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1.
An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk.
J Electrocardiol;
76: 61-65, 2023.
Artículo
en Inglés
| MEDLINE | ID: mdl-36436476
2.
Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke.
Circulation;
143(13): 1287-1298, 2021 03 30.
Artículo
en Inglés
| MEDLINE | ID: mdl-33588584
3.
Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical dataset.
Int J Cardiovasc Imaging;
38(8): 1685-1697, 2022 Aug.
Artículo
en Inglés
| MEDLINE | ID: mdl-35201510
4.
Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank.
Nat Genet;
54(3): 240-250, 2022 03.
Artículo
en Inglés
| MEDLINE | ID: mdl-35177841
5.
Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality.
Nat Biomed Eng;
5(6): 546-554, 2021 06.
Artículo
en Inglés
| MEDLINE | ID: mdl-33558735
6.
Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network.
Nat Med;
26(6): 886-891, 2020 06.
Artículo
en Inglés
| MEDLINE | ID: mdl-32393799
7.
Dopaminergic contributions to hippocampal pathophysiology in schizophrenia: a computational study.
Neuropsychopharmacology;
39(7): 1713-21, 2014 Jun.
Artículo
en Inglés
| MEDLINE | ID: mdl-24469592
8.
Development of antipsychotic medications with novel mechanisms of action based on computational modeling of hippocampal neuropathology.
PLoS One;
8(3): e58607, 2013.
Artículo
en Inglés
| MEDLINE | ID: mdl-23526999
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