Mapping anatomical related entities to human body parts based on wikipedia in discharge summaries

Research output: Contribution to journalArticle

  • External authors:
  • Yipei Wang
  • Xingyu Fan
  • Luoxin Chen
  • Eric I-chao Chang
  • Yan Xu

Abstract

Background
Consisting of dictated free-text documents such as discharge summaries, medical narratives are widely used in medical natural language processing. Relationships between anatomical entities and human body parts are crucial for building medical text mining applications. To achieve this, we establish a mapping system consisting of a Wikipedia-based scoring algorithm and a named entity normalization method (NEN). The mapping system makes full use of information available on Wikipedia, which is a comprehensive Internet medical knowledge base. We also built a new ontology, Tree of Human Body Parts (THBP), from core anatomical parts by referring to anatomical experts and Unified Medical Language Systems (UMLS) to make the mapping system efficacious for clinical treatments.
Result
The gold standard is derived from 50 discharge summaries from our previous work, in which 2,224 anatomical entities are included. The F1-measure of the baseline system is 70.20%, while our algorithm based on Wikipedia achieves 86.67% with the assistance of NEN.
Conclusions
We construct a framework to map anatomical entities to THBP ontology using normalization and a scoring algorithm based on Wikipedia. The proposed framework is proven to be much more effective and efficient than the main baseline system.

Bibliographical metadata

Original languageEnglish
JournalBMC Bioinformatics
Volume20
Issue number1
Early online date17 Aug 2019
DOIs
Publication statusE-pub ahead of print - 17 Aug 2019