{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T18:11:55Z","timestamp":1787595115396,"version":"build-2736575974"},"reference-count":53,"publisher":"Canadian Science Publishing","issue":"4","license":[{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.nrcresearchpress.com\/page\/about\/CorporateTextAndDataMining"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Psychiatry Neurosci"],"published-print":{"date-parts":[[2023,7,4]]},"abstract":"<jats:sec><jats:title>Background:<\/jats:title><jats:p> Delirium is a critically underdiagnosed syndrome of altered mental status affecting more than 50% of older adults admitted to hospital. Few studies have incorporated speech and language disturbance in delirium detection. We sought to describe speech and language disturbances in delirium, and provide a proof of concept for detecting delirium using computational speech and language features. <\/jats:p><\/jats:sec><jats:sec><jats:title>Methods:<\/jats:title><jats:p> Participants underwent delirium assessment and completed language tasks. Speech and language disturbances were rated using standardized clinical scales. Recordings and transcripts were processed using an automated pipeline to extract acoustic and textual features. We used binomial, elastic net, machine learning models to predict delirium status. <\/jats:p><\/jats:sec><jats:sec><jats:title>Results:<\/jats:title><jats:p> We included 33 older adults admitted to hospital, of whom 10 met criteria for delirium. The group with delirium scored higher on total language disturbances and incoherence, and lower on category fluency. Both groups scored lower on category fluency than the normative population. Cognitive dysfunction as a continuous measure was correlated with higher total language disturbance, incoherence, loss of goal and lower category fluency. Including computational language features in the model predicting delirium status increased accuracy to 78%. <\/jats:p><\/jats:sec><jats:sec><jats:title>Limitations:<\/jats:title><jats:p> This was a proof-of-concept study with limited sample size, without a set-aside cross-validation sample. Subsequent studies are needed before establishing a generalizable model for detecting delirium. <\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion:<\/jats:title><jats:p> Language impairments were elevated among patients with delirium and may also be used to identify subthreshold cognitive disturbances. Computational speech and language features are promising as accurate, noninvasive and efficient biomarkers of delirium. <\/jats:p><\/jats:sec>","DOI":"10.1503\/jpn.230026","type":"journal-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T14:15:18Z","timestamp":1688480118000},"page":"E255-E264","source":"Crossref","is-referenced-by-count":3,"title":["Characterizing and detecting delirium with clinical and computational measures of speech and language disturbance"],"prefix":"10.1139","volume":"48","author":[{"given":"Sunny X.","family":"Tang","sequence":"first","affiliation":[{"name":"From the Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, Glen Oaks, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Cong","sequence":"additional","affiliation":[{"name":"From the Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, Glen Oaks, New 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