Automatic identification of recent high impact clinical articles in PubMed to support clinical decision making using time-agnostic features.
J Biomed Inform
; 89: 1-10, 2019 01.
Article
em En
| MEDLINE
| ID: mdl-30468912
ABSTRACT
OBJECTIVES:
Finding recent clinical studies that warrant changes in clinical practice ("high impact" clinical studies) in a timely manner is very challenging. We investigated a machine learning approach to find recent studies with high clinical impact to support clinical decision making and literature surveillance.METHODS:
To identify recent studies, we developed our classification model using time-agnostic features that are available as soon as an article is indexed in PubMed®, such as journal impact factor, author count, and study sample size. Using a gold standard of 541 high impact treatment studies referenced in 11 disease management guidelines, we tested the following null hypotheses (1) the high impact classifier with time-agnostic features (HI-TA) performs equivalently to PubMed's Best Match sort and a MeSH-based Naïve Bayes classifier; and (2) HI-TA performs equivalently to the high impact classifier with both time-agnostic and time-sensitive features (HI-TS) enabled in a previous study. The primary outcome for both hypotheses was mean top 20 precision.RESULTS:
The differences in mean top 20 precision between HI-TA and three baselines (PubMed's Best Match, a MeSH-based Naïve Bayes classifier, and HI-TS) were not statistically significant (12% vs. 3%, pâ¯=â¯0.101; 12% vs. 11%, pâ¯=â¯0.720; 12% vs. 25%, pâ¯=â¯0.094, respectively). Recall of HI-TA was low (7%).CONCLUSION:
HI-TA had equivalent performance to state-of-the-art approaches that depend on time-sensitive features. With the advantage of relying only on time-agnostic features, the proposed approach can be used as an adjunct to help clinicians identify recent high impact clinical studies to support clinical decision-making. However, low recall limits the use of HI-TA for literature surveillance.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Publicações
/
PubMed
/
Tomada de Decisão Clínica
/
Aprendizado de Máquina
Tipo de estudo:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Idioma:
En
Revista:
J Biomed Inform
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Estados Unidos