Clinical researchers, biomedical engineers, and healthcare AI practitioners.
Developing Active Inference agents for bedside clinical waveform data analysis and real-time decision support.
Lead: Franklin Ducatez
This project is archived. It is no longer active, and any participation prompts below describe how it previously operated.
Clinical Waveform Data Based Agent develops Active Inference systems for bedside clinical monitoring. It applies the free energy framework to real-time waveform data — plethysmograph, arterial pressure line, and ventilator curves — to support clinical decision-making in a pediatric intensive care setting.
The project develops Active Inference agent architectures for processing and interpreting clinical waveform data in real time. The goal is to create principled, uncertainty-aware models that can assist clinicians at the bedside by continuously updating belief states from physiological sensor streams. It is a pilot study in a pediatric intensive care setting, working toward a proof of concept before pursuing a clinical trial.
Clinical researchers, biomedical engineers, and AI practitioners with healthcare interests are welcome.
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