18375960. SEGMENTING AND CLASSIFYING UNSTRUCTURED TEXT USING MULTI-TASK NEURAL NETWORKS simplified abstract (GOOGLE LLC)
Contents
- 1 SEGMENTING AND CLASSIFYING UNSTRUCTURED TEXT USING MULTI-TASK NEURAL NETWORKS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SEGMENTING AND CLASSIFYING UNSTRUCTURED TEXT USING MULTI-TASK NEURAL NETWORKS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SEGMENTING AND CLASSIFYING UNSTRUCTURED TEXT USING MULTI-TASK NEURAL NETWORKS
Organization Name
Inventor(s)
Amir Reuven Feder of New York NY (US)
Fan Zhang of Cupertino CA (US)
Ayelet Benjamini of Kfar Saba (IL)
SEGMENTING AND CLASSIFYING UNSTRUCTURED TEXT USING MULTI-TASK NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18375960 titled 'SEGMENTING AND CLASSIFYING UNSTRUCTURED TEXT USING MULTI-TASK NEURAL NETWORKS
Simplified Explanation
The patent application describes a multi-task neural network system designed for processing clinical notes. The system includes a shared neural network that generates text segment embeddings for each text segment in a given text span. It also includes a segmentation neural network to determine if a text segment is a section title, and a section type classification neural network to classify the text segment into one of several section types.
- Shared neural network processes text spans from clinical notes
- Generates text segment embeddings for each text segment
- Segmentation neural network determines if text segment is a section title
- Section type classification neural network classifies text segment into section types
Potential Applications
This technology could be applied in healthcare settings for automating the organization and analysis of clinical notes, improving efficiency and accuracy in medical documentation.
Problems Solved
1. Streamlining the processing of clinical notes 2. Automating the classification of text segments into section types
Benefits
1. Increased efficiency in medical documentation 2. Improved organization and analysis of clinical notes
Potential Commercial Applications
Automated medical transcription services could utilize this technology to enhance their offerings, providing more accurate and organized clinical notes for healthcare providers.
Possible Prior Art
Prior art in this field may include existing systems for text segmentation and classification in various industries, such as natural language processing tools and document management software.
Unanswered Questions
How does this technology handle variations in text structure and content within clinical notes?
The system likely uses advanced algorithms to adapt to different text structures and content variations, ensuring accurate processing and classification of text segments.
What measures are in place to ensure the privacy and security of patient information within the system?
The system likely incorporates encryption protocols, access controls, and other security measures to protect patient data from unauthorized access or breaches.
Original Abstract Submitted
A multi-task neural network system is described. The system includes a shared neural network configured to receive as input a text span from a clinical note, and for each of one or more text segments in the text span, processing the text segment to generate a set of text segment embeddings. The system further includes a segmentation neural network configured to, for each of the one or more text segments, process the respective set of text segment embeddings to determine whether the text segment is a section title or not. The system further includes a section type classification neural network configured to, for each of the one or more text segments, process the respective set of text segment embeddings to classify the text segment into a section type of a plurality of section types.
