Remote intelligent home peritoneal dialysis system, peritoneal dialysis control method, terminal and medium

Abstract

The invention discloses a remote intelligent home peritoneal dialysis system, a peritoneal dialysis control method, a terminal and a medium. The system comprises a patient terminal device, a remote server and a mobile terminal. The patient end device is used for collecting and transmitting multi-dimensional data. And the remote server is used for collecting the multi-dimensional data collected by the patient end equipment, analyzing and processing the multi-dimensional data by using an AI model, and outputting early warning information, a prediction result and a treatment scheme. And the mobile terminal is used for acquiring the early warning information, the prediction result and the treatment scheme pushed by the remote server, and generating a visual data board. The remote intelligent home peritoneal dialysis system provided by the invention is beneficial for helping a patient clearly know the health condition of the patient, meanwhile, a doctor can carry out omnibearing monitoring on the patient, and three-in-one integration of all-dimensional monitoring, AI intelligent decision making and doctor-patient closed-loop management is facilitated.

Description

Technical Field

[0001] The present invention relates to the field of smart medical technology, and in particular to a remote smart home peritoneal dialysis system, a peritoneal dialysis control method, a terminal, and a medium. Background Art

[0002] Peritoneal dialysis (PD) is an important form of replacement therapy for end-stage renal disease. Home PD can be divided into two types: manual PD and automated PD using a fully automated peritoneal dialysis machine (APD). However, home PD is not yet truly accessible. Due to the lack of an effective remote management platform for home PD, most patients still need to regularly travel to the hospital to assess treatment effectiveness and potential complications, significantly consuming their time and energy. Furthermore, some patients with poor compliance often experience problems such as inadequate self-management and a delay in seeking medical attention, which significantly reduces their quality of life and survival rate.

[0003] With the development of technology, automated peritoneal dialysis (APD) is increasingly being accepted by patients due to its many advantages. Current APD machines are equipped with networking capabilities, enabling automatic recording of PD treatment data and data transmission between the physician and the patient. However, treatment data alone cannot fully inform physicians of the patient's treatment effectiveness and health status, enabling remote personalized prescription adjustments and early warning of related complications. Patients are also unable to scientifically understand their health status at home and communicate remotely with their physicians through effective channels. Low compliance with home PD treatment, difficulty monitoring patient health status, and delayed emergency response remain issues that need to be addressed.

[0004] Therefore, the prior art still has defects. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a remote intelligent home peritoneal dialysis system, peritoneal dialysis control method, terminal and medium to address the above-mentioned defects of the prior art. The technical solutions adopted by the present invention are as follows: In a first aspect, the present invention provides a remote intelligent home peritoneal dialysis system, wherein the system comprises: Patient-side devices, which are used for collecting and transmitting multidimensional data, and include any one or more of a fully automatic peritoneal dialysis machine, a body composition analyzer, a non-invasive hemoglobin detector, a home biochemical analyzer, a weight scale, and a blood pressure monitor; A remote server, configured to collect the multidimensional data collected by the patient-side device, analyze and process the multidimensional data using an AI model, and output warning information, prediction results, and treatment plans; The mobile terminal is used to obtain the early warning information, prediction results and treatment plans pushed by the remote server, and generate a visual data dashboard.

[0006] In one implementation, the multidimensional data includes any one or more of the following: ultrafiltration volume and drainage time recorded by a fully automatic peritoneal dialysis machine, total body fluid volume, fat mass and muscle mass assessed by a body composition analyzer, hemoglobin volume detected by a non-invasive hemoglobin detector, electrolytes, creatinine, urea analyzed by a home biochemical analyzer, daily pre- and post-dialysis weight recorded by a weight scale, and daily blood pressure recorded by a sphygmomanometer.

[0007] In one implementation, the mobile terminal includes: a patient mobile terminal and a doctor mobile terminal. The patient mobile terminal obtains health reports and adverse event warnings pushed by a remote server in real time through an APP; the doctor mobile terminal is used to provide a visual data dashboard, dynamically display the patient's multi-dimensional health indicator trends, mark abnormal data and trigger warning prompts.

[0008] In a second aspect, an embodiment of the present invention further provides a peritoneal dialysis control method based on the remote intelligent home peritoneal dialysis system described in the above solution, wherein the method comprises: Collect multi-dimensional data based on patient-side devices; Collect multidimensional data collected by the patient-side device, analyze and process the multidimensional data using an AI model, and output warning information, prediction results, and treatment plans; Obtain the warning information, prediction results and treatment plans pushed by the remote server, and generate a visual data dashboard.

