Identifying anaphylaxis using weakly-supervised prediction models and natural language processing.

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Bibliographic Details
Title: Identifying anaphylaxis using weakly-supervised prediction models and natural language processing.
Authors: Williamson BD; Kaiser Permanente Washington Health Research Institute, Seattle, WA.; University of Washington, Seattle, WA.; Fred Hutchinson Cancer Center, Seattle, WA., Cronkite DJ; Kaiser Permanente Washington Health Research Institute, Seattle, WA., Yu O; Kaiser Permanente Washington Health Research Institute, Seattle, WA., Ramaprasan A; Kaiser Permanente Washington Health Research Institute, Seattle, WA., Fuller S; Kaiser Permanente Washington Health Research Institute, Seattle, WA., Covey J; Kaiser Permanente Washington Health Research Institute, Seattle, WA., Kiniry E; Kaiser Permanente Washington Health Research Institute, Seattle, WA., Park D; Vanderbilt University Medical Center, Nashville, TN., Winter R; Vanderbilt University Medical Center, Nashville, TN., Whitaker J; Vanderbilt University Medical Center, Nashville, TN., McLemore MF; Vanderbilt University Medical Center, Nashville, TN., Wittayanukorn S; US Food and Drug Administration, Silver Spring, MD., Stojanovic D; US Food and Drug Administration, Silver Spring, MD., Zhao Y; US Food and Drug Administration, Silver Spring, MD., Dutcher S; US Food and Drug Administration, Silver Spring, MD., Carrell DS; Kaiser Permanente Washington Health Research Institute, Seattle, WA., Jackson LA; Kaiser Permanente Washington Health Research Institute, Seattle, WA., Nelson JC; Kaiser Permanente Washington Health Research Institute, Seattle, WA.; University of Washington, Seattle, WA., Smith JC; Vanderbilt University Medical Center, Nashville, TN.
Source: MedRxiv : the preprint server for health sciences [medRxiv] 2026 Jun 17. Date of Electronic Publication: 2026 Jun 17.
Publication Type: Journal Article; Preprint
Journal Info: Country of Publication: United States NLM ID: 101767986 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: medRxiv Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
DOI:10.64898/2026.06.09.26355005