Logic-Based MS Type Inference from Noisy Assessment Data
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Solution Overview
Problem
Current diagnostic methods for multiple sclerosis are inadequate in accurately and consistently detecting the type of multiple sclerosis a subject has at a given time, leading to delayed and incorrect treatment decisions, misdiagnosis, and improper clinical study enrollment.
Innovation Solution
A computing system that processes noisy assessment data using type inference logic to select code instances, determine temporal dynamics, and generate alerts for predicted transitions between multiple sclerosis types, thereby inferring the correct type of multiple sclerosis and outputting this information for treatment selection and clinical study enrollment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual diagnostic methods are used to determine multiple sclerosis type, then clinical judgment can be applied, but diagnostic accuracy and consistency deteriorate due to data noise and heterogeneity
Solution Approach 1:
The patent replaces manual clinical judgment with an automated computing system that processes noisy assessment data. The system uses code detection, temporal dynamic analysis, and modulation calculation to objectively determine MS type, substituting the mechanical process of manual diagnosis with an automated information processing system that eliminates human variability and improves diagnostic consistency.
Solution Approach 2:
The patent introduces an intermediary computing system that bridges the gap between noisy clinical data and accurate diagnosis. This intermediary processes the heterogeneous data through multiple stages (code detection, temporal analysis, modulation calculation) to produce a reliable MS type classification, acting as a mediator that transforms raw noisy data into actionable diagnostic information.
2Loss of information
If comprehensive assessment data is collected to improve diagnosis, then diagnostic information increases, but data noise and inconsistency increase
Solution Approach 1:
The patent extracts meaningful diagnostic signals from noisy comprehensive data through a multi-stage process. It detects specific codes related to MS types, isolates relevant temporal dynamics, and calculates modulations that highlight true disease patterns. This extraction process separates useful diagnostic information from the noise inherent in comprehensive assessment data, maintaining both completeness and reliability.
Solution Approach 2:
The patent employs feedback mechanisms where the system continuously refines its analysis by comparing detected codes against expected patterns for different MS types. The temporal dynamic analysis and modulation calculations provide feedback loops that adjust the interpretation of noisy data, improving reliability by validating findings against multiple criteria and correcting for data inconsistencies.
3Productivity
If traditional diagnostic approaches are used, then clinical workflow is maintained, but treatment selection efficiency deteriorates due to delayed and incorrect diagnosis
Solution Approach 1:
The patent performs preliminary automated analysis of assessment data to pre-determine MS type before clinical decision-making. By detecting codes, analyzing temporal dynamics, and calculating modulations in advance, the system prepares diagnostic results ahead of time, enabling faster treatment selection and reducing the time loss associated with traditional diagnostic approaches.
Solution Approach 2:
The patent replaces time-consuming manual diagnostic processes with automated computing system analysis. The system rapidly processes comprehensive assessment data through code detection and temporal analysis, producing MS type classifications much faster than manual methods, thereby improving treatment selection efficiency without sacrificing diagnostic accuracy.
Data Source
AI summary
Techniques disclosed herein relate to inferring a type of multiple sclerosis and determining whether to output an alert based on codes detected within noisy assessment data corresponding to a subject. The noisy assessment data includes content originating from a care provider that identifies a characteristic of the subject or of a treatment for the subject, and the subject has been diagnosed with multiple sclerosis. A temporal dynamic or distribution is determined based on instances of the code detection, and a modulation is determined based on the temporal dynamic or the distribution. It is determined whether an alert criterion is satisfied based on whether the modulation is above a threshold so as to represent noise or a predicted transition across types of multiple sclerosis. The inferred type of multiple sclerosis is output. When the alert criterion is satisfied, an alert is output as well.