[0009] In one implementation, the use of an AI model to analyze and process the multidimensional data and output warning information, prediction results, and treatment plans includes: Perform multi-task prediction during peritoneal dialysis based on a CNN-Transformer-GRU-Attention joint model to obtain the prediction result and the warning information; The treatment plan is output through an architecture that combines convolutional neural networks with fully connected layers.

[0010] In one implementation, the multi-task prediction during peritoneal dialysis is performed based on the CNN-Transformer-GRU-Attention joint model to obtain the prediction result and the warning information, including: Extracting local features and time series information of the multidimensional data based on a one-dimensional convolutional layer in a convolutional neural network, and obtaining important diagnostic information contained in the local features and dynamic change features in the time series information; The Transformer model reconstructs the time series data, extracts the hidden features of different sudden illnesses, and couples the Transformer model input with the GRU layer. An attention mechanism is added after the GRU layer, and different weight coefficients are assigned to the hidden features of different sudden illnesses; The final output values of different tasks are obtained through Softmax function mapping, and the prediction results and the warning information are obtained according to the final output values. The prediction results are used to reflect the probability of occurrence of different sudden diseases.

[0011] In one implementation, the output of the treatment plan using an architecture combining a convolutional neural network and a fully connected layer includes: The multidimensional data is sequentially subjected to one-dimensional convolution to extract features, batch normalization to standardize the data, maximum pooling layer to downsample, and flattening layer to convert the features into one-dimensional vectors; Three treatment parameters are output through the fully connected layer: number of exchanges per day, dialysis cycle time, and dialysate concentration; The treatment plan is generated based on the number of daily exchanges, dialysis cycle time, and dialysate concentration.

[0012] In one implementation, the method further includes: An integrated intelligent consultation platform is used to formulate or adjust prescriptions based on the treatment plan and set parameters through remote tools.

[0013] In a third aspect, an embodiment of the present invention further provides a terminal, wherein the terminal includes a memory, a processor, and a peritoneal dialysis control program for a remote intelligent home peritoneal dialysis system stored in the memory and executable on the processor. When the processor executes the peritoneal dialysis control program for the remote intelligent home peritoneal dialysis system, the steps of the peritoneal dialysis control method for a remote intelligent home peritoneal dialysis system of any one of the above-mentioned solutions are implemented.

[0014] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein a peritoneal dialysis control program for a remote intelligent home peritoneal dialysis system is stored on the computer-readable storage medium, and the peritoneal dialysis control program for the remote intelligent home peritoneal dialysis system implements the steps of the peritoneal dialysis control method for a remote intelligent home peritoneal dialysis system described in any one of the above-mentioned schemes on the computer-readable storage medium.

[0015] Beneficial effects: Compared with the existing technology, the present invention provides a remote intelligent home peritoneal dialysis system, which includes: a patient-side device, a remote server and a mobile terminal. The patient-side device is used to collect and transmit multidimensional data. The remote server is used to collect the multidimensional data collected by the patient-side device, and use the AI model to analyze and process the multidimensional data, and output early warning information, prediction results and treatment plans. The mobile terminal is used to obtain the early warning information, prediction results and treatment plans pushed by the remote server, and generate a visual data dashboard. The remote intelligent home peritoneal dialysis system provided by the present invention is conducive to helping patients clearly understand their own health status, while allowing doctors to monitor patients in all directions, which is conducive to the realization of the trinity of full-dimensional monitoring, AI intelligent decision-making, and closed-loop management of doctors and patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a system framework diagram of the remote intelligent home peritoneal dialysis system provided in an embodiment of the present invention.

[0017] Figure 2 A technical roadmap for AI prediction in a peritoneal dialysis control method based on a remote intelligent home peritoneal dialysis system provided in an embodiment of the present invention.

[0018] Figure 3 A logic diagram for generating personalized prescriptions in a peritoneal dialysis control method for a remote intelligent home peritoneal dialysis system provided in an embodiment of the present invention.

[0019] Figure 4 This is a functional diagram of the mobile APP in an embodiment of the present invention.

[0020] Figure 5 A flowchart of a peritoneal dialysis control method based on a remote intelligent home peritoneal dialysis system provided in an embodiment of the present invention.

[0021] Figure 6 This is a functional block diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0023] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents, operations, or steps, nor must they be executed in the order described. For example, some operations or steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0024] It should be understood that the terms used in this specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0025] It should be understood that, to facilitate a clear description of the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. For example, the first control information and the second control information are merely used to distinguish different control information and do not limit their order.

