Comprehensive anorectal disease management system

Abstract

The invention discloses an anorectal disease comprehensive management system, and relates to the technical field of medical diagnosis, and the evaluation method comprises the following specific steps: biomarker screening and verification: through clinical sample analysis, screening out specific cell factors, inflammatory mediators and microbial metabolites highly related to postoperative respiratory tract infection as biomarkers, by developing and verifying a series of biomarkers, such as specific cytokines, inflammatory mediators and microbial metabolites, highly related to postoperative respiratory tract infection, accurate early warning of postoperative infection risks is achieved, subtle change of the content of the biomarkers in the body of a patient is often earlier than occurrence of clinical symptoms, and therefore, the postoperative respiratory tract infection risk can be early warned. By regularly detecting the biomarkers, early signals of infection can be captured, and the early diagnosis capability provides a precious time window for clinicians, so that the clinicians can take preventive measures when infection symptoms are not shown.

Description

Technical Field

[0001] The present invention relates to the technical field of medical diagnosis, in particular to a comprehensive management system for anorectal diseases. Background Art

[0002] Postoperative respiratory tract infection is one of the common complications after surgical operations. Its occurrence not only prolongs the patient's hospitalization time and increases medical costs, but may also cause serious health consequences and even threaten the patient's life. Therefore, early warning and accurate diagnosis of postoperative respiratory tract infection are of great clinical significance. In recent years, with the rapid development of biotechnology and the continuous accumulation of clinical data, the use of biomarkers to predict and evaluate the risk of postoperative respiratory tract infection has become a research hotspot.

[0003] Although traditional technology has made certain progress in the detection of biomarkers, it still has many shortcomings in the early diagnosis of postoperative respiratory tract infections. First, traditional methods often rely on the detection of a single biomarker, which often lacks sufficient sensitivity and specificity and is difficult to accurately reflect the complex pathological process of postoperative respiratory tract infections. Second, traditional detection methods usually have problems such as cumbersome operation, long detection cycle and high cost, which limits their wide application in clinical practice. Especially in the early diagnosis of biomarkers, traditional technology often cannot capture the subtle changes of biomarkers in patients in a timely and accurate manner, resulting in the inability to achieve early warning and intervention for postoperative respiratory tract infections.

[0004] Therefore, a comprehensive management system for anorectal diseases was developed. By developing and validating a series of highly specific and sensitive biomarkers and combining them with high-throughput detection technology and machine learning algorithms, accurate assessment and early warning of the risk of postoperative respiratory tract infection were achieved. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide a comprehensive management system for anorectal diseases. It can achieve early warning and diagnosis of postoperative respiratory tract infections by developing and verifying a series of biomarkers that are highly correlated with postoperative respiratory tract infections, including specific cytokines, inflammatory mediators and microbial metabolites. It uses high-throughput detection technology to simultaneously detect multiple biomarkers, combines machine learning algorithms to build a risk assessment model, accurately assesses patient risk levels, and provides clinicians with timely warning signals and personalized intervention recommendations.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: anorectal disease comprehensive management system, the specific steps of the evaluation method are:

[0007] S100, biomarker screening and validation: by analyzing clinical samples, screen out specific cytokines, inflammatory mediators and microbial metabolites that are highly correlated with postoperative respiratory tract infection as biomarkers, and verify the sensitivity and specificity of these biomarkers;

[0008] S200, sample collection and processing: At specific time points after surgery, blood and respiratory secretion samples from patients were collected through standardized procedures;

[0009] S300, multi-biomarker joint detection: Use high-throughput detection technology to simultaneously detect the selected biomarkers to obtain data on their content and activity changes in the patient's body;

[0010] S400, risk assessment model construction: Based on the test data and combined with clinical information, a risk assessment model for postoperative respiratory tract infection is constructed to comprehensively evaluate the changes in various biomarkers and give the patient's risk level for postoperative respiratory tract infection;

[0011] S500, Early Warning and Intervention Guidance: Based on the risk assessment results, early warning signals are promptly issued to clinicians and personalized intervention recommendations are provided.

