Multi-task Neural Networks for Clinical Note Section Classification

The patent segments the clinical note organization task into multiple independent neural networks: a shared neural network for generating text embeddings, a segmentation neural network for identifying section titles, and a section type classification neural network for classifying section types. This segmentation allows each network to specialize in a specific function, improving overall accuracy while maintaining manageable complexity through modular architecture.

The shared neural network serves multiple functions: it processes input text, generates text embeddings, and provides features to both the segmentation neural network and section type classification neural network. This multi-functionality reduces redundancy and improves efficiency while maintaining high classification accuracy across different healthcare systems through unified representation learning.

The patent employs parameter changes through neural network training on diverse clinical notes from different healthcare systems. The models learn to adapt to varying section structures, terminology, and formatting styles by adjusting their internal parameters during training, enabling them to generalize accurately across different healthcare systems without requiring system-specific customization.

The patent replaces conventional mechanical parsing methods with neural networks that use deep learning to understand and interpret clinical note structures. This substitution enables the system to handle variability in formatting, terminology, and organization styles through probabilistic models rather than rigid rules, significantly improving adaptability while increasing computational complexity.

The shared neural network performs preliminary processing by generating comprehensive text embeddings that capture semantic meaning, context, and relationships before the segmentation and classification stages. This preliminary action enriches the data representation, enabling subsequent networks to accurately identify section titles and classify types while maintaining processing efficiency through optimized neural network architectures.

The patent implements feedback mechanisms through the joint training of multiple neural networks with shared parameters. The segmentation neural network and section type classification neural network both process the same text embeddings and provide feedback during training to refine the shared representations. This feedback loop ensures that section type correlations are captured accurately while maintaining fast inference through pre-trained models.