A Next?Generation Framework for Adaptive, Transparent, and Physiology?Driven Clinical Decision?Support

Research By: Abhishek Sehgal

Last Updated: 07 February 2025

Confidential: This document is the exclusive property and intellectual property of Abhishek Sehgal. It details one of the approaches in DREAM (MedTwin.AI) project which is developing a AI which creates a medical digital twin of the human body with mathematical modelling and auditable chain of thought. This is a short brief from the overall research.

Abstract

This white paper aims to brief on the blueprint for DREAM project’s Control Biomedical AI system, VECTOR. Designed to simulate and predict complex physiological states in real time by harnessing an interdisciplinary foundation. VECTOR is inspired by physical laws, neuroscience cognitive processes, and engineering techniques to fuse heterogeneous data into actionable clinical insights, research simulations and drug/disease modelling. The AI engine framework blueprints on the least?action principle and free?energy minimization (Friston, 2010) alongside dopamine reward prediction error signals (Schultz, Dayan, & Montague, 1997) and dual?memory architectures (Doya, 2002) to underpin its adaptive learning, with attention heirarchy and context explosion principles to manage structured “Knowledge” and expand “Intelligence” in critical thought and accuracy. By constructing digital twins of patient states, and tying them together in sequence, using techniques such as model predictive control (Camacho & Bordons, 2004) and digital twin simulations (Tao et al., 2019), VECTOR decomposes incoming multi?modal data into hierarchical and graph based nodes which form sub-sectionalised equations of human physiology and chemistry, while tying into a dependent whole with imputed data and structure to form a digital twin. Critical to this is the transparency and detailing of chain?of?thought log (McClelland et al., 2020) tracking every single decision for output, while continuous feedback loops-powered by real?time Bayesian updates (Gelman et al., 2013) and refined via methodologies described in Ramstead et al. (2022)- ensure the system self?refines while rigorously adhering to first?principles. The system digests research publications, and feeds on patient cluster patterns developing and testing equations until variability is eliminated.

1. Introduction

Modern healthcare now captures an ever?expanding array of data, from high?resolution imaging and genomic sequencing to laboratory tests, medication histories, and lifestyle metrics. Yet synthesizing this diverse information into precise, actionable insights remains a formidable challenge, which can now be solved at scale with a merging of LLM AI models and rule-based structuring of underlying “Medical” infrastructure. Traditional models, often focused on single?disease paradigms or simple correlations, struggle to capture the full spectrum of the body’s dynamic interactions. We aim to create a set blueprint for replication and expansion of the human body as a one whole equation which has millions of accounted variable and empirical precedence tied into a tiered logic and context structure.

We do this by unifying first?principles modeling, continuous data ingestion, and multi?agent orchestration into a single framework that generates patient state models (Twins) that are then simulated, and structured to find outcomes and validate precision pathways to diagnostics, treatement and reversion of damage (Zhou et al., 2022). These digital twins not only reflect real?time physiological states but also forecast potential future outcomes, thereby identifying clinical risks before they become apparent. A distinguishing feature of VECTOR is its use of an “ideal state” - an approximation of youthful, optimal physiology- as a “Target State” to assess deviations and guide interventions from preservation and management to restoration. Detailed scenario analyses and distribution curves derived from clinical evidence and recent research (Pandey et al., 2024; Nye, 2023; Tang et al., 2023) enable tailored pharmacological and lifestyle strategies that steer patients toward improved health. Alongside modelling multi-drug and surgery intervention and complexities of outcomes, avoiding cascading failure when restoring say hormonal imbalance in testosterone levels.

(Sel, K., Mohammadi, A., Pettigrew, R. I., & Jafari, R. et al., 2023) Physics-informed neural network example (Layered functions embedded into the Engine)

2. System Architecture and Detailed Mechanisms

VECTOR operates as a closed?loop system in which every module continuously interacts to update the overall model state. Data ingestion begins with the capture of multi?modal inputs - from continuous sensor streams (e.g., continuous glucose monitors and wearable devices) to periodic lab results and clinical notes. Physics?informed neural networks (such as CNNs and LSTMs) process these diverse inputs, enforcing constraints (for example, ensuring non?negative blood pressure values) to produce a comprehensive state vector complete with uncertainty estimates. These variances are throttled, and integrated with MHA span structure on synergy matrices.

