CRRT information processing method and system

Abstract

The invention discloses a CRRT information processing method and system, and relates to the technical field of medical information processing, and the system comprises the following components: S100, real-time data acquisition and integration, S200, intelligent analysis and early warning, S300, medical record management and scientific research support, and S400, personalized treatment suggestion. The intelligent analysis model is constructed to deeply analyze the data, abnormal data modes can be found in time, early warning information is automatically generated, the early warning information comprises detailed information of the abnormal data, possible risk points and suggested intervention measures, medical staff can be helped to quickly respond and take corresponding treatment measures, and the medical staff can be helped to quickly take treatment measures. Therefore, potential risks are effectively avoided, and the safety and effectiveness of CRRT treatment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical information processing, and in particular to a CRRT information processing method and system. Background Art

[0002] In the field of medical information processing technology, CRRT, as an important treatment method, plays an irreplaceable role in the treatment of critically ill patients with acute kidney injury and multiple organ failure. CRRT treatment involves multiple complex physiological parameters and medical equipment. Information management during the treatment process is crucial to ensure the safety and effectiveness of the treatment.

[0003] CRRT, as a form of renal replacement therapy, is widely used in the field of critical care. However, under traditional management methods, traditional methods cannot automatically generate warning information when abnormal data patterns are identified and the warning rules are met. Therefore, it is particularly important to develop a CRRT information processing method and system. Summary of the invention

[0004] The purpose of the present invention is to make up for the deficiencies of the prior art and to provide a CRRT information processing method and system, which can timely detect abnormal data patterns and generate early warning information through intelligent analysis means, thereby helping medical staff to make more accurate diagnosis and treatment decisions.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a CRRT information processing method, characterized in that the method comprises the following specific steps:

[0006] S100, real-time data collection and integration: Through the Internet of Things technology, key parameters of the treatment process are collected from CRRT equipment and blood gas analyzer medical equipment in real time to form a complete big data set;

[0007] S200, intelligent analysis and early warning: According to the data type and characteristics during CRRT treatment, select the algorithm, use historical data to train the selected algorithm, and build an intelligent analysis model. Among them, M represents the output result, n is the number of historical data, i is an index variable used to traverse historical data points, and w i Represents the weight coefficient, FX i Indicates the selected algorithm for historical data point X i The processing function of ∈ is a random error term. The key parameters of the CRRT treatment process collected in real time are input into the trained intelligent analysis model. The algorithm is used to deeply analyze the input data and identify abnormal data patterns.

[0008] According to clinical experience and treatment standards, early warning rules are set. When the intelligent analysis model identifies abnormal data patterns and meets the early warning rules, early warning information is automatically generated. The early warning information should include detailed information of the abnormal data, possible risk points, and recommended intervention measures. The target audience of the early warning information is determined, and the early warning information is pushed to the target audience in a timely manner through SMS, email, and APP push. The response of medical staff to the early warning information and the intervention measures taken are collected, and the effectiveness and accuracy of the early warning information are evaluated. According to the feedback results, the intelligent analysis model is tuned and improved to improve the accuracy and reliability of the early warning;

[0009] S300, medical record management and scientific research support: Build a structured database to store the full cycle data of CRRT treatment for each patient, which is convenient for doctors to review and analyze medical records. At the same time, it provides data export and visualization tools to support scientific researchers in conducting research projects and academic sharing, and promote knowledge accumulation and innovation in the field of CRRT treatment;

[0010] S400, personalized treatment recommendations: Based on patient historical data and real-time monitoring results, using machine learning models, provide patients with personalized treatment plan recommendations, optimize treatment pathways, and improve treatment outcomes.

[0011] Furthermore, the S100 IoT device configuration includes selecting CRRT and blood gas analyzer equipment that support the communication protocol, and accessing the network wirelessly, performing identity authentication and authorization, defining a unified data format, collecting key parameters during the treatment process in real time, integrating and storing the pre-processed data in a distributed database or data warehouse, and implementing data backup and recovery strategies to monitor data quality.

[0012] Furthermore, the S200 uses algorithms to perform in-depth analysis on the input data and identify abnormal data patterns: Among them, A represents the evaluation value of abnormal data, m is the number of features of input data, j is an index variable used to traverse the features of input data, P j is the weight factor of the jth feature, G(Y j ) is the transformation function for the jth feature, α is an adjustment parameter, p is the number of auxiliary features, k is an index variable used to traverse the auxiliary features, H(Z k ) is the transformation function for the kth auxiliary feature, β is the adjustment parameter of the auxiliary feature, and C is a constant term.

[0013] Furthermore, the S200 sets warning rules based on clinical experience and treatment standards, and automatically generates warning information when the intelligent analysis model identifies abnormal data patterns and meets the warning rules: Where W represents the strength of the warning information, γ is an adjustment coefficient, F(A) is the evaluation function of the intelligent analysis model for the abnormal data pattern, D is the severity weight of the abnormal data, q is the number of risk factors considered, i is an index variable used to traverse the risk factors considered, and T i is the weight of the ith risk factor, E i It is an indicator function of the presence or absence of the i-th risk factor. If the risk factor exists, it is 1, and if it does not exist, it is 0. When W exceeds the set warning threshold, a warning message is automatically generated. The warning information content is: detailed information of abnormal data: specifically lists the abnormal data parameter values ​​and the corresponding time points, risk points: based on the characteristics of risk factors and abnormal data, describe the potential risks that may be caused to the patient's health, intervention measures: provide specific response suggestions and review relevant indicators in a timely manner.

[0014] Furthermore, the construction of the CRRT treatment database in S300 requires clear goals, identification and collection of data, selection of database type and design of data model, creation of database and management of metadata, development of data entry interface, implementation of data verification and encryption, and provision of multiple functions to support scientific research and medical record management.

[0015] Furthermore, the data collection and preprocessing in S400 includes collecting data from multiple channels and cleaning, standardizing and feature engineering, selecting machine learning model training, verification and optimization, and inputting real-time monitoring data into the model to generate suggestions and present them.

[0016] Furthermore, the S400 focuses on data privacy protection and model transparency, integrating historical and real-time monitoring data to train the model, and doctors implement treatment and provide feedback for iterative optimization.

[0017] On the other hand, an information processing system for CRRT is characterized in that the system includes real-time data collection and integration, intelligent analysis and early warning, medical record management and scientific research support and personalized treatment suggestions;

[0018] The real-time data collection and integration module: collects key parameters of the treatment process from CRRT equipment and blood gas analyzer medical equipment in real time through the Internet of Things technology to form a complete big data set;

[0019] The intelligent analysis and early warning module: through algorithms, deeply analyzes the collected data, identifies abnormal data patterns, automatically generates early warning information, and promptly reminds medical staff to take intervention measures to ensure safe and effective treatment;

[0020] The medical record management and scientific research support module: builds a structured database to store the full cycle data of CRRT treatment for each patient, which is convenient for doctors to review and analyze medical records. At the same time, it provides data export and visualization tools to support scientific researchers in conducting research projects and academic sharing, and promotes knowledge accumulation and innovation in the field of CRRT treatment;

[0021] The personalized treatment recommendation module: based on the patient's historical data and real-time monitoring results, uses machine learning models to provide patients with personalized treatment plan recommendations, optimize treatment pathways, and improve treatment outcomes.

[0022] Compared with the prior art, an intelligent design method, system and detection method for architectural decoration based on user portraits have the following beneficial effects:

[0023] 1. The present invention constructs an intelligent analysis model to conduct in-depth analysis of data, which can timely discover abnormal data patterns and automatically generate early warning information. These early warning information contain detailed information of abnormal data, possible risk points and recommended intervention measures, which can help medical staff respond quickly and take corresponding treatment measures, thereby effectively avoiding potential risks and improving the safety and effectiveness of CRRT treatment.

[0024] 2. This system builds a structured database to store the full cycle data of CRRT treatment for each patient, which greatly facilitates doctors to review and analyze medical records. At the same time, it also supports data export and visualization tools, allowing scientific researchers to easily obtain the required data for research projects and academic sharing. This function promotes knowledge accumulation and innovation in the field of CRRT treatment, promotes cross-institutional and cross-disciplinary cooperation and exchanges, and makes important contributions to improving the overall medical level. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] 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.

[0026] Figure 1 An operational flow chart of a CRRT information processing method.

[0027] Figure 2 An operation flow chart of the information processing system of CRRT. DETAILED DESCRIPTION

[0028] 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.