[0012] Further, the S100 includes:

[0013] S101, collecting clinical samples from patients with postoperative respiratory tract infection and control patients without postoperative respiratory tract infection, and preprocessing the collected clinical samples;

[0014] S102, screening and validation of candidate biomarkers with significant differences between patients with postoperative respiratory tract infection and control patients;

[0015] S103, select independent validation set samples, use quantitative detection methods to evaluate the expression levels of candidate biomarkers, and calculate the sensitivity and specificity of each biomarker.

[0016] Furthermore, the S200 includes:

[0017] S201, clarify the specific time point of postoperative collection and record it in the patient's medical record. Before collection, verify the patient's identity, assess his / her condition, and explain the collection-related matters to him / her;

[0018] S202, select the elbow vein as the blood collection site, disinfect it, wear sterile gloves, use a sterile blood collection needle, use a blood collection tube containing an anticoagulant to puncture and collect blood, and mark the blood collection tube information;

[0019] S203, after giving the patient aerosolized saline for inhalation, instruct the patient to expectorate into a sterile sputum collector;

[0020] S204, send blood samples to the laboratory in time for centrifugation to obtain serum and plasma, and package and store them as required. Respiratory secretion samples should first remove non-specific substances and then be processed accordingly, and packaged and stored.

[0021] Furthermore, the S300 includes:

[0022] S301, using high-throughput detection technologies such as multi-factor detection chips, liquid phase chips and mass spectrometry to detect biomarkers;

[0023] S302, recording the results of the high-throughput detection technology, analyzing the fluorescence signal and mass / charge ratio parameters of each biomarker, and converting them into data on the content and activity changes of the biomarkers in the patient's body;

[0024] S303, integrating the detection data of each biomarker into a comprehensive report, showing the biomarker content level, change trend and its correlation with the risk of postoperative respiratory tract infection.

[0025] Furthermore, the step S301 of detecting biomarkers by using a multi-factor detection chip through specific antibodies on the chip combined with signal detection technology:

[0026] (1) Design the chip structure and use microfabrication technology to immobilize specific antibodies;

[0027] (2) adding the pretreated sample to the chip so that its biomarkers bind to the specific antibodies on the chip;

[0028] (3) After cleaning the chip, a signal amplification reagent is added to form a complex with the bound biomarker, and the signal is collected by a laser scanner to calculate the biomarker content;

[0029] (4) Correct and normalize the collected data, compare with the preset values ​​to evaluate the biomarker levels, and generate a detailed test report.

[0030] Furthermore, the step S301 implements parallel detection by using a liquid phase chip using coupled probes in combination with a specific device detection signal:

[0031] (1) preparing a liquid chip carrier with distinguishable physical properties, and coupling thereto a probe capable of specifically identifying a biomarker;

[0032] (2) mixing the treated sample with the liquid chip carrier for reaction, and allowing them to fully contact and bind by oscillation;

[0033] (3) Using a flow cytometer to detect the liquid phase chip after the reaction, distinguishing according to the physical properties of the carrier, detecting the signal changes caused by the binding of the biomarker, and recording the relevant data;

[0034] (4) Import the data into the software and convert it into biomarker information, comprehensively analyze the multiple results for comprehensive evaluation and output the test report.

[0035] Furthermore, the step S301 is to ionize the biomarkers by mass spectrometry and then separate and detect them according to their behavior in the electric field:

[0036] (1) The pretreated sample is introduced into a mass spectrometer and the biomarkers are ionized into ions using an ionization source while maintaining their molecular structure;

[0037] (2) Using the electric field, a quadrupole mass filter is used to separate ions based on their physical properties;

[0038] (3) The separated ion signals are captured by a detector and converted into electrical signals to generate a mass spectrum;

[0039] (4) Analyze the mass spectrum to identify the type of biomarker, compare it with the mass spectrum database, and determine the content of the biomarker;

[0040] (5) Output the analysis results in the form of a report, and explain and judge the biological status of the sample, disease diagnosis, and treatment effect evaluation.

