Navigating the consequences of mechanical ventilation in clinical intensive care settings through an evolutionary game-theoretic framework

• URL: http://arxiv.org/abs/2510.15127v1
• Date: Thu, 16 Oct 2025 20:32:52 GMT
• Title: Navigating the consequences of mechanical ventilation in clinical intensive care settings through an evolutionary game-theoretic framework
• Authors: David J. Albers, Tell D. Bennett, Jana de Wiljes, Bradford J. Smith, Peter D. Sottile, J. N. Stroh,
• Abstract summary: Identifying the effects of mechanical ventilation strategies and protocols in critical care requires analyzing data from heterogeneous patient-ventilator systems.<n>This research develops a framework to help understand the consequences of mechanical ventilation (MV) and adjunct care decisions on patient outcome.
• Score: 1.5208420990586384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying the effects of mechanical ventilation strategies and protocols in critical care requires analyzing data from heterogeneous patient-ventilator systems within the context of the clinical decision-making environment. This research develops a framework to help understand the consequences of mechanical ventilation (MV) and adjunct care decisions on patient outcome from observations of critical care patients receiving MV. Developing an understanding of and improving critical care respiratory management requires the analysis of existing secondary-use clinical data to generate hypotheses about advantageous variations and adaptations of current care. This work introduces a perspective of the joint patient-ventilator-care systems (so-called J6) to develop a scalable method for analyzing data and trajectories of these complex systems. To that end, breath behaviors are analyzed using evolutionary game theory (EGT), which generates the necessary quantitative precursors for deeper analysis through probabilistic and stochastic machinery such as reinforcement learning. This result is one step along the pathway toward MV optimization and personalization. The EGT-based process is analytically validated on synthetic data to reveal potential caveats before proceeding to real-world ICU data applications that expose complexities of the data-generating process J6. The discussion includes potential developments toward a state transition model for the simulating effects of MV decision using empirical and game-theoretic elements.

Related papers

• Automated Detection and Mitigation of Dependability Failures in Healthcare Scenarios through Digital Twins [3.188134462843442]
  M-GENGAR is a methodology based on a closed-loop Digital Twin (DT) paradigm for dependability assurance of medical CPSs.<n>M-GENGAR supports the automated synthesis of mitigation strategies, enabling runtime feedback and control within the DT loop.<n>Results show that, in 87.5% of the evaluated scenarios, strategies synthesized through formal game-theoretic analysis stabilize patient vital metrics at least as effectively as human decision-making.
  arXiv  Detail & Related papers  (2026-02-24T15:56:20Z)
• Structured Hybrid Mechanistic Models for Robust Estimation of Time-Dependent Intervention Outcomes [9.820469663506882]
  Estimating intervention effects in dynamical systems is crucial for outcome optimization.<n>Mechanistic models are typically robust, but might be oversimplified.<n>We propose a hybrid mechanistic-data-driven approach to estimate time-dependent intervention outcomes.
  arXiv  Detail & Related papers  (2026-02-11T20:39:41Z)
• Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference [89.5628648718851]
  Causal inference is essential for developing and evaluating medical interventions.<n>Real-world medical datasets are often difficult to access due to regulatory barriers.<n>We present STEAM: a novel method for generating Synthetic data for Treatment Effect Analysis in Medicine.
  arXiv  Detail & Related papers  (2025-10-21T16:16:00Z)
• Clinical NLP with Attention-Based Deep Learning for Multi-Disease Prediction [44.0876796031468]
  This paper addresses the challenges posed by the unstructured nature and high-dimensional semantic complexity of electronic health record texts.<n>A deep learning method based on attention mechanisms is proposed to achieve unified modeling for information extraction and multi-label disease prediction.
  arXiv  Detail & Related papers  (2025-07-02T07:45:22Z)
• Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis [0.1558630944877332]
  This work introduces a comprehensive approach to elucidate the deposition process regimes in Chemical Vapor Deposition (CVD) reactors. Our methodology relies on process outcomes, derived by a detailed CFD model, to identify clusters of "outcomes" corresponding to distinct process regimes. This phenomenon is experimentally validated through Arrhenius plot analysis, affirming the efficacy of our approach.
  arXiv  Detail & Related papers  (2024-05-24T14:10:22Z)
• Adjoint Sensitivity Analysis on Multi-Scale Bioprocess Stochastic Reaction Network [2.6130735302655554]
  We introduce an adjoint sensitivity approach to expedite the learning of mechanistic model parameters. In this paper, we consider enzymatic analysis (SA) representing a multi-scale bioprocess mechanistic model.
  arXiv  Detail & Related papers  (2024-05-07T05:06:45Z)
• Evaluating the Fairness of the MIMIC-IV Dataset and a Baseline Algorithm: Application to the ICU Length of Stay Prediction [65.268245109828]
  This paper uses the MIMIC-IV dataset to examine the fairness and bias in an XGBoost binary classification model predicting the ICU length of stay. The research reveals class imbalances in the dataset across demographic attributes and employs data preprocessing and feature extraction. The paper concludes with recommendations for fairness-aware machine learning techniques for mitigating biases and the need for collaborative efforts among healthcare professionals and data scientists.
  arXiv  Detail & Related papers  (2023-12-31T16:01:48Z)
• QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems [9.29069202652354]
  An Explainable AI for Quantitative analysis (QXAI) framework is proposed with post-hoc model explainability and intrinsic explainability for regression and classification tasks. We adopted the artificial neural networks (ANN) and attention-based Bidirectional LSTM (BiLSTM) models for the prediction of heart rate and classification of physical activities based on sensor data.
  arXiv  Detail & Related papers  (2023-09-19T03:50:30Z)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Data [66.43957431843324]
  We introduce COVID-Net Biochem, a versatile and explainable framework for constructing machine learning models. We apply this framework to predict COVID-19 patient survival and the likelihood of developing Acute Kidney Injury during hospitalization.
  arXiv  Detail & Related papers  (2022-04-24T07:38:37Z)
• Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis [87.31348761201716]
  Crises like the COVID-19 pandemic pose a serious challenge to health-care institutions. BaBSim.Hospital is a tool for capacity planning based on discrete event simulation. We aim to investigate and optimize these parameters to improve BaBSim.Hospital.
  arXiv  Detail & Related papers  (2021-05-16T12:38:35Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.