Key result
A novel interpretable machine learning model identified patients needing advanced heart failure therapies with an F1 score of 43.8%, recall of 51.1%, and precision of 46.9%.
Why the study?
Systems-level barriers limit access to advanced heart failure therapies, creating a need for automated systems to help clinicians evaluate patients at the appropriate time.
Does an interpretable machine learning model accurately identify potential candidates for advanced heart failure therapies in patients with heart failure?
Observational (n=4,694)
Yes
Does an interpretable machine learning model accurately identify potential candidates for advanced heart failure therapies in patients with heart failure?
A novel, interpretable machine learning model can identify heart failure patients who may need evaluation for advanced therapies while providing transparent clinical rules for clinicians.
Answers are grounded in this paper and cite their sources.
Offers a proof-of-concept tool for predicting advanced HF therapy needs; leaves open whether prospective validation.
BACKGROUND: Systems level barriers to heart failure (HF) care limit access to HF advanced therapies (heart transplantation, left ventricular assist devices). There is a need for automated systems that can help clinicians ensure patients with HF are evaluated for HF advanced therapies at the appropriate time to optimize outcomes. METHODS: We performed a retrospective study using the REVIVAL (Registry Evaluation of Vital Information for VADs in Ambulatory Life) and INTERMACS (Interagency Registry for Mechanically Assisted Circulatory Support) registries. We developed a novel machine learning model based on principles of tropical geometry and fuzzy logic that can accommodate clinician knowledge and provide recommendations regarding need for advanced therapies evaluations that are accessible to end-users. RESULTS: The model was trained and validated using data from 4,694 HF patients. When initiated with clinical knowledge from HF and transplant cardiologists, the model achieved an F1 score of 43.8%, recall of 51.1%, and precision of 46.9%. The model achieved comparable performance compared with other commonly used machine learning models. Importantly, our model was 1 of only 3 models providing transparent and parsimonious clinical rules, significantly outperforming the other 2 models. Eleven clinical rules were extracted from the model which can be leveraged in clinical practice. CONCLUSIONS: A machine learning model capable of accepting clinical knowledge and making accessible recommendations was trained to identify patients with advanced HF. While this model was developed for HF care, the methodology has multiple potential uses in other important clinical applications.
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Yao et al. (2022) conducted an observational in Heart failure (n=4,694). Interpretable machine learning model based on tropical geometry and fuzzy logic vs. Other commonly used machine learning models was evaluated on F1 score for identifying need for advanced therapies. A novel interpretable machine learning model identified patients needing advanced heart failure therapies with an F1 score of 43.8%, recall of 51.1%, and precision of 46.9%.
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