CARNA: Characterizing Advanced heart failure Risk and hemodyNAmic
  phenotypes using learned multi-valued decision diagrams
Josephine Lamp Yuxin Wu Steven Lamp Prince Afriyie Kenneth Bilchick Lu Feng Sula Mazimba
Early identification of high risk heart failure (HF) patients is key to
timely allocation of life-saving therapies. Hemodynamic assessments can
facilitate risk stratification and enhance understanding of HF trajectories.
However, risk assessment for HF is a complex, multi-faceted decision-making
process that can be challenging. Previous risk models for HF do not integrate
invasive hemodynamics or support missing data, and use statistical methods
prone to bias or machine learning methods that are not interpretable. To
address these limitations, this paper presents CARNA, a hemodynamic risk
stratification and phenotyping framework for advanced HF that takes advantage
of the explainability and expressivity of machine learned Multi-Valued Decision
Diagrams (MVDDs). This interpretable framework learns risk scores that predict
the probability of patient outcomes, and outputs descriptive patient phenotypes
(sets of features and thresholds) that characterize each predicted risk score.
CARNA incorporates invasive hemodynamics and can make predictions on missing
data. The CARNA models were trained and validated using a total of five
advanced HF patient cohorts collected from previous trials, and compared with
six established HF risk scores and three traditional ML risk models. CARNA
provides robust risk stratification, outperforming all previous benchmarks.
Although focused on advanced HF, the CARNA framework is general purpose and can
be used to learn risk stratifications for other diseases and medical
applications.