EMPOWERING
MINDS
Introduction to Python for Healthcare Data Analysis
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Healthcare is at the forefront of empowering wealth and well-being, impacting societies worldwide. The world has witnessed remarkable progress in life expectancy, medical technology, and personalized medicine. With the advent of Electronic Health Records (EHR), wearables and the global accessibility of healthcare, the opportunities to harness biosignals have never been greater.
Despite these advancements, challenges persist. Shortages of healthcare professionals, especially in remote areas, and the need for remote healthcare access, even in space missions, are pressing concerns. The abundance of healthcare data has also brought about the “curse of data,” making it challenging to extract meaningful patterns from the noise.
The solution to these challenges lies in the realm of Artificial Intelligence (AI) and Machine Learning (ML). These technologies are revolutionizing healthcare by helping doctors, data scientists, and even astronauts ease the burden and enhance the quality of life. AI, particularly Deep Neural Networks (DNN), is reshaping the understanding of biosignals and their potential in improving healthcare.
What to expect
Biosignal Processing for Deep Learning
Gain a comprehensive understanding of biosignal processing techniques tailored for Deep Learning. Explore how to preprocess biosignals effectively to prepare them for AI-driven analysis.
Recurrent Networks (RNNs)
Dive into the world of Recurrent Neural Networks (RNNs), a powerful class of Deep Learning models for sequential data analysis. Learn how RNNs can be applied to biosignal data, allowing you to capture temporal dependencies and patterns.
Convolutional Neural Networks (CNNs)
Uncover the potential of Convolutional Neural Networks (CNNs) in biosignal analysis. Understand how CNNs can be used to automatically extract features from biosignals, making them suitable for classification and prediction tasks.
Biosignal Synthesis (CNNs)
Explore the fascinating realm of biosignal synthesis using Deep Learning techniques. Discover how AI can generate synthetic biosignals, opening up possibilities for research, experimentation, and training in a controlled environment.
Biosignal Classification
Learn how to build powerful Deep Learning models for biosignal classification. Understand the techniques and architectures used to categorize biosignals accurately, enabling applications in disease diagnosis and more.
Prediction
Delve into the world of biosignal prediction. Explore how AI can forecast future biosignal patterns, offering insights into potential health issues or changes in a patient’s condition.
By the end of the workshop
you’ll not only have a solid theoretical foundation but also practical experience in applying Deep Learning techniques to real biosignal datasets. You’ll gain the skills and knowledge needed to make informed decisions in healthcare, research, and various other domains.
Join us in this transformative journey at the Deep Learn Biosignals Workshop
Don’t miss your chance to be part of the future of personalized medicine and data-driven healthcare solutions!
Instructor
David Belo is a distinguished Portuguese professional with a multifaceted career at the intersection of AI, healthcare, and responsible technology. A deep neural network architect by expertise, David seamlessly blends his passions for health, technology, and human understanding.
With a rich academic journey, David earned his Ph.D. in Biomedical Engineering, specializing in deep learning applications for biosignal analysis. His academic prowess extends to supervising master’s students and conducting neural network workshops. Complementing his academic achievements, he boasts more than 15 years of experience in creating, developing, and teaching advanced AI architectures.
David’s journey extends beyond academia. His industry experience includes serving as a Machine Learning Team Lead at Loka, Inc., where he delved into biosignals and AWS solutions architecture. Furthermore, he made a lasting impact as an AI and Biosignal Expert at NASA’s Frontier Development Lab (FDL), contributing his expertise to space sciences.
Notably, David served as a Senior Scientist at Fraunhofer Portugal AICOS, focusing on AI architectures, project management, and MLOps. During his tenure, he made and coordinated the AISym4Med Horizon Europe project, leveraging his expertise in responsible AI.
In parallel, David also possesses a strong background in teaching, which he demonstrated as an Assistant Teacher and Lab Monitor at several institutions, such as Nova University of Lisbon’s Samsung Inovation Campus.
Beyond his professional roles, David is the CEO and Founder of SAFE AI [4U], a company dedicated to responsible AI and AI training, particularly in healthcare. His compassionate leadership, commitment to self-development, and dedication to responsible AI principles make him a transformative force in the field.
David’s expertise extends to numerous publications and research projects, including AISym4Med, ICANs, COTIDIANA, and PrevOccupai. With interests spanning self-development, spirituality, compassion, gaming, fitness, and DIY, he continues to make a profound impact on the world of AI and beyond.