Artificial intelligence-based patient-reported assessment system for inflammatory bowel disease

Abstract

The present invention discloses an inflammatory bowel disease patient report evaluation system based on artificial intelligence, which relates to the technical field of medical treatment. The age group analysis module calculates the historical intestinal damage factor based on the historical data of patients of different ages, and analyzes the impact of different ages on intestinal damage, constructs the corresponding influence coefficient, and combines the blood extraction data to obtain the activity coefficient of the current patient, so as to more accurately judge the patient's inflammation intensity and make up for the shortcomings of traditional evaluation methods under age differences. The intestinal state analysis module uses deep learning technology to construct a morbidity prediction model, and constructs a morbidity complexity coefficient based on imaging data, and obtains a risk assessment coefficient in combination with model fitting. This application of deep learning can more accurately evaluate the complexity of the morbidity and provide a more scientific risk assessment. The report evaluation module compares and analyzes the risk assessment coefficient through preset evaluation thresholds to generate complete and exclusive symptom reports of different levels.

Description

Technical Field

[0001] The present invention relates to the technical field of medical treatment, and in particular to an artificial intelligence-based patient-reported assessment system for inflammatory bowel disease. Background Art

[0002] Inflammatory bowel disease (IBD) is a group of chronic inflammatory bowel diseases, including Crohn's disease (CD) and ulcerative colitis (UC). The incidence of IBD varies worldwide but is generally on the rise.

[0003] Incidence trends: The global incidence of IBD has been increasing over the past few decades, particularly in industrialized countries and some developing countries.

[0004] Regional differences: IBD has a higher incidence in North America, Europe, Australia, and New Zealand, while it has a relatively low incidence in traditional regions of Asia and Africa. However, with the westernization of lifestyles and dietary habits, the incidence of IBD in these regions is also increasing.

[0005] In the medical field, artificial intelligence (AI) is gradually becoming an important tool for improving diagnostic and treatment efficiency, especially in medical image analysis, biomarker detection, and personalized treatment. Specifically, AI-based disease assessment and diagnosis systems have become an indispensable part of clinical practice, among which the patient-reported assessment system for inflammatory bowel disease (IBD) is a typical application scenario. Inflammatory bowel disease is a group of chronic inflammatory bowel diseases, including Crohn's disease and ulcerative colitis. The incidence of inflammatory bowel disease varies worldwide, but is generally on the rise. Therefore, further research on inflammatory bowel disease is needed.

[0006] For patients with inflammatory bowel disease of different age groups, existing assessment methods may have shortcomings when reporting assessments due to the different manifestations of intestinal function impairment. For example, intestinal function parameters of older patients may be more difficult to capture the severity of inflammation, while younger patients may show more obvious intestinal dysfunction, which affects the accuracy of reported assessments. This variability may affect the accuracy of disease assessments, especially when intestinal function impairment becomes an important indicator for evaluating inflammatory activity and disease severity. Traditional assessment methods may ignore this impact, making it difficult to accurately reflect the actual condition. Summary of the Invention

[0007] In response to the deficiencies of the existing technology, the present invention provides an inflammatory bowel disease patient report evaluation system based on artificial intelligence, which solves the problems in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an artificial intelligence-based inflammatory bowel disease patient report evaluation system, including a matter inspection module, a data collection module, an age group analysis module, a bowel status analysis module and a report evaluation module;

[0009] The said matter examination module is used to preliminarily perform biochemical examinations and imaging examinations on patients with inflammatory bowel disease and summarize the examination results to obtain a preliminary exclusive symptom report;

[0010] The data collection module is used to perform feature extraction on the data information in the preliminary exclusive disease report to respectively obtain relevant intestinal function status data information, relevant imaging data information, relevant blood extraction data information, relevant stool sample data and relevant individual identity data information of the current inflammatory bowel disease patient, and to construct an inflammatory bowel disease data set by combining historical data of inflammatory bowel disease patients of different ages;

[0011] The age group analysis module is used to calculate the historical intestinal damage factor Gsyz at different age states based on the historical data of patients with inflammatory bowel disease at different ages. L , based on the historical intestinal damage factor Gsyz at different age states L Analyze the impact of different ages on intestinal damage to construct the corresponding impact coefficient Yxs. Combined with relevant blood extraction data information, obtain the activity coefficient Hyxs of the current inflammatory bowel disease patient. Based on the value of the activity coefficient Hyxs, preliminarily judge the intensity of the current inflammatory bowel disease patient's inflammation. If the inflammation is in an abnormal state, further analysis will be performed.

[0012] The intestinal state analysis module is used to construct a morbidity prediction model using deep learning technology, and construct a morbidity complexity coefficient Bfxs based on relevant imaging data information, and combine the trained morbidity prediction model to obtain the risk assessment coefficient Wpxs through fitting;

[0013] The report evaluation module is used to pre-set an evaluation threshold value P, and compare and analyze the evaluation threshold value P with the risk assessment coefficient Wpxs to comprehensively analyze the severity of the current inflammatory bowel disease patient's symptoms and generate a complete exclusive symptom report.

[0014] Preferably, the matters inspection module is used to perform biochemical examinations and imaging examinations on patients with inflammatory bowel disease in advance, wherein the examination items of the biochemical examination include blood tests, stool tests and intestinal function, and the examination items of the imaging examination include intestinal endoscopy and intestinal ultrasound examination; the examination results obtained through biochemical examinations and imaging examinations are summarized to obtain a preliminary exclusive symptom report.

[0015] Preferably, the data collection module includes a first collection unit, a second collection unit and a third collection unit;

[0016] The first acquisition unit is used to obtain relevant individual identity data information based on the personal information registration form filled out by the current inflammatory bowel disease patient at the hospital front desk, and the relevant individual identity data information includes name, gender, age value Nz, ID number and contact information;

[0017] The second collection unit is used to obtain relevant intestinal function status data information, relevant blood extraction data information and relevant stool sample data of the current inflammatory bowel disease patient based on the biochemical examination content in the preliminary exclusive symptom report, wherein the relevant intestinal function status data information includes the C-reactive protein concentration Crp, the alanine aminotransferase concentration Gmnd and the albumin concentration Bnd in the current inflammatory bowel disease patient; the relevant blood extraction data information includes the white blood cell count Bxs; and the relevant stool sample data includes the fecal calprotectin concentration Bgdz;

[0018] The third acquisition unit is used to obtain relevant image data information based on the imaging examination content in the preliminary exclusive disease report, wherein the relevant image data information includes the number of fistulas in the intestine Lgs and the intestinal wall thickness difference Cbhc;

[0019] The historical data of inflammatory bowel disease patients of different ages refers to the relevant intestinal function status data information, relevant imaging data information, relevant blood extraction data information, relevant stool sample data and relevant individual identity data information of each inflammatory bowel disease patient obtained according to the historical stages in the hospital.

