AI Medical Data Classification via Neural Network Embedding
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Solution Overview
Problem
Current artificial intelligence and natural language processing technologies face challenges in accurately classifying medical histories, particularly in determining whether a History of Present Illness (HPI) is 'extended' or 'brief' due to variations in writing style, presence of medical terms, abbreviations, and numbers with different semantic meanings, which can lead to incorrect classification and impact patient health and reimbursement processes.
Innovation Solution
A system utilizing a neural network with a natural language processor to normalize and classify HPIs by tokenizing, lemmatizing, and replacing named entities and medical terms, followed by embedding tokens into vectors for classification, using a three-layer neural network with an LSTM layer to output a binary classification of 'extended' or 'brief', and retraining the model based on feedback to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing is used to classify HPIs, then classification accuracy is improved, but the complexity of the system increases due to variations in writing style, medical terms, abbreviations, and numbers
Solution Approach 1:
The system segments the HPI classification task into multiple processing stages: tokenization of individual words, lemmatization to reduce words to base forms, named entity recognition to identify medical terms and abbreviations, and sequential processing through multiple neural network layers. This segmentation allows each component to handle specific aspects of the variability in HPI writing styles, improving accuracy while managing complexity through modular design.
Solution Approach 2:
The patent introduces intermediate processing layers between raw input and final classification. These include embedding layers that convert tokens into vector representations, LSTM layers that capture sequential dependencies, and attention mechanisms that weigh important features. These intermediary components act as mediators that transform varied input formats into a standardized representation suitable for accurate classification.
2Ease of operation
If manual classification by healthcare professionals is used, then the process is simple and straightforward, but human error increases and productivity decreases
Solution Approach 1:
The system enables self-service classification where the neural network automatically processes HPIs without requiring manual review by healthcare professionals. The model has been trained to independently handle the complexity of medical terminology and writing style variations, providing automated classification that maintains simplicity for end users while dramatically improving productivity through high-speed processing of multiple HPIs simultaneously.
3Productivity
If automated classification systems are implemented, then productivity is improved, but measurement precision decreases due to difficulty in detecting and measuring semantic meaning in varied medical text
Solution Approach 1:
The system employs dynamic processing through LSTM (Long Short-Term Memory) networks that can adaptively capture sequential dependencies and contextual relationships in HPI text. The attention mechanisms dynamically weight different parts of the input based on their relevance to the classification task, allowing the system to accurately interpret semantic meaning even as processing speed increases. This dynamic approach enables the automated system to maintain high precision while achieving fast classification.
Data Source
AI summary
Apparatus, systems, devices, other articles of manufacture and associated methods are disclosed and described herein to process medical data to generate a classification of the medical data using artificial intelligence. An example apparatus includes a processor to execute instructions to implement a history of past illness (HPI) receiver to receive an HPI formatted as a string, the string including one or more words, the words organized in sentences, a natural language processor to tokenize the one or more words into tokens based on a context associated with at least one of the one or more words and a tensor generator to convert the tokens into hashes, each of the hashes forming a dimension of a tensor based on the context. The apparatus further includes a neural network to embed each of the hashes into vectors, process the vectors to classify the HPI as extended or brief based on a similarity to a set of classified HPIs and output a classification for the HPI. The apparatus further includes an electronic medical record modifier to modify an electronic medical record with the HPI and the classification and to trigger an action with respect to the electronic medical record based on the classification.