Machine Learning Prognosis Prioritization for Complex Diagnostics
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Solution Overview
Problem
Comprehensive diagnoses often go untreated due to the overwhelming complexity of factors required for accurate prioritization, leading to a lack of understanding and application of current knowledge and guidelines.
Innovation Solution
A system and method utilizing a computing device to receive user biological markers, generate a classification machine-learning model, determine diagnostics, rank them using statistical processes, and provide an optimal treatment instruction set through a graphical user interface, incorporating simulation machine-learning processes to prioritize prognoses and generate treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive diagnoses are prioritized using multiple factors, then diagnostic accuracy is improved, but system complexity increases making it overwhelming to stay current
Solution Approach 1:
The system segments the complex diagnostic process into distinct functional modules: a classification machine-learning model for determining diagnostics from biological markers, a statistical machine-learning process for ranking diagnostics with figure of merit calculations, and a simulation machine-learning process for generating prognoses. This segmentation allows each module to handle specific aspects of the diagnostic workflow independently, reducing overall system complexity while maintaining comprehensive diagnostic accuracy.
Solution Approach 2:
The patent introduces an intermediary computational system that acts as a mediator between raw biological marker data and clinical decision-making. This intermediary system automatically processes multiple factors through machine-learning models, generating prioritized diagnostic rankings and treatment recommendations, thereby shielding clinicians from the overwhelming complexity of manually evaluating numerous diagnostic factors.
2Reliability
If comprehensive diagnoses are treated with multiple factors considered, then treatment effectiveness is improved, but the time and resources required increase
Solution Approach 1:
The system performs preliminary actions by pre-processing biological marker data through the classification machine-learning model to determine diagnostics before clinical evaluation. The statistical machine-learning process pre-ranks diagnostics with figure of merit scores, and the simulation machine-learning process pre-generates treatment recommendations. This preliminary automated processing reduces the time required for comprehensive treatment planning while maintaining effectiveness by considering multiple factors in advance.
Solution Approach 2:
The patent replaces manual mechanical processes of diagnostic evaluation and treatment planning with automated machine-learning systems. The classification model automatically determines diagnostics from biological markers, the statistical model automatically ranks them, and the simulation model automatically generates prognoses and treatment recommendations. This substitution eliminates time-consuming manual analysis while preserving comprehensive factor consideration, thereby improving treatment effectiveness without proportionally increasing time investment.
3Productivity
If machine-learning models are used to automate diagnosis, then productivity is improved, but measurement precision may be compromised
Solution Approach 1:
The system incorporates feedback mechanisms where the classification machine-learning model's diagnostic determinations are refined by the statistical machine-learning process through figure of merit calculations. The simulation machine-learning process further refines outcomes by generating prognoses based on treatment simulations. This multi-layered feedback structure allows the system to iteratively improve diagnostic accuracy while maintaining high productivity through automated processing, ensuring that machine-learning outputs are continuously validated and refined.
Data Source
AI summary
A system for prioritizing comprehensive prognoses and generating an associated treatment instruction set, the system comprising a computing device configured to receive at least a user biological marker and select a prognosis using a classification machine-learning model and an associated diagnostic from a biological marker database. Computing device may rank a diagnostic, wherein ranking further comprises a statistical machine-learning process to determine a figure of merit of a diagnostic for a biological marker. Computing device may use a supervised machine-learning process to select a prognosis according to a figure of merit and generate a treatment, ranking an instruction set of the treatment, and simulate the instruction set using a simulation machine-learning process to generate a prognosis, determining a rank for a prognosis, and providing the instruction set that results in the optimal prognosis. Computing device displaying, using a graphical user interface, the treatment instruction set.