Automatic lymphoma classification with sentence subgraph mining from pathology reports.

ABSTRACT:

Objective

Pathology reports are rich in narrative statements that encode a complex web of relations among medical concepts. These relations are routinely used by doctors to reason on diagnoses, but often require hand-crafted rules or supervised learning to extract into prespecified forms for computational disease modeling. We aim to automatically capture relations from narrative text without supervision.

Methods

We design a novel framework that translates sentences into graph representations, automatically mines sentence subgraphs, reduces redundancy in mined subgraphs, and automatically generates subgraph features for subsequent classification tasks. To ensure meaningful interpretations over the sentence graphs, we use the Unified Medical Language System Metathesaurus to ma

SUBMITTER: Luo Y 

PROVIDER: S-EPMC4147603 | biostudies-literature | 2014 Sep-Oct

REPOSITORIES: biostudies-literature