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Goodwin T, Harabagiu SM. A Probabilistic Reasoning Method for Predicting the Progression of Clinical Findings from Electronic Medical Records. AMIA Jt Summits Transl Sci Proc. 2015;2015:61-5
Goodwin, T., & Harabagiu, S. M. (2015). A Probabilistic Reasoning Method for Predicting the Progression of Clinical Findings from Electronic Medical Records. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 201561-5.
Goodwin, Travis, and Harabagiu, Sanda M. "A Probabilistic Reasoning Method for Predicting the Progression of Clinical Findings from Electronic Medical Records." AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science vol. 2015 (2015): 61-5.
Goodwin T, Harabagiu SM. A Probabilistic Reasoning Method for Predicting the Progression of Clinical Findings from Electronic Medical Records. AMIA Jt Summits Transl Sci Proc. 2015 Mar 25;2015:61-5. eCollection 2015. PMID: 26306238; PMCID: PMC4525214.
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AMIA Jt Summits Transl Sci Proc. 2015 Mar 25;2015:61-5. eCollection 2015.

A Probabilistic Reasoning Method for Predicting the Progression of Clinical Findings from Electronic Medical Records.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

Travis Goodwin, Sanda M Harabagiu

Affiliations

  1. University of Texas at Dallas, Richardson, TX, USA.

PMID: 26306238 PMCID: PMC4525214

Abstract

In this paper, we present a probabilistic reasoning method capable of generating predictions of the progression of clinical findings (CFs) reported in the narrative portion of electronic medical records. This method benefits from a probabilistic knowledge representation made possible by a graphical model. The knowledge encoded in the graphical model considers not only the CFs extracted from the clinical narratives, but also their chronological ordering (CO) made possible by a temporal inference technique described in this paper. Our experiments indicate that the predictions about the progression of CFs achieve high performance given the COs induced from patient records.

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