Title : Generalizing the Detection of Interactions in Clinical Guidelines
| Presenter | Veruska Zamborlini |
| Abstract | I'll present about the paper recently presented at HealthINF conference in Rome. This paper presents a method for formally representing Computer-Interpretable Guidelines to deal with multimorbidity. Although some approaches for merging guidelines exist, improvements are still required for combining several sources of information and coping with possibly conflicting pieces of evidence coming from clinical studies. Our main contribution is twofold: (i) we provide general models and rules for representing guidelines that expresses evidence as causation beliefs; (ii) we introduce a mechanism to exploit external medical knowledge acquired from Linked Open Data (Drugbank, Sider, DIKB) to detect potential interactions between recommendations. We apply this framework to merge three guidelines (Osteoarthritis, Diabetes, and Hypertension) in order to illustrate the capability of this approach for detecting potential conflicts between guidelines and eventually propose alternatives. |
Title : Semantic Processing of Medical Text with NLP tools
| Presenter | Zhisheng Huang |
| Abstract | Relation extraction from medical text by using NLP tools has been considered to be one of the important topics in medical knowledge processing. Enhancing those NLP tools with the semantic processing by using some kinds of domain knowledge, such as medical ontologies, would improve the efficiency of medical knowledge extraction. In this talk, we will present an approach how to use the XMedLan NLP tool to obtain the semantic representation of medical knowledge with well-known medical ontologies such as UMLS and SNOMED CT. We will report two use cases of the semantic processing of medical knowledge. The first use case is how to semi-automatically use a rule-based formalization of eligibility criteria for clinical trials when processing clinical text. The second use case is how to convert unstructured knowledge in medical guidelines into structured ones, and how they can be used in searching for new and relevant evidences for evidence-based medical guideline updates. |