[0020] Preferably, the age group analysis module includes a historical data analysis unit, an impact determination unit and an inflammation intensity analysis unit;

[0021] The historical data analysis unit obtains the historical intestinal damage factor Gsyz at different age states based on the relevant intestinal function status data information of each inflammatory bowel disease patient obtained in the historical stage of the hospital. L , specifically obtained according to the following formula:

[0022]

[0023] Where Gmnd represents the alanine aminotransferase concentration, Crp represents the C-reactive protein concentration, Bnd represents the albumin concentration, and a1, a2, and a3 are all weight values.

[0024] Preferably, the impact determination unit is used to analyze the impact of different ages on intestinal damage to construct a corresponding impact coefficient Yxs, which is specifically obtained in the following manner:

[0025]

[0026] Where Nz δ Expressed as the standard deviation of age values, Gsyz Lδ Expressed as the standard deviation of the historical intestinal damage factor, Cov(Nz,Gsyz L ) is expressed as the covariance of age values ​​and historical intestinal damage factors.

[0027] Preferably, the inflammatory bowel disease patients obtained in the hospital in the historical stage are sorted by age to obtain a patient age data column, wherein the sorting method is: sorting by age from small to large; at the same time, the inflammatory bowel disease patient with the closest age value Nz to the current inflammatory bowel disease patient is matched in the patient age data column and marked as the kth inflammatory bowel disease patient, so the influence coefficient Yxs corresponding to the kth inflammatory bowel disease patient is recorded as the kth influence coefficient Yxs k ;

[0028] According to the kth influence coefficient Yxs k , match the kth pair of elements from the patient age data column, the specific element pairing content is (Yxs k ,Yxs n-k+1 );

[0029] The inflammation intensity analysis unit is used to obtain the historical intestinal damage factor Gsyz according to the historical data analysis unit. L Method to obtain the intestinal damage factor Gsyz of current inflammatory bowel disease patients D , and combined with relevant blood extraction data information, obtain the activity coefficient Hyxs of the current inflammatory bowel disease patient, specifically in the following way:

[0030]

[0031] Where, Bxs represents the white blood cell count; Yxs n-k+1 It is represented as the (n-k+1)th influence coefficient; n represents the number of patients with inflammatory bowel disease of different ages in the historical period; k represents the position of the current age value Nz of the inflammatory bowel disease patient in the patient age data column; the value range of k is from 1 to U represents the first correction constant, and b1 and b2 are weight values.

[0032] Preferably, an activity threshold is pre-set, and the activity coefficient Hyxs is compared and analyzed with the activity threshold to preliminarily determine the current inflammatory intensity of the inflammatory bowel disease patient. The specific determination content is as follows:

[0033] If the activity coefficient Hyxs exceeds the activity threshold, it is preliminarily judged that the inflammation of the current inflammatory bowel disease patient is in an abnormal state, and further analysis will be performed at this time;

[0034] If the activity coefficient Hyxs does not exceed the activity threshold, it is preliminarily determined that the inflammation of the current inflammatory bowel disease patient is not in an abnormal state.

[0035] Preferably, the intestinal state analysis module includes an intestinal detection unit and a comprehensive evaluation unit;

[0036] The intestinal detection unit is used to construct a pathological complexity coefficient Bfxs based on relevant image data information and combined with relevant fecal sample data, which is specifically obtained in the following way:

[0037]

[0038] Where Bgdz represents the fecal calprotectin concentration, Lgs represents the number of fistulas, Cbhc represents the intestinal wall thickness difference, R represents the second correction constant, and α and β are weight values.

[0039] Preferably, the comprehensive evaluation unit is used to input the activity coefficient Hyxs and the pathological complexity coefficient Bfxs into the pathological prediction model, and after dimensionless processing, the corresponding data values ​​are mapped to the interval [0,1], and then the risk assessment coefficient Wpxs is obtained according to the following formula:

[0040]

[0041] Where F1 and F2 are weight values, and H represents the third correction constant.

[0042] Preferably, the report evaluation module includes a comparison unit and a report generation unit;

[0043] The comparison unit is used to pre-set an assessment threshold P, which includes a first assessment threshold P1 and a second assessment threshold P2, and the first assessment threshold P1 is greater than the second assessment threshold P2. By comparing and analyzing the risk assessment coefficient Wpxs with the first assessment threshold P1 and the second assessment threshold P2, the severity of the current inflammatory bowel disease patient's condition is comprehensively analyzed. The specific comparison and analysis content is as follows:

[0044] If the risk assessment coefficient Wpxs is less than the second assessment threshold P2, it indicates that the symptom of the current inflammatory bowel disease patient is in a normal state, and a first complete exclusive symptom report will be generated;

[0045] If the second assessment threshold value P2 ≤ the risk assessment coefficient Wpxs ≤ the first assessment threshold value P1, it indicates that the symptom of the current inflammatory bowel disease patient is in a normal state, and a second complete exclusive symptom report is generated;

[0046] If the risk assessment coefficient Wpxs is greater than the first assessment threshold P1, it indicates that the symptom of the current inflammatory bowel disease patient is not in a normal state, and a third complete exclusive symptom report will be generated.

[0047] The report generating unit is used to take corresponding treatment measures according to the first complete exclusive symptom report, the second complete exclusive symptom report and the third complete exclusive symptom report obtained by the comparing unit. The specific contents are as follows:

[0048] If the first complete exclusive symptom report is generated, patients with inflammatory bowel disease will be advised to visit the hospital regularly for checkups, undergoing routine physical examinations and related intestinal examinations every 3-6 months, and will be provided with dietary and lifestyle guidance;

[0049] If a second complete, dedicated symptom report is generated, medication dosages and types will be adjusted for IBD patients; checkups will be increased to every 1-3 months, and additional nutritional support will be provided to IBD patients;

[0050] If a third complete dedicated symptom report is generated, the patient with inflammatory bowel disease will be arranged for hospitalization, and if complications arise, surgical intervention will be considered.

[0051] The present invention provides an artificial intelligence-based patient-reported assessment system for inflammatory bowel disease, which has the following beneficial effects:

[0052] (1) is an innovative solution designed to improve the care and quality of life of IBD patients while providing valuable data to medical professionals and researchers. By using this system, the needs and experiences of IBD patients can be better understood, leading to more personalized and effective care.

