Using machine learning to predict anticoagulation control in atrial fibrillation: a UK Clinical Practice Research Datalink study
<strong>Objective<br></strong> To investigate the predictive performance of machine learning (ML) algorithms for estimating anticoagulation control in patients with atrial fibrillation (AF) who are treated with warfarin. <br><strong> Methods<br></strong> Thi...
Egile Nagusiak: | Gordon, J, Norman, M, Hurst, M, Mason, T, Dickerson, C, Sandler, B, Pollock, KG, Farooqui, U, Groves, L, Tsang, C, Clifton, D, Bakhai, A, Hill, NR |
---|---|
Formatua: | Journal article |
Hizkuntza: | English |
Argitaratua: |
Elsevier
2021
|
Antzeko izenburuak
-
Identification of undiagnosed atrial fibrillation using a machine learning risk-prediction algorithm and diagnostic testing (PULsE-AI) in primary care: a multi-centre randomized controlled trial in England
nork: Hill, NR, et al.
Argitaratua: (2022) -
Identification of undiagnosed atrial fibrillation using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI) in primary care: cost-effectiveness of a screening strategy evaluated in a randomized controlled trial in England
nork: Hill, NR, et al.
Argitaratua: (2022) -
Use of oral anticoagulants in older people with atrial fibrillation in UK general practice: protocol for a cohort study using the Clinical Practice Research Datalink (CPRD) database
nork: Julia Snowball, et al.
Argitaratua: (2019-12-01) -
Comparison of oral anticoagulants for stroke prevention in atrial fibrillation using the UK clinical practice research Datalink Aurum: A reference trial (ARISTOTLE) emulation study.
nork: Emma Maud Powell, et al.
Argitaratua: (2024-08-01) -
Predicting atrial fibrillation in primary care using machine learning
nork: Hill, NR, et al.
Argitaratua: (2019)