A computational framework for converting textual clinical diagnostic criteria into the quality data model.

ABSTRACT:

Background

Constructing standard and computable clinical diagnostic criteria is an important but challenging research field in the clinical informatics community. The Quality Data Model (QDM) is emerging as a promising information model for standardizing clinical diagnostic criteria.

Objective

To develop and evaluate automated methods for converting textual clinical diagnostic criteria in a structured format using QDM.

Methods

We used a clinical Natural Language Processing (NLP) tool known as cTAKES to detect sentences and annotate events in diagnostic criteria. We developed a rule-based approach for assigning the QDM datatype(s) to an individual criterion, whereas we invoked a machine learning algorithm based on the Conditional Random Fields (CRFs) for annotating att

SUBMITTER: Hong N 

PROVIDER: S-EPMC5077690 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature