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

arXiv:2510.15127v3 Announce Type: replace-cross Abstract: Identifying the effects of mechanical ventilation (MV) protocols in critical care requires analyzing data from heterogeneous patient-ventilator systems in the clinical decision-making environment. Multiscale interactions among these coupled components generate a high-dimensional state space that remains sparsely sampled despite extensive data collection. Analysis of existing data is essential for understanding current respiratory management practices and generating testable hypotheses about improvement. The scale and complexity of available data motivate the use of reinforcement learning (RL) to explore data-consistent counterfactual trajectories. However, formulating RL in practical applications requires a spatiotemporally dependent reward process that defines state-to-consequence relationships, their context dependence, and the delays over which consequences emerge. These poorly understood elements are not known \emph{a priori} and inferred from data via hypotheses. To that end, categorized observed states are contrasted according to their relative consequences by solving a game-based inverse problem that identifies a comparison model required for downstream probabilistic and stochastic methods such as reinforcement learning for seeking MV optimization and personalization. The inverted-game inference is validated on synthetic data to reveal potential caveats before proceeding to real-world ICU data applications that expose complexities of the data-generating process. Clinical data applications revealed that both breath-type consequences and their relative ordering are inherently context- and time-dependent, varying across patient subgroups, time, and comparison quantities, and effect timescale. The discussion includes potential developments toward a state transition model for simulating the effects of MV management actions using empirical data and game-inferred comparisons.

Abstract:Identifying the effects of mechanical ventilation (MV) protocols in critical care requires analyzing data from heterogeneous patient-ventilator systems in the clinical decision-making environment. Multiscale interactions among these coupled components generate a high-dimensional state space that remains sparsely sampled despite extensive data collection. Analysis of existing data is essential for understanding current respiratory management practices and generating testable hypotheses about improvement. The scale and complexity of available data motivate the use of reinforcement learning (RL) to explore data-consistent counterfactual trajectories. However, formulating RL in practical applications requires a spatiotemporally dependent reward process that defines state-to-consequence relationships, their context dependence, and the delays over which consequences emerge. These poorly understood elements are not known \emph{a priori} and inferred from data via hypotheses. To that end, categorized observed states are contrasted according to their relative consequences by solving a game-based inverse problem that identifies a comparison model required for downstream probabilistic and stochastic methods such as reinforcement learning for seeking MV optimization and personalization. The inverted-game inference is validated on synthetic data to reveal potential caveats before proceeding to real-world ICU data applications that expose complexities of the data-generating process. Clinical data applications revealed that both breath-type consequences and their relative ordering are inherently context- and time-dependent, varying across patient subgroups, time, and comparison quantities, and effect timescale. The discussion includes potential developments toward a state transition model for simulating the effects of MV management actions using empirical data and game-inferred comparisons.

Quantitative Methods (q-bio.QM); Machine Learning (cs.LG); Optimization and Control (math.OC)

arXiv:2510.15127 [q-bio.QM]

(or arXiv:2510.15127v3 [q-bio.QM] for this version)

From: J.N. Stroh [view email] [v1] Thu, 16 Oct 2025 20:32:52 UTC (3,183 KB) [v2] Sat, 22 Nov 2025 01:08:16 UTC (3,238 KB) [v3] Sat, 1 Aug 2026 16:02:05 UTC (3,150 KB)

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

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