Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure
Author:
Affiliation:
1. Wright State University,Dept. of Computer Sci. and Engr.,Dayton,U.S.A.
2. Wright State University,Dept. of Biological Sciences,Dayton,U.S.A.
3. University of Illinois Chicago,College of Nursing,Chicago,U.S.A.
Funder
Health
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10224635/10224668/10224690.pdf?arnumber=10224690
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1. Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets
2. Diagnosis and length of psychiatric in-patient stay
3. PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models
4. Learning probabilistic phenotypes from heterogeneous EHR data
5. Comorbidities complicating heart failure: changes over the last 15 years
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