Immune Response Scoring for Biomarker-Based Therapy Selection
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Solution Overview
Problem
Clinicians face challenges in determining which patients are likely to benefit from immunotherapies due to the complexity of immune system variables and the difficulty in interpreting biomarker combinations, leading to potential adverse events and high costs.
Innovation Solution
A method and apparatus that determine measurable input parameters associated with immune system conditions, map them to scores, and combine these scores to predict patient response to therapies, incorporating confidence intervals and handling missing data to provide a transparent and interpretable decision support system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive knowledge of multiple immune oncology biomarkers and their combinations is used to assess patient suitability for immunotherapy, then the accuracy of predicting patient response improves, but the complexity of the assessment system increases
Solution Approach 1:
The patent segments the complex immune oncology assessment into distinct modular components: a first machine learning model that processes clinical data and generates a clinical score, and a second machine learning model that processes pathology data and generates a pathology score. These separate modules handle different types of input data independently, making the overall system more manageable and interpretable while maintaining comprehensive assessment capabilities.
Solution Approach 2:
The patent introduces intermediate scoring mechanisms (clinical score and pathology score) that act as mediators between the raw input data and the final treatment recommendation. These intermediate scores simplify the interpretation process by translating complex multi-parameter inputs into standardized numerical outputs that can be more easily integrated and interpreted by clinicians.
2Reliability
If multiple biomarkers and their combinations are analyzed to determine patient suitability for immunotherapy, then the reliability of treatment selection improves, but the difficulty of interpreting the results increases
Solution Approach 1:
The patent replaces manual clinical interpretation of complex biomarker combinations with automated machine learning models. These computational systems process multiple biomarkers simultaneously and generate standardized scores, eliminating the need for clinicians to manually interpret complex interactions between numerous immune oncology parameters while maintaining or improving assessment reliability.
3Ease of operation
If a structured mechanism is provided to assess patient information and applicable therapy, then the ease of clinical decision-making improves, but the device complexity increases
Solution Approach 1:
The patent extracts the complex computational processing from the clinician's workflow and places it within automated machine learning models. The system takes in raw clinical and pathology data, performs complex analyses internally, and outputs simplified treatment recommendations with confidence scores. This allows clinicians to benefit from sophisticated analysis without directly engaging with the computational complexity.
Data Source
AI summary
Aspects and embodiments relate to a method and apparatus configured to perform that method. The method relates to generating an indication of relevance associated with a selected biological phenomenon indicative of condition of an immune system of a patient, that selected biological phenomenon being associated with response of a patient to a chosen therapy. The method comprises steps of: determining one or more measurable input parameters characteristic of the patient and associated with the biological phenomenon; mapping the one or more input parameters to a contribution to a score associated with the selected biological phenomenon; and combining the mapped contributions across the one or more input parameters to calculate the score associated with the selected biological phenomenon. A decision support system in accordance with aspects and embodiments may be configured to provide a score and/or visualisation, for example, a numerical value or graphical representation, relating to one or more underlying biological phenomena contributing to immune response or immune condition of a patient to a therapy. The underlying biological phenomena may be quantified using various biomarkers and/or measurable input parameters.