Classification of clinically useful sentences in clinical evidence resources.

ABSTRACT:

Unlabelled

Most patient care questions raised by clinicians can be answered by online clinical knowledge resources. However, important barriers still challenge the use of these resources at the point of care.

Objective

To design and assess a method for extracting clinically useful sentences from synthesized online clinical resources that represent the most clinically useful information for directly answering clinicians' information needs.

Materials and methods

We developed a Kernel-based Bayesian Network classification model based on different domain-specific feature types extracted from sentences in a gold standard composed of 18 UpToDate documents. These features included UMLS concepts and their semantic groups, semantic predications extracted by SemRep, patient pop

SUBMITTER: Morid MA 

PROVIDER: S-EPMC4836984 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature