Machine Learning Classification for Lower Urinary Tract Symptoms
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Solution Overview
Problem
Current diagnostic methods for lower urinary tract symptoms (LUTS) are inefficient and inaccurate due to the lack of definitive tests or markers, leading to subjective patient-reported symptoms and significant symptomatic overlap between conditions like interstitial cystitis/painful bladder syndrome (IC/BPS) and overactive bladder (OAB), which complicates accurate diagnosis and treatment.
Innovation Solution
The implementation of machine learning algorithms, specifically unsupervised and supervised learning models, to classify patient data from questionnaires and demographic information into novel diagnostic categories, enabling more accurate and efficient diagnosis of LUTS by identifying distinct disease clusters and severity levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic methods based on subjective patient-reported symptoms are used, then the diagnostic process is simple to perform, but the diagnostic accuracy is low due to significant symptomatic overlap between conditions
Solution Approach 1:
The patent replaces traditional manual diagnostic methods with an automated machine learning classification system. The system uses trained algorithms to process patient symptom data and objectively classify LUTS conditions, substituting the subjective clinical judgment process with an automated computational system that applies consistent classification criteria across all patients.
Solution Approach 2:
The patent introduces a machine learning classification system as an intermediary between patient symptom reporting and final diagnosis. This intermediary processes the raw symptom data through trained algorithms that have learned to distinguish between different LUTS conditions, providing an objective bridge that improves diagnostic accuracy while maintaining ease of patient participation.
2Measurement precision
If specialist referral is used for final diagnosis, then diagnostic accuracy may be improved, but the time to diagnosis and treatment is significantly delayed
Solution Approach 1:
The patent enables the diagnostic system to perform classification independently without requiring specialist intervention for every patient. The machine learning model processes patient data and provides diagnostic classifications automatically, allowing the system to serve itself for routine diagnoses while potentially referring only complex cases to specialists, thereby reducing overall wait times.
Solution Approach 2:
The patent implements preliminary automated classification of LUTS conditions before specialist referral is considered. The machine learning system pre-processes patient data and provides initial diagnostic classifications, allowing specialists to review only cases that require their expertise, thus reducing the time loss associated with universal specialist referral while maintaining diagnostic accuracy for complex cases.
3Measurement precision
If traditional classification categories are used, then the diagnostic framework is simple to implement, but the ability to accurately distinguish between overlapping conditions is limited
Solution Approach 1:
The patent segments the broad category of LUTS into distinct diagnostic classes using machine learning. The system divides storage LUTS into specific categories such as overactive bladder, interstitial cystitis/bladder pain syndrome, and other storage LUTS, creating finer-grained classifications that enable better differentiation between conditions with overlapping symptoms while maintaining a structured diagnostic framework.
Solution Approach 2:
The patent changes the parameters used for classification from simple symptom presence/absence to multi-dimensional symptom profiles processed by machine learning algorithms. The system analyzes multiple symptom parameters simultaneously and applies learned weightings and relationships to distinguish between conditions, transforming the classification approach from crude categorical sorting to nuanced parameter-based differentiation.
Data Source
AI summary
Systems and methods are disclosed for diagnosis and treatment of urinary tract symptoms into machine learning based clusters. In some examples, a diagnostic questionnaire is processed by a machine learning model to evaluate a patient's urinary tract health condition and determine a diagnosis based on one or more indications of urinary tract health of the patient. In one example, the machine learning model is trained using datasets labelled according to one or more diagnostic clusters generated by an unsupervised learning model, such as a clustering model. In some examples, a measure of severity of the diagnosis is output by the machine learning model or a second machine learning model.