Gastrointestinal health intelligent science popularization interpretation system based on Internet platform
By integrating user terminals, laboratory information systems, and cloud service platforms through an internet platform, automated sample quality control and risk assessment for gastrointestinal health testing are achieved, generating personalized science popularization interpretation reports. This solves the problem of insufficient sample invalidity verification in existing systems and realizes intelligent integrated interpretation and decision-making for gastrointestinal health testing.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUIZHOU LIDING BIOTECH
- Filing Date
- 2025-12-29
- Publication Date
- 2026-04-17
AI Technical Summary
Existing gastrointestinal health testing systems lack an automatic sample validity verification process, leading to misleading results from invalid samples. Furthermore, users face separate testing procedures and difficult-to-understand professional interpretations, resulting in high costs, slow response times, and difficulty in achieving end-to-end intelligent integrated health decision-making.
Design an intelligent science popularization and interpretation system for gastrointestinal health based on an Internet platform. Integrate user terminals, laboratory information systems and cloud service platforms. The system uses a sample quality control unit to automatically determine the validity of samples, a data fusion analysis unit to conduct risk assessment, and generates personalized science popularization and interpretation reports.
It realizes integrated services and joint intelligent interpretation of gastrointestinal health testing, reduces reliance on manual labor, improves service efficiency and consistency, ensures the reliability of data input and the standardization of interpretation, and supports an end-to-end intelligent technical architecture for multi-source data to integrated health decision-making.
Smart Images
Figure CN121885151A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of health science popularization technology, and in particular to an intelligent science popularization and interpretation system for gastrointestinal health based on an Internet platform. Background Technology
[0002] Early screening for digestive health is a crucial area of preventive medicine and health management. Currently, early risk screening for gastric and intestinal diseases is primarily implemented through two separate pathways. For gastric risks, the ABC method, based on the combined detection of serum pepsinogen and Helicobacter pylori antibodies, is used for initial screening. For intestinal risks, quantitative detection of fecal biomarkers such as fecal occult blood and calprotectin is the main approach. Both screening technologies provide objective laboratory test data. In current practice, users need to complete sampling and submission for both types of tests separately. After testing, the laboratory information system generates a structured test report containing specific indicator values.
[0003] In existing technologies, gastrointestinal testing services are independent, requiring users to undergo two separate processes. The resulting test reports are highly technical and difficult for ordinary users to understand. Furthermore, report interpretation requires manual work by gastroenterologists or other professionals, which is costly, slow, and struggles to ensure consistent interpretation standards. In user-sampling scenarios, sample quality is crucial to the validity of test results, but existing systems generally lack an automatic verification process for sample validity before interpretation, posing a risk of misleading results based on invalid samples. Therefore, it is difficult to meet the end-to-end intelligent system architecture requirements for automated quality control of multi-source data, intelligent fusion analysis, and the generation of integrated health decisions.
[0004] In view of this, a smart science popularization and interpretation system for gastrointestinal health based on an Internet platform is proposed. Summary of the Invention
[0005] This invention provides an intelligent science popularization and interpretation system for gastrointestinal health based on an internet platform, which addresses the problem that existing systems generally lack an automatic verification step for sample validity before generating interpretations, thus posing a risk of misleading based on invalid samples.
[0006] This invention provides an intelligent science popularization and interpretation system for gastrointestinal health based on an internet platform, comprising: The system comprises a user terminal, a laboratory information system, and a cloud service platform, wherein the cloud service platform is communicatively connected to the user terminal and the laboratory information system, respectively. The user terminal is used to collect and upload corresponding multi-source sampling data to the cloud service platform according to the service request submitted by the user; the service request includes a gastrointestinal joint examination request or a single test request; The laboratory information system is used to generate structured test report data containing gastric function markers, intestinal health markers, or combinations thereof after completing the testing of user samples, and send it to the cloud service platform. The cloud service platform includes a sample quality control unit, a data fusion and analysis unit, and a science popularization report generation unit; wherein: The sample quality control unit is used to determine the validity of the received multi-source sampling data and structured test report data according to preset quality control rules; the structured test report data is sent to the data fusion analysis unit only when it is determined that all test items corresponding to the structured test report data are valid. The data fusion and analysis unit is used to perform risk assessment analysis on the markers in the structured test report data according to preset rules, and generate comprehensive risk profile data including gastric risk assessment results and / or intestinal risk assessment results. The science popularization report generation unit is used to receive the comprehensive risk profile data, call the preset content template, generate a personalized science popularization interpretation report, and send it to the user terminal for display. The personalized science popularization interpretation report includes health status, risk assessment, and action suggestions corresponding to the comprehensive risk profile data.
