Collaborative Medical Diagnosis System Using Probabilistic Rules Graph
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Solution Overview
Problem
Healthcare professionals face challenges in efficiently diagnosing diseases and recommending treatments due to the vast and rapidly changing volume of medical data, limited access to datasets, and incompatible data formats, which hinders their ability to provide timely and accurate patient care.
Innovation Solution
A machine-learned collaborative medical diagnosis system that collects and assembles medical data from multiple clinicians, creating a medical knowledge database with a probabilistic rules graph, which is augmented with individual patient treatment outcomes to provide diagnoses and treatment plans, including lifestyle interventions and drug therapies, based on demographic and clinical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If medical data is collected from multiple clinicians and assembled into a comprehensive database, then diagnosis accuracy and treatment effectiveness are improved, but data complexity and integration difficulty increase
Solution Approach 1:
The patent segments medical data into structured categories (patient demographics, clinical measurements, treatment outcomes, lab results) and organizes them into a hierarchical database structure. This segmentation allows clinicians to query specific data types without being overwhelmed by the entire dataset, resolving the contradiction between comprehensive data collection and data management complexity.
Solution Approach 2:
The patent introduces a machine learning intermediary that automatically processes, validates, and integrates data from multiple clinicians. This intermediary layer handles the complexity of data harmonization, format standardization, and quality control, enabling accurate diagnoses without requiring manual data integration efforts.
2Reliability
If medical data is collected and stored for comprehensive analysis, then treatment recommendations are improved, but data security and privacy protection challenges increase
Solution Approach 1:
The patent extracts and separates personally identifiable information (PII) from clinical data, storing it in a secure, access-controlled repository distinct from the clinical database. This extraction allows comprehensive data analysis for improved treatment recommendations while minimizing privacy exposure by limiting PII access to only when absolutely necessary.
Solution Approach 2:
The patent implements automated data anonymization and encryption systems that operate without manual intervention. These self-service security measures automatically protect data privacy through de-identification, encryption at rest and in transit, and automated access logging, enabling reliable treatment recommendations while mitigating privacy risks through systematic protection.
3Productivity
If machine learning techniques are applied to analyze medical data, then identification of trends and emerging therapies is improved, but computational resources and processing time are increased
Solution Approach 1:
The patent applies preliminary data preprocessing, cleaning, and feature extraction before feeding data into machine learning models. This preliminary action reduces data dimensionality and eliminates irrelevant information, enabling more efficient machine learning processing that identifies trends and emerging therapies with reduced computational resource consumption.
4Reliability
If access to certain datasets is limited to subscribers and hospital systems, then data security is maintained, but data accessibility and collaboration are reduced
Solution Approach 1:
The patent creates a multi-functional data access system that serves multiple purposes: secure authenticated access for subscribed clinicians, anonymized aggregate data access for researchers, and automated machine learning access for analysis. This universal system maintains security through role-based access control while enabling broad data accessibility and collaboration across different user types.
Data Source
AI summary
A medical knowledge database including medical knowledge information, medical diagnoses, and medical treatments, is used for machine learning for collaborative medical data metrics. Medical data is collected from a plurality of clinicians serving a first plurality of patients and assembling a medical knowledge database that includes medical knowledge information, medical diagnoses, and medical treatments. The medical knowledge database is a function of demographics and comprises a medical probabilistic rules graph. The medical knowledge database is augmented based on further medical data collected from a second plurality of clinicians. The further medical data is based on individual patient treatment outcomes collected by the second plurality of clinicians. Medical data from a further patient is applied to the medical probabilistic rules graph. A medical diagnosis is provided, based on the medical data applied from a further patient to the rules graph. The medical diagnosis is used to institute a treatment plan.