TY - JOUR
T1 - Description of a Rule-based System for the i2b2 Challenge in Natural Language Processing for Clinical Data
AU - Childs, Lois C.
AU - Enelow, Robert
AU - Simonsen, Lone
AU - Heintzelman, Norris H.
AU - Kowalski, Kimberly M.
AU - Taylor, Robert J
N1 - This article has been found as a ’Free Version’ from the Publisher on May 31, 2021. When access to the article closes, please notify [email protected]
PY - 2009
Y1 - 2009
N2 - The Obesity Challenge, sponsored by Informatics for Integrating Biology and the Bedside (i2b2), a National Center for Biomedical Computing, asked participants to build software systems that could "read" a patient's clinical discharge summary and replicate the judgments of physicians in evaluating presence or absence of obesity and 15 comorbidities. The authors describe their methodology and discuss the results of applying Lockheed Martin's rule-based natural language processing (NLP) capability, ClinREAD. We tailored ClinREAD with medical domain expertise to create assigned default judgments based on the most probable results as defined in the ground truth. It then used rules to collect evidence similar to the evidence that the human judges likely relied upon, and applied a logic module to weigh the strength of all evidence collected to arrive at final judgments. The Challenge results suggest that rule-based systems guided by human medical expertise are capable of solving complex problems in machine processing of medical text.
AB - The Obesity Challenge, sponsored by Informatics for Integrating Biology and the Bedside (i2b2), a National Center for Biomedical Computing, asked participants to build software systems that could "read" a patient's clinical discharge summary and replicate the judgments of physicians in evaluating presence or absence of obesity and 15 comorbidities. The authors describe their methodology and discuss the results of applying Lockheed Martin's rule-based natural language processing (NLP) capability, ClinREAD. We tailored ClinREAD with medical domain expertise to create assigned default judgments based on the most probable results as defined in the ground truth. It then used rules to collect evidence similar to the evidence that the human judges likely relied upon, and applied a logic module to weigh the strength of all evidence collected to arrive at final judgments. The Challenge results suggest that rule-based systems guided by human medical expertise are capable of solving complex problems in machine processing of medical text.
UR - https://doi.org/10.1197/jamia.M3083
U2 - 10.1197/jamia.M3083
DO - 10.1197/jamia.M3083
M3 - Journal article
SN - 1067-5027
VL - 16
SP - 571
EP - 575
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 4
ER -