Document Classification for Personalized Treatment Guidelines
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Solution Overview
Problem
Content repositories like PubMed often lack adequate classification and accessibility, making it difficult for users to efficiently search and utilize large volumes of documents for personalized medicine, especially when institutional licenses or payments are required for full access.
Innovation Solution
A computer system processes documents in content repositories by classifying them into functional and clinical categories, annotating relevant sections, and ranking documents based on query terms using machine learning models to generate personalized treatment guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If documents are stored in large quantities in content repositories, then the quantity of information increases, but accessibility and ease of searching deteriorate
Solution Approach 1:
The patent segments the large corpus of documents by classifying each document into functional categories (e.g., genomic data, clinical data, drug data) and clinical categories (e.g., evidence level, study type). This segmentation enables users to search and access specific types of documents more easily without being overwhelmed by the total volume of information in the repository.
Solution Approach 2:
The patent introduces an intermediary classification and annotation system that acts as a mediator between the raw document corpus and user queries. By adding metadata, annotations, and structured classifications to documents, the system enables efficient searching and retrieval without requiring users to manually browse through all documents.
2Measurement precision
If documents are classified and annotated using machine learning models, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The patent applies machine learning models in advance to automatically classify and annotate documents before they are accessed by users. This preliminary processing extracts key features, assigns categories, and generates metadata upfront, so that when users search for information, the results are already organized and filtered, reducing the need for complex real-time processing during user interactions.
3Loss of information
If full-length research documents are made accessible, then information completeness improves, but loss of substance increases due to licensing requirements
Solution Approach 1:
The patent extracts essential information from full-length research documents by classifying and annotating key findings, data, and conclusions. This extraction creates summarized versions or metadata that capture the most important information without requiring users to access the complete original documents, thereby reducing licensing costs while maintaining information value.
Data Source
AI summary
A computer system processes documents in a content repository. Each document of a plurality of documents is classified into one of a functional category and a clinical category. Each document is annotated using one or more corpora to generate document annotations. Documents satisfying one or more query terms are identified by comparing each query term to the document annotations. The identified documents are ranked based on a determined relevance. Guidelines are produced based on the ranking of the identified documents. Embodiments of the present invention further include a method and program product for processing documents in a content repository in substantially the same manner described above.