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:
- 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)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/618_Paper.pdf