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arXiv AI

Learning response-aware patient dynamics for respiratory support

The paper introduces a response‑aware patient dynamics model that explicitly captures how respiratory support alters a patient’s short‑term physiological trajectory. By decomposing predicted physiological change into baseline dynamics and support‑associated deviations anchored to room‑air reference, the model updates latent patient states across a forecast horizon. Experiments on ICU cohorts from two institutions show that the model matches baseline trajectory predictions overall and consistently improves performance when physiological states are changing.

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Freezing the Physiological Encoder: Explanation Stability Under Bounded Updates of an ICU Model

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Time-Aware Tranformer-Based Prediction Model for AECOPD

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Inferring Relative Consequences of Mechanical Ventilation from Observational Data Using Game-Based Comparisons

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