This repository provides the official PyTorch implementation of "Jeon et al., ADT2R: Adaptive Decision Transformer for Dynamic Treatment Regimes in Sepsis, IEEE Transactions on Neural Networks and Learning Systems (Accept)."
- Contact: E.-J. Jeon ([email protected])
- We propose an Adaptive Decision Transformer for DTR (ADT$^2$R), which recommends an optimal treatment action for each time step depending on the heterogeneity of the sepsis and a patient's evolving health states. Specifically, we devise a trajectory-optimization-based module to be trained with supervision for treatments and adaptively aggregate the multi-head self-attentions by deliberating on various inherent time-varying patterns among sepsis patients. Furthermore, we estimate the patient's health state by adopting an actor-critic algorithm and inform the treatment recommendation learning about its short-term changes.
- MIMIC-III dataset
- Setup MIMIC-III: https://github.com/MIT-LCP/mimic-code
- Sepsis preprocessing: https://github.com/uribyul/py_ai_clinician
- (Supplementary) Mechanical ventilation processing: https://github.com/yinchangchang/DAC/tree/main
- Python 3.9
- PyTorch 2.0