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

Goodwin T, Harabagiu SM - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: University of Texas at Dallas, Richardson, TX, USA.

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.

No MeSH data available.


Related in: MedlinePlus

A probabilistic graphical model encoding the likelihood of any possible progression of clinical findings.
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f2-2092386: A probabilistic graphical model encoding the likelihood of any possible progression of clinical findings.

Mentions: We encoded knowledge using a probabilistic graphical model (PGM), illustrated in Figure 2. In our PGM, nodes correspond to CFs and are represented as binary random variables. Our PGM also encodes knowledge about the CO of CFs. COs are sequences of sets of CFs, denoted as where L is the longest CO inferred from our dataset. Because the PGM encodes knowledge about the entire patient population documented in the dataset, the PGM needed to encode all the possible sets of CFs for each where 0 ≤ i ≤ L. This was achieved by assigning a value of 1 to the random variable of a CF which was observed in the same and a value of 0 to any CF which was not observed in that same . An advantage of the knowledge representation using the PGM stems from the ability to also assign a probability to the random variables, which encapsulates the statistical distribution of the CFs corresponding to the COs across all patients. A second advantage of this knowledge representation stems from the ability to capture statistical dependencies between the random variables, which are represented as edges in the graph. Any edge from a CFx in to a CFy in indicates such a dependency. The statistical dependencies between CFs across successive sets allows us to represent all the possible ways in which CFs may progress from one time interval to the next based on the properties of our clinical dataset. Because we are only considering five different CFs in our dataset, there are 25 = 32 possible statistical dependencies between any to.


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

Goodwin T, Harabagiu SM - AMIA Jt Summits Transl Sci Proc (2015)

A probabilistic graphical model encoding the likelihood of any possible progression of clinical findings.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525214&req=5

f2-2092386: A probabilistic graphical model encoding the likelihood of any possible progression of clinical findings.
Mentions: We encoded knowledge using a probabilistic graphical model (PGM), illustrated in Figure 2. In our PGM, nodes correspond to CFs and are represented as binary random variables. Our PGM also encodes knowledge about the CO of CFs. COs are sequences of sets of CFs, denoted as where L is the longest CO inferred from our dataset. Because the PGM encodes knowledge about the entire patient population documented in the dataset, the PGM needed to encode all the possible sets of CFs for each where 0 ≤ i ≤ L. This was achieved by assigning a value of 1 to the random variable of a CF which was observed in the same and a value of 0 to any CF which was not observed in that same . An advantage of the knowledge representation using the PGM stems from the ability to also assign a probability to the random variables, which encapsulates the statistical distribution of the CFs corresponding to the COs across all patients. A second advantage of this knowledge representation stems from the ability to capture statistical dependencies between the random variables, which are represented as edges in the graph. Any edge from a CFx in to a CFy in indicates such a dependency. The statistical dependencies between CFs across successive sets allows us to represent all the possible ways in which CFs may progress from one time interval to the next based on the properties of our clinical dataset. Because we are only considering five different CFs in our dataset, there are 25 = 32 possible statistical dependencies between any to.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: University of Texas at Dallas, Richardson, TX, USA.

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.

No MeSH data available.


Related in: MedlinePlus