Description
Over 5 million Americans suffer from heart failure, a condition with a 5-year survival that eclipses all cancers apart from that of lung cancer. Conventional understanding of heart failure is simplistic: it is viewed as a single syndrome, despite real heterogeneity. In addition, models predicting outcomes focus on dichotomous results, like 30-day readmission. A novel approach to classification of heart failure may improve our ability to target interventions, improve patient experiences, and predict outcomes.
The Healthcare Cost and Utilization Project is a family of administrative claims databases that describes patient demographics, comorbidities, procedures, acute care utilization and outcomes, such as mortality and readmission. Using the California datasets, which allow linkage of hospital admissions to emergency department visits, we sought to (1) develop a new classification tool for heart failure, (2) predict patient response based on previous visits, (3) predict survival time.
In this pilot study, we propose novel tree-based frameworks for the classification of heart failure patients that can also be used to predict clinical response, health care utilization and mortality. The pilot sample contains 822 patients with heart failure who are randomly picked from a total sample of 211284 patients. The median number of encounters per patient was 3 (IQR: 5); each are associated with up to 168 variables. By applying random forest approaches to this pilot sample, we have performed classification of patients with heart failure and identified important predictors of outcomes. Going forward, we will refine the model and apply to the entire data set to produce broadly applicable insights.
Included in
Bioinformatics Commons, Cardiovascular Diseases Commons, Health Information Technology Commons, Public Health Commons
Applying novel tree-based frameworks to big data for classification of heart failure patients and prediction of clinical responses
Over 5 million Americans suffer from heart failure, a condition with a 5-year survival that eclipses all cancers apart from that of lung cancer. Conventional understanding of heart failure is simplistic: it is viewed as a single syndrome, despite real heterogeneity. In addition, models predicting outcomes focus on dichotomous results, like 30-day readmission. A novel approach to classification of heart failure may improve our ability to target interventions, improve patient experiences, and predict outcomes.
The Healthcare Cost and Utilization Project is a family of administrative claims databases that describes patient demographics, comorbidities, procedures, acute care utilization and outcomes, such as mortality and readmission. Using the California datasets, which allow linkage of hospital admissions to emergency department visits, we sought to (1) develop a new classification tool for heart failure, (2) predict patient response based on previous visits, (3) predict survival time.
In this pilot study, we propose novel tree-based frameworks for the classification of heart failure patients that can also be used to predict clinical response, health care utilization and mortality. The pilot sample contains 822 patients with heart failure who are randomly picked from a total sample of 211284 patients. The median number of encounters per patient was 3 (IQR: 5); each are associated with up to 168 variables. By applying random forest approaches to this pilot sample, we have performed classification of patients with heart failure and identified important predictors of outcomes. Going forward, we will refine the model and apply to the entire data set to produce broadly applicable insights.
Comments
We submit our poster draft together with the abstract. The poster is based on results from the pilot project, and will be updated with results from the full dataset.