2023
Gill, Simrat; Karwath, Andreas; Uh, Hae-Won; Cardoso, Victor Roth; Gu, Zhujie; Barsky, Andrey; Slater, Luke; Acharjee, Animesh; Duan, Jinming; DallÓlio, Lorenzo; el Bouhaddani, Said; Chernbumroong, Saisakul; Stanbury, Mary; Haynes, Sandra; Asselbergs, Folkert W; Grobbee, Diederick; Eijkemans, Marinus; Gkoutos, Georgios; Kotecha, Dipak; group BigData@Heart Consortium,
Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare: artificial intelligence framework Journal Article
In: European Heart Journal, 2023, ISSN: 0195-668X, (textcopyright The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.).
Abstract | Links | BibTeX | Tags: artificial intelligence, cardiology, EHR, health data science
@article{9b9767f517a040f4822591145f8c61a8,
title = {Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare: artificial intelligence framework},
author = {Simrat Gill and Andreas Karwath and Hae-Won Uh and Victor Roth Cardoso and Zhujie Gu and Andrey Barsky and Luke Slater and Animesh Acharjee and Jinming Duan and Lorenzo DallÓlio and Said el Bouhaddani and Saisakul Chernbumroong and Mary Stanbury and Sandra Haynes and Folkert W Asselbergs and Diederick Grobbee and Marinus Eijkemans and Georgios Gkoutos and Dipak Kotecha and group BigData@Heart Consortium },
doi = {10.1093/eurheartj/ehac758},
issn = {0195-668X},
year = {2023},
date = {2023-01-11},
urldate = {2023-01-11},
journal = {European Heart Journal},
publisher = {Öxford University Press},
abstract = {Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management.Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity."},
note = {textcopyright The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.},
keywords = {artificial intelligence, cardiology, EHR, health data science},
pubstate = {published},
tppubtype = {article}
}
2021
KV, Bunting; SK, Gill; A, Sitch; S, Mehta; K, O'Connor; GY, Lip; P, Kirchhof; VY, Strauss; K, Rahimi; AJ, Camm; M, Stanbury; M, Griffith; JN, Townend; GV, Gkoutos; control in permanent trial group, RAte Therapy Evaluation Atrial Fibrillation (RATE-AF)
Improving the diagnosis of heart failure in patients with atrial fibrillation. Journal Article
In: Heart (British Cardiac Society), 2021.
Links | BibTeX | Tags: artificial intelligence, cardiology, health data science
@article{PMID:33692093,
title = {Improving the diagnosis of heart failure in patients with atrial fibrillation.},
author = {Bunting KV and Gill SK and Sitch A and Mehta S and O'Connor K and Lip GY and Kirchhof P and Strauss VY and Rahimi K and Camm AJ and Stanbury M and Griffith M and Townend JN and Gkoutos GV and RAte Therapy Evaluation Atrial Fibrillation (RATE-AF) control in permanent trial group},
doi = {10.1136/heartjnl-2020-318557},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Heart (British Cardiac Society)},
keywords = {artificial intelligence, cardiology, health data science},
pubstate = {published},
tppubtype = {article}
}