Cardiovascular Care in the Era of Machine Learning enabled Personalized Medicine

Tailoring medical interventions to a particular patient and pathology has been termed personalized medicine. The outcome of cancer treatments is improved when the intervention is timed in accordance with the patient’s internal time. Yet, one challenge of personalized medicine is how to consider the biological time of the patient. Prerequisite for this so-called chronotherapy is an accurate characterization of the internal circadian time of the patient. As an alternative to time-consuming measurements in a sleep-laboratory, recent studies in chronobiology predict circadian time by applying machine learning approaches and mathematical modelling to easier accessible observables such as gene expression. Embedding these results into the mathematical dynamics between clock and cancer in mammals, we review the precision of predictions and the potential usage with respect to cancer treatment and discuss whether the patient’s internal time and circadian observables, may provide an additional indication for individualized treatment timing. Besides the health improvement, timing treatment may imply financial advantages, by ameliorating side effects of treatments, thus reducing costs. Summarizing the advances of recent years, this review brings together the current clinical standard for measuring biological time, the general assessment of circadian rhythmicity, the usage of rhythmic variables to predict biological time and models of circadian rhythmicity.

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Machine Learning and the Future of Cardiovascular Care

Journal of the American College of Cardiology ◽

10.1016/j.jacc.2020.11.030 ◽

2021 ◽

Vol 77 (3) ◽

pp. 300-313

Author(s):

Giorgio Quer ◽

Ramy Arnaout ◽

Michael Henne ◽

Rima Arnaout

Keyword(s):

Machine Learning ◽

The Future ◽

Cardiovascular Care

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ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE

Proceedings of the Shevchenko Scientific Society. Medical Sciences ◽

10.25040/ntsh2021.02.07 ◽

2021 ◽

Vol 65 (2) ◽

Author(s):

Oleksandr Dudin ◽

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Ozar Mintser ◽

Oksana Sulaieva ◽

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...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Image Analysis ◽

Deep Learning ◽

Personalized Medicine ◽

Digital Pathology ◽

Automated Image Analysis ◽

Learning Methods ◽

Machine Learning Methods ◽

Wide Range

Introduction. Over the past few decades, thanks to advances in algorithm development, the introduction of available computing power, and the management of large data sets, machine learning methods have become active in various fields of life. Among them, deep learning possesses a special place, which is used in many spheres of health care and is an integral part and prerequisite for the development of digital pathology. Objectives. The purpose of the review was to gather the data on existing image analysis technologies and machine learning tools developed for the whole-slide digital images in pathology. Methods: Analysis of the literature on machine learning methods used in pathology, staps of automated image analysis, types of neural networks, their application and capabilities in digital pathology was performed. Results. To date, a wide range of deep learning strategies have been developed, which are actively used in digital pathology, and demonstrated excellent diagnostic accuracy. In addition to diagnostic solutions, the integration of artificial intelligence into the practice of pathomorphological laboratory provides new tools for assessing the prognosis and prediction of sensitivity to different treatments. Conclusions: The synergy of artificial intelligence and digital pathology is a key tool to improve the accuracy of diagnostics, prognostication and personalized medicine facilitation

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Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome

Diagnostics ◽

10.3390/diagnostics11091631 ◽

2021 ◽

Vol 11 (9) ◽

pp. 1631

Author(s):

Il Bin Kim ◽

Seon-Cheol Park

Keyword(s):

Machine Learning ◽

Personalized Medicine ◽

Imaging Techniques ◽

Language Game ◽

Symptom Clusters ◽

Machine Learning Algorithms ◽

Depressive Syndrome ◽

Research Network ◽

Imaging Data ◽

Clinical Cohort

The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, “a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed” as the “next-generation treatment for mental disorders” by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining symptom clusters and selecting antidepressants. Potentially applicable suggestions to realize machine learning-based personalized medicine for depressive syndrome are also provided herein.

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MACHINE LEARNING FOR DETECTING EPISTASIS INTERACTIONS AND ITS RELEVANCE TO PERSONALIZED MEDICINE IN ALZHEIMER’S DISEASE: SYSTEMATIC REVIEW

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237221500472 ◽

2021 ◽

pp. 2150047

Author(s):

Marwa M. Abd El Hamid ◽

Mohamed Shaheen ◽

Mai S. Mabrouk ◽

Yasser M. K. Omar

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Personalized Medicine ◽

Complex Diseases ◽

Genetic Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Nucleotide Polymorphisms ◽

Learning Techniques

Alzheimer’s disease (AD) is a progressive disease that attacks the brain’s neurons and causes problems in memory, thinking, and reasoning skills. Personalized Medicine (PM) needs a better and more accurate understanding of the relationship between human genetic data and complex diseases like AD. The goal of PM is to tailor the treatment of a case person to his individual properties. PM requires the prediction of a person’s disease from genetic data, and its success depends on the accurate detection of genetic biomarkers. Single Nucleotide polymorphisms (SNPs) are considered the most prevalent type of variation in the human genome. Epistasis has a biological relevance to complex diseases and has an important impact on PM. Detection of the most significant epistasis interactions associated with complex diseases is a big challenge. This paper reviews several machine learning techniques and algorithms to detect the most significant epistasis interactions in Alzheimer’s disease. We discuss many machine learning techniques that can be used for detecting SNPs’ combinations like Random Forests, Support Vector Machines, Multifactor Dimensionality Reduction, Neural Network, and Deep Learning. This review paper highlights the pros and cons of these techniques and explains how they can be applied in an efficient framework to apply knowledge discovery and data mining in AD disease.

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