\/ror.org\/040r8fr65","id-type":"ROR","asserted-by":"publisher"}],"name":"Aarhus University Hospital - Psychiatry"},{"name":"Center for Humanities Computing, Aarhus University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakob Gr\u00f8hn","family":"Damgaard","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01aj84f44","id-type":"ROR","asserted-by":"publisher"}],"name":"Aarhus University"},{"id":[{"id":"https:\/\/ror.org\/040r8fr65","id-type":"ROR","asserted-by":"publisher"}],"name":"Aarhus University Hospital - 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Published by Cambridge University Press on behalf of Scandinavian College of Neuropsychopharmacology","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided that no alterations are made and the original article is properly cited. The written permission of Cambridge University Press or the rights holder(s) must be obtained prior to any commercial use and\/or adaptation of the article.","name":"license","label":"License","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}],"article-number":"e19"}}