Statistics in cardiovascular medicine: there is still gold in the old

Heuvel, Edwin R. van den; Vasan, Ramachandran S.

doi:10.1136/heartjnl-2018-313303

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…During the procedure, the program learns and evolves from data that have been previously provided instead. This emerges in the scientific fields of statistics and computer science [8].…”

Section: Machine Learningmentioning

confidence: 99%

Artificial Intelligence in Heart Failure: Friend or Foe?

Bourazana,

Xanthopoulos,

Briasoulis

et al. 2024

Life

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In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the “garbage in, garbage out” issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians’ hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.

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“…During the procedure, the program learns and evolves from data that have been previously provided instead. This emerges in the scientific fields of statistics and computer science [8].…”

Section: Machine Learningmentioning

confidence: 99%

Artificial Intelligence in Heart Failure: Friend or Foe?

Bourazana,

Xanthopoulos,

Briasoulis

et al. 2024

Life

View full text Add to dashboard Cite

show abstract

“…These studies are inspiring and illustrated that AI-based models could be further applied to the early detection of ICB-dependent toxicities. Finally, the further development of AI-driven algorithms in cancer immunotherapy can accelerate progress in terms of developing accurate biomarkers and governance algorithms for predicting the response to treatment, evaluating drug resistance, predicting patient survival and analyzing minimal residual disease, which enables clinicians to more precisely provide patients with the most effective standard of care, providing a supreme advantage for each patient 110 , 111 , 112 .…”

Section: Application Of Ai In Current Challenges Of Immunotherapymentioning

confidence: 99%

Applying artificial intelligence for cancer immunotherapy

Wang

Zeng

et al. 2021

Acta Pharmaceutica Sinica B

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Artificial intelligence (AI) is a general term that refers to the use of a machine to imitate intelligent behavior for performing complex tasks with minimal human intervention, such as machine learning; this technology is revolutionizing and reshaping medicine. AI has considerable potential to perfect health-care systems in areas such as diagnostics, risk analysis, health information administration, lifestyle supervision, and virtual health assistance. In terms of immunotherapy, AI has been applied to the prediction of immunotherapy responses based on immune signatures, medical imaging and histological analysis. These features could also be highly useful in the management of cancer immunotherapy given their ever-increasing performance in improving diagnostic accuracy, optimizing treatment planning, predicting outcomes of care and reducing human resource costs. In this review, we present the details of AI and the current progression and state of the art in employing AI for cancer immunotherapy. Furthermore, we discuss the challenges, opportunities and corresponding strategies in applying the technology for widespread clinical deployment. Finally, we summarize the impact of AI on cancer immunotherapy and provide our perspectives about underlying applications of AI in the future.

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“…The final stage consists of the data classification. It may include various AI techniques, including Artificial Neural Networks, Support Vector Machines, Decision Trees, and Ensemble Learning algorithms [65]. Finally, the different measurements and machine learning methods will be more accurate using the Big Data concept in healthcare [66][67][68][69].…”

Section: Bio-signals Acquisition and Processingmentioning

confidence: 99%

Mobile 5P-Medicine Approach for Cardiovascular Patients

Pires

Denysyuk

Villasana

et al. 2021

Sensors

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Medicine is heading towards personalized care based on individual situations and conditions. With smartphones and increasingly miniaturized wearable devices, the sensors available on these devices can perform long-term continuous monitoring of several user health-related parameters, making them a powerful tool for a new medicine approach for these patients. Our proposed system, described in this article, aims to develop innovative solutions based on artificial intelligence techniques to empower patients with cardiovascular disease. These solutions will realize a novel 5P (Predictive, Preventive, Participatory, Personalized, and Precision) medicine approach by providing patients with personalized plans for treatment and increasing their ability for self-monitoring. Such capabilities will be derived by learning algorithms from physiological data and behavioral information, collected using wearables and smart devices worn by patients with health conditions. Further, developing an innovative system of smart algorithms will also focus on providing monitoring techniques, predicting extreme events, generating alarms with varying health parameters, and offering opportunities to maintain active engagement of patients in the healthcare process by promoting the adoption of healthy behaviors and well-being outcomes. The multiple features of this future system will increase the quality of life for cardiovascular diseases patients and provide seamless contact with a healthcare professional.

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Statistics in cardiovascular medicine: there is still gold in the old

Cited by 5 publications

References 8 publications

Artificial Intelligence in Heart Failure: Friend or Foe?

Artificial Intelligence in Heart Failure: Friend or Foe?

Applying artificial intelligence for cancer immunotherapy

Mobile 5P-Medicine Approach for Cardiovascular Patients

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