[0007] Furthermore, the sample quality control unit performs sample validity assessment on the received multi-source sampling data and structured test report data according to preset quality control rules, including: Identify the types of test items included in the structured test report data, wherein the types of test items include at least one of gastric function test items and intestinal health test items; If a gastric function test item is identified, the first quality control process is executed, which includes a pass / fail judgment on the blood spot image based on the multi-source sampling data. If a gut health testing item is identified, a second quality control process is executed, which includes determining the delivery timeliness of the time metadata based on the structured test report data.
[0008] Furthermore, the execution of the first quality control process includes: Extract fingertip bloodstain images from the multi-source sampling data; Determine whether the fingertip bloodstain image meets the preset qualification standard, and record the first determination result; Simultaneously, based on the time metadata of the structured test report data, the transportation timeliness of the gastric function test sample is judged, and the second judgment result is recorded; A sample for a gastric function test is deemed valid only if both the first and second judgment results are passed.
[0009] Furthermore, the execution of the second quality control process includes: The collection time and laboratory receipt time of the intestinal health test sample are obtained from the time metadata of the structured test report data. Calculate the transportation time from data collection to receipt; Determine whether the transportation time does not exceed the preset threshold for the validity period of intestinal sample transportation; If the limit is not exceeded, the sample for the intestinal health test is deemed valid.
[0010] Furthermore, the sample quality control unit confirms that all samples corresponding to the structured test report data are valid, including: If a test is identified that includes both gastric function testing and intestinal health testing, the first quality control process and the second quality control process are executed in parallel or sequentially. A confirmation conclusion that the overall quality control has passed is generated only when both the gastric function test sample and the intestinal health test sample are deemed valid.
[0011] Furthermore, the preset rules in the data fusion analysis unit include gastric risk assessment rules for gastric function testing projects and intestinal risk assessment rules for intestinal health testing projects; wherein: The gastric risk assessment rule is based on pepsinogen I, pepsinogen II, the ratio of pepsinogen I to pepsinogen II, and Helicobacter pylori antibody status, and maps the test data to multiple gastric risk levels according to a preset ABC stratification method. The intestinal risk assessment rule is based on the quantitative values of fecal occult blood and calprotectin. By comparing and combining the test values with preset thresholds, the test data is mapped to multiple intestinal risk levels.
[0012] Furthermore, the data fusion analysis unit performs risk assessment analysis according to preset rules, including: Extract gastric function markers and / or gut health markers contained in the structured test report data; Based on the type of biomarker extracted, the corresponding risk assessment rules are applied; specifically, when a gastric function biomarker is extracted, the gastric risk level is calculated using the gastric risk assessment rules; when a gut health biomarker is extracted, the gut risk level is calculated using the gut risk assessment rules. The calculated gastric risk level and / or intestinal risk level are combined to form a comprehensive risk profile.
[0013] Furthermore, the generation of comprehensive risk profile data, including gastric risk assessment results and / or intestinal risk assessment results, includes: When the structured test report data contains both gastric function markers and gut health markers, the comprehensive risk profile data is a joint risk profile that includes gastric risk level and gut risk level. When the structured test report data only contains gastric function markers, the comprehensive risk profile data is a single risk profile that only contains the gastric risk level; When the structured test report data only contains intestinal health markers, the comprehensive risk profile data is a single risk profile containing only intestinal risk levels.
[0014] Furthermore, the preset content templates include: stomach health interpretation templates corresponding to different stomach risk levels, intestinal health interpretation templates corresponding to different intestinal risk levels, and combined gastrointestinal interpretation templates corresponding to combinations of stomach risk levels and intestinal risk levels.
[0015] Furthermore, the science popularization report generation unit generates personalized science popularization interpretation reports, including: Based on the type of the comprehensive risk profile data, the corresponding content template is invoked; wherein, if the comprehensive risk profile data is a joint risk profile, the gastrointestinal joint interpretation template is invoked; if the comprehensive risk profile data is a single risk profile containing only the gastric risk level, the corresponding gastric health interpretation template is invoked; if the comprehensive risk profile data is a single risk profile containing only the intestinal risk level, the corresponding intestinal health interpretation template is invoked. The specific risk levels from the comprehensive risk profile data are filled into the invoked content template to generate the personalized science popularization interpretation report, which includes a description of the corresponding health status, risk assessment, and action suggestions.
