Thesaurus-Based Hierarchical Semantic Grouping of Medical Terms in Information Extraction.

In this paper we describe a semantic approach for grouping medical terms into a hierarchy of concepts based on the UMLS meta-thesaurus. The context of this work is Medical Recap, a Web system that automatically extracts risk information from PubMed abstracts, and then aggregates this knowledge into dependence graphs or Bayesian networks.

Extracting Dependence Relations from Unstructured Medical Text.

Dependence relations among disease and risk factors are a key ingredient in risk modeling and decision support models. Currently such information is either provided by experts (costly and time consuming) or extracted from data (if available). The published medical literature represents a promising source of such knowledge; however its manual processing is practically infeasible. While a number of solutions have been introduced to add structure to biomedical literature, none adequately recover dependence relations. The objective of our research is to build such an automatic dependence extraction solution, based on a sequence of natural language processing steps, which take as input a set of MEDLINE abstracts and provide as output a list of structured dependence statements. This paper presents a hybrid pipeline approach, a combination of rule-based and machine learning algorithms. We found that this approach outperforms a strictly rule-based approach.