[0041] Furthermore, the S400 includes:

[0042] S401, cleaning the raw data provided by multi-biomarker joint detection and integrating clinical information for pre-processing;

[0043] S402, Identification of biomarker signatures that contribute to risk assessment of postoperative respiratory tract infection;

[0044] S403, selecting an exponentially weighted comprehensive risk assessment function as the basis of the risk assessment model to construct the model, dividing the training data set for training, and evaluating the performance on the validation set;

[0045] S404, optimize and adjust the model according to the validation results, and verify its effectiveness and clinical practical value with an independent test set;

[0046] S405, deploying the model in practice, and setting thresholds to classify patients into different risk levels based on the model output results and combined with actual clinical conditions.

[0047] Furthermore, the S403 selects an exponentially weighted comprehensive risk assessment function as the basis for constructing a risk assessment model, and sets a set of risk characteristic variables X1, X2, ..., X n , the risk assessment model is constructed through the exponentially weighted comprehensive risk assessment function, and the exponentially weighted comprehensive risk assessment function is: Where: A and B are constant terms used to adjust the overall scale and offset of the model, W i is the corresponding feature variable X i The weight coefficient indicates the influence of the feature on the risk, and β is an adjustment parameter used to control the growth rate and shape of the function.

[0048] Furthermore, the S500 includes

[0049] S501, after receiving the risk assessment result, a pre-set threshold is used to generate a warning signal containing the patient's key information, and the signal is communicated to the clinician through multiple channels;

[0050] S502, collecting and integrating the patient's clinical information, matching measures from the intervention strategy library based on this and the risk assessment results, and generating personalized intervention recommendations;

[0051] S503, provide guidance and support to clinicians in implementing the intervention, record the implementation of intervention measures and regularly evaluate their effectiveness, and provide feedback to clinicians based on patient-related data and evaluation results.

[0052] Compared with the existing technology, the anorectal disease comprehensive management system has the following beneficial effects:

[0053] 1. The present invention achieves accurate early warning of postoperative infection risk by developing and verifying a series of biomarkers that are highly correlated with postoperative respiratory tract infections, such as specific cytokines, inflammatory mediators and microbial metabolites. The subtle changes in the content of these biomarkers in the patient's body are often earlier than the onset of clinical symptoms. Therefore, by regularly testing these biomarkers, early signals of infection can be captured. This early diagnosis capability provides clinicians with a valuable "time window", allowing them to take preventive measures before the symptoms of infection appear, thereby effectively controlling the development of infection, reducing the occurrence of complications, and improving the patient's recovery speed and treatment effect.

[0054] 2. By detecting and analyzing these specific biomarkers, the postoperative respiratory tract infection risk assessment method of the present invention can not only achieve early diagnosis, but also provide a tailored treatment plan for each patient. Different combinations of biomarkers may indicate different infection types, development stages and patient-specific physiological states. Therefore, based on the risk assessment results of biomarkers, doctors can more accurately select drugs, adjust dosages and optimize treatment plans. This personalized treatment strategy not only helps to improve the effectiveness of treatment and reduce unnecessary drug use, but also reduces side effects and adverse reactions during treatment, further improving patients' treatment experience and recovery speed.

[0055] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0057] Figure 1 Flowchart of a method for assessing the risk of postoperative respiratory tract infection. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] Embodiment 1:

[0060] The comprehensive management system for anorectal diseases includes the following steps: biomarker screening and verification, sample collection and processing, multi-biomarker joint detection, surface coating, and risk assessment model construction.

