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The digitization of data in hospitals has enabled the collection of mixed temporal data for surgical procedures. These datasets describe patients through an event trace and a multivariate time series. Leveraging this digitized data presents a unique opportunity to develop innovative anesthesia training methods, enabling real-time scenario generation driven by machine learning models. This paper aims to develop a model that (i) simulates the evolution of the physiological variables of a digital patient under anesthesia, in response to medical actions, and (ii) predicts the actions performed by the virtual medical team, with the simulator user being the sole human agent executing actions through the simulator interface. The challenge lies in achieving two goals within a single model: modeling asynchronous data streams for time series forecasting and event prediction. A key difficulty in our medical case is the sparsity of the event sequences. We found no models addressing the combined prediction of event-contextualized time series and time-series-contextualized events. To bridge this gap, we explore four deep learning architectures that we have tailored for unsupervised representation learning and joint prediction tasks: (i) a stacked LSTM (Long Short-Term Memory) network, (ii) a Graph Neural Network (GNN), (iii) the Vanilla Transformer, and (iv) its variant, the Informer. We evaluate these models on a real-world anesthesia dataset, focusing on overall predictive performance and computational efficiency. Our proof-of-concept study demonstrates that the Transformer outperforms the three other models which show significant shortcomings in predicting medical events.

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Two-in-One Models for Event Prediction and Time Series Forecasting. Comparison of Four Deep Learning Approaches to Simulate a Digital Patient Under Anesthesia

  • 会议论文

  • 发表日期：

    2025年

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  • Quentin Victor,
  • Ianis Clavier,
  • Hugo Boisaubert,
  • Fabien Picarougne,
  • Corinne Lejus-Bourdeau,
  • Christine Sinoquet

摘要

The digitization of data in hospitals has enabled the collection of mixed temporal data for surgical procedures. These datasets describe patients through an event trace and a multivariate time series. Leveraging this digitized data presents a unique opportunity to develop innovative anesthesia training methods, enabling real-time scenario generation driven by machine learning models. This paper aims to develop a model that (i) simulates the evolution of the physiological variables of a digital patient under anesthesia, in response to medical actions, and (ii) predicts the actions performed by the virtual medical team, with the simulator user being the sole human agent executing actions through the simulator interface. The challenge lies in achieving two goals within a single model: modeling asynchronous data streams for time series forecasting and event prediction. A key difficulty in our medical case is the sparsity of the event sequences. We found no models addressing the combined prediction of event-contextualized time series and time-series-contextualized events. To bridge this gap, we explore four deep learning architectures that we have tailored for unsupervised representation learning and joint prediction tasks: (i) a stacked LSTM (Long Short-Term Memory) network, (ii) a Graph Neural Network (GNN), (iii) the Vanilla Transformer, and (iv) its variant, the Informer. We evaluate these models on a real-world anesthesia dataset, focusing on overall predictive performance and computational efficiency. Our proof-of-concept study demonstrates that the Transformer outperforms the three other models which show significant shortcomings in predicting medical events.