Estimating disease prevalence from drug utilization data using the Random Forest algorithm.

ABSTRACT:

Background

Aggregated claims data on medication are often used as a proxy for the prevalence of diseases, especially chronic diseases. However, linkage between medication and diagnosis tend to be theory based and not very precise. Modelling disease probability at an individual level using individual level data may yield more accurate results.

Methods

Individual probabilities of having a certain chronic disease were estimated using the Random Forest (RF) algorithm. A training set was created from a general practitioners database of 276 723 cases that included diagnosis and claims data on medication. Model performance for 29 chronic diseases was evaluated using Receiver-Operator Curves, by measuring the Area Under the Curve (AUC).

Results

The diseases for which model pe

SUBMITTER: Slobbe LCJ 

PROVIDER: S-EPMC6660107 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature