Prescriptive Element Selection via Loss Function Minimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Accurate and informed selection of prescriptive elements in healthcare is challenging due to the multitude of variables that need to be analyzed, leading to potential fruitless spending on unnecessary treatments that may worsen health conditions.
Innovation Solution
A system and method utilizing a computing device with a prescriptive generator module and loss function module to receive diagnosis descriptors and prescriptive training data, generate a prescriptive model using supervised machine-learning, and minimize a loss function to select an optimal prescriptive element based on user implementation inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple variables are analyzed to improve prescriptive element selection accuracy, then selection accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of prescriptive element selection into distinct functional modules: a prescriptive generator module that creates candidate elements, a loss function module that evaluates them, and a minimization mechanism that selects the optimal element. This segmentation allows each module to handle specific aspects of the analysis independently, managing complexity while maintaining high selection accuracy through comprehensive variable analysis.
2Ease of manufacture
If traditional methods are used for prescriptive element selection, then implementation is simpler, but unnecessary treatments are prescribed leading to fruitless spending
Solution Approach 1:
The system incorporates feedback through the loss function module, which evaluates candidate prescriptive elements against multiple variables including user implementation inputs. This feedback mechanism continuously refines the selection process by comparing predicted outcomes with actual or expected results, enabling the system to identify and eliminate unnecessary treatments while optimizing resource allocation. The iterative nature of loss function minimization ensures that only the most appropriate prescriptive elements are selected.
Data Source
AI summary
A system for selecting a prescriptive element based on user implementation inputs. The system includes at least a computing device and a prescriptive generator module operating on the at least a computing device. A prescriptive generator module is configured to receive at least a diagnosis descriptor from a user client device, receive prescriptive training data, and generate using a supervised machine-learning process a prescriptive model that produces an output containing a plurality of prescriptive elements. The system includes a loss function module operating on the at least a computing device. The loss function module is configured to receive from a user client device at least a user implementation response and generate a loss function as a function of the at least a user implementation response and the plurality of prescriptive elements. The loss function module minimizes the loss function and selects a prescriptive element as a function of minimizing the loss function. The loss function module transmits the selected prescriptive element to a user client device.