Arnaldo Dimagli

Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis

2020 · 116 Zit. · Journal of Thoracic and Cardiovascular Surgery

Can machine learning improve mortality prediction following cardiac surgery?

2020 · 37 Zit. · European Journal of Cardio-Thoracic Surgery

A survey of retractions in the cardiovascular literature

2021 · 36 Zit. · International Journal of Cardiology

Comparison of machine learning techniques in prediction of mortality following cardiac surgery: analysis of over 220 000 patients from a large national database

2023 · 33 Zit. · European Journal of Cardio-Thoracic Surgery

Development and Evaluation of a Natural Language Processing System for Curating a Trans-Thoracic Echocardiogram (TTE) Database

2023 · 18 Zit. · Bioengineering

Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis

2024 · 17 Zit. · JMIRx Med

Performance drift is a major barrier to the safe use of machine learning in cardiac surgery

2023 · 4 Zit. · medRxiv

Development and Evaluation of a Natural Language Processing System for curating a Trans-Thoracic Echocardiogram (TTE) database

2023 · 2 Zit. · Preprints.org

1657 Systematic Review and Meta-Analysis of Mortality Risk Prediction Models in Adult Cardiac Surgery

2021 · 1 Zit. · British journal of surgery

Authors’ Response to Peer Reviews of “Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis”

2024 · 0 Zit. · JMIRx Med