Discovering and validating disease subtypes for heart failure using unsupervised machine learning methods
| Autor: | Fatemifar, G, Lumbers, RT, Swerdlow, DI, Denaxas, S |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Science & Technology
Cardiac & Cardiovascular Systems Peripheral Vascular Disease Cardiovascular System & Hematology Cardiovascular System & Cardiology Electronic health records (EHRs) Heart failure 1103 Clinical Sciences Life Sciences & Biomedicine 1102 Cardiorespiratory Medicine and Haematology 1117 Public Health and Health Services |
| Zdroj: | Scientific Sessions of the American-Heart-Association / Resuscitation Science Symposium |
| Popis: | Notable heterogeneity exists in the clinical presentation of heart failure (HF) patients. Current subtype classifications are based on ejection fraction may not fully capture the aetiological and prognostic heterogeneity of HF. The use of unsupervised machine learning (ML) approaches, such as cluster analysis, on large-scale observational data from electronic health records (EHR), can enable the discovery of novel subtypes and guide the characterization of their clinical manifestation. Clustering methods can group HF patients based on similarities between their clinical features without making a priori assumptions about the distribution of the data. We sought to discover, characterize and replicate HF subtypes by applying a clustering method on a heterogeneous HF population derived from phenotypically rich EHR. Characterization of HF subtypes using EHR derived variable may enable more precise large-scale genomic analysis to inform better prevention, diagnostic and treatment strategies. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|