Identifying emergency department patients at high risk for opioid overdose using natural language processing and machine learning.
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| Title: | Identifying emergency department patients at high risk for opioid overdose using natural language processing and machine learning. |
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| Authors: | Sharp A; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America. Electronic address: asharp@challiance.org., Parry GJ; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America., Pérez GR; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America., Mullin BO; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America., Yang X; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America., Kumar A; Institute for Behavioral Health, Heller School for Social Policy & Management Brandeis University, United States of America., Creedon T; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America., Flores M; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America., Fischer CM; Department of Emergency Medicine, Mount Auburn Hospital, United States of America., Schuman-Olivier Z; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America., Moyer M; John Snow, Inc., United States of America., Tran NM; Department of Health Policy, Vanderbilt University, United States of America., Cook BL; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America. |
| Source: | Journal of substance use and addiction treatment [J Subst Use Addict Treat] 2025 Aug; Vol. 175, pp. 209718. Date of Electronic Publication: 2025 May 03. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Elsevier Inc Country of Publication: United States NLM ID: 9918541186406676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2949-8759 (Electronic) Linking ISSN: 29498759 NLM ISO Abbreviation: J Subst Use Addict Treat Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
| FullText | Text: Availability: 0 |
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| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 40324654 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Identifying emergency department patients at high risk for opioid overdose using natural language processing and machine learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Sharp+A%22">Sharp A</searchLink>; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America. Electronic address: asharp@challiance.org.<br /><searchLink fieldCode="AU" term="%22Parry+GJ%22">Parry GJ</searchLink>; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America.<br /><searchLink fieldCode="AU" term="%22Pérez+GR%22">Pérez GR</searchLink>; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America.<br /><searchLink fieldCode="AU" term="%22Mullin+BO%22">Mullin BO</searchLink>; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America.<br /><searchLink fieldCode="AU" term="%22Yang+X%22">Yang X</searchLink>; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America.<br /><searchLink fieldCode="AU" term="%22Kumar+A%22">Kumar A</searchLink>; Institute for Behavioral Health, Heller School for Social Policy & Management Brandeis University, United States of America.<br /><searchLink fieldCode="AU" term="%22Creedon+T%22">Creedon T</searchLink>; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America.<br /><searchLink fieldCode="AU" term="%22Flores+M%22">Flores M</searchLink>; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America.<br /><searchLink fieldCode="AU" term="%22Fischer+CM%22">Fischer CM</searchLink>; Department of Emergency Medicine, Mount Auburn Hospital, United States of America.<br /><searchLink fieldCode="AU" term="%22Schuman-Olivier+Z%22">Schuman-Olivier Z</searchLink>; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America.<br /><searchLink fieldCode="AU" term="%22Moyer+M%22">Moyer M</searchLink>; John Snow, Inc., United States of America.<br /><searchLink fieldCode="AU" term="%22Tran+NM%22">Tran NM</searchLink>; Department of Health Policy, Vanderbilt University, United States of America.<br /><searchLink fieldCode="AU" term="%22Cook+BL%22">Cook BL</searchLink>; Health Equity Research Lab, Cambridge Health Alliance, Cambridge, MA, United States of America; Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%229918541186406676%22">Journal of substance use and addiction treatment</searchLink> [J Subst Use Addict Treat] 2025 Aug; Vol. 175, pp. 209718. <i>Date of Electronic Publication: </i>2025 May 03. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Elsevier+Inc%22">Elsevier Inc </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>9918541186406676 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2949-8759 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2229498759%22">29498759 </searchLink><i>NLM ISO Abbreviation: </i>J Subst Use Addict Treat <i>Subsets: </i>MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=40324654 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.josat.2025.209718 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 209718 Titles: – TitleFull: Identifying emergency department patients at high risk for opioid overdose using natural language processing and machine learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sharp A – PersonEntity: Name: NameFull: Parry GJ – PersonEntity: Name: NameFull: Pérez GR – PersonEntity: Name: NameFull: Mullin BO – PersonEntity: Name: NameFull: Yang X – PersonEntity: Name: NameFull: Kumar A – PersonEntity: Name: NameFull: Creedon T – PersonEntity: Name: NameFull: Flores M – PersonEntity: Name: NameFull: Fischer CM – PersonEntity: Name: NameFull: Schuman-Olivier Z – PersonEntity: Name: NameFull: Moyer M – PersonEntity: Name: NameFull: Tran NM – PersonEntity: Name: NameFull: Cook BL IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: 2025 Aug Type: published Y: 2025 Identifiers: – Type: issn-electronic Value: 2949-8759 Numbering: – Type: volume Value: 175 Titles: – TitleFull: Journal of substance use and addiction treatment Type: main |
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