Neural Network Medical Text Report Section Segmentation
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Solution Overview
Problem
Existing methods for section segmentation in medical text reports are inefficient due to sub-optimal accuracy and reliability in mapping sentences to pre-defined class labels, and require labor-intensive hand-crafting of features that fail to generalize well across different layouts.
Innovation Solution
A computer-implemented method using neural networks to generate sentence representations by combining word-level and aggregated representations, allowing for accurate and reliable section classification of medical text reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If hand-crafted features are used for text classification, then the classification can be performed with simpler models, but the accuracy and reliability of sentence-to-section mapping deteriorates
Solution Approach 1:
The patent replaces manual feature engineering (mechanical process) with automatic feature learning through neural networks. The system uses embedding layers to automatically transform words into dense vector representations, and uses convolutional and recurrent neural networks to automatically learn hierarchical features from the text, eliminating the need for hand-crafted features while significantly improving section mapping accuracy
Solution Approach 2:
The patent transforms the classification approach by changing from discrete hand-crafted features to continuous dense vector representations. The embedding layers convert sparse word indices into dense vectors that capture semantic relationships, and the neural networks process these continuous representations to achieve superior classification performance compared to traditional discrete feature methods
2Ease of manufacture
If hand-crafted features are used for text classification, then the implementation process is simpler, but the labor intensity increases due to manual feature engineering
Solution Approach 1:
The system implements self-service through automatic feature learning. The neural networks automatically learn relevant features from the raw text data without human intervention. The embedding layers automatically capture word semantics, and the convolutional and recurrent layers automatically learn hierarchical patterns, making the system self-sufficient and eliminating manual feature engineering labor
Solution Approach 2:
The patent replaces the manual mechanical process of feature engineering with an automated computational process. Instead of experts manually designing and selecting features, the system uses neural networks to automatically learn and extract features from the data, significantly reducing labor intensity while improving consistency and reproducibility
3Loss of time
If hand-crafted features are used for text classification, then the model training is faster, but the generalization performance on unseen layouts deteriorates
Solution Approach 1:
The patent implements dynamic feature learning that adapts to different text layouts and styles. The neural networks learn features directly from the data, allowing them to automatically adapt to variations in report formats, section headings, and text structures. This dynamic adaptation capability enables the system to generalize effectively to unseen layouts without requiring re-engineering of features
Solution Approach 2:
The system changes from fixed hand-crafted features to learnable parameters that can adapt to different contexts. The embedding vectors and network weights are trained on diverse data and can adjust to capture the characteristics of different report layouts, enabling the model to maintain high performance across varied text structures and formats
Data Source
AI summary
A framework for segmenting a medical text report into sections is disclosed. For each sentence of the report, a first sentence representation is determined by inputting a word-level context representation for each sentence sequentially into a neural network. A second sentence representation is determined by inputting an aggregated representation for each sentence sequentially into another neural network. For each sentence, a third sentence representation is determined based on a combination of the first and second sentence representations, and a section classification for the sentence is determined by inputting the third sentence representation into a section classifier. Each sentence is assigned the section classification determined for the sentence.