  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.