This state vector is then decomposed into atomic context nodes that represent fundamental clinical facts—individual vital signs, lab values, and similar data points. These nodes are organized hierarchically within a dynamic knowledge graph, while specialized agents continuously update, merge, or split nodes as new data become available. Every decision is recorded in a transparent chain?of?thought log (McClelland et al., 2020), providing a full audit trail that facilitates review by clinicians and developers. This is further expanded upon with multiple layers, having an equational, disease, drug and non-drug layer on top of baseline modelling.

Multiple orchestrator functions coordinates specialized rule-based engines (Agents)—ranging from neuro?symbolic reasoning modules to reinforcement learning agents inspired by dopamine reward prediction error signals (Schultz et al., 1997) and generative models that draft explanations and simulate alternative scenarios. Continuous communication among these agents ensures that any discrepancies trigger immediate re?evaluation. Concurrently, a digital twin of the patient is simulated using physics?informed differential equations. This twin not only forecasts future states through stochastic and counterfactual analysis but also informs therapeutic planning via methods such as model predictive control (Camacho & Bordons, 2004) and Monte Carlo tree search.

Safety is enforced through a rule?based validation process that compares each intervention against established clinical guidelines and verifies compliance with first?principles (e.g., mass?balance and receptor saturation limits). Any violation triggers alerts for manual review. Once recommendations are generated, they are either presented to clinicians with detailed explanations or directly implemented in closed?loop systems, with continuous monitoring and feedback loops (Gelman et al., 2013; Ramstead et al., 2022) ensuring iterative refinement. The system grows in reasoning and intelligence with self-restructuring for accuracy rewarded by principle establishment where baseline building blocks are presented and proven.

3. Data Ingestion, Contextual Integration, and Interdependency Mapping (Context Engine Only)

Data ingestion in VECTOR is both robust and multifaceted. Real?time streams from sensors, such as continuous glucose monitors and wearable devices, are captured via advanced frameworks (e.g., Apache Kafka), while periodic laboratory results and electronic health record extracts are processed through dedicated ETL pipelines. Concurrently, language model–assisted pipelines embed new research documents, facilitating semantic retrieval and the rapid integration of emerging findings (Nye, 2023).

Raw data is normalized using dimensionless δ-factors, for example:

;which standardizes measurements across modalities. Each measurement is then transformed into a context node enriched with metadata (timestamp, source, units) and organized within a dynamic knowledge graph (e.g., implemented using Neo4j). Unstructured text is embedded into vector representations and stored in a vector database such as Pinecone, enabling rapid and accurate semantic searches.

The overall computational flow is organized as a layered, interdependent hierarchy. Initially, raw data is normalized into δ?factors, then segmented into atomic nodes, and finally processed by disease?specific modules that compute specialized factors for conditions like type 2 diabetes, chronic kidney disease, autoimmune disorders, and neurological diseases. Both pharmacological and non?pharmacological interventions are modeled and combined within a multi?agent synergy matrix. Overlay validation continuously compares the full model’s predictions against simpler, well?validated baselines. The aggregated and validated synergy then updates the patient state via digital twin simulations, with continuous feedback from observed outcomes driving further refinement.

To prevent cascading failures, the system incorporates safeguards such as automatic reversion to simpler models when advanced expansions breach physiological constraints, variance throttling to diminish the influence of components with high uncertainty, and a directed acyclic graph (DAG) structure to avoid cyclic dependencies and infinite feedback loops.

4. Iterative Refinement, Clinical Validation, and Integration

VECTOR continuously learns and refines its models through a combination of online updates, utilizing techniques such as particle filters and reinforcement learning and offline retrospective analyses. Bayesian inference (Gelman et al., 2013) plays a crucial role in fine?tuning model parameters by comparing predictions with real?world outcomes. Each discrepancy is recorded in the chain?of?thought log, ensuring that the system evolves based on actual performance.

Clinical validation is achieved through retrospective analyses of historical data as well as prospective pilot trials in clinical settings. In every instance, major treatment recommendations or parameter updates are subject to a human?in?the?loop review by clinicians, with automated alerts and audit trails ensuring patient safety. Overlay validation continuously compares advanced models with simpler baselines; if discrepancies exceed predetermined thresholds, the system reverts to the more reliable approach.

Integration with clinical systems is seamless. Standardized protocols, such as HL7 FHIR, facilitate continuous data exchange with electronic health records and wearable devices. Clinicians are provided with real?time dashboards that display key physiological variables, synergy parameters, and detailed chain?of?thought logs, while scenario testing tools enable the simulation of various intervention impacts. Comprehensive audit logging and robust safety checks ensure both regulatory compliance and the highest standards of patient care.

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