Academic Journal
Scalable incident detection via natural language processing and probabilistic language models
| Title: | Scalable incident detection via natural language processing and probabilistic language models |
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| Authors: | Colin G. Walsh, Drew Wilimitis, Qingxia Chen, Aileen Wright, Jhansi Kolli, Katelyn Robinson, Michael A. Ripperger, Kevin B. Johnson, David Carrell, Rishi J. Desai, Andrew Mosholder, Sai Dharmarajan, Sruthi Adimadhyam, Daniel Fabbri, Danijela Stojanovic, Michael E. Matheny, Cosmin A. Bejan |
| Source: | Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024) |
| Publisher Information: | Nature Portfolio, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Medicine LCC:Science |
| Subject Terms: | Medicine, Science |
| More Details: | Abstract Post marketing safety surveillance depends in part on the ability to detect concerning clinical events at scale. Spontaneous reporting might be an effective component of safety surveillance, but it requires awareness and understanding among healthcare professionals to achieve its potential. Reliance on readily available structured data such as diagnostic codes risks under-coding and imprecision. Clinical textual data might bridge these gaps, and natural language processing (NLP) has been shown to aid in scalable phenotyping across healthcare records in multiple clinical domains. In this study, we developed and validated a novel incident phenotyping approach using unstructured clinical textual data agnostic to Electronic Health Record (EHR) and note type. It’s based on a published, validated approach (PheRe) used to ascertain social determinants of health and suicidality across entire healthcare records. To demonstrate generalizability, we validated this approach on two separate phenotypes that share common challenges with respect to accurate ascertainment: (1) suicide attempt; (2) sleep-related behaviors. With samples of 89,428 records and 35,863 records for suicide attempt and sleep-related behaviors, respectively, we conducted silver standard (diagnostic coding) and gold standard (manual chart review) validation. We showed Area Under the Precision-Recall Curve of ~ 0.77 (95% CI 0.75–0.78) for suicide attempt and AUPR ~ 0.31 (95% CI 0.28–0.34) for sleep-related behaviors. We also evaluated performance by coded race and demonstrated differences in performance by race differed across phenotypes. Scalable phenotyping models, like most healthcare AI, require algorithmovigilance and debiasing prior to implementation. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-024-72756-7 |
| Access URL: | https://doaj.org/article/5b2eef6f8ea54433a9250e4c3c6abd1c |
| Accession Number: | edsdoj.5b2eef6f8ea54433a9250e4c3c6abd1c |
| Database: | Directory of Open Access Journals |
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| ISSN: | 20452322 |
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| DOI: | 10.1038/s41598-024-72756-7 |
| Published in: | Scientific Reports |
| Language: | English |