[0016] As can be seen from the above technical solutions, the present invention has the following advantages: This invention integrates a user terminal, a laboratory information system, and a cloud service platform. The cloud service platform includes a built-in sample quality control unit, a data fusion analysis unit, and a science popularization report generation unit. By receiving multi-source sampling data from users and structured test reports from the laboratory, the sample quality control unit automatically judges and screens sample validity according to preset rules, ensuring the reliability of data input. The data fusion analysis unit, based on preset gastric and intestinal risk assessment rules, performs fusion analysis on the biomarker data that has passed quality control, generating a comprehensive risk profile. Finally, the science popularization report generation unit uses the profile to call corresponding templates and automatically generates a personalized science popularization interpretation report covering health status, risk assessment, and action recommendations. This invention achieves integrated services and joint intelligent interpretation of early gastric and intestinal screening on a single internet platform. Through preset assessment rules and templates, it realizes automatic standardized conversion from data to science popularization content, significantly reducing reliance on manual labor, improving service efficiency and consistency, and realizing an end-to-end intelligent technical architecture from multi-source data to integrated health decision-making. Attached Figure Description
[0017] Figure 1 This is a schematic diagram of the architecture of an intelligent science popularization and interpretation system for gastrointestinal health based on an Internet platform, as described in this invention. Figure 2 This is a schematic diagram of the workflow of the sample quality control unit in this invention; Figure 3 This is a schematic diagram of the workflow of the data fusion analysis unit and the popular science report generation unit in this invention. Detailed Implementation
[0018] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “corresponding to,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0019] Example 1 Please see Figure 1The system in this application embodiment includes a user terminal, a laboratory information system, and a cloud service platform. The cloud service platform is communicatively connected to both the user terminal and the laboratory information system. The user terminal is used to collect and upload corresponding multi-source sampling data to the cloud service platform according to the service requests submitted by the user. The service requests include gastrointestinal joint testing requests or single-item testing requests. The laboratory information system is used to generate structured test report data containing gastric function markers, intestinal health markers, or combinations thereof after completing the testing of the user's samples, and send it to the cloud service platform. The cloud service platform includes a sample quality control unit, a data fusion analysis unit, and a science popularization report generation unit. Among them, the sample quality control unit is used to process the received multi-source samples according to preset quality control rules. The system uses data and structured test report data to determine sample validity. Only when the sample corresponding to all test items in the structured test report data is confirmed to be valid is the structured test report data sent to the data fusion analysis unit. The data fusion analysis unit performs risk assessment analysis on the biomarkers in the structured test report data according to preset rules, generating comprehensive risk profile data that includes gastric risk assessment results and / or intestinal risk assessment results. The science popularization report generation unit receives the comprehensive risk profile data, calls preset content templates, generates personalized science popularization interpretation reports, and sends them to the user terminal for display. The personalized science popularization interpretation reports include health status, risk assessment, and action recommendations corresponding to the comprehensive risk profile data.
[0020] The workflow of the system of the present invention is described in detail below: First, users submit gastrointestinal health testing service requests via mobile applications or WeChat mini-programs. This service request is instantiated in the system as a data object containing user identification, a combination of testing items, and order status. The testing item combination can include gastric function testing only, intestinal health testing only, or a combined gastrointestinal test. Based on the selected testing item, the user terminal guides the user through the corresponding home sampling procedure. Specifically, for gastric function testing, the user is guided to use a lancet to collect blood from their fingertip and drip it onto a special blood collection card to form a blood spot, which is then captured by the terminal's built-in camera. For intestinal health testing, the user is guided to collect a stool sample and record the sampling time. The user terminal integrates the collected blood spot image, sampling time record, and other information into a multi-source sampling data packet, binds this data packet to the user's service request, and transmits it encrypted over the internet to the cloud service platform.
[0021] After the physical sample is delivered and accepted, the laboratory information system initiates the testing process. The system associates the sample with the registered user information and testing items in the system using a unique barcode attached to the sample tube or blood collection card. Upon completion of the test, the laboratory information system generates a structured test report. This data uses a machine-readable standardized format and includes key test fields: for gastric function tests, the report includes pepsinogen I and II values, the pepsinogen I to pepsinogen II ratio, Helicobacter pylori antibody status and typing, and corresponding sample collection and laboratory receipt timestamps; for intestinal health tests, the report includes fecal occult blood and calprotectin quantification values, along with corresponding timestamps. The laboratory information system pushes the structured test report data to a cloud service platform via a pre-configured application programming interface (API).
