Identifying clinical concepts in unstructured clinical notes using existing knowledge within the corpus

Abstract

This paper, reports on the results of research which is based originally on the 2009 criteria and corpus of "The Obesity Challenge ", defined by Informatics for Integrating Biology and the Bedside (i2b2), a National Center for Biomedical Computing. In the original task, i2b2 asked participants to build software systems that could process a corpus of noisy patient's clinical discharge summaries and report on patients' condition. The ultimate aim was to compare the judgments of physicians in evaluating the patient condition to a machine performance over such a corpus. The authors used a collection of resources to lexically and semantically characterize the diseases and their associated signs, symptoms. In this approach, they combined dictionary look-up, rule-based, and machine-learning methods along with taking advantage of existing knowledge within the clinical notes to reduce the usage of customized rules and increase the consistency of the performance over various types of noisy corpora. The performance was strengthened by information extracted from the patient notes via an internal redundancy module to overcome False Positives (FPs) and False Negatives (FNs) arising from the noisy nature of corpus. The methods were applied to a collection of 507 previously unseen noisy patient discharge summaries, and the Judgments were evaluated against a manually provided gold standard. The overall ranking of the participating Research groups were primarily based on the macro-averaged F-measure over 16 Classes of diseases. The implemented method achieved the micro-averaged F-measure of 96.9% (ranked within the top 7 out of 28 research groups) where there was no statistical significant difference between top 7 teams in micro F-measure. The highest F-Measure was 97.2%. Comparison of the results of this approach to results of other submitted classical approaches showed using existing knowledge within clinical notes can boost the accuracy of classifiers without extensive usage of rules and customization and therefore has potential for a more consistent performance and more efficient processing over various type of noisy corpora.