[0029] Embodiment 1

[0030] First, select CRRT equipment and blood gas analyzer medical equipment that support the Internet of Things communication protocol. These equipment should have data output interfaces and be able to send data according to the predetermined communication protocol. The medical equipment should be connected to the local area network or wide area network of the medical institution by wired or wireless means to ensure that the equipment can transmit data stably and securely. In order to ensure the security and privacy of the data, the connected medical equipment should be authenticated and authorized to access to prevent unauthorized access and data leakage. A unified data format and transmission protocol should be defined to ensure that the data collected from each medical device is consistent and parseable. This usually includes defining the data field, data type, data length, and encoding method. Through the Internet of Things technology, key parameters of the treatment process can be collected from CRRT equipment and blood gas analyzer medical equipment in real time. These parameters may include treatment dose, machine operating parameters, blood gas test results, filter service life, and coagulation status. The collected raw data should be cleaned, filtered, and formatted to remove noise, duplicate or invalid data to ensure data accuracy and availability.

[0031] The pre-processed data is integrated according to certain logical relationships and organizational structures to form a complete big data set. This may include arranging data at different time points in chronological order, associating data from different devices according to the treatment process, and storing the integrated data in a distributed database or data warehouse for subsequent data analysis and processing. The database or data warehouse should have high availability, scalability and security, and be able to support the storage of large amounts of data and efficient data access. In order to ensure the reliability and security of the data, the database or data warehouse should be backed up regularly, and a corresponding data recovery plan should be formulated. The integrity of the data should be checked regularly to ensure that all key parameters are correctly collected and stored. By comparing data from different data sources or at different time points, the consistency and accuracy of the data can be verified. Machine learning algorithms or statistical methods can be used to detect and identify abnormal data, such as values ​​outside the normal range and sudden changes in trends, so that they can be processed and corrected in a timely manner.

[0032] According to the data types and characteristics during CRRT treatment, select algorithms, use historical data to train the selected algorithms, and build intelligent analysis models Among them, M represents the output result, n is the number of historical data, i is an index variable used to traverse historical data points, and w i Represents the weight coefficient, FX i Indicates the selected algorithm for historical data point X i The processing function of ∈ is a random error term. The key parameters of the CRRT treatment process collected in real time are input into the trained intelligent analysis model. The algorithm is used to deeply analyze the input data and identify abnormal data patterns. Among them, A represents the evaluation value of abnormal data, m is the number of features of input data, j is an index variable used to traverse the features of input data, P j is the weight factor of the jth feature, G(Y j ) is the transformation function for the jth feature, α is an adjustment parameter, p is the number of auxiliary features, k is an index variable used to traverse the auxiliary features, H(Z k ) is the transformation function for the kth auxiliary feature, β is the adjustment parameter of the auxiliary feature, and C is a constant term;

[0033] According to clinical experience and treatment standards, early warning rules are set. When the intelligent analysis model identifies abnormal data patterns and meets the early warning rules, early warning information is automatically generated. Where W represents the strength of the warning information, γ is an adjustment coefficient, F(A) is the evaluation function of the intelligent analysis model for the abnormal data pattern, D is the severity weight of the abnormal data, q is the number of risk factors considered, i is an index variable used to traverse the risk factors considered, and T i is the weight of the ith risk factor, E i It is an indicator function of the presence or absence of the i-th risk factor. If the risk factor exists, it is 1, and if it does not exist, it is 0. When W exceeds the set warning threshold, a warning message is automatically generated. The warning message content: Detailed information of abnormal data: specifically lists the abnormal data parameter values ​​and the corresponding time points, risk points: based on the characteristics of risk factors and abnormal data, describe the potential risks to the patient's health, intervention measures: provide specific response suggestions, and review relevant indicators in a timely manner. The warning information should include detailed information of the abnormal data, possible risk points, and recommended intervention measures. Determine the target audience of the warning information, and push the warning information to the target audience in a timely manner through SMS, email, and APP push methods. Collect the medical staff's response to the warning information and the intervention measures taken, evaluate the effectiveness and accuracy of the warning information, and tune and improve the intelligent analysis model based on the feedback results to improve the accuracy and reliability of the warning.