[0022] Upon receiving multi-source sampling data from user terminals and structured test report data from the laboratory information system, the cloud service platform initiates an automated processing flow. The sample quality control unit within the platform first parses the received structured test report data, identifying the specific test items involved. For example, it identifies whether the test is a gastric function test, a gut health test, or both by parsing the item code field in the data. Next, the unit performs judgments according to preset quality control rules: for the identified gastric function test item, the unit determines whether the sample meets the preset pass / fail criteria based on the multi-source sampling data packet; it extracts the sample collection time and laboratory receiving time from the test report data, calculates the time difference between the two, and determines whether the sample transportation time exceeds the validity period set for gastric function samples. For the identified gut health test item, it calculates the transportation time based on the timestamp in the test report data and determines whether it exceeds the validity period set for gut samples. The sample quality control unit summarizes the quality control judgment results for all identified test items. Only when the judgment results for all items are passed is the structured test report data marked as valid and allowed to proceed to subsequent processing stages. The data fusion and analysis unit receives valid test report data that has passed quality control. This unit has a built-in risk assessment rule base and parses the report data: if the data contains gastric function markers, it invokes gastric risk assessment rules to automatically calculate and output the gastric risk level based on the specific values and status. If the data contains gut health markers, it invokes gut risk assessment rules to compare these values with preset clinical thresholds and maps them to the corresponding gut risk level according to preset combination logic. This unit encapsulates the calculated gastric and / or gut risk levels into a structured, comprehensive risk profile data object.
[0023] Finally, the science popularization report generation unit receives comprehensive risk profile data from the data fusion and analysis unit. This unit includes a content template library storing pre-written science popularization text modules corresponding to different risk levels and their combinations. Based on the type of comprehensive risk profile data, the generation unit retrieves and calls the corresponding gastrointestinal joint interpretation template from the template library. It fills the specific levels and key values from the profile data into the reserved positions of the selected template, automatically generating a complete personalized science popularization interpretation report. This report includes sections such as gastric mucosal status assessment, intestinal health risk analysis, comprehensive recommendations, and action plans. After the report is generated, the unit sends the interpretation report back to the user terminal that initiated the service request via push notification service, and it is fully presented in the user's application interface, thus completing an end-to-end service process from home data collection and laboratory testing to cloud-based intelligent analysis and interpretation.
[0024] Example 2 Please see Figure 2 The working process of the sample quality control unit of the present invention will be described in detail below: In this embodiment, the sample quality control unit judges the sample validity of the received multi-source sampling data and structured test report data according to preset quality control rules, including the following steps: 101. Identify the types of test items included in the structured test report data, including at least one of gastric function test items and intestinal health test items; The type of test item is determined by parsing the preset item code or test category field in the test report data. If the report data contains data fields such as pepsinogen I, pepsinogen II, and Helicobacter pylori antibody, it is identified as containing gastric function test items; if the report data contains data fields such as fecal occult blood quantitative value and calprotectin quantitative value, it is identified as containing intestinal health test items. A report data may contain both types, or it may contain only one of them. The essence of this identification step is to determine which types of test samples the system needs to perform subsequent quality control logic for.
[0025] 201. If a gastric function test item is identified, the first quality control process is executed. The first quality control process includes a pass / fail assessment of blood spot images based on multi-source sampling data. 1. Extract fingertip bloodstain images from multi-source sampling data; 2. Determine whether the fingertip bloodstain image meets the preset acceptance criteria and record the first judgment result; 3. Simultaneously, based on the time metadata of the structured test report data, the transportation timeliness of the gastric function test samples is judged, and the second judgment result is recorded; 4. A sample for a gastric function test is deemed valid if and only if both the first and second judgment results are passed.
[0026] Specifically, the system extracts the fingertip bloodstain image file uploaded by the user terminal from the multi-source sampling data packet bound to the current user and order. A preset bloodstain image qualification judgment module processes the image file. This module integrates a trained machine learning model that automatically analyzes key visual features such as the area coverage, color uniformity, and morphological integrity of the bloodstain region in the image. The system compares the feature values output by the model with a preset qualification threshold range: if the bloodstain area is greater than the minimum requirement, the color meets the standard, and the morphology is intact and without abnormalities, it is judged to meet the preset qualification standard, and a first judgment result is generated: Pass; otherwise, it is judged to fail. Simultaneously, the system parses the metadata related to the gastric function test sample in the structured test report data, extracting the sample collection timestamp and the laboratory receipt timestamp. The difference between the two timestamps is calculated to obtain the actual transportation time from user collection to laboratory receipt. The calculated actual transportation time is compared with the preset maximum allowable transportation validity period for the gastric function test project. If the actual time does not exceed this validity period threshold, a second judgment result is generated: Pass.
