Medical Concept Normalization in User-Generated Text

Medical Concept Normalization in User-Generated Text

Manousogiannis, Manolis (TU Delft Electrical Engineering, Mathematics and Computer Science)

Bozzon, Alessandro (mentor)
Sips, RJ (mentor)
Mesbah, Sepideh (mentor)
Houben, Geert-Jan (graduation committee)
Rellermeyer, Jan (graduation committee)

Delft University of Technology

Computer Science | Data Science and Technology

2019-09-13

Online social networks have revolutionized the way people interact with each other nowadays. Users often share their experiences in various health - related topics like disease symptoms, drug treatments and other medical related issues in order to discuss with other patients dealing with similar conditions. During the production of a new drug, important drug properties like possible Adverse Drug Reactions (ADRs) are monitored through a phase of clinical trials. However, due to various factors that can not be easily measured in those trials, patients can potentially experience adverse events that were not related to their treatment before, or were related to it in a much smaller frequency. Therefore, the automatic detection of Adverse Events in online networks is gaining an increasing popularity among researchers in the biomedical community, as it can offer a valuable complementary source of information, next to the traditional approaches of reporting those events to the corresponding Food and Drug Association. From an NLP perspective, this task poses a significant challenge as there is a large gap between the informal language used in social media and the formal medical language used to officially describe a medical concept. Moreover, there is an absence of large annotated datasets, as the manual labeling of an adverse effect mentions is a time-consuming and often ambiguous procedure which also requires some sort of medical expertise. In this work we propose a novel machine learning approach to normalize Adverse Drug Effect mentions in user-generated text to a standard vocabulary from a medical Ontology. The evaluation results of the proposed model demonstrates a competitive performance among the current state of the art techniques, posing the potential feasibility of our model in the medical concept normalization domain.

Named Entity Linking
medical concept normalization
few shot learning
Natural Language Processing

http://resolver.tudelft.nl/uuid:2ded3311-51df-484a-9afa-c713eca1a36b

Student theses

master thesis

© 2019 Manolis Manousogiannis