Medical text inspection report analysis method based on large model

Abstract

The invention discloses a medical text inspection report analysis method based on a large model. The report analysis method comprises the following steps: inputting a to-be-analyzed medical inspection report; preprocessing the medical examination report to obtain a preprocessed examination report; key data are extracted from the preprocessing inspection report, and key indexes are obtained; performing data standardization processing on the key indexes to obtain standardized formatted data; performing large model analysis on the standardized formatted data to obtain an analysis result; and generating an interpretation report according to the analysis result. The method for accurately extracting the structured test data from the unstructured medical text is realized. The system can be compatible with reports from different sources, and has wide applicability.

Description

Technical Field

[0001] This invention relates to the field of medical laboratory report analysis, and more particularly to a method for analyzing medical laboratory reports based on a large model. Background Technology

[0002] A complete blood count (CBC) is a crucial clinical laboratory test used to assess a patient's health and diagnose various diseases. Reports include multiple indicators such as red blood cell count and hemoglobin levels, typically presented in a structured table format. Currently, hospitals mostly use automated blood analyzers to generate reports; however, manual interpretation suffers from inefficiency and inconsistency, and differences in reference ranges and formats among different laboratories increase the difficulty of cross-institutional interpretation. In recent years, advancements in large language modeling technology have provided new approaches to medical laboratory report analysis. These models can automatically extract key information from the text, understand medical terminology, assess abnormal results, and generate interpretations, potentially improving analytical efficiency and standardization while providing more accurate clinical recommendations when combined with professional medical knowledge.

[0003] Existing technical solutions mainly include manual interpretation, rule and template systems, and traditional NLP and machine learning methods. Manual interpretation involves doctors or laboratory technicians reviewing report values, comparing them to reference ranges to identify abnormalities, and providing explanations based on clinical context. Rule and template systems automatically generate explanations by extracting abnormal indicators from reports using pre-defined templates for interpreting common abnormalities. Traditional NLP and machine learning methods utilize techniques such as keyword matching and regularization rules to extract indicator values ​​from text and combine them with classification models to determine the causes of abnormalities.

[0004] Disadvantages of existing technology: Manual interpretation relies on experience, which is inefficient and inconsistent.

[0005] The rules and template systems are rigid, have high maintenance costs, and lack flexibility and scalability.

[0006] Traditional NLP and machine learning methods rely on handcrafted features, are sensitive to format changes, and lack model depth. Deep semantic understanding capabilities are insufficient to capture the complex relationships between indicators. Summary of the Invention

[0007] In view of the above problems, the present invention is proposed to provide a large-model-based method for analyzing medical text examination reports, which overcomes or at least partially solves the above problems.

[0008] According to one aspect of the present invention, a method for analyzing medical text examination reports based on a large model is provided, the report analysis method comprising: Enter the medical test report to be analyzed; The medical test report is preprocessed to obtain a preprocessed test report; Key data are extracted from the preprocessed test report to obtain key indicators; The key indicators are subjected to data standardization processing to obtain standardized formatted data; Perform large-scale model analysis on the standardized data to obtain analysis results; An interpretation report is generated based on the analysis results.

[0009] Optionally, the input medical test report to be analyzed specifically includes: receiving blood routine reports in various formats as input, including PDF electronic reports or image scans.

[0010] Optionally, the preprocessing of the medical test report to obtain a preprocessed test report specifically includes: performing format verification and cleaning on the input report to remove redundant information and invalid data.

[0011] Optionally, the step of extracting key data and obtaining key indicators from the preprocessed inspection report specifically includes: using optical character recognition and visual language models to obtain key indicators from the chart data in the recognition report, and converting them into structured data.

[0012] Optionally, the key indicators specifically include: various blood cell counts and numerical results, and reference ranges.

[0013] Optionally, the step of standardizing the key indicators to obtain standardized formatted data specifically includes: The extracted indicator data were normalized to unify units and formats, and the reference range was adjusted as needed to eliminate the impact of differences between different laboratories.

[0014] Optionally, the step of performing large-scale model analysis on the standardized data to obtain analysis results specifically includes: The standardized structured data is input into a large language model trained in medicine, and prompt words guide the model to conduct in-depth analysis and interpretation of the data; The model will output natural language analysis results for the report.

[0015] Optionally, the natural language analysis results include the identification of abnormal indicators, inference of possible causes, and clinical recommendations.

[0016] Optionally, generating the interpretation report based on the analysis results specifically includes: The analysis results generated by the large model are structured and organized to form a clear and easy-to-read interpretation report; The results can be rendered into a visual webpage or exported as a PDF for doctors or patients to view, as needed.

[0017] This invention provides a method for analyzing medical test reports based on a large model. The method includes: inputting the medical test report to be analyzed; preprocessing the medical test report to obtain a preprocessed test report; extracting key data from the preprocessed test report to obtain key indicators; standardizing the key indicators to obtain standardized formatted data; performing large model analysis on the standardized formatted data to obtain analysis results; and generating an interpretation report based on the analysis results. This method accurately extracts structured test data from unstructured medical text. The system is compatible with reports from different sources and has wide applicability.

[0018] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a medical text examination report analysis method based on a large model, provided as an embodiment of the present invention. Detailed Implementation

[0021] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0022] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.

