Clinical Data-Driven Probabilistic Graph Processing

Travis Goodwin, Sanda Harabagiu


Abstract
Electronic Medical Records (EMRs) encode an extraordinary amount of medical knowledge. Collecting and interpreting this knowledge, however, belies a significant level of clinical understanding. Automatically capturing the clinical information is crucial for performing comparative effectiveness research. In this paper, we present a data-driven approach to model semantic dependencies between medical concepts, qualified by the beliefs of physicians. The dependencies, captured in a patient cohort graph of clinical pictures and therapies is further refined into a probabilistic graphical model which enables efficient inference of patient-centered treatment or test recommendations (based on probabilities). To perform inference on the graphical model, we describe a technique of smoothing the conditional likelihood of medical concepts by their semantically-similar belief values. The experimental results, as compared against clinical guidelines are very promising.
Anthology ID:
L14-1495
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
101–108
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/618_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Travis Goodwin and Sanda Harabagiu. 2014. Clinical Data-Driven Probabilistic Graph Processing. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 101–108, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
Clinical Data-Driven Probabilistic Graph Processing (Goodwin & Harabagiu, LREC 2014)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/618_Paper.pdf