Biomarker Knowledge Graph for Adaptive Health Interventions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern healthcare systems rely predominantly on pharmacological treatments, often sidelining other interventions that could synergistically improve long-term health outcomes, and lack a holistic, dynamic approach that integrates diverse biomarker data to tailor interventions over time.
Innovation Solution
A multi-modal biomarker framework that integrates biochemical, biophysical, diagnostic imaging, emotional, and behavioral data to generate a dynamic intervention plan, escalating through behavioral, nutritional, pharmacological, device-based, surgical, and genetic interventions to achieve biomarker ranges correlating with improved health and wellbeing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pharmacological treatments are used as the cornerstone of managing conditions, then symptom management and disease progression control are effective, but other proven interventions are sidelined and long-term outcomes are suboptimal
Solution Approach 1:
The patent combines multiple intervention types (behavioral, nutritional, pharmacological, device-based, surgical, and genetic) into a unified treatment framework. The system integrates these diverse interventions into a coordinated plan that addresses multiple aspects of patient health simultaneously, rather than relying solely on pharmacological treatments.
Solution Approach 2:
The treatment plan is dynamically adjusted based on longitudinal biomarker data. The system continuously monitors patient responses and adapts the intervention strategy over time, transitioning from static pharmacological prescriptions to dynamic, personalized treatment protocols that evolve with patient needs.
2Adaptability or versatility
If existing integrative medicine initiatives leverage genetic or biochemical markers, then treatment tailoring is improved, but the scope remains limited and multiple dimensions of biomarker data are not integrated
Solution Approach 1:
The system serves multiple functions by integrating various types of biomarker data (genetic, biochemical, biophysical, imaging, behavioral, and emotional) into a single comprehensive platform. This multi-functional approach allows the system to analyze diverse data types simultaneously and generate integrated treatment recommendations across multiple health dimensions.
Solution Approach 2:
The patent creates a composite biomarker profile by combining multiple types of biomarker data (genetic, biochemical, biophysical, imaging, behavioral, emotional) into a unified health assessment. This composite approach synthesizes information from different data sources to provide a holistic view of patient health status.
3Measurement precision
If clinical practice guidelines are siloed by specialty, then individual specialty expertise is maintained, but coordinated care and holistic health assessment are compromised
Solution Approach 1:
The digital platform acts as an intermediary that connects different specialty practices and coordinates their efforts. The system integrates inputs from various specialists while maintaining the precision of their individual assessments, and synthesizes these into a coordinated comprehensive care plan.
4Measurement precision
If a narrow slice of biomarker data is emphasized, then specific molecular insights are gained, but a complete snapshot of patient health is not achieved
Solution Approach 1:
The system creates a composite health profile by integrating multiple types of biomarker data including genetic, biochemical, biophysical, imaging, behavioral, and emotional markers. This composite approach maintains the precision of individual molecular insights while synthesizing them into a complete health assessment.
Data Source
AI summary
Provided are methods, systems including one or more processors, and computer-readable media storing instructions which, when executed by the system, cause the system to execute the method, the method including: obtaining biomarker data representing biomarker levels for different molecules for a patient; determining, based on the obtained data and using a knowledge graph linking intervention modules, target biomarker ranges, and outcomes, that at least one of the biomarker levels for the patient do not fall within the one or more target biomarker ranges; selecting, based on the obtained data and using the knowledge graph, one of the respective intervention modules associated with the biomarker level that does not fall within the target biomarker range; selecting an intervention associated with the selected intervention module; and generating, by an outputter, an output indicating the selected intervention.