[0034] Determine the main purpose of the database, including medical record review, analysis, and scientific research support, identify the CRRT treatment-related data that needs to be stored, such as basic patient information, treatment parameters, physiological indicators, and complication records, communicate with doctors and researchers to understand their specific needs and usage habits, select the appropriate database type based on the amount of data and access frequency, design the data table structure, field type, and index based on the results of the data collection phase, create the database and table on the selected database platform, establish a data dictionary, clarify the meaning and value range of each field, manage metadata, and ensure data consistency and accuracy;

[0035] Develop a user-friendly data entry interface to facilitate medical staff to quickly and accurately enter patient information and treatment data. Perform real-time verification during the data entry process to ensure data integrity and accuracy. Encrypt sensitive data to ensure patient privacy and security. Develop a medical record review function to allow doctors to query patient information and treatment data based on specific conditions. Provide data analysis tools to support statistical analysis and trend prediction of data. Develop a report generation function to allow doctors to generate medical records or scientific research reports based on analysis results.

[0036] It provides data export function and supports exporting data to common formats to facilitate subsequent analysis by researchers. It develops data visualization tools and supports multiple visualization forms such as charts, curve charts, and scatter plots to help researchers intuitively understand data and analysis results. It provides API interfaces to allow researchers to access data in the database programmatically and conduct more complex analysis and research. It regularly monitors database performance, optimizes it as needed, and formulates data backup strategies to ensure data security; it can quickly restore data when necessary, provides training and support for medical staff and researchers to ensure that they can use databases and related tools proficiently, supports researchers to create and manage scientific research projects, records research processes, analysis results and conclusions, builds academic sharing platforms, encourages researchers to publish research results and participate in academic exchanges, promotes cross-institutional and cross-disciplinary cooperation and collaboration, and jointly promotes knowledge accumulation and innovation in the field of CRRT treatment.

[0037] Collect historical data of patients from electronic medical record systems, laboratory testing systems, and imaging systems, including but not limited to basic information, past medical history, treatment plans, physiological indicators, and drug reactions. Use wearable devices and sensors to monitor patients' physiological parameters and biomarker levels in real time. Clean the collected data, remove duplicate, abnormal, or missing values, and perform standardization to ensure data consistency and comparability. Based on medical knowledge and machine learning algorithms, extract meaningful features from the raw data and build a feature set for subsequent model training.

[0038] According to the complexity of the problem and the characteristics of the data, a model is selected. Historical data is used as a training set to train the selected machine learning model so that it can learn the laws and patterns in the data. The performance of the model is evaluated through cross-validation methods, and model parameters are adjusted and the model structure is optimized based on the evaluation results to improve the prediction accuracy and generalization ability. The patient data monitored in real time is input into the trained machine learning model. The model makes predictions based on the input data and outputs personalized treatment plan recommendations, including drug dosage adjustment and treatment path optimization. The prediction results are converted into easy-to-understand personalized treatment recommendations, which are presented to patients and doctors through electronic medical record systems and mobile application channels.

[0039] Doctors develop and implement personalized treatment plans based on the patient's specific situation and the model's recommendations, continue to monitor the patient's physiological parameters and biomarker levels in real time, evaluate the treatment effect, and feed the treatment effect data back to the machine learning model for further training and optimization of the model to improve the accuracy and reliability of subsequent recommendations. During the data collection, processing and use process, relevant laws, regulations and ethical standards must be strictly observed to ensure the security and confidentiality of patient privacy. Before collecting patient data, the patient's informed consent should be obtained, and the purpose of the data and possible risks should be clearly informed. The transparency and interpretability of the machine learning model should be improved so that patients and doctors can understand how the model makes decisions.

[0040] Embodiment 2

[0041] Through the Internet of Things technology, the patient's treatment parameters and physiological indicators are collected in real time from the CRRT equipment and blood gas analyzer. These parameters include but are not limited to blood flow, replacement fluid volume, electrolyte concentration, heart rate, blood pressure, and blood oxygen saturation. The collected data are cleaned, filtered, and formatted to form a complete big data set. These data are integrated and stored in a distributed database or data warehouse for subsequent analysis and processing. A data backup and recovery strategy is also implemented to ensure the security and reliability of the data. At the same time, data quality monitoring is carried out, including integrity checking, consistency verification, and abnormal data detection to ensure the accuracy and validity of the data.

