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.