Investigating the consequences of mechanical ventilation in clinical intensive care settings through an evolutionary game-theoretic framework
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908793826181120 |
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| author | Albers, David J. Bennett, Tell D. de Wiljes, Jana Hripcsak, George Smith, Bradford J. Sottile, Peter D. Stroh, J. N. |
| author_facet | Albers, David J. Bennett, Tell D. de Wiljes, Jana Hripcsak, George Smith, Bradford J. Sottile, Peter D. Stroh, J. N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15127 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Investigating the consequences of mechanical ventilation in clinical intensive care settings through an evolutionary game-theoretic framework Albers, David J. Bennett, Tell D. de Wiljes, Jana Hripcsak, George Smith, Bradford J. Sottile, Peter D. Stroh, J. N. Quantitative Methods Machine Learning Optimization and Control 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. |
| title | Investigating the consequences of mechanical ventilation in clinical intensive care settings through an evolutionary game-theoretic framework |
| topic | Quantitative Methods Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2510.15127 |