Our manuscript about heart failure was recently published in the Journal of Cardiovascular Translational Research. This is a highly respected peer reviewed journal on cardiovascular research. JCTR is the journal of choice for professionals seeking information about emerging technologies, therapies and diagnostics, preclinical research, and first-in-man clinical trials.
The title of our manuscript was: A Machine Learning Methodology for Identification and Triage of Congestive Heart Failure Exacerbations*. Inadequate at-home management and self-awareness of heart failure (HF) exacerbations are known to be leading causes of the greater than 1 million estimated HF-related hospitalizations in The USA alone. Most current at-home HF management protocols include paper guidelines or exploratory health applications that lack rigor and validation at the level of the individual patient. We report on a novel triage methodology that uses machine learning predictions for real-time detection and assessment of exacerbations. The machine-learned triage approach in this study performed favorably when compared to individual heart specialists in a broad range of statistical performance measures in both exacerbation identification and severity assessment.
Please read our article on Springer – CLICK HERE
Heart failure (HF) is a chronic, progressive condition in which the heart muscle is unable to pump sufficient blood to meet the body’s circulatory and oxygenation needs. At present, the global prevalence of HF is estimated to include 38 million people with an associated direct and indirect cost burden of $108 billion dollars.
*Morrill, J., Qirko, K., Kelly, J. et al. A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations. J. of Cardiovasc. Trans. Res. (2021). Published: August 28th, 2021