@inproceedings{goodwin-harabagiu-2014-clinical,
title = "Clinical Data-Driven Probabilistic Graph Processing",
author = "Goodwin, Travis and
Harabagiu, Sanda",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/618_Paper.pdf",
pages = "101--108",
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.",
}
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%0 Conference Proceedings
%T Clinical Data-Driven Probabilistic Graph Processing
%A Goodwin, Travis
%A Harabagiu, Sanda
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F goodwin-harabagiu-2014-clinical
%X 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.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/618_Paper.pdf
%P 101-108
Markdown (Informal)
[Clinical Data-Driven Probabilistic Graph Processing](http://www.lrec-conf.org/proceedings/lrec2014/pdf/618_Paper.pdf) (Goodwin & Harabagiu, LREC 2014)
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).