[0027] The core logic of the entire process is an AND relationship: the system only determines the gastric function test sample to be valid if and only if both the first judgment result generated in step 2 and the second judgment result generated in step 4 are passed. Failure in either step will result in the gastric test sample being deemed invalid.
[0028] 301. If a gut health testing item is identified, a second quality control process is executed, which includes judging the delivery timeliness of time metadata based on structured test report data.
[0029] 1. Obtain the collection time and laboratory receipt time of the intestinal health test sample from the time metadata of the structured test report data; 2. Calculate the transportation time from data collection to receipt; 3. Determine whether the transportation time has not exceeded the preset threshold for the validity period of intestinal sample transportation; 4. If the limit is not exceeded, the sample for the intestinal health test is deemed valid.
[0030] Specifically, the system extracts two key time points—collection time and laboratory receipt time—from the metadata related to the structured test report and the intestinal health test sample. These time points are recorded by the user during sampling and reported via the terminal, and automatically recorded by the laboratory information system upon sample receipt. Based on these two time points, the system calculates the actual transportation time from the user's collection of the fecal sample to the laboratory's formal receipt of the sample. This actual transportation time is compared to a pre-set transportation validity period threshold specifically for intestinal health testing projects. This threshold can be set to 24 hours, typically shorter than that for gastric function samples. If the calculated transportation time does not exceed the pre-set threshold, the sample for the intestinal health testing project is deemed valid. The principle behind this determination is that the analytes in fecal samples may degrade over time at room temperature, and the biological reliability of the test results will significantly decrease for samples exceeding a specific time window.
[0031] In this embodiment, the sample quality control unit confirms that all samples corresponding to the structured test report data are valid, including the following: 1. If a test is identified that includes both gastric function testing and intestinal health testing, the first and second quality control processes are executed in parallel or sequentially. 2. A confirmation conclusion that the overall quality control has passed will be generated only if both the gastric function test sample and the intestinal health test sample are deemed valid.
[0032] After identifying the test item types in step 101, the system clarifies the set of items requiring quality control. If the set includes both gastric function tests and intestinal health tests, the system will schedule execution units to simultaneously initiate the first quality control process (for the stomach) and the second quality control process (for the intestines), or execute these two processes sequentially depending on system resources. Each process runs independently and outputs its valid or invalid judgment for a specific type of sample. The final overall quality control confirmation follows this set logic: the system will only generate an overall quality control pass confirmation signal when both the gastric function test sample and the intestinal health test sample are deemed valid. This principle ensures that in a gastrointestinal combined testing scenario, the system will not allow mixed results of partially valid and partially invalid data to pass, thus guaranteeing the overall quality reliability of all data to be analyzed from the source before it flows into the core analysis module. If any sample is deemed invalid, the system will not generate an overall pass conclusion and will interrupt subsequent processes, providing feedback on the specific reason for the quality control failure to the user terminal.
[0033] Please see Figure 3 The workflow of the data fusion analysis unit and the science popularization report generation unit in this invention will be described in detail below: In this embodiment, the preset rules in the data fusion analysis unit include gastric risk assessment rules for gastric function testing and intestinal risk assessment rules for intestinal health testing. The gastric risk assessment rules are based on pepsinogen I, pepsinogen II, the ratio of pepsinogen I to pepsinogen II, and Helicobacter pylori antibody status, and map the test data to multiple gastric risk levels according to a preset ABC stratification method. The intestinal risk assessment rules are based on fecal occult blood quantitative values and calprotectin quantitative values, and map the test data to multiple intestinal risk levels by comparing and combining the test values with preset thresholds.
[0034] Specifically, the gastric risk assessment rules follow a pre-defined ABC stratification method, a standardized risk classification logic. The principle of this rule is as follows: the system determines whether the Helicobacter pylori antibody status is positive, combines the concentration values of pepsinogen I and pepsinogen II, and their ratio (the pepsinogen ratio), and compares this with pre-stored numerical threshold ranges in the rule base. The rule maps the output to four distinct risk levels: Type A corresponds to Helicobacter pylori negative and normal pepsinogen levels; Type B corresponds to Helicobacter pylori positive but normal pepsinogen levels; Type C corresponds to Helicobacter pylori positive and normal pepsinogen levels (PGI). 70 and the ratio of pepsinogen PGI to PGI II Case 3; Type D corresponds to Helicobacter pylori negativity but pepsinogen PGI 70 and the ratio of pepsinogen PGI to PGI II Case 3. Each type corresponds to a different gastric mucosal condition and degree of gastric cancer risk. The preset thresholds in the intestinal risk assessment rules are quantitative cutoff values determined based on a large amount of clinical research data. The principle of this rule is to make judgments through logical combination: comparing the user's actual test values with these thresholds, if fecal occult blood is positive and calprotectin is significantly elevated, it is mapped to a high-risk level; if only one is positive or only slightly elevated, it is mapped to a medium-risk level; if both are negative, it is mapped to a low-risk level. The above thresholds and mapping logic are all preset during system deployment and can be maintained and updated through the system management backend according to the latest medical consensus.