[0042] The collected data is deeply analyzed through algorithms that have been trained and optimized with historical data to identify abnormal data patterns. Among them, A represents the evaluation value of abnormal data, m is the number of features of input data, j is an index variable used to traverse the features of input data, P j is the weight factor of the jth feature, G(Y j ) is the transformation function for the jth feature, α is an adjustment parameter, p is the number of auxiliary features, k is an index variable used to traverse the auxiliary features, H(Z k) is the transformation function for the kth auxiliary feature, β is the adjustment parameter of the auxiliary feature, and C is a constant term. When one or more indicators of the patient are detected to be abnormal, an early warning message will be automatically generated. Where W represents the strength of the warning information, γ is an adjustment coefficient, F(A) is the evaluation function of the intelligent analysis model for the abnormal data pattern, D is the severity weight of the abnormal data, q is the number of risk factors considered, i is an index variable used to traverse the risk factors considered, and T i is the weight of the ith risk factor, E i It is an indicator function of the presence or absence of the i-th risk factor. If the risk factor exists, it is 1, and if it does not exist, it is 0. When W exceeds the set warning threshold, a warning message is automatically generated. The warning message content: Detailed information of abnormal data: specifically lists the abnormal data parameter values ​​and the corresponding time points, risk points: based on the characteristics of risk factors and abnormal data, describe the potential risks that may be caused to the patient's health, intervention measures: provide specific response suggestions, and review relevant indicators in a timely manner. The warning information contains detailed information of abnormal data, possible risk points and recommended intervention measures. The warning information is pushed to the target audience in a timely manner, including doctors, nurses and medical staff, through SMS, email or APP push. In this way, medical staff can quickly understand the patient's condition and take appropriate treatment measures.

[0043] After receiving the warning information, medical staff will immediately check the patient's detailed data and possible risk points. They will take corresponding treatment measures based on the recommended intervention measures provided and their own clinical experience and judgment. For example, if the patient's electrolyte concentration is detected to be abnormal, the medical staff may adjust the electrolyte composition and concentration in the replacement fluid. If a sudden drop in blood pressure is detected, the medical staff may give pressor drugs or adjust blood flow. During the treatment process, medical staff continuously monitor the patient's condition and adjust the treatment plan as needed. At the same time, they will feedback the intervention measures taken and the patient's treatment effects to the system.

[0044] The intelligent analysis model is tuned and improved based on the feedback information provided by medical staff. This feedback information includes the intervention measures taken by medical staff, the treatment effects of patients and any new abnormal data patterns. Through continuous feedback and optimization, the system can gradually improve the accuracy and reliability of early warnings. In this way, in future treatment processes, the system can more accurately identify abnormal data patterns and provide medical staff with more effective recommended intervention measures. In addition, the system also provides medical record management and scientific research support functions. It builds a structured database to store the full cycle data of CRRT treatment for each patient. These data provide doctors with a basis for medical record review and analysis, and also provide scientific researchers with valuable research resources.

[0045] Based on the patient's historical data and real-time monitoring results, the system uses machine learning models to provide patients with personalized treatment plan recommendations. These recommendations are designed to optimize the treatment path and improve treatment outcomes. Doctors can develop more precise treatment plans based on the personalized treatment recommendations provided by the system, combined with the patient's specific conditions and clinical needs. At the same time, they can continue to monitor the patient's treatment outcomes and use feedback for iterative optimization of the model.

[0046] 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. A CRRT information processing method, characterized in that: The specific steps of this method are: S100, real-time data collection and integration: Through the Internet of Things technology, key parameters of the treatment process are collected from CRRT equipment and blood gas analyzer medical equipment in real time to form a complete big data set; S200, intelligent analysis and early warning: According to the data type and characteristics during CRRT treatment, select the algorithm, use historical data to train the selected algorithm, and build an intelligent analysis model. Among them, M represents the output result, n is the number of historical data, i is an index variable used to traverse historical data points, and w i represents the weight coefficient, F(X i ) represents the selected algorithm for the historical data point X i The processing function of ∈ is a random error term. The key parameters of the CRRT treatment process collected in real time are input into the trained intelligent analysis model. The algorithm is used to deeply analyze the input data and identify abnormal data patterns. According to clinical experience and treatment standards, early warning rules are set. When the intelligent analysis model identifies abnormal data patterns and meets the early warning rules, early warning information is automatically generated. The early warning information should include detailed information of the abnormal data, possible risk points, and recommended intervention measures. The target audience of the early warning information is determined, and the early warning information is pushed to the target audience in a timely manner through SMS, email, and APP push. The response of medical staff to the early warning information and the intervention measures taken are collected, and the effectiveness and accuracy of the early warning information are evaluated. According to the feedback results, the intelligent analysis model is tuned and improved to improve the accuracy and reliability of the early warning; S300, medical record management and scientific research support: Build a structured database to store the full cycle data of CRRT treatment for each patient, which is convenient for doctors to review and analyze medical records. At the same time, it provides data export and visualization tools to support scientific researchers in conducting research projects and academic sharing, and promote knowledge accumulation and innovation in the field of CRRT treatment; S400, personalized treatment recommendations: Based on patient historical data and real-time monitoring results, using machine learning models, provide patients with personalized treatment plan recommendations, optimize treatment pathways, and improve treatment outcomes.

