Neural Network Patient Outcome Forecasting
Solution Overview
Problem
Healthcare practitioners lack a systematic method to accurately predict patient outcomes based on historical drug intake data and administer multiple drugs to patients with multiple ailments during overlapping treatment windows, leading to inefficiencies in precision medicine modeling and cure-ability forecasting.
Innovation Solution
A system utilizing a neural network within a healthcare decision cloud system that analyzes historical and current patient data, including medication prescriptions and medical tests, to generate rules and forecasts for patient outcomes, providing precision medicine forecasts and alerts for potential medicine issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used for patient outcome prediction, then the system is simple and easy to implement, but the prediction accuracy and precision are insufficient
Solution Approach 1:
The patent replaces traditional mechanical/statistical prediction methods with a neural network system that processes patient data. The neural network learns complex patterns from historical patient information, current medical data, and medication prescriptions to generate accurate outcome forecasts, substituting simple algorithms with an intelligent system capable of handling multiple variables and interactions.
Solution Approach 2:
The patent introduces a neural network as an intermediary between raw patient data and outcome predictions. This intermediary processes and interprets complex relationships in the data, including drug interactions and treatment effectiveness, that cannot be directly observed or calculated using traditional methods.
2Adaptability or versatility
If multiple drugs are administered to patients with multiple ailments, then treatment coverage is improved, but the risk of drug interactions and overdoses increases
Solution Approach 1:
The patent implements a feedback mechanism where the neural network continuously analyzes patient data and provides forecasts about treatment outcomes and potential drug interactions. This feedback loop allows practitioners to adjust prescriptions based on predicted risks, preventing harmful interactions before they occur while maintaining comprehensive treatment coverage.
Solution Approach 2:
The patent applies preliminary anti-action by using the neural network to predict and prevent drug interactions before they harm the patient. The system analyzes potential combinations of medications and alerts practitioners to risky interactions in advance, allowing them to modify prescriptions proactively rather than reacting to adverse events after they occur.
3Measurement precision
If historical patient data is extensively analyzed, then prediction accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on extensive historical patient data before actual use. This initial training phase processes large volumes of data to build the network's knowledge base and predictive capabilities. Once trained, the system can quickly generate predictions for new patients without requiring real-time processing of the entire historical dataset, thus maintaining high accuracy while reducing operational processing time.
Data Source
AI summary
A system may receive a query for a patient evaluation. The system may then receive historical patient information or hospital data, current medical information, patient information, and testing information. The system may then use the received information to generate a set of rules using a neural network and uses the rules to process an evaluation for the patient.