[0035] In this embodiment, the data fusion analysis unit performs risk assessment analysis according to preset rules, including the following steps: 1. Extract gastric function markers and / or gut health markers from the structured test report data; 2. Apply the corresponding risk assessment rules according to the type of the extracted biomarker; when a gastric function biomarker is extracted, the gastric risk level is calculated using the gastric risk assessment rules; when a gut health biomarker is extracted, the gut risk level is calculated using the gut risk assessment rules. 3. Combine the calculated gastric risk level and / or intestinal risk level into comprehensive risk profile data.
[0036] Specifically, the system iterates through all fields of the test report data, identifying and extracting data related to gastric function, such as pepsinogen I and pepsinogen II values, and Helicobacter pylori antibody status, as well as data related to gut health, such as fecal occult blood and calprotectin levels. If gastric function markers are present, the system calls the decision logic code corresponding to the gastric risk assessment rules, inputting specific data such as pepsinogen I, pepsinogen II, and Helicobacter pylori antibody status, and runs a pre-coded ABC stratification judgment process. After a series of conditional judgments, a clear gastric risk level result is finally output. If gut health markers are present, the system calls the decision logic code corresponding to the gut health risk assessment rules, comparing the fecal occult blood and calprotectin levels with preset thresholds in the rule base one by one, and making judgments based on preset combination logic, ultimately outputting a clear gut health risk level result. Finally, the system creates a structured data object, namely comprehensive risk profile data, and fills in the gastric risk level result or gut health risk level result, or both, generated during the rule application phase as attribute values for this data object. For example, if a user undergoes a combined gastrointestinal examination and both samples pass quality control, the data object will simultaneously contain two key fields: gastric risk level: type C, and intestinal risk level: medium risk.
[0037] In this embodiment, comprehensive risk profile data is generated, including gastric risk assessment results and / or intestinal risk assessment results, as follows: 1. When the structured test report data contains both gastric function markers and intestinal health markers, the comprehensive risk profile data is a joint risk profile that includes gastric risk level and intestinal risk level; When the structured test report data only contains gastric function markers, the comprehensive risk profile data is a single risk profile that only contains the gastric risk level; When the structured test report data only contains intestinal health markers, the comprehensive risk profile data is a single risk profile containing only intestinal risk levels.
[0038] Specifically, the form of the comprehensive risk profile data is entirely determined by the content of the input structured test report data. The system dynamically determines the profile type by judging which categories of markers were successfully processed during the rule application phase. The determination principle is as follows: The condition for forming a joint risk profile is that the input report data, after parsing, simultaneously contains two types of effective markers: gastric function and intestinal health. Furthermore, the data fusion analysis unit successfully runs risk assessment rules on these two types of markers and outputs the corresponding risk levels. The condition for forming a single risk profile is that the input report data contains only one type of effective marker, i.e., only gastric function markers or only intestinal health markers. In this case, the risk profile data object generated by the system has a single structure, carrying only one dimension of risk assessment conclusion, such as only a gastric risk level of type B, or only an intestinal risk level of low risk. This provides an accurate data foundation for subsequently generating specialized science popularization reports targeting a single organ. This flexible design allows the system to seamlessly support three different service modes: combined gastrointestinal testing, single gastric function testing, and single intestinal health testing. Under a unified data processing and analysis framework, it outputs risk profile data with a consistent structure but highly personalized content to adapt to diverse user needs.
[0039] In this embodiment, the preset content templates include: a stomach health interpretation template corresponding to different stomach risk levels, an intestinal health interpretation template corresponding to different intestinal risk levels, and a combined gastrointestinal interpretation template corresponding to a combination of stomach risk level and intestinal risk level.