[0053] (2) Through the event inspection module, patients with inflammatory bowel disease are subjected to a variety of biochemical and imaging examinations, and the results are summarized to obtain a preliminary exclusive symptom report, further ensuring a comprehensive understanding of the patient's condition. The data collection module extracts features from the data in the preliminary report, obtains relevant intestinal function status, imaging data, blood extraction data, stool sample data and individual identity information, and combines historical data to construct an inflammatory bowel disease data set. This data precision processing helps to improve the accuracy of the analysis and ensure personalized evaluation. The age group analysis module calculates the historical intestinal damage factor Gsyz based on the historical data of patients of different ages. LThe module analyzes the impact of different ages on intestinal damage, constructs a corresponding influence coefficient Yxs, and combines this with blood extraction data to obtain the patient's current activity coefficient Hyxs. This allows for a more accurate assessment of the patient's inflammatory intensity, addressing the limitations of traditional assessment methods due to age differences. The intestinal status analysis module utilizes deep learning technology to construct a disease prediction model and constructs a disease complexity coefficient Bfxs based on imaging data. Combined with model fitting, this module derives a risk assessment coefficient Wpxs. This application of deep learning enables more accurate assessment of disease complexity and provides a more scientific risk assessment. The report evaluation module compares and analyzes the risk assessment coefficient Wpxs using a preset assessment threshold P, generating complete, customized disease reports at different levels. Based on the assessment results, the system provides appropriate treatment recommendations, further ensuring the relevance and practicality of the report content. In summary, through the integrated application of multiple modules and the in-depth application of artificial intelligence technology, this system not only improves the accuracy and personalization of disease assessment for patients with inflammatory bowel disease, but also enhances the scientific and practical nature of report generation, effectively supporting clinical decision-making and personalized treatment.

[0054] (2) By calculating the historical intestinal damage factor Gsyz at different age states L , helping the system effectively identify intestinal damage in patients of different age groups, thereby providing data support for personalized treatment. The impact determination unit analyzes the impact of different ages on intestinal damage and constructs the corresponding impact coefficient Yxs. By calculating the standard deviation and covariance of age values ​​and historical intestinal damage factors, it can accurately assess the specific impact of age on intestinal damage. This scientific assessment method can reveal differences in intestinal damage among patients of different age groups, thereby better adjusting and optimizing treatment plans. The inflammation intensity analysis unit will accurately determine the current patient's inflammation intensity based on the age group analysis results and relevant data. This analysis not only considers the patient's age factor, but also integrates the specific manifestations of intestinal function, helping to determine the specific intensity of the patient's inflammation and its potential health risks. In summary, by integrating and analyzing historical data from different age groups, the system can accurately assess intestinal damage and inflammation intensity, thereby improving the level of personalized assessment and treatment of inflammatory bowel disease, and has a significant beneficial effect on patient health management.

[0055] (3) The system creates a patient age data column by sorting the inflammatory bowel disease patients obtained in the hospital in historical stages in order of age from young to old. This sorting method ensures the systematization and standardization of patient data. The system can effectively find the kth inflammatory bowel disease patient closest to the current patient's age value Nz in the data column and mark its corresponding influence coefficient Yxs. Through this precise matching process, the system can obtain the historical data most relevant to the current patient's age, thereby providing reliable benchmark data for inflammation intensity analysis.

[0056] (4) Through detailed analysis and hierarchical management of the risk assessment coefficient Wpxs, the system provides highly personalized treatment recommendations and can formulate scientific treatment plans based on the patient's specific condition. This personalized management approach can effectively improve treatment efficacy and the patient's quality of life. Through regular symptom assessment and report generation, doctors can monitor the patient's health status in a timely manner and make timely interventions based on the actual situation. This dynamic health monitoring and intervention capability further improves the efficiency of disease control and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a block diagram of the artificial intelligence-based inflammatory bowel disease patient report assessment system of the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0059] Inflammatory bowel disease (IBD) is a group of chronic inflammatory bowel diseases, including Crohn's disease (CD) and ulcerative colitis (UC). The incidence of IBD varies worldwide but is generally on the rise.

[0060] Over the past few decades, the global incidence of IBD has been increasing, particularly in industrialized and some developing countries. IBD has a higher incidence in North America, Europe, Australia, and New Zealand, whereas it is relatively low in traditionally affected regions of Asia and Africa. However, with the westernization of lifestyles and dietary habits, the incidence of IBD in these regions is also increasing. North America has one of the highest incidences of IBD, with an estimated 10 to 15 new cases per 100,000 people in the United States each year. The incidence of IBD in Europe is similar to that in North America, with even higher rates in some regions. The incidence of IBD in Asia is lower, but has been on the rise in recent years, particularly in more urbanized areas.

[0061] Genetic factors play a significant role in the development of IBD. Environmental factors, including dietary habits, smoking, antibiotic use, and environmental pollution, may also be associated with the onset of IBD. An abnormal immune system response is also a significant factor in the development of IBD.

[0062] A healthy lifestyle, including a balanced diet, regular exercise, and smoking cessation, may help reduce the risk of IBD. Medication is the mainstay of treatment for IBD and includes anti-inflammatory drugs, immunosuppressants, and biologics. In some cases, surgery may be necessary, especially for patients who do not respond to medication.

[0063] The incidence of IBD is increasing worldwide, particularly in industrialized and developing countries and urban areas. Understanding the trends and influencing factors of IBD is crucial for developing public health policies and allocating healthcare resources. Furthermore, strengthening IBD prevention, early diagnosis, and treatment is crucial for improving patients' quality of life and reducing the healthcare burden.

[0064] Example 1

[0065] See also Figure 1 The present invention provides an artificial intelligence-based inflammatory bowel disease patient report evaluation system, which includes a matter inspection module, a data collection module, an age group analysis module, an intestinal state analysis module and a report evaluation module;

[0066] The said matter examination module is used to preliminarily perform biochemical examinations and imaging examinations on patients with inflammatory bowel disease and summarize the examination results to obtain a preliminary exclusive symptom report;

[0067] The data collection module is used to perform feature extraction on the data information in the preliminary exclusive disease report to respectively obtain relevant intestinal function status data information, relevant imaging data information, relevant blood extraction data information, relevant stool sample data and relevant individual identity data information of the current inflammatory bowel disease patient, and to construct an inflammatory bowel disease data set by combining historical data of inflammatory bowel disease patients of different ages;

[0068] The data collected by the data collection module includes:

[0069] Patient Global Impression of Change: Current data on bowel function in patients with inflammatory bowel disease include patient electronic diaries, bowel movement frequency, and the Crohn's Disease Activity Index (CDAI), an 8-item disease activity index comprised of 3 patient-reported and 5 physician-reported / laboratory items (physical examination and 1 laboratory parameter [hematocrit]).

[0070] Defecation count, Abdominal Pain Numeric Rating Scale (NRS), Defecation Urgency NRS, Patient Global Severity Rating (PGRS) were obtained through patient electronic diaries, Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-Fatigue), Inflammatory Bowel Disease Questionnaire, Abdominal Pain Numeric Rating Scale, Patient Global Severity Rating, etc. were obtained through tablet devices;

[0071] The data collection module extracts data in a way that is already known in the art and will not be described in detail here.

