Deep Learning for Healthcare

From medical embeddings to generative models for synthetic Electronic Health Records — this series covers the main deep learning families applied to healthcare data. Each article focuses on a different architecture, its clinical motivations, and practical implementations.

Topics covered: representation learning for clinical codes, convolutional networks for medical imaging, recurrent models for patient trajectories, attention mechanisms for interpretability, memory-augmented networks for clinical reasoning, graph neural networks for molecular property prediction, and generative models for privacy-preserving synthetic data.

Articles

1 Medical Embeddings for Healthcare
Deep neural networks and medical embeddings for healthcare prediction. Learning dense representations of diagnoses, procedures, and medications from clinical data.

word2vec Med2Vec EHR representation learning
2 CNNs for Medical Imaging
Convolutional neural networks applied to medical imaging — from X-rays and CT scans to histopathology. Architectures, transfer learning, and clinical deployment.

CNN medical imaging transfer learning radiology
3 RNNs for Patient Trajectories
Recurrent neural networks for modelling patient trajectories over time. LSTMs, GRUs, and temporal patterns in clinical sequences for diagnosis prediction.

LSTM GRU temporal sequences clinical events
4 Attention Mechanisms for Interpretable Healthcare AI
Attention mechanisms and the RETAIN model — making deep learning interpretable for clinicians. Reverse time attention for explainable clinical predictions.

attention RETAIN interpretability explainable AI
5 Memory Networks and Transformers for Healthcare
Memory-augmented networks and transformer architectures for clinical reasoning. External memory, multi-hop attention, and medical question answering.

transformers memory networks clinical reasoning BERT
6 Graph Neural Networks for Drug Discovery
Graph neural networks and message passing for molecular property prediction. Modelling molecules as graphs for drug discovery and toxicity prediction.

GNN MPNN molecular graphs drug discovery
7 Generative Models for Healthcare
Variational autoencoders and GANs for synthetic healthcare data generation. Privacy-preserving synthetic EHR, data augmentation, and de-identification.

VAE GAN synthetic data privacy