NLP Model for Predicting Cellular Abnormalities
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Solution Overview
Problem
The increasing volume of data generated daily poses inefficiencies in sorting and decision-making, as much data is either ignored or abandoned, leading to undesirable outcomes, particularly in operational flows where timely evaluation is crucial.
Innovation Solution
A system configured with software, firmware, or a combination of hardware that processes data streams to identify messages related to dependent users, employs natural language processing to evaluate unstructured data for subjective indicators, assigns weights, determines a composite abnormality score, and generates reports on cellular abnormalities present in dependent users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored and sorted through manually, then data can be evaluated, but the time required creates inefficiencies and data may be ignored or abandoned
Solution Approach 1:
The patent replaces manual mechanical sorting and evaluation of data with automated natural language processing systems. The NLP model automatically analyzes unstructured data in clinical notes to identify subjective indicators, extract assessments, and determine cellular abnormalities, eliminating the need for manual data sorting while maintaining evaluation completeness.
Solution Approach 2:
The system enables self-service by allowing the data to be automatically processed and evaluated without human intervention. The NLP model independently evaluates unstructured data, assigns weights to subjective indicators, and generates composite abnormality scores, freeing human users from the time-consuming task of manual data sorting.
2Measurement precision
If a comprehensive evaluation of all data is performed, then accurate predictive determinations can be made, but the complexity of processing increases
Solution Approach 1:
The patent segments the complex evaluation process into distinct hierarchical levels: extracting subjective indicators from unstructured data, assigning weights to individual indicators, combining them into composite scores, and finally making predictive determinations. This segmentation simplifies the overall complexity while maintaining comprehensive evaluation and high accuracy.
Solution Approach 2:
The system applies local quality by treating different types of data and indicators with specialized processing methods. Each subjective indicator is evaluated with appropriate weight based on its specific correspondence to cellular abnormalities, allowing the system to handle diverse data types with tailored approaches rather than a single complex method.
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.