Protocol for Designing a Model to Predict the Likelihood of Psychosis From Electronic Health Records Using Natural Language Processing and Machine Learning.
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| Title: | Protocol for Designing a Model to Predict the Likelihood of Psychosis From Electronic Health Records Using Natural Language Processing and Machine Learning. |
|---|---|
| Authors: | Stavers-Sosa, Icelini1,2 icelini.x.stavers-sosa@kp.org, Cronkite, David J.2, Gerstley, Lawrence D.3, Kelley, Ann2, Kiel, Linda2, Kline-Simon, Andrea H.3, Marafino, Ben J.3, Ramaprasan, Arvind2, Carrell, David S.2, Hirschtritt, Matthew E.1,3,4 |
| Source: | Permanente Journal. Sep2024, Vol. 28 Issue 3, p23-36. 14p. |
| Database: | Academic Search Ultimate |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: asn DbLabel: Academic Search Ultimate An: 180313982 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=180313982 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.7812/TPP/23.139 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 23 Titles: – TitleFull: Protocol for Designing a Model to Predict the Likelihood of Psychosis From Electronic Health Records Using Natural Language Processing and Machine Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Stavers-Sosa, Icelini – PersonEntity: Name: NameFull: Cronkite, David J. – PersonEntity: Name: NameFull: Gerstley, Lawrence D. – PersonEntity: Name: NameFull: Kelley, Ann – PersonEntity: Name: NameFull: Kiel, Linda – PersonEntity: Name: NameFull: Kline-Simon, Andrea H. – PersonEntity: Name: NameFull: Marafino, Ben J. – PersonEntity: Name: NameFull: Ramaprasan, Arvind – PersonEntity: Name: NameFull: Carrell, David S. – PersonEntity: Name: NameFull: Hirschtritt, Matthew E. IsPartOfRelationships: – BibEntity: Dates: – D: 16 M: 09 Text: Sep2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 15525767 Numbering: – Type: volume Value: 28 – Type: issue Value: 3 Titles: – TitleFull: Permanente Journal Type: main |
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