Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future

Yasmin, Farah; Shah, Syed Muhammad Ismail; Naeem, Ayesha; Shujauddin, Syed Muhammad; Jabeen, Adina; Kazmi, Sana; Siddiqui, Sarush Ahmed; Kumar, Pankaj; Salman, Shiza; Hassan, Syed Adeel; Dasari, Chandrashekhar; Choudhry, Ali Sanaullah; Mustafa, Ahmad; Chawla, Sanchit; Lak, Hassan Mehmood

doi:10.31083/j.rcm2204121

Cited by 65 publications

(43 citation statements)

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“…Incorporating technology into the health system with regularity strengthens the healthcare environment, improves major health outcomes, and takes healthcare to another level: precision medicine [ 102 ]. The future involves hybrid environments capable of offering e-health under a more holistic concept: telemedicine and remote liaison with patients, continuous remote monitoring through wearables, adding artificial intelligence to routine decision making [ 103 , 69 ] as well as predicting major cardiovascular events with simple tools such as the electrocardiogram or biomarkers as old as the human voice itself ( Fig. 2 ) [ 104 ].…”

Section: Clinical Trialsmentioning

confidence: 99%

Reshaping care in the aftermath of the pandemic. Implications for cardiology health systems

Jordán-Ríos¹,

Nuzzi²,

Bromage³

et al. 2023

European Journal of Internal Medicine

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Section: Clinical Trialsmentioning

confidence: 99%

Reshaping care in the aftermath of the pandemic. Implications for cardiology health systems

Jordán-Ríos¹,

Nuzzi²,

Bromage³

et al. 2023

European Journal of Internal Medicine

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“…AI is one of the key technologies responsible for the evolution of eHealth, bringing multiple advantages to the field of healthcare, such as the following: Improvements in diagnosis accuracy, e.g., detecting heart failure [ 2 ]. Risk prediction, e.g., predicting antibiotic resistance through machine learning [ 3 ], cardiovascular disease prediction from AI-based models [ 4 ], and using deep learning to predict cardiac indices [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

Pap

Oniga

2022

IJERPH

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Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.

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“…Machine learning methodologies have been employed for the detection of heart failure decompensations using existing data. 57 Data mining techniques of existing health records have been used to analyze heart failure detection, severity estimation, prediction of readmission, and impending decompensation, 58 using predictor features such as demographic and laboratory data, ECG tracings, echocardiogram findings, and physiological parameters, particularly heart rate variability (Table 2 ). 57 , 58 , 62 , 63 There are fewer studies that target short‐term predictions of decompensation rather than a long‐term risk of hospitalization.…”

Section: Introductionmentioning

confidence: 99%

“… 57 Data mining techniques of existing health records have been used to analyze heart failure detection, severity estimation, prediction of readmission, and impending decompensation, 58 using predictor features such as demographic and laboratory data, ECG tracings, echocardiogram findings, and physiological parameters, particularly heart rate variability (Table 2 ). 57 , 58 , 62 , 63 There are fewer studies that target short‐term predictions of decompensation rather than a long‐term risk of hospitalization. Candelieri et al explored the use of decision trees and support vector models in the prediction of heart failure decompensation, finding that a “hyper‐solution” framework encompassing an optimized ensemble of support vector models showed an accuracy of 87.35% and sensitivity of 90.91% in the early detection of heart failure decompensation.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence and digital health for volume maintenance in hemodialysis patients

Sandys

Sexton

O’Seaghdha

2022

Hemodialysis International

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Chronic fluid overload is associated with morbidity and mortality in hemodialysis patients. Optimizing the diagnosis and treatment of fluid overload remains a priority for the nephrology community. Although current methods of assessing fluid status, such as bioimpedance and lung ultrasound, have prognostic and diagnostic value, no single system or technique can be used to maintain euvolemia. The difficulty in maintaining and assessing fluid status led to a publication by the Kidney Health Initiative in 2019 aimed at fostering innovation in fluid management therapies. This review article focuses on the current limitations in our assessment of extracellular volume, and the novel technology and methods that can create a new paradigm for fluid management. The cardiology community has published research on multiparametric wearable devices that can create individualized predictions for heart failure events. In the future, similar wearable technology may be capable of tracking fluid changes during the interdialytic period and enabling behavioral change. Machine learning methods have shown promise in the prediction of volume-related adverse events. Similar methods can be leveraged to create accurate, automated predictions of dry weight that can potentially be used to guide ultrafiltration targets and interdialytic weight gain goals.

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Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future

Cited by 65 publications

References 0 publications

Reshaping care in the aftermath of the pandemic. Implications for cardiology health systems

Reshaping care in the aftermath of the pandemic. Implications for cardiology health systems

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

Artificial intelligence and digital health for volume maintenance in hemodialysis patients

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