Rule-based and Machine Learning Hybrid System for Patient Cohort Selection

Abstract

Clinical trials play a critical role in medical studies. However, identifying and selecting cohorts for such trials can be a troublesome task since patients must match a set of complex pre-determined criteria. Patient selection requires a manual analysis of clinical narratives in patients’ records, which is a time-consuming task for medical researchers. In this work, natural language processing (NLP) techniques were used to perform automatic patient cohort selection. The approach herein presented was developed and tested on the 2018 n2c2 Track 1 Shared-Task dataset where each patient record is annotated with 13 selection criteria. The resulting hybrid approach is based on heuristics and machine learning and attained a micro-average and macro-average F1-score of 0.8844 and 0.7271, respectively, in the n2c2 test set. Part of the source code resultant from this work is available at https://github.com/ruiantunes/2018-n2c2-track-1/.

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