Machine learning-based coreference resolution of concepts in clinical documents
ABSTRACT Coreference resolution of concepts, although a very active area in the natural language processing community, has not yet been widely applied to clinical documents. Accordingly, the 2011 i2b2 competition focusing on this area is a timely and useful challenge. The objective of this research was to collate coreferent chains of concepts from a corpus of clinical documents. These concepts are in the categories of person, problems, treatments, and tests.
A machine learning approach based on graphical models was employed to cluster coreferent concepts. Features selected were divided into domain independent and domain specific sets. Training was done with the i2b2 provided training set of 489 documents with 6949 chains. Testing was done on 322 documents.
The learning engine, using the un-weighted average of three different measurement schemes, resulted in an F measure of 0.8423 where no domain specific features were included and 0.8483 where the feature set included both domain independent and domain specific features.
Our machine learning approach is a promising solution for recognizing coreferent concepts, which in turn is useful for practical applications such as the assembly of problem and medication lists from clinical documents.
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ABSTRACT: Identification of co-referent entity mentions inside text has significant importance for other natural language processing (NLP) tasks (e.g.event linking). However, this task, known as co-reference resolution, remains a complex problem, partly because of the confusion over different evaluation metrics and partly because the well-researched existing methodologies do not perform well on new domains such as clinical records. This paper presents a variant of the influential mention-pair model for co-reference resolution. Using a series of linguistically and semantically motivated constraints, the proposed approach controls generation of less-informative/sub-optimal training and test instances. Additionally, the approach also introduces some aggressive greedy strategies in chain clustering. The proposed approach has been tested on the official test corpus of the recently held i2b2/VA 2011 challenge. It achieves an unweighted average F1 score of 0.895, calculated from multiple evaluation metrics (MUC,B(3) and CEAF scores). These results are comparable to the best systems of the challenge. What makes our proposed system distinct is that it also achieves high average F1 scores for each individual chain type (Test: 0.897, Person: 0.852, Problem: 0.855, Treatment: 0.884). Unlike other works, it obtains good scores for each of the individual metrics rather than being biased towards a particular metric.Journal of Biomedical Informatics 04/2013; 46(3). DOI:10.1016/j.jbi.2013.03.007 · 2.48 Impact Factor
- Journal of the American Medical Informatics Association 12/2013; 20(e2):e206-11. DOI:10.1136/amiajnl-2013-002428 · 3.93 Impact Factor
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ABSTRACT: Implementation of Electronic Health Record (EHR) systems continues to expand. The massive number of patient encounters results in high amounts of stored data. Transforming clinical data into knowledge to improve patient care has been the goal of biomedical informatics professionals for many decades, and this work is now increasingly recognized outside our field. In reviewing the literature for the past three years, we focus on "big data" in the context of EHR systems and we report on some examples of how secondary use of data has been put into practice.Yearbook of medical informatics 01/2014; 9(1):97-104. DOI:10.15265/IY-2014-0003