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Explore the Garbh-Ini Project through this comprehensive conference talk presented by experts Shinjini Bhatnagar, Bapu Koundinya Desiraju, Ram Thiruvengadam, and Himanshu Sinha. Delve into the intersection of machine learning and healthcare as part of the "Machine Learning for Health and Disease" program organized by the International Centre for Theoretical Sciences. Gain insights into how modern mathematical and computational techniques can be applied to better understand health-related data across multiple domains. Learn about various machine learning techniques, including logistic regression, tree-based methods, support vector machines, Bayesian methods, and deep networks, with examples of their applicability in biomedicine and health. Discover applications in predicting patient outcomes, analyzing medical imaging data, genomic variant analysis, and inferring patterns in large-scale heterogeneous data. Bridge the gap between mathematical modeling and clinical problems while exploring the potential of building public health databases as resources. Suitable for PhD students in STEM fields, medical students, postdoctoral fellows, faculty, and professionals in science, engineering, and medicine.