[0040] Specifically, the content template library is a collection of structured texts pre-built and stored in the cloud service platform database. Each template is a document framework with a fixed format and variable slots. The stomach health interpretation templates are categorized according to stomach risk levels (Type A, Type B, Type C, Type D), with one template for each level. The gut health interpretation templates are categorized according to gut risk levels (low, medium, high), with one template for each level. The combined gastrointestinal interpretation templates are specifically designed for different combinations of stomach and gut risks; their content not only integrates descriptions of the individual states of the stomach and intestines but also focuses on providing integrated health management action recommendations. All templates follow a three-part structure: health status description, risk assessment, and action recommendations, and the text content is reviewed by medical experts to ensure its scientific rigor and accessibility.
[0041] In this embodiment, the science popularization report generation unit generates a personalized science popularization interpretation report, including the following steps: 1. Based on the type of comprehensive risk profile data, call the corresponding content template; if the comprehensive risk profile data is a joint risk profile, call the gastrointestinal joint interpretation template; if the comprehensive risk profile data is a single risk profile containing only the gastric risk level, call the corresponding gastric health interpretation template; if the comprehensive risk profile data is a single risk profile containing only the intestinal risk level, call the corresponding intestinal health interpretation template. 2. Fill the specific risk levels from the comprehensive risk profile data into the called content template to generate the personalized science popularization interpretation report, which includes the corresponding health status description, risk assessment and action suggestions.
[0042] Specifically, the system reads the structure of the profile data to identify whether it contains a single risk level or two risk levels. If it's a combined risk profile, the system uses the gastric risk level value plus the intestinal risk level value as the key to retrieve the corresponding gastrointestinal combined interpretation template from the template library. If it's a single risk profile (stomach only), it uses the gastric risk level value as the key to retrieve the corresponding gastric health interpretation template; if it's a single risk profile (intestine only), it uses the intestinal risk level value as the key to retrieve the corresponding intestinal health interpretation template. After a successful match, the system loads the corresponding template content into memory. Next, the system parses the predefined variable slots in the called template, extracts the corresponding specific values from the comprehensive risk profile data and its associated original structured test report data, and accurately fills these values into the corresponding slot positions in the template. Simultaneously, the system integrates the risk level description into the text. After filling, the system automatically combines the paragraphs and adds standardized formatting and visualization elements, ultimately generating a complete personalized science popularization interpretation report document. This document is the final result directly presented to the user, and its content achieves a precise correspondence with the user's individual test data and risk level.
[0043] It is understood that those skilled in the art can combine various implementation methods in the above embodiments under the guidance of the above examples to obtain technical solutions with multiple implementation methods.
[0044] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. An Internet platform-based intelligent popular science interpretation system for gastrointestinal health, characterized in that, include: The system comprises a user terminal, a laboratory information system, and a cloud service platform, wherein the cloud service platform is communicatively connected to the user terminal and the laboratory information system, respectively. The user terminal is used to collect and upload corresponding multi-source sampling data to the cloud service platform according to the service request submitted by the user; the service request includes a gastrointestinal joint examination request or a single test request; The laboratory information system is used to generate structured test report data containing gastric function markers, intestinal health markers, or combinations thereof after completing the testing of user samples, and send it to the cloud service platform. The cloud service platform includes a sample quality control unit, a data fusion and analysis unit, and a science popularization report generation unit; wherein: The sample quality control unit is used to determine the validity of the received multi-source sampling data and structured test report data according to preset quality control rules; the structured test report data is sent to the data fusion analysis unit only when it is determined that all test items corresponding to the structured test report data are valid. The data fusion and analysis unit is used to perform risk assessment analysis on the markers in the structured test report data according to preset rules, and generate comprehensive risk profile data including gastric risk assessment results and / or intestinal risk assessment results. The science popularization report generation unit is used to receive the comprehensive risk profile data, call the preset content template, generate a personalized science popularization interpretation report, and send it to the user terminal for display. The personalized science popularization interpretation report includes health status, risk assessment, and action suggestions corresponding to the comprehensive risk profile data. 2.The Internet platform-based intelligent popular science interpretation system for gastrointestinal health according to claim 1, characterized in that, The sample quality control unit performs sample validity assessment on the received multi-source sampling data and structured test report data according to preset quality control rules, including: Identify the types of test items included in the structured test report data, wherein the types of test items include at least one of gastric function test items and intestinal health test items; If a gastric function test item is identified, the first quality control process is executed, which includes a pass / fail judgment on the blood spot image based on the multi-source sampling data. If a gut health testing item is identified, a second quality control process is executed, which includes determining the delivery timeliness of the time metadata based on the structured test report data. 3.The Internet platform-based intelligent science popularization interpretation system for gastrointestinal health according to claim 2, characterized in that, The execution of the first quality control process includes: Extract fingertip bloodstain images from the multi-source sampling data; Determine whether the fingertip bloodstain image meets the preset qualification standard, and record the first determination result; Simultaneously, based on the time metadata of the structured test report data, the transportation timeliness of the gastric function test sample is judged, and the second judgment result is recorded; A sample for a gastric function test is deemed valid only if both the first and second judgment results are passed.
