Medical Report Generation Using Style Analysis
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Solution Overview
Problem
The generation of medical reports relies heavily on human effort, resulting in varying writing styles that can depend on hospitals, specimen types, and physician report writing habits, leading to inconsistencies and inefficiencies.
Innovation Solution
A method and apparatus that utilize machine learning to analyze writing styles from historical texts, convert medical data into draft text conforming to a template, and generate customized medical reports through the assistance of a language model, ensuring consistency and readability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If medical reports are generated manually by physicians, then the reports can be customized according to individual judgment, but the writing styles vary depending on hospitals, specimen types, and physician habits leading to inconsistency
Solution Approach 1:
The patent segments the report generation process into multiple components: template structure, writing style analysis, and content generation. By separating these elements, the system maintains consistent templates and writing styles while allowing customization of specific report content based on medical data and physician preferences.
Solution Approach 2:
The system analyzes historical texts to extract writing style parameters (common words, contextual relationships) and uses these parameters to generate reports that match desired styles. This allows the same template to produce consistent output across different physicians and hospitals while maintaining customization capabilities.
2Reliability
If manual report generation is used, then physicians can exercise professional judgment in writing, but the process requires significant time and effort
Solution Approach 1:
The system performs preliminary actions by pre-defining report templates, analyzing historical writing styles, and preparing the structural framework before actual report generation. This allows physicians to review and modify pre-generated content rather than writing from scratch, reducing time while maintaining professional judgment.
Solution Approach 2:
The system enables self-service report generation by automatically creating draft reports based on medical data and learned writing styles. Physicians can then review and make necessary modifications, shifting the workload from complete manual writing to selective review and editing.
3Productivity
If automated generation is implemented, then time and effort are reduced, but the reports may lack customization and readability
Solution Approach 1:
The system incorporates feedback mechanisms by analyzing historical texts to learn writing styles and using this learned information to improve generated reports. The feedback loop ensures that automated reports progressively match the readability and customization characteristics of manually written reports while maintaining high generation speed.
Solution Approach 2:
The patent introduces an intermediary layer between automated generation and final output: the writing style analysis module acts as a mediator that translates learned stylistic patterns into generation parameters. This intermediary ensures that automated reports maintain readability and customization without requiring complete manual rewriting.
Data Source
AI summary
A generation method and generation apparatus of a medical report are provided. In the method, the writing style is analyzed from multiple historical texts, where the writing style includes multiple common words in the historical text and the contextual relationships that connect those common words; the medical data is converted into draft text that conforms to the template text, where the template text is a report that conforms to a preset style; and by using the draft text and writing style as input data of the language model, an output report that conforms to the writing style is generated, where the language model selects sentences that conform to the writing style.