Este artigo é um Preprint
Preprints são relatos preliminares de pesquisa que não foram certificados pela revisão por pares. Eles não devem ser considerados para orientar a prática clínica ou comportamentos relacionados à saúde e não devem ser publicados na mídia como informação estabelecida.
Preprints publicados online permitem que os autores recebam feedback rápido, e toda a comunidade científica pode avaliar o trabalho independentemente e responder adequadamente. Estes comentários são publicados juntamente com os preprints para qualquer pessoa ler e servir como uma avaliação pós-publicação.
Discovering Social Determinants of Health from Case Reports using Natural Language Processing: Algorithmic Development and Validation (preprint)
medrxiv; 2022.
Preprint
em Inglês
| 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.
Texto completo:
Disponível
Coleções:
Preprints
Base de dados:
medRxiv
Assunto principal:
COVID-19
Idioma:
Inglês
Ano de publicação:
2022
Tipo de documento:
Preprint
Similares
MEDLINE
...
LILACS
LIS