[0072] The age group analysis module is used to calculate the historical intestinal damage factor Gsyz at different age states based on the historical data of patients with inflammatory bowel disease at different ages. L , based on the historical intestinal damage factor Gsyz at different age states L Analyze the impact of different ages on intestinal damage to construct the corresponding impact coefficient Yxs. Combined with relevant blood extraction data information, obtain the activity coefficient Hyxs of the current inflammatory bowel disease patient. Based on the value of the activity coefficient Hyxs, preliminarily judge the intensity of inflammation in the current inflammatory bowel disease patient. If the inflammation of the current inflammatory bowel disease patient is in an abnormal state, further analysis will be performed;

[0073] The intestinal state analysis module is used to construct a morbidity prediction model using deep learning technology, and construct a morbidity complexity coefficient Bfxs based on relevant imaging data information, and combine the trained morbidity prediction model to obtain the risk assessment coefficient Wpxs through fitting;

[0074] The report evaluation module is used to pre-set an evaluation threshold value P, and compare and analyze the evaluation threshold value P with the risk assessment coefficient Wpxs to comprehensively analyze the severity of the current inflammatory bowel disease patient's symptoms and generate a complete exclusive symptom report.

[0075] During operation, the system first uses the event-based examination module to conduct comprehensive biochemical and imaging examinations on patients. These examination results are summarized into a preliminary, dedicated symptom report, providing a solid foundation for subsequent data analysis and evaluation. Secondly, the data collection module extracts features from the data within the preliminary, dedicated symptom report, covering the patient's intestinal function status, imaging data, blood and stool sample data, and individual identity information. Combined with historical data from patients of different ages, this module constructs a comprehensive inflammatory bowel disease dataset. This process further ensures the comprehensiveness and multidimensionality of the data, providing a basis for accurate analysis and decision-making. The introduction of the age-group analysis module is a significant advantage of the system. By analyzing historical data from patients of different age groups, the system can calculate age-related intestinal injury factors and further analyze the impact of age on intestinal injury, constructing the corresponding influence coefficient Yxs. Combined with the patient's blood extraction data, the system can accurately obtain the patient's current inflammatory activity coefficient Hyxs, thereby preliminarily determining the intensity of inflammation. This analysis method considers the impact of age on intestinal function damage, overcomes the assessment flaw of traditional methods that ignore assessments across different age groups, and improves the reliability of the assessment results. In addition, the intestinal status analysis module uses deep learning technology to construct the disease complexity coefficient Bfxs by processing relevant imaging data, and combines it with the trained disease prediction model to finally fit the patient's risk assessment coefficient Wpxs. This method not only improves the system's ability to predict complex diseases, but also further improves the accuracy of the assessment. Finally, the report evaluation module compares and analyzes the risk assessment coefficient Wpxs through a pre-set assessment threshold P, comprehensively analyzes the severity of the current patient's disease, and generates a corresponding complete and exclusive disease report. This process further ensures the clarity and operability of the evaluation results and can provide reliable support for clinical decision-making. In short, the system further improves the accuracy and efficiency of the condition assessment of patients with inflammatory bowel disease by integrating multiple functions such as biochemical examinations, data feature extraction, age group analysis, deep learning prediction and report generation, and has broad clinical application value.

[0076] Example 2

[0077] Please refer to Figure 1 Specifically: the item inspection module is used to conduct biochemical examinations and imaging examinations on patients with inflammatory bowel disease in advance, wherein the inspection items of the biochemical examination include blood tests, stool tests and intestinal function, and the inspection items of the imaging examination include intestinal endoscopy and intestinal ultrasound; the inspection results obtained through biochemical examinations and imaging examinations are summarized to obtain a preliminary exclusive symptom report.

[0078] The data collection module includes a first collection unit, a second collection unit and a third collection unit;

[0079] The first acquisition unit is used to obtain relevant individual identity data information based on the personal information registration form filled out by the current inflammatory bowel disease patient at the hospital front desk, and the relevant individual identity data information includes name, gender, age value Nz, ID number and contact information;

[0080] Specifically, the hospital uses the electronic medical record system (EMR) or patient management system (PMS) to enter the patient's basic personal information into the system. This information includes but is not limited to: name, gender, age, ID number and contact information. The hospital will verify whether the registered personal information is consistent with the information on the ID card to ensure that there is no error or false information. If the patient's information is incomplete or incorrect, the hospital staff will ask the patient to provide additional information or make corrections;

[0081] The second collection unit is used to obtain relevant intestinal function status data information, relevant blood extraction data information, and relevant stool sample data of the current inflammatory bowel disease patient based on the biochemical examination content in the preliminary exclusive symptom report, wherein the relevant intestinal function status data information includes the C-reactive protein concentration Crp, the alanine aminotransferase concentration Gmnd, and the albumin concentration Bnd in the current inflammatory bowel disease patient;

[0082] The relevant blood extraction data information includes white blood cell count Bxs;

[0083] The relevant stool sample data include fecal calprotectin concentration Bgdz;

[0084] The third acquisition unit is used to obtain relevant image data information based on the imaging examination content in the preliminary exclusive disease report, wherein the relevant image data information includes the number of fistulas in the intestine Lgs and the intestinal wall thickness difference Cbhc;

[0085] And using dimensionless processing technology, the relevant data information in the inflammatory bowel disease data set is dimensionlessly processed, so that the units between the information are unified;

[0086] The historical data of inflammatory bowel disease patients of different ages refers to the relevant intestinal function status data information, relevant imaging data information, relevant blood extraction data information, relevant stool sample data and relevant individual identity data information of each inflammatory bowel disease patient obtained according to the historical stages in the hospital.

[0087] In this embodiment, through the biochemical examination and imaging examination conducted by the matter inspection module, the system can obtain multi-dimensional data including blood tests, stool tests, intestinal function tests, intestinal endoscopy and intestinal ultrasound examination. This comprehensive data collection and aggregation makes the generation of preliminary exclusive symptom reports more accurate and comprehensive, laying a solid foundation for subsequent evaluation and analysis. The data collection module systematically collects the patient's personal information, including name, gender, age, ID number and contact information, through the first acquisition unit. This ensures the accurate recording and effective tracking of each patient's individual identity information, further avoids assessment bias caused by incomplete or erroneous information, and improves the accuracy and reliability of the system. The second acquisition unit extracts key biochemical data from the preliminary exclusive symptom report. These data help to understand the patient's physiological status in detail and support more accurate disease activity assessment. The third acquisition unit provides detailed imaging data. These data can reflect the actual pathological changes in the intestine, provide the necessary basic data for the intestinal status analysis module, and help to assess the complexity of the pathology. By integrating historical data from patients with inflammatory bowel disease of different age groups, the system can analyze and compare the impact of different ages on intestinal function and pathological status. This method not only helps identify the special pathological characteristics of different age groups, but also improves the accuracy of prediction and judgment of the current patient's condition, thereby enhancing the scientific nature and accuracy of the report evaluation. Based on the summarized biochemical and imaging data, the system can construct a detailed symptom report, and conduct a comprehensive analysis according to the set evaluation threshold to generate a targeted and exclusive symptom report. This not only monitors the patient's condition in real time, but also formulates personalized treatment plans based on specific evaluation results, thereby improving the efficiency and effectiveness of disease management. In short, this system effectively improves the accuracy of reports from patients with inflammatory bowel disease through multi-faceted data collection, precise data processing, and comprehensive evaluation and analysis, providing doctors with a more reliable basis for diagnosis and treatment, and helping to improve patients' treatment outcomes and quality of life.

