Practical LLMs for Healthcare

Turn noisy wearable PPG signals into reliable health insights by building generative Transformer and diffusion models in Python, from raw sensor data to real-time alerts and on-device deployment.

Key Features

• Apply Transformer architectures to real-world PPG signals from smartwatches and fitness trackers
• Build denoising, synthetic data generation, and streaming inference pipelines end-to-end
• Deploy optimized generative models on low-power wearable hardware using Python and Keras

Book Description

Wearable devices generate a constant stream of photoplethysmography (PPG) data, but most of it is noisy and far removed from clean datasets used in academic research. Large Language Models and Transformers are usually linked with text, yet the same sequence-modeling principles powering chatbots suit physiological time series just as well.

Practical LLMs for Healthcare reframes generative AI as a toolkit for biosignal engineering. You will load, visualize, and clean raw PPG data in Python before learning why classical filters fall short of learned representations, then tokenize signals, build a denoising autoencoder, and construct Transformer blocks tuned for long, high-frequency time series. The book then covers self-supervised pretraining, synthetic data generation with diffusion models, and streaming inference for real-time monitoring. You will design alerting logic, personalize models per user, and optimize them for low-power hardware with quantization, pruning, and ONNX or TFLite export. Later chapters extend the pipeline to multimodal fusion with ECG and accelerometer data, and add Retrieval-Augmented Generation to ground physiological events in medical knowledge. By the end, you will design, train, and deploy generative models that turn raw wearable signals into trustworthy, real-time health insights.

What you will learn

• Preprocess and tokenize continuous PPG time-series for Transformer input
• Build generative denoising models to reconstruct clean signals from noisy wearable data
• Apply attention mechanisms adapted for long, high-frequency physiological sequences
• Train self-supervised foundation models on unlabeled PPG sensor data
• Generate synthetic PPG signals using diffusion models for privacy-safe dataset augmentation
• Implement streaming inference pipelines for real-time health monitoring
• Optimize and export models for deployment on low-power wearable hardware

Who this book is for

This book is for machine learning engineers, AI engineers, data scientists, health researchers, and software developers working with wearable sensors or health-tech applications who want to apply modern generative AI beyond text and images. Readers should be comfortable with Python, NumPy, and basic neural network concepts (experience with Keras or PyTorch is helpful). No prior knowledge of biology, signal processing, or healthcare is required.