Human Digital Twin Modeling for Multimorbidity Risk Prediction
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Solution Overview
Problem
Current diagnostic and treatment methods in precision medicine are based on population averages and mono-morbid approaches, leading to high uncertainty and potential diagnosis errors due to the complex topology of multimorbidity, which are not adequately addressed by existing AI and ML technologies.
Innovation Solution
A deterministic patient model, referred to as a Human Digital Twin (HDT), incorporating a base model and health attributes to simulate and predict individual patient outcomes using deterministic modeling, accounting for dynamic complexity and interdependent systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If population averages and mono-morbid approaches are used for diagnosis and treatment, then the methods are simple and based on established statistics, but the uncertainty level is high and diagnosis errors occur due to ignoring complex multimorbidity topology
Solution Approach 1:
The patent segments the complex health system into multiple independent modules including a base model layer, health attributes layer, and scenario parameters layer. Each module handles specific aspects of patient data (genetic traits, blood lipid profile, medical imaging, environmental factors), allowing the system to manage complexity through modular decomposition while maintaining high diagnostic reliability through integrated simulation.
Solution Approach 2:
The patent performs preliminary actions by pre-generating multiple scenario parameters and modeling potential health outcomes before actual clinical decisions are made. The system pre-calculates occurrence probabilities for various disease states and therapeutic responses, enabling clinicians to review predicted outcomes and make informed decisions in advance, thereby reducing diagnostic uncertainty.
2Reliability
If experience or static data is used to infer patient outcomes based on population averages, then the approach is straightforward, but a high level of uncertainty is an inescapable outcome that misses important circumstantial data
Solution Approach 1:
The patent adds a temporal and scenario-based dimension to static population data by simulating multiple possible future health states. Instead of relying on single-point statistical averages, the system models dynamic transitions across various health scenarios, capturing circumstantial data about how patient outcomes may evolve under different conditions, thereby reducing information loss about individual patient circumstances.
Solution Approach 2:
The patent systematically varies multiple parameters simultaneously (genetic traits, environmental factors, lifestyle variables, disease states) to generate diverse simulation scenarios. This multi-parameter approach captures the complex interactions between different circumstantial factors that static single-parameter statistical methods miss, improving prediction reliability by accounting for the full range of relevant variables.
3Measurement precision
If multiple scenario parameters are generated and modeled to predict individual patient outcomes, then precision medicine goals are achieved with higher certainty, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent divides the complex modeling system into distinct functional segments: a base model component that establishes fundamental health relationships, an attributes component that integrates patient-specific data, and a scenario simulation component that generates outcome predictions. This segmentation allows each module to be optimized independently while maintaining high overall prediction precision through their coordinated integration.
Solution Approach 2:
The patent creates a digital twin (virtual copy) of the patient's health system that can be simulated repeatedly under different scenarios without affecting the actual patient. This virtual copy encapsulates the complex relationships and parameters, allowing numerous simulations to be performed efficiently on the model rather than requiring complex real-world experimentation, thereby achieving high prediction precision with manageable system complexity.
Data Source
AI summary
A patient's health is modeled through multiple scenarios. A base model incorporates rules that govern a response by a human being to one or more diseases, as well as a relation between health metrics and the diseases. Health attributes of the patient are obtained. From the base model and the health attributes, a patient model is generated. The patient model is modeled under different parameters to generate health metrics. From an analysis of the parameters and the resulting health metrics, occurrence probabilities for each of the sets of parameters are determined. Risks are identified, which indicate a likelihood of the patient transitioning from the initial state to an adverse outcome such as a diseased state. A report provides a diagnosis of the patient and one or more remedies/interventions that are predicted, based on the plural sets of parameters and resulting health metrics, to avoid or prevent an adverse outcome.