Machine Learning - Based Bleeding Risk Predictions in Atrial Fibrillation Patients on Direct Oral Anticoagulants.
Rahul ChaudharyMehdi NourelahiFloyd W ThomaWalid F GelladWei-Hsuan Lo-CiganicKevin P BlidenPaul A GurbelMatthew D NealSandeep K JainAditya BhonsaleSuresh R MulukutlaYanshan WangMatthew E HarinsteinSamir SabaShyam VisweswaranPublished in: medRxiv : the preprint server for health sciences (2024)
Our findings demonstrate the superior performance of ML models compared to conventional bleeding risk scores and identify novel risk factors highlighting the potential for personalized bleeding risk assessment in AF patients on DOACs.
Keyphrases
- atrial fibrillation
- direct oral anticoagulants
- risk assessment
- end stage renal disease
- ejection fraction
- risk factors
- newly diagnosed
- venous thromboembolism
- peritoneal dialysis
- heart failure
- prognostic factors
- coronary artery disease
- human health
- patient reported
- climate change
- deep learning
- left ventricular
- breast cancer risk