2. The CRRT information processing method according to claim 1, characterized in that: The S100 IoT device configuration includes selecting CRRT and blood gas analyzer equipment that supports the communication protocol, accessing the network wirelessly, performing identity authentication and authorization, defining a unified data format, collecting key parameters during the treatment process in real time, integrating and storing the pre-processed data in a distributed database or data warehouse, and implementing data backup and recovery strategies to monitor data quality.

3. The CRRT information processing method according to claim 1, characterized in that: The S200 uses algorithms to perform in-depth analysis on the input data and identify abnormal data patterns: Among them, A represents the evaluation value of abnormal data, m is the number of features of input data, j is an index variable used to traverse the features of input data, P j is the weight factor of the jth feature, G(Y j ) is the transformation function for the jth feature, α is an adjustment parameter, p is the number of auxiliary features, k is an index variable used to traverse the auxiliary features, H(Z k ) is the transformation function for the kth auxiliary feature, β is the adjustment parameter of the auxiliary feature, and C is a constant term.

4. The CRRT information processing method according to claim 1, characterized in that: The S200 sets warning rules based on clinical experience and treatment standards. When the intelligent analysis model identifies abnormal data patterns and meets the warning rules, it automatically generates warning information: Where W represents the strength of the warning information, γ is an adjustment coefficient, F(A) is the evaluation function of the intelligent analysis model for the abnormal data pattern, D is the severity weight of the abnormal data, q is the number of risk factors considered, i is an index variable used to traverse the risk factors considered, and T i is the weight of the ith risk factor, E i It is an indicator function of the presence or absence of the i-th risk factor. If the risk factor exists, it is 1, and if it does not exist, it is 0. When W exceeds the set warning threshold, a warning message is automatically generated. The warning information content is: detailed information of abnormal data: specifically lists the abnormal data parameter values ​​and the corresponding time points, risk points: based on the characteristics of risk factors and abnormal data, describe the potential risks that may be caused to the patient's health, intervention measures: provide specific response suggestions and review relevant indicators in a timely manner.

5. The CRRT information processing method according to claim 1, characterized in that: The construction of the CRRT treatment database in S300 requires clear goals, identification and collection of data, selection of database type and design of data model, creation of database and management of metadata, development of data entry interface, implementation of data verification and encryption, and provision of multiple functions to support scientific research and medical record management.

6. The CRRT information processing method according to claim 1, characterized in that: The data collection and preprocessing in S400 includes collecting data from multiple channels and cleaning, standardizing and feature engineering, selecting machine learning model training, verification and optimization, and inputting real-time monitoring data into the model to generate suggestions and present them.

7. The CRRT information processing method according to claim 6, characterized in that: The S400 focuses on data privacy protection and model transparency, integrating historical and real-time monitoring data to train the model, and doctors implement treatment and provide feedback for iterative optimization.

8. The CRRT information processing system according to any one of claims 1 to 7, characterized in that: The system includes real-time data collection and integration module, intelligent analysis and early warning module, medical record management and scientific research support module and personalized treatment recommendation module; The real-time data collection and integration module: collects key parameters of the treatment process from CRRT equipment and blood gas analyzer medical equipment in real time through the Internet of Things technology to form a complete big data set; The intelligent analysis and early warning module: through algorithms, deeply analyzes the collected data, identifies abnormal data patterns, automatically generates early warning information, and promptly reminds medical staff to take intervention measures to ensure safe and effective treatment; The medical record management and scientific research support module: builds a structured database to store the full cycle data of CRRT treatment for each patient, which is convenient for doctors to review and analyze medical records. At the same time, it provides data export and visualization tools to support scientific researchers in conducting research projects and academic sharing, and promotes knowledge accumulation and innovation in the field of CRRT treatment; The personalized treatment recommendation module: based on the patient's historical data and real-time monitoring results, uses machine learning models to provide patients with personalized treatment plan recommendations, optimize treatment pathways, and improve treatment outcomes.

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