  Background and Significance: Selecting cohorts for a clinical trial typically
requires costly and time-consuming manual chart reviews resulting in poor
participation. To help automate the process, National NLP Clinical Challenges
(N2C2) conducted a shared challenge by defining 13 criteria for clinical trial
cohort selection and by providing training and test datasets. This research was
motivated by the N2C2 challenge.
  Methods: We broke down the task into 13 independent subtasks corresponding to
each criterion and implemented subtasks using rules or a supervised machine
learning model. Each task critically depended on knowledge resources in the
form of task-specific lexicons, for which we developed a novel model-driven
approach. The approach allowed us to first expand the lexicon from a seed set
and then remove noise from the list, thus improving the accuracy.
  Results: Our system achieved an overall F measure of 0.9003 at the challenge,
and was statistically tied for the first place out of 45 participants. The
model-driven lexicon development and further debugging the rules/code on the
training set improved overall F measure to 0.9140, overtaking the best
numerical result at the challenge.
  Discussion: Cohort selection, like phenotype extraction and classification,
is amenable to rule-based or simple machine learning methods, however, the
lexicons involved, such as medication names or medical terms referring to a
medical problem, critically determine the overall accuracy. Automated lexicon
development has the potential for scalability and accuracy.
