Neural Signal Detection Using Adverse Event Dependency Modeling
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Solution Overview
Problem
Existing signal detection methods, particularly in pharmacovigilance, suffer from false alerts and missed signals due to the complexity of adverse event relationships, which are not adequately addressed by traditional statistical or machine learning methodologies, leading to inefficiencies and potential harm to patients.
Innovation Solution
The implementation of deep learning techniques through neural networks to model complex dependencies between adverse events, products, and other factors, enabling more accurate signal detection by reducing false alerts and improving timeliness of signal identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional statistical or machine learning methodologies are used for signal detection, then the system is simpler to implement, but the accuracy of signal detection deteriorates due to false alerts and missed signals
Solution Approach 1:
The patent segments the signal detection process into multiple independent components: a neural network processor that performs complex pattern recognition, a signal score calculator that computes detection metrics, and a threshold comparison module that determines signal presence. This segmentation allows each component to specialize in specific tasks, improving overall detection accuracy while managing system complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary neural network processor between the raw signal input and the final detection output. This intermediary component transforms complex adverse event data into structured representations that can be more effectively analyzed, bridging the gap between traditional methodologies and improved accuracy requirements.
2Measurement precision
If deep learning techniques are implemented to model complex dependencies, then the accuracy of signal detection improves, but the computational resources and time required increase
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network processor on extensive adverse event data before actual signal detection. This pre-processing step allows the system to learn complex patterns and relationships in advance, so that during actual operation, the neural network can quickly process new data with high accuracy without requiring extensive computational time for each detection task.
3Measurement precision
If deep learning techniques are implemented to model complex dependencies, then the accuracy of signal detection improves, but the device complexity increases
Solution Approach 1:
The patent segments the signal detection process into multiple independent parts: a neural network processor for pattern recognition, a signal score calculator for metric computation, and a threshold comparison module for decision-making. This segmentation allows each component to handle specific aspects of the complex task, improving accuracy while managing overall system complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary neural network processor between raw signal input and final detection output. This intermediary transforms complex adverse event data into structured representations, bridging the gap between traditional methodologies and improved accuracy requirements while containing complexity within a dedicated component.
Data Source
AI summary
Systems, methods, and other embodiments associated with neural signal detection are described. In one embodiment, for a plurality of reports: create a vector embedding for each of a set of information objects included in a report by an embedding layer of a neural signal detection network. The set of information objects includes a target object. Represent the report by a representation layer in a manner that describes correlation between occurrence of a target event and the information objects and accounts for dependencies between the information objects that make up the report. Model a set of events including the target event based on the representation of the report by a logit layer. Determine an occurrence probability for the target event, given the modeled set of events. Identify the presence of a signal by comparing a summary occurrence probability for the target event across the plurality of reports against a comparator probability.