4. The Internet platform-based intelligent popular science interpretation system for gastrointestinal health according to claim 2, characterized in that, The execution of the second quality control process includes: The collection time and laboratory receipt time of the intestinal health test sample are obtained from the time metadata of the structured test report data. Calculate the transportation time from data collection to receipt; Determine whether the transportation time does not exceed the preset threshold for the validity period of intestinal sample transportation; If the limit is not exceeded, the sample for the intestinal health test is deemed valid. 5.The Internet platform-based intelligent science popularization interpretation system for gastrointestinal health according to claim 2, characterized in that, The sample quality control unit confirms that all samples corresponding to the structured test report data are valid, including: If a test is identified that includes both gastric function testing and intestinal health testing, the first quality control process and the second quality control process are executed in parallel or sequentially. A confirmation conclusion that the overall quality control has passed is generated only when both the gastric function test sample and the intestinal health test sample are deemed valid. 6.The Internet platform-based intelligent science popularization interpretation system for gastrointestinal health according to claim 1, characterized in that, The preset rules in the data fusion analysis unit include gastric risk assessment rules for gastric function testing projects and intestinal risk assessment rules for intestinal health testing projects; wherein: The gastric risk assessment rule is based on pepsinogen I, pepsinogen II, the ratio of pepsinogen I to pepsinogen II, and Helicobacter pylori antibody status, and maps the test data to multiple gastric risk levels according to a preset ABC stratification method. The intestinal risk assessment rule is based on the quantitative values of fecal occult blood and calprotectin. By comparing and combining the test values with preset thresholds, the test data is mapped to multiple intestinal risk levels. 7.The Internet platform-based intelligent science popularization interpretation system for gastrointestinal health according to claim 6, characterized in that, The data fusion and analysis unit performs risk assessment and analysis according to preset rules, including: Extract gastric function markers and / or gut health markers contained in the structured test report data; Based on the type of biomarker extracted, the corresponding risk assessment rules are applied; specifically, when a gastric function biomarker is extracted, the gastric risk level is calculated using the gastric risk assessment rules; when a gut health biomarker is extracted, the gut risk level is calculated using the gut risk assessment rules. The calculated gastric risk level and / or intestinal risk level are combined to form a comprehensive risk profile. 8.The Internet platform-based intelligent science popularization interpretation system for gastrointestinal health according to claim 7, characterized in that, The generation of comprehensive risk profile data, including gastric risk assessment results and / or intestinal risk assessment results, includes: When the structured test report data contains both gastric function markers and gut health markers, the comprehensive risk profile data is a joint risk profile that includes gastric risk level and gut risk level. When the structured test report data only contains gastric function markers, the comprehensive risk profile data is a single risk profile that only contains the gastric risk level; When the structured test report data only contains intestinal health markers, the comprehensive risk profile data is a single risk profile containing only intestinal risk levels. 9.The Internet platform-based intelligent science popularization interpretation system for gastrointestinal health according to claim 8, characterized in that, The preset content templates include: stomach health interpretation templates corresponding to different stomach risk levels, intestinal health interpretation templates corresponding to different intestinal risk levels, and combined gastrointestinal interpretation templates corresponding to combinations of stomach and intestinal risk levels.
10. The intelligent science popularization and interpretation system for gastrointestinal health based on an internet platform as described in claim 9, characterized in that, The science popularization report generation unit generates personalized science popularization interpretation reports, including: Based on the type of the comprehensive risk profile data, the corresponding content template is invoked; wherein, if the comprehensive risk profile data is a joint risk profile, the gastrointestinal joint interpretation template is invoked; if the comprehensive risk profile data is a single risk profile containing only the gastric risk level, the corresponding gastric health interpretation template is invoked; if the comprehensive risk profile data is a single risk profile containing only the intestinal risk level, the corresponding intestinal health interpretation template is invoked. The specific risk levels from the comprehensive risk profile data are filled into the invoked content template to generate a personalized science popularization interpretation report that includes a description of the corresponding health status, risk assessment, and action suggestions.