Authors:
Rui Antunes
;
João Figueira Silva
;
Arnaldo Pereira
and
Sérgio Matos
Affiliation:
DETI/IEETA, University of Aveiro, Campus Universitário de Santiago, Aveiro and Portugal
Keyword(s):
Electronic Health Record, Patient Cohort Selection, Machine Learning, Rule-based.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Electronic Health Records and Standards
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Knowledge-Based Systems
;
Pattern Recognition and Machine Learning
;
Symbolic Systems
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/.