Data-Driven Simulator for Mechanical Circulatory Support with Domain Adversarial Neural Process

• URL: http://arxiv.org/abs/2405.18536v1
• Date: Tue, 28 May 2024 19:07:12 GMT
• Title: Data-Driven Simulator for Mechanical Circulatory Support with Domain Adversarial Neural Process
• Authors: Sophia Sun, Wenyuan Chen, Zihao Zhou, Sonia Fereidooni, Elise Jortberg, Rose Yu,
• Abstract summary: Existing mechanical simulators for MCS rely on oversimplifying assumptions and are insensitive to patient-specific behavior. We use a neural process architecture to capture the probabilistic relationship between MCS pump levels and aortic pressure measurements with uncertainty. Empirical results with an improvement of 19% in non-stationary trend prediction establish DANP as an effective tool for clinicians.
• Score: 15.562905335917408
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mechanical Circulatory Support (MCS) devices, implemented as a probabilistic deep sequence model. Existing mechanical simulators for MCS rely on oversimplifying assumptions and are insensitive to patient-specific behavior, limiting their applicability to real-world treatment scenarios. To address these shortcomings, our model Domain Adversarial Neural Process (DANP) employs a neural process architecture, allowing it to capture the probabilistic relationship between MCS pump levels and aortic pressure measurements with uncertainty. We use domain adversarial training to combine simulation data with real-world observations, resulting in a more realistic and diverse representation of potential outcomes. Empirical results with an improvement of 19% in non-stationary trend prediction establish DANP as an effective tool for clinicians to understand and make informed decisions regarding MCS patient treatment.

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