Physiological World Models for Human State Transitions

• URL: http://arxiv.org/abs/2608.15309v1
• Date: Sat, 15 Aug 2026 16:26:10 GMT
• Title: Physiological World Models for Human State Transitions
• Abstract summary: We propose the Physiological World Model (PWM), an event-conditioned framework for learning changes at the level of the whole person.<n>We describe four capability levels, from state representation to bounded intervention planning, together with four data acquisition and validation protocols.<n>This framework provides a practical path towards personalized health management, behavioural intervention design and clinician-supervised decision support.
• Score: 16.270018313593514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continuous multimodal sensing now allows human physiology to be observed throughout daily life rather than only during occasional clinical visits. However, most health artificial intelligence systems are designed to recognize current states, estimate risks or analyse individual biomarkers. They do not directly model how physiological states change in response to real-world events, behaviours, contexts and interventions. Here we propose the Physiological World Model (PWM), an event-conditioned framework for learning these changes at the level of the whole person. We introduce the HumanState Transition Token, a structured, quality-scored unit that connects the physiological state before an event with the event or action, relevant context and intervention information, the physiological trajectory after the event, observed outcomes and data quality. We describe four capability levels, from state representation to bounded intervention planning, together with four data acquisition and validation protocols. We also propose six benchmark tasks covering HumanState representation, forecasting across multiple timescales, individualized response prediction, simulation of alternative interventions, bounded planning and reliability under distribution shift. Together, this framework provides a practical path towards personalized health management, behavioural intervention design and clinician-supervised decision support, while clearly separating prediction from causal inference and making uncertainty, safety, governance and limits of use explicit.

Related papers

• Medical world models in healthcare: foundations, applications, and challenges for trustworthy clinical translation [9.298791886645303]
  Medical world models offer a framework for extending medical artificial intelligence beyond static prediction.<n>This Review defines the conceptual boundaries, technical foundations, application domains, and evidence requirements of the field.
  arXiv  Detail & Related papers  (2026-07-28T03:35:47Z)
• You Are in Control of Your State: Why Human Outcomes Are Controllable Through Causal State Intervention [1.1470070927586018]
  We argue that human outcomes are controllable in a precise and operational sense through interventions that target the state and its weighting at the moment a decision is being formed.<n>We derive seven testable predictions, list six operational requirements for state-aware systems, and discuss implications for digital health, education, AI personalisation, and personal agency.
  arXiv  Detail & Related papers  (2026-05-26T18:49:41Z)
• From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction [61.12883122613684]
  This Review focuses on intervention-aware disease trajectory modeling in clinical AI--methods estimating patient-specific longitudinal disease evolution and assessing trajectory changes under alternative treatments.<n>We organize the field around six linked components: three decision tasks (factual forecasting, counterfactual estimation, policy evaluation) and three data-generating mechanisms (disease evolution, treatment assignment, observation process) that determine identifiability.<n>We present the first unified framework bridging forecasting, counterfactual trajectories, and policy evaluation across discrete/continuous time, explicitly addressing treatment assignment, time-varying confounding, and observation bias.
  arXiv  Detail & Related papers  (2026-05-16T10:45:26Z)
• Toward World Modeling of Physiological Signals with Chaos-Theoretic Balancing and Latent Dynamics [11.273542534850506]
  We introduce NormWear-2, a world model that encodes both multivariate physiological signals and clinical intervention variables into a shared latent space.<n>Our approach combines inference from prior pre-trained knowledge (intuition) with instant non-parametric latent state transition adaptation (insight)<n>We evaluate the world model performance across diverse real-world physiological datasets spanning heterogeneous temporal resolutions and intervention regimes.
  arXiv  Detail & Related papers  (2026-05-14T23:06:15Z)
• Simulating clinical interventions with a generative multimodal model of human physiology [49.828582089394445]
  HealthFormer is a decoder-only transformer that models the human physiological trajectory generatively.<n>We tokenise each participant's health trajectory across 667 measurements spanning seven domains.<n>HealthFormer improves prediction for 27 of 30 incident-disease and mortality endpoints, exceeding established clinical risk scores in every comparison.
  arXiv  Detail & Related papers  (2026-04-30T14:10:30Z)
• From Brain Models to Executable Digital Twins: Execution Semantics and Neuro-Neuromorphic Systems [51.82266520875928]
  This survey introduces physically constrained executability as a unifying perspective for comparing approaches at the level of execution.<n>I propose a taxonomy of execution regimes ranging from isolated offline models to coordinated co-simulation, to continuously executing digital twins sustained by online data.<n>The executability concept clarifies why accuracy alone is insufficient, and motivates an agenda centered on semantic interoperability, hybrid-time correctness, evaluation protocols, scalable assimilation, and safe closed-loop validation.
  arXiv  Detail & Related papers  (2026-04-15T07:34:46Z)
• From Prediction to Intervention: The Evolution of AI in Biomedicine [67.45502291821956]
  We argue that AI in biomedicine is undergoing a structural transition.<n>We define disease-level models as systems that explicitly represent the state, dynamics, and intervention response of biological processes.<n>It follows directly from the structure of biomedical decision-making and defines the next stage of AI in medicine.
  arXiv  Detail & Related papers  (2026-04-14T17:49:51Z)
• Toward World Models for Epidemiology [45.965729493797824]
  We argue that computational epidemiology is a natural and underdeveloped setting for world models.<n>We introduce a conceptual framework for epidemiological world models, formulating epidemics as controlled, partially observed dynamical systems.<n>We present three case studies that illustrate why explicit world modeling is necessary for policy-relevant reasoning.
  arXiv  Detail & Related papers  (2026-04-10T17:39:20Z)
• CLARITY: Medical World Model for Guiding Treatment Decisions by Modeling Context-Aware Disease Trajectories in Latent Space [49.74032713886216]
  CLARITY is a medical world model that forecasts disease evolution directly within a structured latent space.<n>It explicitly integrates time intervals (temporal context) and patient-specific data (clinical context) to model treatment-conditioned progression as a smooth, interpretable trajectory.
  arXiv  Detail & Related papers  (2025-12-08T20:42:10Z)
• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)

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.