Artificial Intelligence-Driven Structurization of Diagnostic Information in Free-Text Pathology Reports.

ABSTRACT:

Background

Free-text sections of pathology reports contain the most important information from a diagnostic standpoint. However, this information is largely underutilized for computer-based analytics. The vast majority of NLP-based methods lack a capacity to accurately extract complex diagnostic entities and relationships among them as well as to provide an adequate knowledge representation for downstream data-mining applications.

Methods

In this paper, we introduce a novel informatics pipeline that extends open information extraction (openIE) techniques with artificial intelligence (AI) based modeling to extract and transform complex diagnostic entities and relationships among them into Knowledge Graphs (KGs) of relational triples (RTs).

Results

Evaluation studies ha

SUBMITTER: Giannaris PS 

PROVIDER: S-EPMC7045509 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature