Artificial intelligence in cardiovascular medicine: Applications in the diagnosis of infarction and prognosis of heart failure

Díaz, John Jaime Sprockel

doi:10.1016/b978-0-12-817133-2.00013-6

Cited by 4 publications

(4 citation statements)

References 46 publications

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“…To our knowledge, sex and racial biases have not been previously evaluated in the context of ML models for ACS diagnosis. Unlike most ML-based cardiovascular diagnosis and prediction studies that rely upon ECG data as inputs, 18,40,[42][43][44][45][46] our models did not include ECG data. There are several issues with relying on ECG data for ACS diagnosis.…”

Section: Discussionmentioning

confidence: 99%

A machine learning approach for unbiased diagnosis of acute coronary syndrome in the emergency department

Zeng

Xiang

Allen

et al. 2022

Preprint

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Importance: Despite sex and race disparities in the symptom presentation, diagnosis, and management of acute coronary syndrome (ACS), these differences have not been investigated in the development and validation of machine learning (ML) models using individualized patient information from electronic health records (EHRs) to diagnose ACS. Objective: To evaluate ML-based ACS diagnosis performance across different subpopulations in a multi-site emergency department (ED) setting and determine how bias mitigating techniques influence ML performance. Design, Setting, and Participants: This retrospective observational study included data from 2,334,316 ED patients ( >18 years) from January 2007 to June 2020. Exposure: Logistic regression (LR) and neural network (NN) models were assessed in ED encounters grouped by sex, race, presence or absence of chest pain, EHR data quality, and timeliness of several key ED procedures. Prejudice regularization, reweighting, and within-subpopulation training were evaluated for bias mitigation. Main Outcomes/Measures: Metrics including area under the receiver operating characteristic (AUROC) were used to assess performances. Results: We analyzed 4,268,165 ED visits in which patient demographics by race were 67.40% White, 19.20% Black, 2.40% Asian, and 11.00% Other or Unknown. Patient composition was 54.80% female and 45.20% male. Both models’ AUROCs were significantly higher in White vs. Black patients (LR: z-score = 3.23 and NN: 4.26 for NN; P < 0.0006), in males vs. females (z-score = 3.81 for LR and 4.16 for NN; P < 0.0001) and in no chest pain subpopulation vs. chest pain (z-score = 13.32 for LR and 17.70 for NN; P < 0.0001). Prejudice regularization and reweighting techniques did not reduce biases. Training in race-specific and sex-specific training populations also did not yeild statistically signficant improvements in ML algorithm performance. Chest pain-specific training led to significantly improved AUROC.Conclusion: EHR-derived ML models trained and tested within similar demographic subpopulations and symptom groups may perform better than ML models that are trained in random populations, and provide less biased clinical decision support for ACS diagnosis.

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Section: Discussionmentioning

confidence: 99%

A machine learning approach for unbiased diagnosis of acute coronary syndrome in the emergency department

Zeng

Xiang

Allen

et al. 2022

Preprint

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“…Krittanawong et al [9] Dilsizian et al [10] Johnson et al [11] Díaz et al [12] Madani et al [13] Krittanawong et al [14] 2017…”

Section: Cardiologymentioning

confidence: 99%

SARS-CoV-2: Has artificial intelligence stood the test of time

Sajid

Ahmed

Waqar

et al. 2022

Chinese Medical Journal

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Artificial intelligence (AI) has proven time and time again to be a game-changer innovation in every walk of life, including medicine. Introduced by Dr. Gunn in 1976 to accurately diagnose acute abdominal pain and list potential differentials, AI has since come a long way. In particular, AI has been aiding in radiological diagnoses with good sensitivity and specificity by using machine learning algorithms. With the coronavirus disease 2019 pandemic, AI has proven to be more than just a tool to facilitate healthcare workers in decision making and limiting physician-patient contact during the pandemic. It has guided governments and key policymakers in formulating and implementing laws, such as lockdowns and travel restrictions, to curb the spread of this viral disease. This has been made possible by the use of social media to map severe acute respiratory syndrome coronavirus 2 hotspots, laying the basis of the “smart lockdown” strategy that has been adopted globally. However, these benefits might be accompanied with concerns regarding privacy and unconsented surveillance, necessitating authorities to develop sincere and ethical government–public relations.

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“…AI has played a vital role in disease classification and risk stratification. Two categories of AI technologies, i.e., ML and DL have long been active in CVD risk management (Díaz, 2020). The ML (Bishop, 2006;Díaz, 2020) refers to the broad class of probabilistic, distance, and rule-based models used in the characterization of diseases and risk stratification, whereas, the DL (Krizhevsky et al, 2012;LeCun et al, 2015) refers to multiple layers of brainmimicking neural networks employed for the same.…”

Section: Evidence Of Machine Learning In Cvd Risk Assessmentmentioning

confidence: 99%

“…Two categories of AI technologies, i.e., ML and DL have long been active in CVD risk management (Díaz, 2020). The ML (Bishop, 2006;Díaz, 2020) refers to the broad class of probabilistic, distance, and rule-based models used in the characterization of diseases and risk stratification, whereas, the DL (Krizhevsky et al, 2012;LeCun et al, 2015) refers to multiple layers of brainmimicking neural networks employed for the same. ML-based solutions have been adapted for risk stratification in different applications such as stroke (Acharya et al, 2013b;Cuadrado-Godia et al, 2018a;Jamthikar et al, 2020b,a;Martis et al, 2013).…”

Section: Evidence Of Machine Learning In Cvd Risk Assessmentmentioning

confidence: 99%

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

2020

Reviews in Cardiovascular Medicine

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Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AIbased platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

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Artificial intelligence in cardiovascular medicine: Applications in the diagnosis of infarction and prognosis of heart failure

Cited by 4 publications

References 46 publications

A machine learning approach for unbiased diagnosis of acute coronary syndrome in the emergency department

A machine learning approach for unbiased diagnosis of acute coronary syndrome in the emergency department

SARS-CoV-2: Has artificial intelligence stood the test of time

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

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