Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.

ABSTRACT:

Background

Electronic health records are invaluable for medical research, but much of the information is recorded as unstructured free text which is time-consuming to review manually.

Aim

To develop an algorithm to identify relevant free texts automatically based on labelled examples.

Methods

We developed a novel machine learning algorithm, the 'Semi-supervised Set Covering Machine' (S3CM), and tested its ability to detect the presence of coronary angiogram results and ovarian cancer diagnoses in free text in the General Practice Research Database. For training the algorithm, we used texts classified as positive and negative according to their associated Read diagnostic codes, rather than by manual annotation. We evaluated the precision (positive predictive value) and

SUBMITTER: Wang Z 

PROVIDER: S-EPMC3261909 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature