Carpentieri B. Big Data Analysis and AI for Medical Sciences 2024

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Carpentieri B. Big Data Analysis and AI for Medical Sciences 2024

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Textbook in PDF format Our modern society is characterized by an unprecedented ability to generate vast amounts of data. The use of big data in science is driving the development of a new scientific paradigm. Smart search and learning computer algorithms are utilized to extract mean ingful patterns from large datasets and generate new knowledge that can be applied to model the behavior of complex real-world systems much faster than by using traditional scientific laws and theories. The name of the new game is machine learning, deep learning, and artificial intelligence. These data-driven research methodologies are already paving the way for advanced discoveries in numerous scientific disciplines. In healthcare industry and research, the data-driven modeling approach is opening new frontiers, for example, enabling to produce more accurate diagnoses, to facilitate the design of drugs, to innovate treatment protocols and prevent diseases, to produce personalized Preface Introduction Fuzzy Logic for Knowledge-Driven and Data-Driven Modeling in Biomedical Sciences Application of Machine Learning Algorithms to Diagnosis and Prognosis of Chronic Wounds Deep Learning Techniques for Gene Identification in Cancer Prevention Deep Learning for Network Biology Deep Learning-Based Reduced Order Models for Cardiac Electrophysiology The Potential of Microbiome Big Data in Precision Medicine: Predicting Outcomes Through Machine Learning Predictive Patient Stratification Using Artificial Intelligence and Machine Learning Hybrid Data-Driven and Numerical Modeling of Articular Cartilage A Hybrid of Differential Evolution and Minimization of Metabolic Adjustment for Succinic and Ethanol Production Analysis Pipelines and a Platform Solution for Next-Generation Sequencing Data Artificial Intelligence: From Drug Discovery to Clinical Pharmacology Using AI to Steer Brain Regeneration: The Enhanced Regenerative Medicine Paradigm Towards Better Ways to Assess Predictive Computing in Medicine: On Reliability, Robustness, and Utility Legal Aspects of AI in the Biomedical Field. The Role of Interpretable Models The Long Path to Usable AI