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Objective: Natural language processing can help minimize human interventionin identifying patients meeting eligibility criteria for clinical trials, butthere is still a long way to go to obtain a general and systematic approachthat is useful for researchers. We describe two methods taking a step in thisdirection and present their results obtained during the n2c2 challenge oncohort selection for clinical trials. Materials and Methods: The first methodis a weakly supervised method using an unlabeled corpus (MIMIC) to build asilver standard, by producing semi-automatically a small and very precise setof rules to detect some samples of positive and negative patients. This silverstandard is then used to train a traditional supervised model. The secondmethod is a terminology-based approach where a medical expert selects theappropriate concepts, and a procedure is defined to search the terms and checkthe structural or temporal constraints. Results: On the n2c2 dataset containingannotated data about 13 selection criteria on 288 patients, we obtained anoverall F1-measure of 0.8969, which is the third best result out of 45participant teams, with no statistically significant difference with thebest-ranked team. Discussion: Both approaches obtained very encouraging resultsand apply to different types of criteria. The weakly supervised method requiresexplicit descriptions of positive and negative examples in some reports. Theterminology-based method is very efficient when medical concepts carry most ofthe relevant information. Conclusion: It is unlikely that much more annotateddata will be soon available for the task of identifying a wide range of patientphenotypes. One must focus on weakly or non-supervised learning methods usingboth structured and unstructured data and relying on a comprehensiverepresentation of the patients.

Hybrid Approaches for our Participation to the n2c2 Challenge on Cohort Selection for Clinical Trials