ReviewReviews in cardiovascular medicine2023
Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline.
Review in Reviews in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cardiotoxicity in pediatric oncology: a systematic review and meta-analysis.Pediatric research · 2025Pooled it
- GCE: A Framework for Interpretable Nonlinear Hazard Modeling in Cardiac Sarcoma Survival Using SEER Data.Bioengineering (Basel, Switzerland) · 2026Article
- Performance of Traditional Cardiovascular Risk Scores and Objective Optimization in Cancer Survivors.Current oncology (Toronto, Ont.) · 2026Article
- Chemotherapy-induced cardiotoxicity in breast cancer: mechanisms, diagnostic advances, and emerging protective strategies.American journal of physiology. Heart and circulatory physiology · 2025Review
- Healthcare Management in Cardio-Oncology, Clinical Strategies and Future Perspectives: A Narrative Review.Healthcare (Basel, Switzerland) · 2025Review
- Risk prediction of QTc prolongation occurrence in cancer patients treated with commonly used oral tyrosine kinase inhibitors: machine learning modeling or conventional statistical analysis better?BMC medical informatics and decision making · 2025Article
- Applications, challenges and future directions of artificial intelligence in cardio-oncology.European journal of clinical investigation · 2025Review
- AI and Smart Devices in Cardio-Oncology: Advancements in Cardiotoxicity Prediction and Cardiovascular Monitoring.Diagnostics (Basel, Switzerland) · 2025Review
- Review
- Current strategies for prevention of cancer therapy-related cardiotoxicity: pharmacological, non-pharmacological and emerging approaches.Frontiers in cardiovascular medicine · 2025Review
- Editorial: Interpretable predictive analytics for precision cardio-oncology preventive care.Frontiers in cardiovascular medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A growing body of evidence on a wide spectrum of adverse cardiac events following oncologic therapies has led to the emergence of cardio-oncology as an increasingly relevant interdisciplinary specialty. This also calls for better risk-stratification for patients undergoing cancer treatment. Machine learning (ML), a popular branch discipline of artificial intelligence that tackles complex big data problems by identifying interaction patterns among variables, has seen increasing usage in cardio-oncology studies for risk stratification. The objective of this comprehensive review is to outline the application of ML approaches in cardio-oncology, including deep learning, artificial neural networks, random forest and summarize the cardiotoxicity identified by ML. The current literature shows that ML has been applied for the prediction, diagnosis and treatment of cardiotoxicity in cancer patients. In addition, role of ML in gender and racial disparities for cardiac outcomes and potential future directions of cardio-oncology are discussed. It is essential to establish dedicated multidisciplinary teams in the hospital and educate medical professionals to become familiar and proficient in ML in the future.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.