proprietary natural language processing nlp algorithm (Optum Inc)
86
Structured Review
Optum Inc
proprietary natural language processing nlp algorithm
Proprietary Natural Language Processing Nlp Algorithm, supplied by Optum Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/nlp+algorithm/nlp+optum+system/pm40952619-77-38-37
Average 86 stars, based on 1 article reviews
Proprietary Natural Language Processing Nlp Algorithm, supplied by Optum Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/nlp+algorithm/nlp+optum+system/pm40952619-77-38-37
Average 86 stars, based on 1 article reviews
proprietary natural language processing nlp algorithm - by Bioz Stars,
2026-10
86/100 stars
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other:Article Title: A cohort study of predictors of short-term nonfatal suicidal and self-harm events among individuals with mental health disorders treated in the emergency department. Article Snippet: Background: Patients presenting to emergency departments (EDs) for mental health problems have an elevated short-term risk of repeat ED visits, subsequent hospitalization, and suicide.. Objective: Use health records to identify predictors of nonfatal suicidal or self-harm events following emergency department visits of individuals with mental health disorders.. Methods: Electronic health record data from 2015 to 2022 were used to identify ED visits with mental health diagnoses for individuals 10 and older; extract 408 potential predictors including demographic, historical and baseline clinical characteristics from structured and unstructured data; and subsequent suicidal and self-harm events. Article Title: Method used to identify adenomyosis and potentially undiagnosed adenomyosis in a large, U.S. electronic health record database. Article Snippet: Funding information AbbVie Abstract Background: The prevalence of adenomyosis is underestimated due to lack of a specific diagnostic code and diagnostic delays given most diagnoses occur at hysterectomy.. Objectives: To identify women with adenomyosis using indicators derived from natural language processing (NLP) of clinical notes in the Optum Electronic Health Record database (2014–2018), and to estimate the prevalence of potentially undiagnosed adenomyosis.. Methods: An NLP algorithm identified mentions of adenomyosis in clinical notes that were highly likely to represent a diagnosis. |