[0061] First, enter the biomarker screening and validation stage (S100), and divide the patients who undergo surgical operations in the hospital into a postoperative respiratory infection group and a non-infected control group according to their conditions. Collect clinical samples of the two groups of patients, including blood and respiratory secretions, and uniformly number them, record the collection time and basic information of the patients, and perform a series of pretreatment operations such as centrifugation, removal of non-specific substances, and packaging and storage according to standard procedures. The pretreated samples are comprehensively screened to screen out candidate biomarkers with significant differences between patients with postoperative respiratory tract infection and control patients, select independent validation set samples, and use quantitative detection methods to evaluate the expression levels of candidate biomarkers, calculate the sensitivity, specificity and AUC value of each biomarker, and determine its effectiveness as a diagnostic marker.

[0062] Next, enter the sample collection and processing stage (S200). According to clinical and research needs, the postoperative collection time point is clarified and recorded in the patient's medical record. Before collection, the patient's identity is verified, his condition is assessed, the purpose of collection and precautions are explained, and the elbow vein is selected as the blood collection site. After disinfection, medical staff wear sterile gloves for puncture and blood collection. Blood is collected using a blood collection tube containing an anticoagulant, shaken to mix and the blood collection tube information is marked. After giving the patient a nebulized saline solution for inhalation, he is guided to expectorate correctly into a sterile sputum collector, and the blood sample is sent to the laboratory in time, the serum and plasma are separated by centrifugation, and stored in aliquots. After removing non-specific substances from the respiratory secretion sample, the sample is processed accordingly and stored in aliquots.

[0063] Subsequently, the multi-biomarker joint detection stage (S300) is entered, and high-throughput detection technology is used to focus on multi-factor detection chips to detect biomarkers through specific antibodies on the chip combined with signal detection technology. Liquid phase chips use coupled probes combined with specific equipment detection signals to achieve parallel detection. Mass spectrometry technology ionizes biomarkers and separates and detects them according to their behavior in the electric field. The results of high-throughput detection technology are recorded, and the fluorescence signals and mass / charge ratio parameters of each biomarker are analyzed and converted into data on the content and activity changes of biomarkers in the patient's body. The detection data of each biomarker are integrated into a comprehensive report to display the content level, change trend and correlation with the risk of postoperative respiratory tract infection.

[0064] Then, the risk assessment model construction phase (S400) is entered. The raw data of the multi-biomarker joint detection are cleaned and standardized, and clinical information is integrated to identify the biomarker characteristics that contribute to the risk assessment of postoperative respiratory tract infection. Dimensionality reduction is performed to reduce redundant information, and an exponentially weighted comprehensive risk assessment function is selected as the basis of the model. The training data set is divided for training, and the model performance is evaluated on the validation set. The model is optimized and adjusted according to the validation results. An independent test set is used to verify the effectiveness and clinical practical value of the model. The model is deployed in an actual medical environment, and thresholds are set to divide patients into different risk levels.

[0065] Finally, the early warning and intervention guidance stage (S500) is entered. After receiving the risk assessment results, the preset threshold is judged and an early warning signal is generated. The signal is transmitted to the clinician through the hospital information system and mobile phone APP, and the patient's clinical information is collected and integrated. Combined with the risk assessment results, measures are matched from the intervention strategy library to generate personalized intervention recommendations, including preventive medication and enhanced monitoring. Guidance and support for intervention implementation are provided to clinicians, the implementation of intervention measures is recorded, and their effectiveness is regularly evaluated. Feedback is provided to clinicians based on patient-related data and evaluation results to continuously optimize intervention strategies.

[0066] This embodiment provides a scientific and efficient method for risk assessment of postoperative respiratory tract infection through systematic sample collection, screening and verification of differential biomarkers, application of high-throughput detection technology, construction and application of risk assessment model, and early warning and intervention steps.

[0067] Embodiment 2:

[0068] The comprehensive anorectal disease management system in this embodiment places particular emphasis on the specific steps from using multiple technologies to detect biomarkers to building a risk assessment model.

