NLP Vector Generation for Medical Record Prediction Accuracy
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Solution Overview
Problem
Natural language processing and machine learning systems face challenges in capturing temporal information and handling data structure incompatibility, leading to inaccurate data analytics and prediction results in fields like healthcare, where electronic medical records vary greatly in format and content.
Innovation Solution
The system generates encounter vectors and client vectors using natural language processing models like transformers to encode temporal information from electronic medical records, transforming them into a universal format for machine learning models, thereby improving data prediction accuracy and eliminating the need for manual data curation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If natural language processing systems process electronic medical records in various formats, then the system can handle diverse data sources, but the data structure incompatibility leads to inaccurate prediction results
Solution Approach 1:
The patent transforms unstructured electronic medical record data into structured vector representations, changing the parameter format from diverse text formats to standardized numerical vectors. This allows the system to maintain adaptability across different data sources while ensuring prediction accuracy through consistent vectorized input to machine learning models
Solution Approach 2:
The patent introduces natural language processing models as an intermediary layer between diverse electronic medical record formats and the prediction system. This intermediary automatically transforms various data formats into unified vector representations, resolving structure incompatibility without requiring manual data curation while maintaining prediction accuracy
2Stability of the object's composition
If manual data curation is performed to standardize medical records, then data structure compatibility improves, but the time and resources required increase significantly
Solution Approach 1:
The patent implements self-service data standardization through automated natural language processing models that independently transform electronic medical records into consistent vector formats without human intervention. The system automatically handles data structure consistency while eliminating the time-consuming manual curation process
Solution Approach 2:
The patent replaces the mechanical process of manual data curation with automated natural language processing and machine learning models. This substitution maintains data structure consistency through algorithmic transformation while dramatically reducing the time and human resources required for data preparation
3Device complexity
If temporal information is not captured in medical records, then data processing is simpler, but prediction accuracy for health states deteriorates
Solution Approach 1:
The patent applies preliminary action by embedding temporal information into the vector representations during the initial data transformation phase. The natural language processing models incorporate time-related features into the vectorized data before it reaches the prediction system, ensuring that temporal context is preserved without increasing processing complexity later in the pipeline
Data Source
AI summary
Methods, apparatuses, systems, computing devices, computing entities, and/or the like are provided. An example method may include retrieving one or more record data elements associated with a client identifier; generating one or more encounter vectors based at least in part on the one or more record data elements; generating a client vector based at least in part on the one or more encounter vectors and a first natural language processing model; generating a prediction data element based at least in part on the client vector and a machine learning model; and perform at least one data operation based at least in part on the prediction data element.