[0088] Example 3

[0089] Please refer to Figure 1 ,Specifically: the age group analysis module includes a historical data analysis unit, an ,impact determination unit and an inflammation intensity analysis unit;

[0090] The historical data analysis unit obtains the historical intestinal damage factor Gsyz at different age states based on the relevant intestinal function status data information of each inflammatory bowel disease patient obtained in the historical stage of the hospital. L , specifically obtained according to the following formula:

[0091]

[0092] Wherein, Gmnd represents the alanine aminotransferase concentration, Crp represents the C-reactive protein concentration, Bnd represents the albumin concentration, a1, a2, and a3 are all weight values, where 0<a1≤1, 0<a2≤1, 0<a3≤1, and a1+a2+a3=1.

[0093] The above-mentioned alanine aminotransferase concentration Gmnd can be measured by a blood analyzer, such as a fully automatic biochemical analyzer, which measures the alanine aminotransferase level in the blood by spectral analysis or electrochemical methods;

[0094] C-reactive protein (Crp) is an acute phase protein synthesized by the intestinal tract and belongs to the pentameric protein family. It rises rapidly during inflammation or infection, usually with a significant increase in levels within 6 hours of the onset of inflammation and reaching a peak within 24-48 hours. It can usually be monitored using an immunoturbidimeter or an automated biochemical analyzer.

[0095] The albumin concentration Bnd can be monitored and obtained by a blood analyzer, usually by colorimetry or immunoturbidimetry in a fully automatic biochemical analyzer. This method can accurately measure the albumin level in the blood.

[0096] The impact determination unit is used to analyze the impact of different ages on intestinal damage to construct the corresponding impact coefficient Yxs, which is specifically obtained in the following way:

[0097]

[0098] Where Nz δ Expressed as the standard deviation of age values, Gsyz Lδ Expressed as the standard deviation of the historical intestinal damage factor, Cov(Nz,Gsyz L ) is expressed as the covariance of age values ​​and historical intestinal damage factors.

[0099] It should be noted that adults generally have more stable intestinal function, with intestinal enzyme and protein production reaching their peak. Adults also have a more standardized response to inflammation, with stronger production and response to inflammatory markers such as CRP, SAA, and white blood cell count. Due to stable intestinal function, adults have a more balanced regulation and response to inflammation.

[0100] However, with aging, the intestines gradually show signs of aging, the number of intestinal cells decreases, the intestinal regeneration capacity weakens, and the activity of intestinal enzymes may decrease. The inflammatory response in the elderly may exhibit different characteristics. Due to the decline in intestinal function, the production of acute phase response proteins such as CRP may decrease, resulting in a weakened or atypical inflammatory response. In summary, the intestinal function of adults is relatively stable, and the inflammatory response is more standard; while the intestinal function of the elderly is in a state of decline, the inflammatory response may be weaker and atypical. These regularities not only affect the clinical manifestations of inflammatory diseases, but also need to be considered when formulating treatment plans. In addition, inflammatory bowel disease, which mainly includes Crohn's disease and ulcerative colitis, is generally more common in young, middle-aged and elderly people.

[0101] In this embodiment, the historical data analysis unit can obtain the historical intestinal damage factor Gsyz at different age states by analyzing the relevant intestinal function status data of each inflammatory bowel disease patient obtained in the hospital's historical stage. L The calculation of this factor considers multiple parameters, further ensuring a comprehensive assessment of intestinal functional impairment. This precise calculation reveals the characteristics and trends of intestinal impairment across different age groups, providing a solid data foundation for subsequent analysis. The impact determination unit analyzes the impact of different ages on intestinal impairment based on age values ​​and the standard deviation and covariance of historical intestinal impairment factors. This process constructs the impact coefficient Yxs by calculating the standard deviation and covariance of age values ​​and historical intestinal impairment factors. This method allows the system to identify and quantify the specific impact of age on intestinal impairment, improving the ability to provide personalized assessments for patients of different age groups. The inflammation intensity analysis unit utilizes the results of historical data analysis and impact determination to conduct a detailed analysis of the current patient's inflammation intensity. Combining the impact coefficient Yxs derived from historical data with the current patient's biochemical test results, the system can more accurately determine the severity of inflammation. This analysis not only improves the accuracy of the assessment but also helps physicians better understand the specific impact of different age groups on inflammatory bowel disease. By comprehensively considering the impact of different ages on intestinal impairment and the results of the inflammation intensity analysis, the system can provide personalized treatment recommendations for patients. Based on precise analysis results, doctors can adjust treatment plans to suit the patient's specific age-related needs, thereby improving treatment outcomes and the patient's quality of life. The system converts complex age-related data and inflammation analysis results into easy-to-understand assessment reports, providing doctors with data-driven decision support. In summary, the system not only improves the diagnostic accuracy of inflammatory bowel disease by analyzing intestinal damage and assessing inflammation intensity in patients of different age groups, but also optimizes treatment plans and decision support.

[0102] Example 4

[0103] Please refer to Figure 1Specifically, the inflammatory bowel disease patients obtained in the historical stage of the hospital are sorted by age to obtain the patient age data column, wherein the sorting method is: sorting by age from small to large; at the same time, the inflammatory bowel disease patient with the closest age value Nz to the current inflammatory bowel disease patient is matched in the patient age data column and marked as the kth inflammatory bowel disease patient, so the influence coefficient Yxs corresponding to the kth inflammatory bowel disease patient is recorded as the kth influence coefficient Yxs k ;

[0104] According to the kth influence coefficient Yxs k , match the kth pair of elements from the patient age data column, the specific element pairing content is (Yxs k ,Yxs n-k+1 );

[0105] Here, the pairing process for the patient age data series exhibits symmetry because the pairing is performed from the ends of the series toward the middle. This symmetry allows us to find the "pairing" element for each element. Symmetry is a property of an object that remains unchanged under certain transformations.

[0106] Example: For the data column {y1, y2, y3, y4, y5}, n = 5, the first pair: (y1, y5), the second pair: (y2, y4), the third pair: (y3, y3), that is, the middle element matches itself;

[0107] The inflammation intensity analysis unit is used to obtain the historical intestinal damage factor Gsyz according to the historical data analysis unit. L Method to obtain the intestinal damage factor Gsyz of current inflammatory bowel disease patients D , and combined with relevant blood extraction data information, obtain the activity coefficient Hyxs of the current inflammatory bowel disease patient, specifically in the following way:

[0108]

[0109] Where, Bxs represents the white blood cell count; Yxs n-k+1 It is represented as the (n-k+1)th influence coefficient; n represents the number of patients with inflammatory bowel disease of different ages in the historical period; k represents the position of the current age value Nz of the inflammatory bowel disease patient in the patient age data column; the value range of k is from 1 to in is a symbol indicating “round up”; U represents the first correction constant, b1 and b2 are both weight values, wherein 0<b1≤1, 0<b2≤1, and b1+b2=1.

[0110] White blood cell count (Bxs) indicates the number of white blood cells (leukocytes) in the blood. They are components of the immune system. Inflammation and infection often lead to elevated white blood cell counts, particularly increases in neutrophils. This can usually be monitored and obtained using a hematology analyzer. This method can be used to accurately measure albumin levels in the blood using colorimetry or immunoturbidimetry.

[0111] The activity threshold is set in advance. By comparing and analyzing the activity coefficient Hyxs with the activity threshold, the intensity of inflammation in the current inflammatory bowel disease patient can be preliminarily judged. The specific judgment content is as follows:

[0112] If the activity coefficient Hyxs exceeds the activity threshold, it is preliminarily judged that the inflammation of the current inflammatory bowel disease patient is in an abnormal state, and further analysis will be performed at this time;

[0113] If the activity coefficient Hyxs does not exceed the activity threshold, it is preliminarily determined that the inflammation of the current inflammatory bowel disease patient is not in an abnormal state.

[0114] In this embodiment, the system sorts historical IBD patients within the hospital by age, ensuring accurate data analysis. Within the patient age data column, the system matches the patient with the closest age value Nz to the current IBD patient and labels them as the kth IBD patient. This allows the system to accurately identify reference data with a similar age to the current patient, ensuring the relevance and practicality of historical data analysis. Based on the kth influence coefficient Yxs, the system matches the elements associated with the kth patient from the patient age data column. This scientific calculation method effectively reflects the intestinal damage status of patients of different age groups and provides a solid foundation for analyzing inflammation intensity. The inflammation intensity analysis unit combines historical intestinal damage factors with relevant blood extraction data to calculate the activity coefficient Hyxs for the current IBD patient, further ensuring an accurate assessment of the patient's inflammatory status and mitigating the problem of inaccurate analysis of the patient's inflammatory status caused by age-related decline in intestinal function. A pre-set activity threshold, when compared with Hyxs, effectively distinguishes whether a patient's inflammation is abnormal. Based on the comparison of the activity coefficient Hyxs with the set activity threshold, the system can make a preliminary judgment on the patient's current inflammatory status. If the activity coefficient Hyxs exceeds the threshold, the system will conduct further analysis to promptly detect and handle abnormal situations. This preliminary judgment can help doctors quickly screen out patients who need special attention and improve the efficiency of clinical decision-making. Through precise age matching and scientific activity coefficient assessment, the system can provide personalized treatment recommendations for each patient. This personalized assessment method not only improves the accuracy of diagnosis, but also optimizes the treatment plan, so that each patient can receive the most suitable treatment, thereby improving the treatment effect and the patient's quality of life.

[0115] Through precise analysis of historical data and dynamic evaluation of real-time data, the system provides medical personnel with data-driven decision support. This data-driven approach helps doctors better understand patients' conditions and make more accurate clinical decisions, thereby improving patients' health management and treatment outcomes. In summary, through systematic age matching, influence coefficient calculation, and activity coefficient Hyxs assessment, this system can improve the assessment accuracy and personalized treatment level of patients with inflammatory bowel disease, providing patients with more accurate and efficient medical services.

[0116] Example 5

[0117] Please refer to Figure 1 ,Specifically: the intestinal state analysis module includes an intestinal detection unit and a comprehensive evaluation unit;

[0118] The intestinal detection unit is used to construct a pathological complexity coefficient Bfxs based on relevant image data information and combined with relevant fecal sample data, which is specifically obtained in the following way:

[0119]

[0120] Wherein, Bgdz represents the fecal calprotectin concentration, Lgs represents the number of fistulas, Cbhc represents the intestinal wall thickness difference, R represents the second correction constant, and α and β are both weight values, where 0<α≤1, 0<β≤1, and α+β=1.

[0121] The fecal calprotectin concentration (Bgdz) mentioned above is a non-invasive marker for assessing intestinal inflammation. Calprotectin is a protein released by activated white blood cells. Higher calprotectin levels indicate more active intestinal inflammation. Calprotectin can usually be detected using fecal calprotectin testing instruments, such as enzyme-linked immunosorbent assays (ELISAs) or fluorescent immunoassays, which can quantitatively analyze calprotectin concentrations in fecal samples.

[0122] The number of fistulas Lgs can be monitored and obtained through imaging examinations such as magnetic resonance imaging (MRI) or computed tomography (CT);

[0123] The intestinal wall thickness difference Cbhc can be obtained by monitoring through endoscopic ultrasound (EUS) or abdominal ultrasound. EUS or abdominal ultrasound measures the thickness of the intestinal wall through high-frequency sound wave reflection. Doctors can calculate the difference by comparing the thickness of different parts.

[0124] In this embodiment, the intestinal detection unit constructs a pathological complexity coefficient Bfxs by combining relevant imaging data and stool sample data. The construction of this coefficient depends on multiple indicators, such as fecal calprotectin concentration Bgdz, the number of fistulas Lgs, and the intestinal wall thickness difference Cbhc. This comprehensive consideration method can reflect the complexity of the patient's intestinal pathology in many aspects and provide accurate quantification of the disease status. The system integrates the results of imaging examinations and stool sample analysis, considering the impact of multiple indicators on pathological complexity. Specifically, fecal calprotectin concentration Bgdz helps to assess the intestinal inflammatory response, while the number of fistulas Lgs and the intestinal wall thickness difference Cbhc provide structural information of the lesion. This comprehensive information can more accurately assess the patient's pathological complexity and assist doctors in making scientific treatment decisions. Using the results of the comprehensive assessment, doctors can obtain detailed information about the patient's intestinal status, thereby optimizing personalized treatment plans. Long-term tracking and evaluation can also help doctors predict the development trend of the disease and improve the prognosis and management of patients. In summary, the intestinal detection unit provides support for the intestinal status assessment of patients with inflammatory bowel disease by combining multidimensional data to construct the pathological complexity coefficient Bfxs. This method not only improves the accuracy of disease assessment, but also optimizes treatment plans and patient prognosis monitoring capabilities.

[0125] Example 6

[0126] Please refer to Figure 1 Specifically, the comprehensive evaluation unit is used to input the activity coefficient Hyxs and the pathological complexity coefficient Bfxs into the pathological prediction model, and after dimensionless processing, the corresponding data values ​​are mapped to the interval [0,1], and then the risk assessment coefficient Wpxs is obtained according to the following formula:

[0127]

[0128] Where F1 and F2 are weight values, H represents the third correction constant, where 0<F1≤1, 0<F2≤1, and F1+F2=1. The weight value can be obtained by referring to the hierarchical analysis method;

[0129] The initial model was constructed using a convolutional neural network in deep learning technology. The initial model was trained and tested using relevant data information in the inflammatory bowel disease dataset. The trained initial model was used as a state recognition model. Feature information within the state recognition model was obtained. The state recognition model was trained and tested using the obtained feature information. The trained state recognition model was used as a pathological prediction model.

[0130] The report evaluation module includes a comparison unit and a report generation unit;

[0131] The comparison unit is used to pre-set an assessment threshold P, which includes a first assessment threshold P1 and a second assessment threshold P2, and the first assessment threshold P1 is greater than the second assessment threshold P2. By comparing and analyzing the risk assessment coefficient Wpxs with the first assessment threshold P1 and the second assessment threshold P2, the severity of the current inflammatory bowel disease patient's condition is comprehensively analyzed. The specific comparison and analysis content is as follows:

[0132] If the risk assessment coefficient Wpxs is less than the second assessment threshold P2, it indicates that the symptom of the current inflammatory bowel disease patient is in a normal state, and a first complete exclusive symptom report will be generated;

[0133] If the second assessment threshold value P2 ≤ the risk assessment coefficient Wpxs ≤ the first assessment threshold value P1, it indicates that the symptom of the current inflammatory bowel disease patient is in a normal state, and a second complete exclusive symptom report is generated;

[0134] If the risk assessment coefficient Wpxs is greater than the first assessment threshold P1, it indicates that the symptom of the current inflammatory bowel disease patient is not in a normal state, and a third complete exclusive symptom report will be generated.

[0135] The report generating unit is used to take corresponding treatment measures according to the first complete exclusive symptom report, the second complete exclusive symptom report and the third complete exclusive symptom report obtained by the comparing unit. The specific contents are as follows:

[0136] If the first complete exclusive symptom report is generated, patients with inflammatory bowel disease will be advised to visit the hospital for regular checkups, including routine physical examinations and related intestinal examinations every 3-6 months, such as blood tests, fecal calprotectin tests, and ultrasound examinations. Dietary and lifestyle guidance will also be provided to patients with inflammatory bowel disease, encouraging them to maintain a low-inflammatory diet, such as a low-fat, low-sugar diet, avoid lactose or gluten, and engage in moderate physical exercise;

[0137] If a second complete, dedicated symptom report is generated, the dosage and type of medication for IBD patients will be adjusted, such as increasing the use of immunosuppressants or biologics to strengthen inflammation control. At the same time, the frequency of examinations will be increased to once every 1-3 months to promptly detect changes in the condition and make adjustments. Additionally, additional nutritional support will be provided to IBD patients, such as protein and vitamin supplements, to improve intestinal repair and overall health.

[0138] If a third complete dedicated symptom report is generated, the patient with inflammatory bowel disease will be hospitalized and may require intravenous medication and nutritional support. If complications occur, such as intestinal perforation or severe stenosis, surgical intervention will be considered.

[0139] It should be noted that the hierarchical analysis method is an analysis method that combines qualitative and quantitative methods. It can decompose complex problems into multiple levels. By comparing the importance of factors at each level, it can help decision makers make decisions on complex problems and determine the final decision plan. In this process, the hierarchical analysis method can be used to determine the weight values ​​of these indicators.

[0140] In this embodiment, the comprehensive assessment unit calculates the risk assessment coefficient Wpxs by inputting the activity coefficient Hyxs and the disease complexity coefficient Bfxs into the disease prediction model. This process makes symptom assessment more accurate, effectively distinguishing between conditions of varying severity, and providing patients with a clear diagnostic basis. The comparison unit compares and analyzes the risk assessment coefficient Wpxs based on the preset first and second assessment thresholds P1 and P2. The system can generate three different complete and customized disease reports. This multi-level reporting mechanism provides appropriate advice and treatment plans based on the patient's actual condition, enhancing the effectiveness of personalized medical services. The report generation unit generates corresponding treatment recommendations based on different disease reports. For patients in normal condition, regular checkups and maintaining a healthy lifestyle are recommended. For patients whose condition changes, medication adjustment and increased checkup frequency are recommended. For patients with severe conditions, hospitalization and possible surgical intervention are recommended. This dynamic adjustment strategy responds to changes in the condition in a timely manner, ensuring that patients receive appropriate treatment. By providing detailed, personalized treatment recommendations, such as adjusting medication dosages, increasing checkup frequency, and providing nutritional support, the system can optimize patient management and treatment outcomes. This refined management can not only improve the patient's condition, but also improve the patient's overall health. Through precise risk assessment coefficients and multi-level symptom reports, the system can detect changes in the condition and potential risks earlier, allowing for timely intervention. This early detection and intervention capability helps avoid further deterioration of the condition and improves the patient's quality of life and prognosis. In general, the system can further improve the management of patients with inflammatory bowel disease, optimize treatment plans, and provide timely intervention when the condition changes, thereby improving patients' health and quality of life through precise symptom assessments and dynamically adjusted treatment strategies.

[0141] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence-based patient-reported assessment system for inflammatory bowel disease, characterized by: It includes item inspection module, data collection module, age group analysis module, intestinal status analysis module and report evaluation module; The said matter examination module is used to preliminarily perform biochemical examinations and imaging examinations on patients with inflammatory bowel disease and summarize the examination results to obtain a preliminary exclusive symptom report; The data collection module is used to perform feature extraction on the data information in the preliminary exclusive disease report to respectively obtain relevant intestinal function status data information, relevant imaging data information, relevant blood extraction data information, relevant stool sample data and relevant individual identity data information of the current inflammatory bowel disease patient, and combine it with historical data of inflammatory bowel disease patients of different ages; The age group analysis module is used to calculate the historical intestinal damage factors at different age states based on the historical data of patients with inflammatory bowel disease at different ages. , based on historical intestinal damage factors at different age states , analyze the impact of different ages on intestinal damage to construct the corresponding influence coefficient Yxs, and perform dimensionless processing on the historical intestinal damage factors, influence coefficients, and related blood extraction data information. Then, combine the processed data to obtain the activity coefficient Hyxs of the current inflammatory bowel disease patient. Based on the value of the activity coefficient Hyxs, preliminarily judge the inflammation intensity of the current inflammatory bowel disease patient. If the inflammation of the current inflammatory bowel disease patient is in an abnormal state, further analysis is performed; The age group analysis module includes a historical data analysis unit, an impact determination unit and an inflammation intensity analysis unit; The historical data analysis unit is used to obtain historical intestinal damage factors at different age levels , specifically obtained according to the following formula: ； In the formula, Gmnd represents the concentration of alanine aminotransferase, Crp represents the concentration of C-reactive protein, and Bnd represents the concentration of albumin. 、 and All are weight values; The impact determination unit is used to analyze the impact of different ages on intestinal damage to construct the corresponding impact coefficient Yxs, which is specifically obtained in the following way: ； Where, Expressed as the standard deviation of age values, Expressed as the standard deviation of the historical intestinal damage factor, Expressed as the covariance of age values ​​and historical intestinal damage factors; The inflammation intensity analysis unit is used to obtain historical intestinal damage factors according to the historical data analysis unit. To obtain the intestinal damage factors of current inflammatory bowel disease patients , and combined with relevant blood extraction data information, obtain the activity coefficient Hyxs of the current inflammatory bowel disease patient, specifically in the following way: ； Where, 、 and They represent the values ​​obtained after dimensionless processing of white blood cell count Bxs, the n-k+1th influence coefficient Yxs and the current intestinal damage factor GsyzD; n represents the number of patients with inflammatory bowel disease of different ages in the historical stage; k represents the position of the age value Nz of the current inflammatory bowel disease patient in the patient age data column; the value range of k is from 1 to ; U represents the first correction constant, and All are weight values; The intestinal state analysis module is used to construct a morbidity prediction model using deep learning technology, and based on relevant image data information and relevant fecal sample data, and after dimensionless processing of corresponding indicators in the image data information and fecal sample data, construct a morbidity complexity coefficient Bfxs, and combine it with the trained morbidity prediction model to obtain the risk assessment coefficient Wpxs by fitting; The intestinal state analysis module includes an intestinal detection unit and a comprehensive evaluation unit; The intestinal detection unit is used to construct a pathological complexity coefficient Bfxs based on relevant image data information and combined with relevant fecal sample data, which is specifically obtained in the following way: ； Where, 、 and They represent the values ​​obtained by dimensionless processing of fecal calprotectin concentration Bgdz, number of fistulas Lgs and intestinal wall thickness difference Cbhc, respectively. R represents the second correction constant, where and All are weight values; The comprehensive evaluation unit is used to input the activity coefficient Hyxs and the pathological complexity coefficient Bfxs into the pathological prediction model, and after dimensionless processing, the corresponding data values ​​are mapped to the interval Then, the risk assessment coefficient Wpxs is obtained according to the following formula: ； Where, and are all weight values, H represents the third correction constant; The report evaluation module is used to pre-set an evaluation threshold value P, and compare and analyze the evaluation threshold value P with the risk assessment coefficient Wpxs to comprehensively analyze the severity of the current inflammatory bowel disease patient's symptoms and generate a complete exclusive symptom report.

