Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

BACKGROUND: Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30‐day readmission following an acute myocardi...

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Detalles Bibliográficos
Autores principales: Brown, Jeremiah R., Ricket, Iben M., Reeves, Ruth M., Shah, Rashmee U., Goodrich, Christine A., Gobbel, Glen, Stabler, Meagan E., Perkins, Amy M., Minter, Freneka, Cox, Kevin C., Dorn, Chad, Denton, Jason, Bray, Bruce E., Gouripeddi, Ramkiran, Higgins, John, Chapman, Wendy W., MacKenzie, Todd, Matheny, Michael E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9075435/
https://www.ncbi.nlm.nih.gov/pubmed/35322668
http://dx.doi.org/10.1161/JAHA.121.024198

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9075435/
https://www.ncbi.nlm.nih.gov/pubmed/35322668
http://dx.doi.org/10.1161/JAHA.121.024198

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