{ "item_title" : "Integrated Meta- Learning Into Biomedical Diagnostics", "item_author" : [" Monika Kumari", "Irfan Ali", "Mohnish Muchhal "], "item_description" : "Meta-learning methods have to be properly applied in a wide range of healthcare settings through a huge number of theoretical and experimental studies. Meta-learning in health care remains in its infancy and has as yet not appealed to a larger scientific community, even with the developments. Additional work attempts need to be done to bridge a gap in the literature by combining existing breakthroughs and their practical applications since it is the first systematic survey targeting meta-learning within this area. The scope of the adaptation is where meta-learning also diverges from base learning. Whereas its acquisition is all about acquiring proficiency in a number of learning tasks, base-level acquisition is aimed at acquiring knowledge in one particular learning task. These are data integration across heterogeneous modalities, model interpretability, privacy and security, and real-world scalability optimization. Addressing these areas will enable meta-learning to become a foundation of the next generation of healthcare technologies, providing more accurate, personalized, and efficient diagnostic systems that can ultimately lead to better patient outcomes.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/6/20/844/151/620844151X_b.jpg", "price_data" : { "retail_price" : "50.00", "online_price" : "50.00", "our_price" : "50.00", "club_price" : "50.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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