Physiological Risk Weighting for Multi-Condition Diagnosis Pathways
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Solution Overview
Problem
Conventional medical diagnosis of conditions like cancer is challenging due to the absence of single symptoms directing clinicians to specific diagnoses, and the dispersed nature of relevant information, making early identification difficult.
Innovation Solution
A method using machine learning models to determine medical condition risks and provide diagnosis pathways based on physiological values, incorporating weighting and Bayesian models to refine risk assessments and pathway selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional diagnostic methods are used, then clinicians can maintain simplicity in the diagnostic process, but the ability to accurately identify medical conditions at early stages deteriorates due to vague and overlapping symptoms
Solution Approach 1:
The diagnostic system segments the complex task of medical diagnosis into multiple independent machine learning models, each specialized in evaluating specific symptoms or risk factors for particular medical conditions. This allows the system to handle complexity through modular components rather than a monolithic approach, improving diagnostic accuracy while maintaining manageable system complexity.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between the clinician and the diagnostic decision. These models process raw symptom data and generate structured risk assessments, acting as intermediaries that transform unstructured clinical information into actionable diagnostic insights without replacing the clinician's final decision-making authority.
2Measurement precision
If comprehensive information from multiple sources is collected to improve diagnosis, then diagnostic accuracy improves, but the complexity of information management and processing increases
Solution Approach 1:
The machine learning models are designed with multi-functionality to handle various types of diagnostic information (symptoms, risk factors, patient history) through unified processing frameworks. This universal approach allows the system to integrate diverse information sources without requiring separate processing mechanisms for each data type, reducing overall information processing complexity.
Solution Approach 2:
The system transforms diverse diagnostic information into standardized numerical parameters and risk scores that can be processed by machine learning algorithms. By converting qualitative clinical information into quantifiable parameters, the system simplifies information processing while maintaining diagnostic accuracy.
3Measurement precision
If multiple diagnostic pathways are considered to improve diagnosis accuracy, then the ability to identify appropriate treatment paths improves, but the time required for decision-making increases
Solution Approach 1:
The machine learning models perform preliminary analysis of diagnostic pathways by pre-calculating risk scores and probability assessments for multiple potential diagnoses based on initial symptom input. This preliminary action filters out unlikely diagnoses before the clinician needs to make final decisions, reducing the time required to evaluate multiple diagnostic pathways.
Solution Approach 2:
The system implements feedback mechanisms where the machine learning models continuously refine their assessments based on additional clinical information as it becomes available. This iterative feedback process allows the system to converge on the most likely diagnosis efficiently, reducing the time needed to evaluate multiple pathways by eliminating unlikely options early in the diagnostic process.
Data Source
AI summary
Disclosed herein is a method for diagnosing a medical condition in a patient. The method comprises: obtaining, from the patient, a plurality of physiological values; implementing a first model configured to determine risk values for at least one of a plurality of medical conditions, based on the physiological values. Implementing the first model comprises: obtaining a first risk value for the at least one medical condition, based on a first one of the obtained physiological values; and weighting the first risk value based on a second one of the obtained physiological values, to determine a total risk value of the at least one medical condition for the patient.