Identifying clinically important COPD sub-types using data-driven approaches in primary care population based electronic health records.
Maria PikoulaJennifer Kathleen QuintFrancis NissenHarry HemingwayLiam SmeethSpiros DenaxasPublished in: BMC medical informatics and decision making (2019)
COPD patients can be sub-classified into groups with differing risk factors, comorbidities, and prognosis, based on data included in their primary care records. The identified clusters confirm findings of previous clustering studies and draw attention to anxiety and depression as important drivers of the disease in young, female patients.
Keyphrases
- primary care
- electronic health record
- end stage renal disease
- newly diagnosed
- risk factors
- ejection fraction
- chronic kidney disease
- chronic obstructive pulmonary disease
- prognostic factors
- peritoneal dialysis
- patient reported outcomes
- lung function
- working memory
- single cell
- artificial intelligence
- air pollution
- deep learning
- rna seq