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Several indices exist to classify Congestive Heart Failure (CHF) patients' propensity for early mortality; however, they are primarily based on limited data and are not intuitive to use at the point of care. We investigate a novel, data-driven, risk assessment and visualization approach to investigate mortality prediction of CHF patients using data retrieved from an intensively digitized hospital's data repository. Combining well-known, computationally efficient, dimensionality reduction (DR)doi:10.3233/shti190230 pmid:31437932 fatcat:umoanobdovhsregfwvw4673hne