{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T11:58:26Z","timestamp":1772884706974,"version":"3.50.1"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2018,10,30]],"date-time":"2018-10-30T00:00:00Z","timestamp":1540857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["P50 MH106933"],"award-info":[{"award-number":["P50 MH106933"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["4R13LM011411"],"award-info":[{"award-number":["4R13LM011411"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Develop an approach, One-class-at-a-time, for triaging psychiatric patients using machine learning on textual patient records. Our approach aims to automate the triaging process and reduce expert effort while providing high classification reliability.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>The One-class-at-a-time approach is a multistage cascading classification technique that achieves higher triage classification accuracy compared to traditional multiclass classifiers through 1) classifying one class at a time (or stage), and 2) identification and application of the highest accuracy classifier at each stage. The approach was evaluated using a unique dataset of 433 psychiatric patient records with a triage class label provided by \u201cI2B2 challenge,\u201d a recent competition in the medical informatics community.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The One-class-at-a-time cascading classifier outperformed state-of-the-art classification techniques with overall classification accuracy of 77% among 4 classes, exceeding accuracies of existing multiclass classifiers. The approach also enabled highly accurate classification of individual classes\u2014the severe and mild with 85% accuracy, moderate with 64% accuracy, and absent with 60% accuracy.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>The triaging of psychiatric cases is a challenging problem due to the lack of clear guidelines and protocols. Our work presents a machine learning approach using psychiatric records for triaging patients based on their severity condition.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>The One-class-at-a-time cascading classifier can be used as a decision aid to reduce triaging effort of physicians and nurses, while providing a unique opportunity to involve experts at each stage to reduce false positive and further improve the system\u2019s accuracy.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocy109","type":"journal-article","created":{"date-parts":[[2018,7,26]],"date-time":"2018-07-26T19:29:50Z","timestamp":1532633390000},"page":"1481-1487","source":"Crossref","is-referenced-by-count":20,"title":["Machine learning for psychiatric patient triaging: an investigation of cascading classifiers"],"prefix":"10.1093","volume":"25","author":[{"given":"Vivek Kumar","family":"Singh","sequence":"first","affiliation":[{"name":"Information Systems and Decision Sciences, MUMA College of Business, University of South Florida, Tampa, Florida, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Utkarsh","family":"Shrivastava","sequence":"additional","affiliation":[{"name":"Haworth College of Business, Department of Business Information Systems, Western Michigan University, Kalamazoo, Michigan, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina","family":"Bouayad","sequence":"additional","affiliation":[{"name":"College of Business, Information Systems and Business Analytics, Florida International University, Miami, Florida, USA"},{"name":"HSR&D Center of Innovation on Disability and Rehabilitation Research (CINDRR), James A. 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