Artificial Intelligence in Cardiology: Present and Future

López-Jiménez, Francisco; Attia, Zachi I.; Arruda-Olson, Adelaide M.; Carter, Rickey E.; Chareonthaitawee, Panithaya; Jouni, Hayan; Kapa, Suraj; Lerman, Amir; Luong, Christina; Medina‐Inojosa, José R.; Noseworthy, Peter A.; Pellikka, Patricia A.; Redfield, Margaret M.; Roger, Véronique L.; Sandhu, Gurpreet S.; Senecal, Conor; Friedman, Paul A.

doi:10.1016/j.mayocp.2020.01.038

Cited by 161 publications

(110 citation statements)

References 98 publications

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“…For instance, hospital systems are already using natural language processing technologies to extract diagnostic information from radiology and pathology reports and clinical notes. [19][20][21] As these capacities are shifted on to tasks for identifying clinically significant symptoms of SARS-CoV-2 infection, 22 hazards of embedding inequality will also increase. Where human biases are recorded in clinical notes, these discriminatory patterns will probably infiltrate the natural language processing supported AI models that draw on them.…”

Section: Key Messagesmentioning

confidence: 99%

Does “AI” stand for augmenting inequality in the era of covid-19 healthcare?

Leslie

Mazumder

Peppin³

et al. 2021

BMJ

103

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Section: Key Messagesmentioning

confidence: 99%

Does “AI” stand for augmenting inequality in the era of covid-19 healthcare?

Leslie

Mazumder

Peppin³

et al. 2021

BMJ

103

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“…It is a nontechnical term that typically refers to ML-computer algorithms that are able to independently find patterns in large amounts of data. 1 Deep learning, a subfield of ML, uses neural networks to find these patterns and learn the relationships between provided input and desired outcomes. ML is already being employed in all cardiology subspecialties, with applications ranging from workflow optimization to disease diagnosis and prognostication, therapeutic intervention decision support, and research.…”

Section: Artificial Intelligence In Cardiologymentioning

confidence: 99%

“…ML is already being employed in all cardiology subspecialties, with applications ranging from workflow optimization to disease diagnosis and prognostication, therapeutic intervention decision support, and research. 1 Its uptake has been directed at 2 general goals: (1) providing human-like capabilities at scale and (2) developing new insights by analyzing existing data sets individually or in novel combinations (essentially moving beyond current human capacity).…”

Section: Artificial Intelligence In Cardiologymentioning

confidence: 99%

Digital health innovation in cardiology

Ladejobi

Cruz

Attia

et al. 2020

Cardiovascular Digital Health Journal

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“…We used a feed‐forward neural network framework designed as a multi‐layer perceptron (MLP) 12,13 . The concept of a ‘perceptron’ originates from a probabilistic model for information storage and organization in the brain, which represents how information about the physical world is sensed, in what form is information remembered, and how information retained in memory to influence recognition and behaviour 14 .…”

Section: Methodsmentioning

confidence: 99%

Artificial neural network‐based prediction of prolonged length of stay and need for post‐acute care in acute coronary syndrome patients undergoing percutaneous coronary intervention

Kulkarni¹,

Thangam

Amin

2020

Eur J Clin Investigation

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Background Prolonged length of stay (LOS) and post‐acute care after percutaneous coronary intervention (PCI) is common and costly. Risk models for predicting prolonged LOS and post‐acute care have limited accuracy. Our goal was to develop and validate models using artificial neural networks (ANN) to predict prolonged LOS > 7days and need for post‐acute care after PCI. Methods We defined prolonged LOS as ≥7 days and post‐acute care as patients discharged to: extended care, transitional care unit, rehabilitation, other acute care hospital, nursing home or hospice care. Data from 22 675 patients who presented with ACS and underwent PCI was shuffled and split into a derivation set (75% of dataset) and a validation dataset (25% of dataset). Calibration plots were used to examine the overall predictive performance of the MLP by plotting observed and expected risk deciles and fitting a lowess smoother to the data. Classification accuracy was assessed by a receiver‐operating characteristic (ROC) and area under the ROC curve (AUC). Results Our MLP‐based model predicted prolonged LOS with an accuracy of 90.87% and 88.36% in training and test sets, respectively. The post‐acute care model had an accuracy of 90.22% and 86.31% in training and test sets, respectively. This accuracy was achieved with quick convergence. Predicted probabilities from the MLP models showed good (prolonged LOS) to excellent calibration (post‐acute care). Conclusions Our ANN‐based models accurately predicted LOS and need for post‐acute care. Larger studies for replicability and longitudinal studies for evidence of impact are needed to establish these models in current PCI practice.

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Artificial Intelligence in Cardiology: Present and Future

Cited by 161 publications

References 98 publications

Does “AI” stand for augmenting inequality in the era of covid-19 healthcare?

Does “AI” stand for augmenting inequality in the era of covid-19 healthcare?

Digital health innovation in cardiology

Artificial neural network‐based prediction of prolonged length of stay and need for post‐acute care in acute coronary syndrome patients undergoing percutaneous coronary intervention

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