Diagnostic Risk Scoring With Weighted Physiological Inputs
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Solution Overview
Problem
Conventional medical diagnosis of conditions like cancer is challenging due to the lack of clear symptoms and the need for comprehensive information that is often dispersed across multiple sources, making it difficult to identify the condition or appropriate diagnosis pathway efficiently.
Innovation Solution
A method using machine learning techniques, including a first model to determine risk values for medical conditions based on physiological values and demographic data, and a second model to select diagnosis pathways, utilizing Bayesian models to iteratively update and refine the models with patient data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional diagnostic methods are used, then medical practitioners can maintain simplicity in the diagnostic process, but diagnostic accuracy and efficiency deteriorate due to the inability to integrate dispersed information from multiple sources
Solution Approach 1:
The patent merges multiple information sources (patient records, clinical guidelines, research data) into a single integrated diagnostic system. The machine learning model consolidates dispersed data from various sources into a unified risk assessment framework, enabling comprehensive analysis without requiring practitioners to manually synthesize information from multiple separate systems.
Solution Approach 2:
The diagnostic system is designed with multi-functionality to handle diverse data types and diagnostic scenarios. The machine learning model can process various physiological values, patient factors, and clinical parameters simultaneously, providing a universal diagnostic tool that adapts to different medical conditions and diagnostic pathways while maintaining a single integrated interface.
2Reliability
If comprehensive information from multiple sources is integrated, then diagnostic accuracy improves, but the complexity of the diagnostic system increases
Solution Approach 1:
The patent replaces manual information integration processes with automated machine learning systems. Instead of requiring practitioners to manually collect, organize, and analyze data from multiple sources, the system uses computational algorithms to automatically process and synthesize information, substituting mechanical human effort with automated computational processes.
Solution Approach 2:
The diagnostic system performs self-service by automatically gathering, processing, and analyzing data from multiple sources without requiring manual intervention. The machine learning model autonomously integrates information from patient records, clinical guidelines, and research databases, generating diagnostic recommendations independently while providing support to practitioners.
3Productivity
If machine learning models are implemented to analyze multiple physiological values, then diagnostic efficiency improves, but computational complexity increases
Solution Approach 1:
The patent segments the diagnostic process into distinct computational stages handled by different machine learning models. The first model processes physiological values and patient factors to generate risk scores, while the second model analyzes clinical guidelines and research data to determine diagnosis pathways. This segmentation divides complex computational tasks into manageable modules, improving efficiency while organizing complexity.
Solution Approach 2:
The system performs preliminary actions by pre-processing and pre-analyzing data before final diagnostic determination. Machine learning models pre-calculate risk scores based on physiological values and pre-identify relevant diagnosis pathways from clinical guidelines, preparing structured information in advance that accelerates the final diagnostic decision-making 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.