Clinical-GAN

Clinical-GAN forecasts clinical trajectories by predicting future medical codes from time-ordered clinical records to support early disease trajectory prediction.

Key Features:

• Transformer-based GAN Architecture: A Generator and a Discriminator both built on Transformer mechanisms combine generative adversarial training with sequence modeling.
• Medical codes as time-ordered sequences: Patients' medical codes are represented as tokenized, time-ordered sequences analogous to language models to capture temporal dependencies.
• Adversarial Training: The Generator is trained adversarially against the Discriminator to improve realism and robustness of predicted future medical codes.
• Multi-head attention for local interpretation: Multi-head attention mechanisms enable local interpretation of which elements of a patient's history influence predictions.
• Handling clinical-data challenges: The model addresses long-range dependencies, irregular intervals between admissions, and non-stationarity in clinical data.

Scientific Applications:

• Precision medicine: Early prediction of disease trajectories and subsequent medical visits to inform personalized interventions and anticipate potential complications.
• Validation and benchmarking: Performance evaluated on the Medical Information Mart for Intensive Care IV v1.0 (MIMIC-IV v1.0) dataset (over 500,000 visits from ~196,000 adult patients, 2008–2019) with reported improvements over baseline methods and existing works.

Methodology:

Clinical data are tokenized as sequences processed by a Transformer-based Generator; a Transformer-based Discriminator evaluates generated predictions against actual outcomes in adversarial training, and multi-head attention is used to provide local interpretability.