Name
Title | ||
|---|---|---|
Latent topic discovery of clinical concepts from hospital discharge summaries of a heterogeneous patient cohort |
Abstract | ||
|---|---|---|
Patients in critical care often exhibit complex disease patterns. A fundamental challenge in clinical research is to identify clinical features that may be characteristic of adverse patient outcomes. In this work, we propose a data-driven approach for phenotype discovery of patients in critical care. We used Hierarchical Dirichlet Process (HDP) as a non-parametric topic modeling technique to automatically discover the latent “topic” structure of diseases, symptoms, and findings documented in hospital discharge summaries. We show that the latent topic structure can be used to reveal phenotypic patterns of diseases and symptoms shared across subgroups of a patient cohort, and may contain prognostic value in stratifying patients' post hospital discharge mortality risks. Using discharge summaries of a large patient cohort from the MIMIC II database, we evaluate the clinical utility of the discovered topic structure in identifying patients who are at high risk of mortality within one year post hospital discharge. We demonstrate that the learned topic structure has statistically significant associations with mortality post hospital discharge, and may provide valuable insights in defining new feature sets for predicting patient outcomes. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/EMBC.2014.6943952 | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference |
Keywords | Field | DocType |
data mining,diseases,document handling,medical information systems,patient care,HDP,Hierarchical Dirichlet Process,MIMIC II database,adverse patient outcomes,clinical concepts,clinical features,clinical research,clinical utility,complex disease patterns,critical care,data-driven approach,documented summaries,findings,heterogeneous patient cohort,hospital discharge summaries,latent topic discovery,latent topic structure,mortality post hospital discharge,nonparametric topic modeling technique,patient post hospital discharge mortality risks,phenotype discovery,phenotypic patterns,prognostic value,symptoms | Risk of mortality,Computer vision,Topic structure,Hierarchical Dirichlet process,Disease,Intensive care medicine,Artificial intelligence,Clinical research,Bioinformatics,Topic model,Cohort,Medicine | Conference |
Volume | ISSN | Citations |
2014 | 1557-170X | 2 |
PageRank | References | Authors |
0.41 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lehman, L.-W. | 1 | 2 | 0.41 |
| William J. Long | 2 | 218 | 27.94 |
| Mohammed Saeed | 3 | 7 | 2.31 |
| Roger G. Mark | 4 | 243 | 30.74 |