Signal Processing Using NLP for Predictive Cellular Abnormality Detection
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Solution Overview
Problem
The growing volume of unstructured data in operational environments leads to inefficiencies and undesirable outcomes due to the time required to sort through it, with much of the data being ignored or abandoned, impacting decision-making processes.
Innovation Solution
A system utilizing a natural language processing model to evaluate unstructured data from messages in a data stream, identify subjective indicators, assign weights, determine a composite abnormality score, and generate reports on potential cellular abnormalities in dependent users based on these indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If unstructured data is stored and evaluated in operational flows, then decision-making capability is improved, but time consumption increases and data is ignored or abandoned
Solution Approach 1:
The system performs preliminary action by pre-processing and filtering unstructured data before it enters operational flows. The data is evaluated and prepared in advance, so when it is needed for decision-making, it is already sorted and ready for use, eliminating the need for time-consuming sorting during operational execution.
Solution Approach 2:
The system extracts only the relevant information from unstructured data using evaluation criteria. By taking out only the necessary data elements and filtering out irrelevant information, the system reduces the volume of data that needs to be processed in real-time, thereby reducing time consumption while maintaining decision-making capability.
2Quantity of substance
If unstructured data is processed using traditional methods, then data completeness is maintained, but processing efficiency decreases
Solution Approach 1:
The system replaces traditional mechanical data processing methods with automated evaluation algorithms and criteria. Instead of manually sorting and processing unstructured data, the system uses computational models that can rapidly analyze data completeness and extract meaningful information, significantly improving processing efficiency while maintaining data completeness.
Solution Approach 2:
The system changes the parameters of data processing by introducing new evaluation criteria and transformation methods. The unstructured data is converted into structured formats with new parameters that enable efficient processing, allowing the system to maintain data completeness while achieving high processing efficiency through parameter transformation.
3Speed
If data is sorted and processed in real-time, then responsiveness is improved, but system complexity increases
Solution Approach 1:
The system segments the data processing function into separate modules: a data collection component, an evaluation component with criteria, and a response component. This segmentation allows each module to operate independently and optimally, improving response time while reducing overall system complexity by dividing the processing tasks into manageable units.
Solution Approach 2:
The system introduces an intermediary evaluation layer that sits between data collection and decision-making. This intermediary component processes unstructured data according to predefined criteria and outputs structured information, enabling real-time responsiveness while simplifying the overall system architecture by providing a clear interface between data sources and operational flows.
Data Source
AI summary
In some examples, unstructured data is evaluated using a natural language processing model to output a set of subjective indicators. These subjective indicators are scored using a predictive model to determine whether a dependent user has or is likely to develop a particular condition such as a cellular abnormality.