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.

Extracting risk modeling information from medical articles.

Risk modeling in healthcare is both ubiquitous and in its infancy. On the one hand, a significant proportion of medical research focuses on determining the factors that influence the incidence, severity and treatment of diseases, which is a form of risk identification. Those studies typically investigate the micro-level of risk modeling, i.e., the existence of dependences between a reduced set of hypothesized (or demonstrated) risk factors and a focus disease or treatment. On the other hand, the macro-level of risk modeling, i.e., articulating how a large number of such risk factors interact to affect diseases and treatments is not widespread, though essential for medical decision support modeling. By exploiting advances in natural language processing, we believe that information contained in unstructured texts such as medical articles could be extracted to facilitate aggregation into macro-level risk models.

Semantic annotation for concept-based cross-language medical information retrieval.

We present a framework for concept-based cross-language information retrieval in the medical domain, which is under development in the MUCHMORE project. Our approach is based on using the Unified Medical Language System (UMLS) as the primary source of semantic data. Documents and queries are annotated with multiple layers of linguistic information. Linguistic processing includes part-of-speech tagging, morphological analysis, phrase recognition and the identification of medical terms and semantic relations between them. The paper describes experiments in monolingual and cross-language document retrieval, performed on a corpus of medical abstracts. Results show that linguistic processing, especially lemmatization and compound analysis for German, is a crucial step in achieving a good baseline performance. On the other hand, they show that semantic information, specifically the combined use of concepts and relations, increases the performance in monolingual and cross-language retrieval.