[0069] Biomarkers are detected using a multi-factor detection chip through specific antibodies on the chip combined with signal detection technology. According to the type of biomarker to be detected, the chip structure is designed and specific antibodies are fixed on the chip. The collected patient biological samples are pretreated to remove impurities and interfering substances to make them suitable for reaction with the chip. The pretreated samples are dripped onto the chip to allow the biomarkers in the samples to react with the specific antibodies on the chip. The reaction is fully carried out under appropriate temperature and humidity conditions to ensure that the biomarkers can firmly bind to the corresponding antibodies. The chip is cleaned to remove unbound substances, and signal amplification reagents are added to amplify the signal. The chip is scanned with a laser scanner to collect signals, and the content of biomarkers is calculated according to the signal intensity through professional data analysis software. The collected data is corrected and normalized to eliminate errors caused by experimental conditions and equipment differences. The corrected data is compared with the preset normal range values ​​of biomarkers to evaluate the levels of biomarkers. Based on the evaluation results, a detailed test report is generated.

[0070] Parallel detection is achieved through the use of coupled probes on a liquid chip combined with detection signals from specific devices, and a liquid chip carrier with distinguishable physical properties is prepared. The probes that can specifically identify biomarkers are coupled to the liquid chip carrier through specific chemical coupling technology, and the processed biological sample is mixed with the liquid chip carrier and oscillated under appropriate conditions so that the biomarkers in the sample are in full contact and combination with the probes on the carrier. The liquid chip after the reaction is detected using a flow cytometer, and the signal changes caused by the binding of biomarkers are detected. The detected data is collected and recorded, and the collected data is imported into a special data processing software and converted into biomarker information. The concentration of each biomarker is calculated based on the relationship between signal intensity and biomarker content. The detection results of multiple biomarkers are combined for a comprehensive evaluation, a preliminary assessment of the patient's risk of postoperative respiratory tract infection is made, and a test report is output.

[0071] After the biomarkers are ionized by mass spectrometry technology, they are separated and detected based on their behavior in the electric field. The pretreated biological sample is introduced into the mass spectrometer, and the biomarkers are ionized into ions using an ionization source while trying to maintain the integrity of their molecular structure. With the help of the electric field, a quadrupole mass filter is used to separate the ions according to the differences in their physical properties. The separation of different ions is achieved by adjusting the electric field parameters. The separated ion signals are captured by the detector and converted into electrical signals. According to the intensity and time information of the electrical signal, a mass spectrum is generated, the mass spectrum is analyzed, the type of biomarker is identified, and the content of the biomarker is determined based on the signal intensity of the biomarker ions in the mass spectrum combined with the known standard curve. The analysis results are output in the form of a report, which should include the identification results of the biomarkers, the content measurement values, the comparison with the normal range, and the explanation and judgment of the biological status of the sample, disease diagnosis, and treatment effect evaluation.

[0072] The detection data of each biomarker are integrated into a comprehensive report, which shows the content level, change trend and its correlation with the risk of postoperative respiratory tract infection of the biomarker, providing basic data support for subsequent risk assessment.

[0073] Integrate the raw data provided by the joint detection of multiple biomarkers, remove missing values, outliers and illogical data points, ensure the integrity and accuracy of the data, standardize the data, eliminate the impact of different dimensions and distribution differences, encode clinical information into quantifiable indicators, and integrate it with the biomarker data to form a complete data set. Screen out biomarkers and clinical characteristics that have a significant contribution to the risk assessment of postoperative respiratory tract infection, and perform dimensionality reduction on the screened features to reduce the computational complexity and improve the generalization ability of the model.

