Lecture 3.2.6: Anomaly detection models + TinyML & edge deployment

In Lecture 3.2.6 of the Masters in Health Data Science program, we explore anomaly detection models, TinyML, and edge deployment in healthcare AI systems. This lecture covers how to define “normal” in medical data and detect deviations using statistical, machine learning, and deep learning approaches. You’ll learn about point, contextual, and collective anomalies, along with real-world healthcare applications such as sepsis detection, ECG monitoring, and glucose tracking. We also dive into advanced methods including Isolation Forest, Autoencoders, One-Class SVM, LSTM-based temporal models, and Prophet forecasting. Performance evaluation techniques such as Precision, Recall, F1 Score, ROC vs PR curves, and real-world clinical metrics are explained in detail. The lecture then transitions into TinyML and edge computing, showing how machine learning models can run on low-power devices for real-time decision-making. Key concepts include: • Model quantization and optimization • On-device inference vs cloud computing • Latency, privacy, and cost considerations • Microcontroller deployment constraints Finally, we discuss real-world challenges such as model drift, alert fatigue, explainability, federated learning, and regulatory considerations for Software as a Medical Device (SaMD). 📌 Perfect for students, researchers, and professionals working in: • Health Data Science • AI in Healthcare • Biomedical Engineering • Edge AI & IoT Systems Subscribe to our channel for more Digital Health, Health Data Science, Health Economics, Medical Entrepreneurship, Robotics, and Academic Research content. ❤️ Like | 💬 Comment | 🔔 Subscribe & Turn On Notifications 🌐 FOLLOW US ON SOCIAL MEDIA Facebook: https://www.facebook.com/UniversalDigitalHealth/ Twitter (X): https://twitter.com/UniDigiHealth LinkedIn: https://www.linkedin.com/company/universal-digital-health/ Instagram: https://www.instagram.com/universaldigitalhealth/ TikTok: https://www.tiktok.com/@universaldigitalhealth 🎓 FREE MASTERS PROGRAMS 1️⃣ Health Data Science Masters https://healthdatasciencemasters.com/ 2️⃣ Global Health Economics Masters https://healtheconomicsmasters.com/ 3️⃣ Medical Entrepreneurship Masters https://medicalentrepreneurshipmasters.com/ 4️⃣ Medical Robotics Masters http://medicalroboticsmasters.com/ 🌍 OUR PLATFORMS & WEBSITES • Universal Digital Health (UDH) https://universaldigitalhealth.com/ • UDH Learning Management System https://learn.universaldigitalhealth.com/ • Nazish Masood Research Center (NMRC) https://nazishmasoodresearch.org/ • Health Innovation Journal (HIJ) https://healthinnovationjournal.com/hij • Tashafe https://tashafe.org/ • Health Rahber https://healthrahber.com/ 📚 POPULAR PLAYLISTS • How to Launch Your Own Academic Journal (OJS & Indexing) https://www.youtube.com/playlist?list=PLbk8Qfk7_hvnY95Y4XqZjPPrkUqXiiwrg • Free Systematic Review & Meta-Analysis Workshop https://www.youtube.com/playlist?list=PLbk8Qfk7_hvnfB0bCttRZS0JIyH6olcgx • R & Python Data Analysis in Health Research https://www.youtube.com/playlist?list=PLbk8Qfk7_hvkfUVYXDrhspqtfAU-IvZE6 • Survival Analysis in Health Research (Using R) https://www.youtube.com/playlist?list=PLbk8Qfk7_hvlvhltJye-Xq4JQm8d3LI6k • Python for Health Professionals https://www.youtube.com/playlist?list=PLbk8Qfk7_hvnWk5W2_BFttO00KUCEQwNV 🤝 JOIN OUR RESEARCH & INNOVATION COMMUNITIES • Health Innovation Journal Internship https://chat.whatsapp.com/Lonzvpe1RBREqH8QoZV1n3 • Grant Writing Team https://chat.whatsapp.com/FLgTMd5KggFJlBmtzOQVTh • Healthcare Research (Middle East) https://chat.whatsapp.com/HsjrZtXkLpPDLStrp8NOMp • Universal Digital Health Community https://chat.whatsapp.com/CRVvwvJggAXG0Z7JO8CfeQ • Nazish Masood Research Center Community https://chat.whatsapp.com/KBpFk6cl6JV0UEYxWREKYy • Digital Health Reviews / Meta / LTE Community https://chat.whatsapp.com/KxjM9soe1LsEKobicNwqs9 • Medical Robotics Community https://chat.whatsapp.com/C8THQKTxiAvBkuI6ra1z7T 📌 Universal Digital Health is committed to strengthening health systems globally, especially in LMICs, through structured education, research capacity building, digital innovation, and entrepreneurship.