Personalized Treatment Planning via Deformed Efficacy Models
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Solution Overview
Problem
Current medical therapies and treatments are often tested statistically and do not account for the unique genetic and physiological characteristics of individual patients, rendering personalized medicine impractical or infeasible.
Innovation Solution
Methods and systems that generate personalized treatment plans by deforming generic efficacy-estimation functions using patient-specific information and limited experimental data, optimizing control variables to create patient-specific efficacy-estimation functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If statistical testing methods are used for treatment efficacy evaluation, then general applicability is improved, but patient-specific accuracy deteriorates
Solution Approach 1:
The patent applies local quality by transforming a generic efficacy-estimation function into a patient-specific function through deformation using individual patient characteristics (genetic markers, physiological parameters, treatment history). This allows the system to maintain a universal base model while locally adapting it to each patient's unique attributes, thereby achieving both general applicability and patient-specific accuracy
Solution Approach 2:
The system changes parameters by adjusting the efficacy-estimation function based on patient-specific data including genetic markers, physiological parameters, and treatment responses. The deformation process modifies function parameters to reflect individual patient characteristics, transitioning from population-level statistical averages to personalized predictions
2Measurement precision
If personalized treatment evaluation is implemented, then treatment accuracy for individual patients is improved, but system complexity deteriorates
Solution Approach 1:
The system performs preliminary action by pre-collecting and storing patient characteristics data (genetic markers, physiological parameters, treatment history) and pre-processing this information into structured formats. This preliminary preparation enables the deformation process to efficiently generate patient-specific efficacy functions without requiring complex real-time computations during treatment decision-making
Solution Approach 2:
The patent introduces an intermediary deformation process that bridges the gap between simple statistical models and complex personalized evaluations. This intermediary layer transforms generic efficacy estimates into patient-specific predictions by incorporating individual characteristics through a systematic deformation approach, avoiding the need for entirely complex personalized models while still achieving accuracy
3Measurement precision
If extensive patient experimentation is conducted to personalize treatment, then efficacy estimation accuracy is improved, but time consumption and cost deteriorate
Solution Approach 1:
The system applies partial action by using a limited set of key patient characteristics (selected genetic markers, relevant physiological parameters, and prior treatment responses) rather than requiring exhaustive experimentation. The deformation process efficiently leverages this partial information to generate sufficiently accurate patient-specific efficacy functions without the need for extensive time-consuming trials
Solution Approach 2:
The patent uses copying by creating patient-specific efficacy functions as deformations of a proven generic efficacy-estimation function. Rather than developing entirely new models for each patient through extensive experimentation, the system copies the validated general model and adapts it to individual patients, significantly reducing the time and resources required while maintaining accuracy
Data Source
AI summary
The current document is directed to methods and systems that generate personalized treatment and therapy plans for patients. Currently disclosed implementations of these methods and systems maintain one or more databases that store general patient information as well as information about different types of treatments and therapies, including generic and patient-specific efficacy models that provide estimates of the efficacy of a treatment plan prior to application of the treatment encoded in the treatment plan. Based on the results of a limited number of experiments conducted on a particular patient, on extensive treatment histories for large numbers of patients, and/or on the treatment history for the particular patient, the currently disclosed methods and systems generate a treatment plan by deforming a generic efficacy model and then using the deformed model to identify optimal or near-optimal values for control variables that together represent the treatment plan.