How <i>cy pres</i> promotes transdisciplinary convergence science: an academic health center for women’s cardiovascular and brain health

Villablanca, Amparo; Dugger, Brittany N.; Nuthikattu, Saivageethi; Chauhan, Joohi; Cheung, Samson; Chuah, Chen-Nee; Garrison, Siedah L.; Milenkovic, Dragan; Norman, Jennifer E.; Oliveira, Luca Cerny; Smith, Bridgette P.; Brown, Susan D.

doi:10.1017/cts.2023.705

Cited by 1 publication

(1 citation statement)

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“…It is now widely accepted that vascular risk factors that increase the chances of heart disease, such as hypertension, diabetes, obesity, and hyperlipidemia, also compromise cerebrovascular health [76]. It is crucial to detect and control these modifiable risk factors that exist throughout a person's lifetime, as managing them early on is currently the only known prevention method [77]. Therefore, there is a pressing need for more efficient strategies to combat multimorbidity and speed up early diagnosis and management [78].…”

Section: Discussionmentioning

confidence: 99%

Towards a Deeper Understanding: Utilizing Machine Learning to Investigate the Association between Obesity and Cognitive Decline—A Systematic Review

Veneziani,

Grimaldi,

Marra

et al. 2024

JCM

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Background/Objectives: Several studies have shown a relation between obesity and cognitive decline, highlighting a significant global health challenge. In recent years, artificial intelligence (AI) and machine learning (ML) have been integrated into clinical practice for analyzing datasets to identify new risk factors, build predictive models, and develop personalized interventions, thereby providing useful information to healthcare professionals. This systematic review aims to evaluate the potential of AI and ML techniques in addressing the relationship between obesity, its associated health consequences, and cognitive decline. Methods: Systematic searches were performed in PubMed, Cochrane, Web of Science, Scopus, Embase, and PsycInfo databases, which yielded eight studies. After reading the full text of the selected studies and applying predefined inclusion criteria, eight studies were included based on pertinence and relevance to the topic. Results: The findings underscore the utility of AI and ML in assessing risk and predicting cognitive decline in obese patients. Furthermore, these new technology models identified key risk factors and predictive biomarkers, paving the way for tailored prevention strategies and treatment plans. Conclusions: The early detection, prevention, and personalized interventions facilitated by these technologies can significantly reduce costs and time. Future research should assess ethical considerations, data privacy, and equitable access for all.

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Section: Discussionmentioning

confidence: 99%

Towards a Deeper Understanding: Utilizing Machine Learning to Investigate the Association between Obesity and Cognitive Decline—A Systematic Review

Veneziani,

Grimaldi,

Marra

et al. 2024

JCM

View full text Add to dashboard Cite

show abstract

How cy pres promotes transdisciplinary convergence science: an academic health center for women’s cardiovascular and brain health

Abstract: Smith BP, and Brown SD. How cy pres promotes transdisciplinary convergence science: an academic health center for women's cardiovascular and brain health.

Cited by 1 publication

References 77 publications

Towards a Deeper Understanding: Utilizing Machine Learning to Investigate the Association between Obesity and Cognitive Decline—A Systematic Review

Towards a Deeper Understanding: Utilizing Machine Learning to Investigate the Association between Obesity and Cognitive Decline—A Systematic Review

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