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1.
BMC Med Res Methodol ; 24(1): 108, 2024 May 09.
Artigo em Inglês | MEDLINE | ID: mdl-38724903

RESUMO

OBJECTIVE: Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming process. This study aims to (1) develop two disease-specific annotated corpora, one for human papillomavirus (HPV) associated diseases and the other for pneumococcal-associated pediatric diseases (PAPD), and (2) optimize machine- and deep-learning models to facilitate automation of the SLR abstract screening. METHODS: This study constructed two disease-specific SLR screening corpora for HPV and PAPD, which contained citation metadata and corresponding abstracts. Performance was evaluated using precision, recall, accuracy, and F1-score of multiple combinations of machine- and deep-learning algorithms and features such as keywords and MeSH terms. RESULTS AND CONCLUSIONS: The HPV corpus contained 1697 entries, with 538 relevant and 1159 irrelevant articles. The PAPD corpus included 2865 entries, with 711 relevant and 2154 irrelevant articles. Adding additional features beyond title and abstract improved the performance (measured in Accuracy) of machine learning models by 3% for HPV corpus and 2% for PAPD corpus. Transformer-based deep learning models that consistently outperformed conventional machine learning algorithms, highlighting the strength of domain-specific pre-trained language models for SLR abstract screening. This study provides a foundation for the development of more intelligent SLR systems.


Assuntos
Aprendizado de Máquina , Infecções por Papillomavirus , Humanos , Infecções por Papillomavirus/diagnóstico , Economia Médica , Algoritmos , Avaliação de Resultados em Cuidados de Saúde/métodos , Aprendizado Profundo , Indexação e Redação de Resumos/métodos
2.
J Am Med Inform Assoc ; 27(9): 1437-1442, 2020 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-32569358

RESUMO

Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.


Assuntos
Betacoronavirus , Técnicas de Laboratório Clínico/classificação , Infecções por Coronavirus/diagnóstico , Logical Observation Identifiers Names and Codes , Pneumonia Viral/diagnóstico , Terminologia como Assunto , COVID-19 , Teste para COVID-19 , Infecções por Coronavirus/classificação , Registros Eletrônicos de Saúde , Humanos , Pandemias , SARS-CoV-2
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