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Discovering Social Determinants of Health from Case Reports using Natural Language Processing: Algorithmic Development and Validation (preprint)
medrxiv; 2022.
Preprint
in English
| medRxiv | ID: ppzbmed-10.1101.2022.11.30.22282946
ABSTRACT
Background:
Social determinants of health are non-medical factors that influence health outcomes (SDOH). There is a wealth of SDOH information available via electronic health records, clinical reports, and social media, usually in free texts format, which poses a significant challenge and necessitates the use of natural language processing (NLP) techniques to extract key information.Objective:
The objective of this research is to advance the automatic extraction of SDOH from clinical texts. Setting and Data The case reports of COVID-19 patients from the published literature are curated to create a corpus. A portion of the data is annotated by experts to create gold labels, and active learning is used for corpus re-annotation.Methods:
A named entity recognition (NER) framework is developed and tested to extract SDOH along with a few prominent clinical entities (diseases, treatments, diagnosis) from the free texts. The proposed model consists of three deep neural networks-A Transformer-based model, a BiLSTM model and a CRF module.Results:
The proposed NER implementation achieves an accuracy (F1-score) of 92.98% on our test set and generalizes well on benchmark data. A careful analysis of case examples demonstrates the superiority of the proposed approach in correctly classifying the named entities.Conclusions:
NLP can be used to extract key information, such as SDOH from free texts. A more accurate understanding of SDOH is needed to further improve healthcare outcomes.
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Main subject:
COVID-19
Language:
English
Year:
2022
Document Type:
Preprint
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