[0074] The exponential weighted comprehensive risk assessment function is selected as the model basis to build the model. There are a set of risk characteristic variables X1, X2,…, X n , the risk assessment model is constructed through the exponentially weighted comprehensive risk assessment function, and the exponentially weighted comprehensive risk assessment function is: Where: A and B are constant terms used to adjust the overall scale and offset of the model, which can be determined by optimizing on the training data, W i is the corresponding feature variable X iThe weight coefficient represents the degree of influence of the feature on the risk. It is learned and adjusted on the training data through the gradient descent method. β is a regulating parameter used to control the growth rate and shape of the function. It is adjusted according to the characteristics of the data and the specific needs of risk assessment. When β approaches 0, the function approaches the linear combination form. When β increases, the function is more sensitive to the part with larger eigenvalues, so that the prominent influence of high-risk features can be captured. In the training process, a data set with known risk results is used to continuously adjust A and B by minimizing the difference between the predicted risk value R and the actual risk result. 、 W i and β so that the model can assess risk as accurately as possible.

[0075] According to the performance evaluation results of the validation set, the model parameters and structure are adjusted to further improve the model performance. An independent test set is used to verify the effectiveness and clinical practical value of the optimized model to ensure the generalization ability of the model on unknown data. The optimized model is deployed in the clinical information system. According to the risk assessment results output by the model and combined with the actual clinical situation, reasonable thresholds are set to divide patients into different risk levels, providing clinicians with personalized risk assessment reports and decision support recommendations.

[0076] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. Anorectal disease comprehensive management system, characterized by: The specific steps of the management system are: S100, biomarker screening and validation: by analyzing clinical samples, screen out specific cytokines, inflammatory mediators and microbial metabolites that are highly correlated with postoperative respiratory tract infection as biomarkers, and verify the sensitivity and specificity of these biomarkers; S200, sample collection and processing: At specific time points after surgery, blood and respiratory secretion samples from patients were collected through standardized procedures; S300, multi-biomarker joint detection: Use high-throughput detection technology to simultaneously detect the selected biomarkers to obtain data on their content and activity changes in the patient's body; S400, risk assessment model construction: Based on the test data and combined with clinical information, a risk assessment model for postoperative respiratory tract infection is constructed to comprehensively evaluate the changes in various biomarkers and give the patient's risk level for postoperative respiratory tract infection; S500, Early Warning and Intervention Guidance: Based on the risk assessment results, early warning signals are promptly issued to clinicians and personalized intervention recommendations are provided.

2. The anorectal disease comprehensive management system according to claim 1, characterized in that: The S100 includes: S101, collecting clinical samples from patients with postoperative respiratory tract infection and control patients without postoperative respiratory tract infection, and preprocessing the collected clinical samples; S102, screening and validation of candidate biomarkers with significant differences between patients with postoperative respiratory tract infection and control patients; S103, select independent validation set samples, use quantitative detection methods to evaluate the expression levels of candidate biomarkers, and calculate the sensitivity and specificity of each biomarker.

3. The anorectal disease integrated management system according to claim 1, characterized in that: The S200 includes: S201, clarify the specific time point of postoperative collection and record it in the patient's medical record. Before collection, verify the patient's identity, assess his / her condition, and explain the collection-related matters to him / her; S202, select the elbow vein as the blood collection site, disinfect it, wear sterile gloves, use a sterile blood collection needle, use a blood collection tube containing an anticoagulant to puncture and collect blood, and mark the blood collection tube information; S203, after giving the patient aerosolized saline for inhalation, instruct the patient to expectorate into a sterile sputum collector; S204, send blood samples to the laboratory in time for centrifugation to obtain serum and plasma, and package and store them as required. Respiratory secretion samples should first remove non-specific substances and then be processed accordingly, and packaged and stored.

4. The anorectal disease comprehensive management system according to claim 1, characterized in that: The S300 includes: S301, using high-throughput detection technologies such as multi-factor detection chips, liquid phase chips and mass spectrometry to detect biomarkers; S302, recording the results of the high-throughput detection technology, analyzing the fluorescence signal and mass / charge ratio parameters of each biomarker, and converting them into data on the content and activity changes of the biomarkers in the patient's body; S303, integrating the detection data of each biomarker into a comprehensive report, showing the biomarker content level, change trend and its correlation with the risk of postoperative respiratory tract infection.

5. The anorectal disease integrated management system according to claim 4, characterized in that: The step of detecting biomarkers in S301 by using a multi-factor detection chip through specific antibodies on the chip combined with signal detection technology: (1) Design the chip structure and use microfabrication technology to immobilize specific antibodies; (2) adding the pretreated sample to the chip so that its biomarkers bind to the specific antibodies on the chip; (3) After cleaning the chip, a signal amplification reagent is added to form a complex with the bound biomarker, and the signal is collected by a laser scanner to calculate the biomarker content; (4) Correct and normalize the collected data, compare with the preset values ​​to evaluate the biomarker levels, and generate a detailed test report.

6. The anorectal disease integrated management system according to claim 4, characterized in that: The step of S301 realizing parallel detection by using a liquid phase chip using coupled probes in combination with a specific device detection signal: (1) preparing a liquid chip carrier with distinguishable physical properties, and coupling thereto a probe capable of specifically identifying a biomarker; (2) mixing the treated sample with the liquid chip carrier for reaction, and allowing them to fully contact and bind by oscillation; (3) Using a flow cytometer to detect the liquid phase chip after the reaction, distinguishing according to the physical properties of the carrier, detecting the signal changes caused by the binding of the biomarker, and recording the relevant data; (4) Import the data into the software and convert it into biomarker information, comprehensively analyze the multiple results for comprehensive evaluation and output the test report.

7. The anorectal disease integrated management system according to claim 4, characterized in that: The step of S301 is to ionize the biomarkers by mass spectrometry and then separate and detect them according to their behavior in the electric field: (1) The pretreated sample is introduced into a mass spectrometer and the biomarkers are ionized into ions using an ionization source while maintaining their molecular structure; (2) Using the electric field, a quadrupole mass filter is used to separate ions based on their physical properties; (3) The separated ion signals are captured by a detector and converted into electrical signals to generate a mass spectrum; (4) Analyze the mass spectrum to identify the type of biomarker, compare it with the mass spectrum database, and determine the content of the biomarker; (5) Output the analysis results in the form of a report, and explain and judge the biological status of the sample, disease diagnosis, and treatment effect evaluation.

8. The anorectal disease integrated management system according to claim 1, characterized in that: The S400 includes: S401, cleaning the raw data provided by multi-biomarker joint detection and integrating clinical information for pre-processing; S402, Identification of biomarker signatures that contribute to risk assessment of postoperative respiratory tract infection; S403, selecting an exponentially weighted comprehensive risk assessment function as the basis of the risk assessment model to construct the model, dividing the training data set for training, and evaluating the performance on the validation set; S404, optimize and adjust the model according to the validation results, and verify its effectiveness and clinical practical value with an independent test set; S405, deploying the model in practice, and setting thresholds to classify patients into different risk levels based on the model output results and combined with actual clinical conditions.

9. The anorectal disease integrated management system according to claim 8, characterized in that: The S403 selects an exponentially weighted comprehensive risk assessment function as the basis for constructing a risk assessment model, and sets a set of risk characteristic variables X1, X2, ..., X n , the risk assessment model is constructed through the exponentially weighted comprehensive risk assessment function, and the exponentially weighted comprehensive risk assessment function is: Where: A and B are constant terms used to adjust the overall scale and offset of the model, W i is the corresponding feature variable X i The weight coefficient indicates the influence of the feature on the risk, and β is an adjustment parameter used to control the growth rate and shape of the function.

10. The anorectal disease integrated management system according to claim 1, characterized in that: The S500 includes S501, after receiving the risk assessment result, a pre-set threshold is used to generate a warning signal containing the patient's key information, and the signal is communicated to the clinician through multiple channels; S502, collecting and integrating the patient's clinical information, matching measures from the intervention strategy library based on this and the risk assessment results, and generating personalized intervention recommendations; S503, provide guidance and support to clinicians in implementing the intervention, record the implementation of intervention measures and regularly evaluate their effectiveness, and provide feedback to clinicians based on patient-related data and evaluation results.

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