Statistical Classifier for IBS Biomarker Diagnosis
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Solution Overview
Problem
Diagnosing irritable bowel syndrome (IBS) is challenging due to its similar symptoms with other intestinal diseases or disorders, leading to difficulty in differentiating IBS from conditions like inflammatory bowel disease (IBD), which hampers early and effective treatment.
Innovation Solution
A method involving the detection of specific diagnostic markers such as TNF-related weak inducer of apoptosis (TWEAK) and other biomarkers, combined with symptom profiling, using a learning statistical classifier system to classify samples as IBS or non-IBS, aiding in accurate diagnosis and differentiation from other intestinal disorders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic methods based on symptom profiling alone are used, then the diagnostic process remains simple and accessible, but diagnostic accuracy is insufficient due to symptom overlap with other intestinal diseases
Solution Approach 1:
The diagnostic approach segments the evaluation into multiple independent components: symptom profiling, biomarker detection (such as fecal calprotectin, lactoferrin, or inflammatory markers), and statistical classification. Each component addresses a specific aspect of diagnosis, allowing the system to handle complexity systematically while improving overall accuracy through integrated analysis.
Solution Approach 2:
Biomarkers serve as intermediary elements that bridge the gap between symptom presentation and definitive diagnosis. These objective markers (e.g., fecal calprotectin levels, lactoferrin concentrations) provide measurable data that mediates between the subjective symptom report and the need for accurate differentiation from other intestinal conditions, enabling more precise classification without requiring complex invasive procedures.
2Reliability
If multiple biomarkers and statistical algorithms are integrated for diagnosis, then differentiation between IBS and other intestinal diseases improves, but the complexity of the diagnostic system increases
Solution Approach 1:
The diagnostic system merges multiple data sources—symptom profiles, biomarker measurements (such as fecal calprotectin, lactoferrin, inflammatory markers), and statistical classification algorithms—into a unified diagnostic framework. This integration allows the system to leverage the strengths of each component while achieving reliable differentiation between IBS and other intestinal diseases through combined analysis.
Solution Approach 2:
The system utilizes changes in biomarker parameters (such as fecal calprotectin concentration, lactoferrin levels, or inflammatory marker ratios) as objective indicators to differentiate disease states. By monitoring and comparing these parameter changes against established thresholds and patterns, the system achieves reliable diagnosis while managing complexity through standardized measurement criteria.
3Measurement precision
If a learning statistical classifier system is used to classify samples, then diagnostic accuracy and differentiation capability improve, but the complexity of data processing and analysis increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing data into structured formats before classification. Symptom profiles are standardized, biomarker measurements are normalized, and reference ranges are pre-established. This preliminary organization simplifies the subsequent statistical classification process while maintaining high accuracy, as the learning algorithm receives ready-to-analyze structured data rather than raw, unprocessed information.
Data Source
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AI summary
The present invention provides methods, systems, and code for accurately classifying whether a sample from an individual is associated with irritable bowel syndrome (IBS). In particular, the present invention is useful for classifying a sample from an individual as an IBS sample using a statistical algorithm and/or empirical data. The present invention is also useful for ruling out one or more diseases or disorders that present with IBS-like symptoms and ruling in IBS using a combination of statistical algorithms and/or empirical data. Thus, the present invention provides an accurate diagnostic prediction of IBS and prognostic information useful for guiding treatment decisions.