ArticleBMC medical informatics and decision making2022
Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 22 citations in OpenAlex.
- Predicting 30-day readmission after heart failure hospitalization using interpretable machine learning: evidence from a 20-year population-based rural registry.Medical & biological engineering & computing · 2026Article
- Applying Artificial Intelligence and machine learning in precision nutrition.Nature communications · 2026Review
- Explainable and interpretable models for predicting early-onset hypertension in the Tlalpan 2020 cohort.Frontiers in digital health · 2026Article
- A novel hybrid explainable artificial intelligence modelling approach for smart manufacturing.The International journal, advanced manufacturing technology · 2026Article
- Machine Learning-Driven Prediction of One-Year Readmission in HFrEF Patients: The Key Role of Inflammation.Clinical interventions in aging · 2025Article
- Analysis of the survival time of patients with heart failure with reduced ejection fraction: a Bayesian approach via a competing risk parametric model.BMC cardiovascular disorders · 2024Article
- Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives.Archives of computational methods in engineering : state of the art reviews · 2023Review
- More than a Feeling: Dermatological Changes Impacted by Spaceflight.Research square · 2023Article
- Improvement of a prediction model for heart failure survival through explainable artificial intelligence.Frontiers in cardiovascular medicine · 2023Article
- Predicting weight loss success on a new Nordic diet: an untargeted multi-platform metabolomics and machine learning approach.Frontiers in nutrition · 2023Article
- Multi-objective Symbolic Regression to Generate Data-driven, Non-fixed Structure and Intelligible Mortality Predictors using EHR: Binary Classification Methodology and Comparison with State-of-the-art.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Editorial: Advances and challenges in remote monitoring of patients with heart failure.Frontiers in cardiovascular medicine · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundHeart failure is a clinical syndrome characterised by a reduced ability of the heart to pump blood. Patients with heart failure have a high mortality rate, and physicians need reliable prognostic predictions to make informed decisions about the appropriate application of devices, transplantation, medications, and palliative care. In this study, we demonstrate that combining symbolic regression with the Cox proportional hazards model improves the ability to predict death due to heart failure compared to using the Cox proportional hazards model alone.
methodsWe used a newly invented symbolic regression method called the QLattice to analyse a data set of medical records for 299 Pakistani patients diagnosed with heart failure. The QLattice identified non-linear mathematical transformations of the available covariates, which we then used in a Cox model to predict survival.
resultsAn exponential function of age, the inverse of ejection fraction, and the inverse of serum creatinine were identified as the best risk factors for predicting heart failure deaths. A Cox model fitted on these transformed covariates had improved predictive performance compared with a Cox model on the same covariates without mathematical transformations.
conclusionSymbolic regression is a way to find transformations of covariates from patients' medical records which can improve the performance of survival regression models. At the same time, these simple functions are intuitive and easy to apply in clinical settings. The direct interpretability of the simple forms may help researchers gain new insights into the actual causal pathways leading to deaths.
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