Open AccessA Novel Metric for Developing Easy-to-Use and Accurate Clinical Prediction Models
Author(s) -
Sei J. Lee,
Alexander K. Smith,
L. Grisell DiazRamirez,
Kenneth E. Covinsky,
Siqi Gan,
Catherine L. Chen,
W. John Boscardin
Publication year - 2021
Publication title -
medical care
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.632
H-Index - 178
eISSN - 1537-1948
pISSN - 0025-7079
DOI - 10.1097/mlr.0000000000001510
Subject(s) - metric (unit) , computer science , data science , engineering , operations management
Guidelines recommend that clinicians use clinical prediction models to estimate future risk to guide decisions. For example, predicted fracture risk is a major factor in the decision to initiate bisphosphonate medications. However, current methods for developing prediction models often lead to models that are accurate but difficult to use in clinical settings.