[0023] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0024] This invention integrates technologies such as natural language processing, prompting engineering, and large-scale language models to automatically parse and deeply analyze test reports, generating structured interpretation results and health recommendations.

[0025] like Figure 1 As shown, a medical text examination report analysis method based on a large model is proposed. The report analysis method includes: inputting the medical examination report to be analyzed; preprocessing the medical examination report to obtain a preprocessed examination report; extracting key data from the preprocessed examination report to obtain key indicators; performing data standardization processing on the key indicators to obtain standardized formatted data; performing large model analysis on the standardized formatted data to obtain analysis results; and generating an interpretation report based on the analysis results.

[0026] The specific detailed steps include: Input the medical test report to be analyzed: The system accepts blood routine reports in various formats as input, such as PDF electronic reports or image scans.

[0027] Preprocessing: The input report is formatted and cleaned to remove redundant information and invalid data, preparing it for subsequent processing.

[0028] Key data extraction: Using optical character recognition (OCR) and visual language (VL) models, key indicators such as various blood cell counts and their numerical results and reference ranges are obtained from the chart data in the recognition report and converted into structured data.

[0029] Data standardization: The extracted indicator data are normalized, the units and formats are standardized, and the reference range is adjusted as needed to eliminate the impact of differences between different laboratories.

[0030] Large-scale model analysis: Standardized structured data is input into a medically trained large-scale language model. Carefully designed prompts guide the model to conduct in-depth analysis and interpretation of the data. The model will output natural language analysis results of the report, including the identification of abnormal indicators, inferences about possible causes, and clinical recommendations.

[0031] Results Generation and Output: The analysis results generated by the large model are structured and organized into a clear and easy-to-read interpretation report. The results can be rendered into visual web pages or exported as PDFs for doctors or patients to view, as needed.

[0032] Beneficial effects: Automated Parsing of Multi-Source Heterogeneous Reports: An automated processing workflow supporting multiple formats (PDF, images, etc.) of laboratory reports is proposed, including OCR recognition, VL model recognition of complex charts, text cleaning, and key data extraction. This method enables the accurate extraction of structured laboratory data from unstructured medical text. This approach allows the system to be compatible with reports from different sources, making it widely applicable.

[0033] Personalized and Context-Aware Interpretation Generation: The analysis in this invention is based not only on the test values ​​themselves, but also on the patient's individual characteristics and contextual information (such as age, gender, symptoms, medical history, etc.). By providing this information in prompts or incorporating individualized cases into model training, the generated report recommendations are more tailored to the patient's specific situation, achieving truly personalized medical advice output. This is something that traditional general templates cannot achieve and is a significant innovation of this invention.

[0034] Automated Report Generation and Visualization: This feature achieves end-to-end automation from data parsing to report generation. The system can automatically generate structured natural language reports based on analysis results and produce visual charts to aid understanding. This automated report generation technology reduces the workload of manual report writing while ensuring the standardization and aesthetics of the report content, improving information delivery efficiency and user experience.

[0035] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for analyzing a medical text examination report based on a large model, the method comprising: The report analysis method comprises: inputting a medical test report to be analyzed; preprocessing the medical test report to obtain a pretreated test report; extracting key data from the pretreated test report to obtain key indicators; performing data standardization processing on the key indicators to obtain standardized formatted data; performing large model analysis on the standardized formatted data to obtain analysis results; generating an interpretation report according to the analysis results.

2. The method of claim 1, wherein the method further comprises: The inputting of the medical test report to be analyzed specifically comprises: receiving blood routine reports in various formats as input, including PDF electronic reports or image scans. 3.The method of claim 1, wherein, The preprocessing of the medical test report to obtain a pretreated test report specifically comprises: performing format checking and cleaning on the input report to remove redundant information and invalid data. 4.The method of claim 1, wherein, The extracting of key data from the pretreated test report to obtain key indicators specifically comprises: using optical character recognition and visual language models to recognize chart data in the report to obtain key indicators and convert them into structured data.

5. The method of claim 4, wherein the method further comprises: The key indicators specifically include: blood cell counts and numerical results, reference ranges.

6. The method of claim 1, wherein the method further comprises: The data standardization processing of the key indicators to obtain standardized formatted data specifically comprises: normalizing the extracted indicator data, unifying units and formats, and adjusting reference ranges as needed to eliminate the effects of differences between different laboratories.

7. The method of claim 1, wherein the method further comprises: The large model analysis of the standardized formatted data to obtain analysis results specifically comprises: inputting the standardized structured data into a large language model trained in medicine, guiding the model to analyze and interpret the data in depth through prompt words; the model outputs natural language analysis results of the report.

8. The method of claim 7, wherein the method further comprises: The natural language analysis results include identification of abnormal indicators, inference of possible causes, and clinical suggestions.

9. The method of claim 1, wherein the method further comprises: determining a plurality of medical text inspection report analysis results based on the plurality of medical text inspection report analysis models; and outputting the plurality of medical text inspection report analysis results. The generation of an interpretation report according to the analysis results specifically comprises: structuring the analysis results generated by the large model to form a clear and easy-to-read interpretation report; rendering the results as a visual web page or exporting them as a PDF format as needed for doctors or patients to view.

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