2. The artificial intelligence-based inflammatory bowel disease patient-reported assessment system according to claim 1, characterized in that: The matters inspection module is used to conduct biochemical examinations and imaging examinations on patients with inflammatory bowel disease in advance, wherein the examination items of the biochemical examination include blood tests, stool tests and intestinal function, and the examination items of the imaging examination include intestinal endoscopy and intestinal ultrasound examination; the examination results obtained through biochemical examinations and imaging examinations are summarized to obtain a preliminary exclusive symptom report.

3. The artificial intelligence-based inflammatory bowel disease patient-reported assessment system according to claim 1, characterized in that: The data collection module includes a first collection unit, a second collection unit and a third collection unit; The first acquisition unit is used to obtain relevant individual identity data information based on the personal information registration form filled out by the current inflammatory bowel disease patient at the hospital front desk, and the relevant individual identity data information includes name, gender, age value Nz, ID number and contact information; The second collection unit is used to obtain relevant intestinal function status data information, relevant blood extraction data information and relevant stool sample data of the current inflammatory bowel disease patient based on the biochemical examination content in the preliminary exclusive symptom report, wherein the relevant intestinal function status data information includes the C-reactive protein concentration Crp, the alanine aminotransferase concentration Gmnd and the albumin concentration Bnd in the current inflammatory bowel disease patient; the relevant blood extraction data information includes the white blood cell count Bxs; and the relevant stool sample data includes the fecal calprotectin concentration Bgdz; The third acquisition unit is used to obtain relevant image data information based on the imaging examination content in the preliminary exclusive disease report, wherein the relevant image data information includes the number of fistulas in the intestine Lgs and the intestinal wall thickness difference Cbhc; The historical data of inflammatory bowel disease patients of different ages refers to the relevant intestinal function status data information, relevant imaging data information, relevant blood extraction data information, relevant stool sample data and relevant individual identity data information of each inflammatory bowel disease patient obtained according to the historical stages in the hospital.

4. The artificial intelligence-based inflammatory bowel disease patient-reported assessment system according to claim 3, characterized in that: Sort the inflammatory bowel disease patients obtained in the hospital in the historical stage by age to obtain the patient age data column, where the sorting method is: sort by age from small to large; at the same time, match the inflammatory bowel disease patient with the age value Nz closest to the current inflammatory bowel disease patient in the patient age data column and mark it as the kth inflammatory bowel disease patient, so the influence coefficient Yxs corresponding to the kth inflammatory bowel disease patient is recorded as the kth influence coefficient ; According to the kth influence coefficient , match the kth pair of elements from the patient age data column, the specific element pairing content is .

5. The artificial intelligence-based inflammatory bowel disease patient-reported assessment system according to claim 4, characterized in that: The activity threshold is set in advance. By comparing and analyzing the activity coefficient Hyxs with the activity threshold, the intensity of inflammation in the current inflammatory bowel disease patient can be preliminarily judged. The specific judgment content is as follows: If the activity coefficient Hyxs exceeds the activity threshold, it is preliminarily judged that the inflammation of the current inflammatory bowel disease patient is in an abnormal state, and further analysis will be performed at this time; If the activity coefficient Hyxs does not exceed the activity threshold, it is preliminarily determined that the inflammation of the current inflammatory bowel disease patient is not in an abnormal state.

6. The artificial intelligence-based inflammatory bowel disease patient-reported assessment system according to claim 1, characterized in that: The report evaluation module includes a comparison unit and a report generation unit; The comparison unit is used to pre-set an assessment threshold P, which includes a first assessment threshold P1 and a second assessment threshold P2, and the first assessment threshold P1 is greater than the second assessment threshold P2. By comparing and analyzing the risk assessment coefficient Wpxs with the first assessment threshold P1 and the second assessment threshold P2, the severity of the current inflammatory bowel disease patient's condition is comprehensively analyzed. The specific comparison and analysis content is as follows: If the risk assessment coefficient Wpxs is less than the second assessment threshold P2, it indicates that the symptom of the current inflammatory bowel disease patient is in a normal state, and a first complete exclusive symptom report will be generated; If the second assessment threshold value P2 ≤ the risk assessment coefficient Wpxs ≤ the first assessment threshold value P1, it indicates that the symptom of the current inflammatory bowel disease patient is in a normal state, and a second complete exclusive symptom report is generated; If the risk assessment coefficient Wpxs is greater than the first assessment threshold P1, it indicates that the symptom of the current inflammatory bowel disease patient is not in a normal state, and a third complete exclusive symptom report will be generated. The report generating unit is used to take corresponding treatment measures according to the first complete exclusive symptom report, the second complete exclusive symptom report and the third complete exclusive symptom report obtained by the comparing unit. The specific contents are as follows: If the first complete exclusive symptom report is generated, patients with inflammatory bowel disease will be advised to visit the hospital regularly for checkups, undergoing routine physical examinations and related intestinal examinations every 3-6 months, and will be provided with dietary and lifestyle guidance; If a second complete, dedicated symptom report is generated, medication dosages and types will be adjusted for IBD patients; checkups will be increased to every 1-3 months, and additional nutritional support will be provided to IBD patients; If a third complete dedicated symptom report is generated, the patient with inflammatory bowel disease will be arranged for hospitalization, and if complications arise, surgical intervention will be considered.

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