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

Abstract : Objective: Natural language processing can help minimize human intervention in identifying patients meeting eligibility criteria for clinical trials, but there is still a long way to go to obtain a general and systematic approach that is useful for researchers. We describe two methods taking a step in this direction and present their results obtained during the n2c2 challenge on cohort selection for clinical trials. Materials and Methods: The first method is a weakly supervised method using an unlabeled corpus (MIMIC) to build a silver standard, by producing semi-automatically a small and very precise set of rules to detect some samples of positive and negative patients. This silver standard is then used to train a traditional supervised model. The second method is a terminology-based approach where a medical expert selects the appropriate concepts, and a procedure is defined to search the terms and check the structural or temporal constraints. Results: On the n2c2 dataset containing annotated data about 13 selection criteria on 288 patients, we obtained an overall F1-measure of 0.8969, which is the third best result out of 45 participant teams, with no statistically significant difference with the best-ranked team. Discussion: Both approaches obtained very encouraging results and apply to different types of criteria. The weakly supervised method requires explicit descriptions of positive and negative examples in some reports. The terminology-based method is very efficient when medical concepts carry most of the relevant information. Conclusion: It is unlikely that much more annotated data will be soon available for the task of identifying a wide range of patient phenotypes. One must focus on weakly or non-supervised learning methods using both structured and unstructured data and relying on a comprehensive representation of the patients.
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https://hal.archives-ouvertes.fr/hal-02406975
Contributor : Xavier Tannier <>
Submitted on : Thursday, December 12, 2019 - 12:03:30 PM
Last modification on : Saturday, February 15, 2020 - 1:44:54 AM

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  • HAL Id : hal-02406975, version 1
  • ARXIV : 1903.07879

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Xavier Tannier, Nicolas Paris, Hugo Cisneros, Christel Daniel, Matthieu Doutreligne, et al.. Hybrid Approaches for our Participation to the n2c2 Challenge on Cohort Selection for Clinical Trials. 2019. ⟨hal-02406975⟩

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