[0026] Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0027] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0028] The remote intelligent home peritoneal dialysis system of the present invention includes: a patient-side device, a remote server and a mobile terminal. Figure 1 As shown, the patient-side device, remote server and mobile terminal of this embodiment can realize data transmission and interaction. The patient-side device includes any one or more of a fully automatic peritoneal dialysis machine, a body composition analyzer, a non-invasive hemoglobin detector, a home biochemical analyzer, a weight scale, and a blood pressure monitor. The patient-side device is used for collecting and transmitting multidimensional data, and uploading the collected multidimensional data to the remote server. The remote server is deployed in the cloud based on AI deployment, and is used to collect the multidimensional data collected by the patient-side device, and use the AI model to analyze and process the multidimensional data, output early warning information, prediction results and treatment plans, and feed back the early warning information, prediction results and treatment plans to the mobile terminal. The mobile terminal includes: a patient mobile terminal and a doctor mobile terminal, which are used to obtain the early warning information, prediction results and treatment plans pushed by the remote server, and generate a visual data dashboard.

[0029] Specifically, the patient-side device of this embodiment deploys multiple devices, including an automated peritoneal dialysis machine, a body composition analyzer, a non-invasive hemoglobin detector, a home biochemical analyzer, a blood pressure monitor, a weight scale, etc. After daily treatment using the automated peritoneal dialysis machine, the fully automated peritoneal dialysis machine will record treatment parameters such as ultrafiltration volume and drainage time. The patient will then use the body composition analyzer to assess body composition, such as total body fluid volume, fat mass, and muscle mass. A non-invasive hemoglobin detector will be used to quantitatively measure hemoglobin, which is a direct indicator for determining the presence of renal anemia. A blood pressure monitor will be used to record daily blood pressure. A weight scale will be used to record daily weight before and after dialysis. A thermometer will be used to record body temperature. A home biochemical analyzer will be used regularly to perform home tests on blood and urine to test important indicators such as electrolytes, creatinine, and urea. The above-recorded data constitutes the multidimensional data, which is used to analyze adverse event warnings and predict complications.

[0030] In this embodiment, the multidimensional data described has significant clinical significance. The ultrafiltration volume and drainage time recorded by the fully automated peritoneal dialysis machine can be used as data for analyzing peritonitis complications. The total body water content recorded by the body composition analyzer can assess the water load of dialysis patients and, combined with blood pressure changes and heart rate, provide data support for early warning of heart failure. Blood pressure and hemoglobin levels provide data support for monitoring complications of renal anemia. Electrolytes provide data support for timely early warning of electrolyte imbalances. Furthermore, the patient's daily dry weight, total body fluid volume, therapeutic ultrafiltration volume, blood pressure, and biochemical indicators (creatinine, urea, electrolytes) serve as data sources for tailoring personalized treatment plans. To interconnect data from these instruments with different protocols, a multimode gateway (i.e., one that supports multiple communication protocols) is required to achieve standardized data transmission and upload data recorded by all devices to a remote server.

[0031] After receiving the multidimensional data, the remote server first determines whether the collected multidimensional data has obvious abnormalities based on a simple threshold, and provides graded warnings, which are sent to the patient's mobile terminal and the doctor's mobile terminal respectively. Specifically, this embodiment uses an AI model to provide early warnings for related complications. The specific technical route is as follows: Figure 2As shown. This embodiment uses a CNN-Transformer-GRU-Attention joint model for multi-task prediction during peritoneal dialysis. CNN (Convolutional Neural Network) is a convolutional neural network, and the core of the Transformer model is the attention mechanism, which allows the model to simultaneously consider information at all time steps when processing sequence data, thereby better capturing long-range dependencies. GRU (Gate Recurrent Unit) is a type of recurrent neural network. Attention is an attention model. First, one-dimensional convolution is used to extract local features and time series information from the input patient data, obtaining important diagnostic information contained in the local features and dynamic change characteristics in the time series information (such as changes in the collected multidimensional data). The time series data is then further reconstructed using the Transformer model, efficiently extracting deeper and more abstract hidden features of different sudden illnesses. This allows for better processing of time series information of a certain length, effectively capturing long-term dependencies, namely, the relationship between various important diagnostic information and changes in the multidimensional data. This embodiment couples the input of the Transformer model with a GRU layer. Compared to RNNs (Recurrent Neural Networks), which are prone to vanishing or exploding gradients in long sequences, making them ineffective at learning long-range dependencies, and LSTMs (Long Short-Term Memory Networks), which are complex, computationally intensive, and feature numerous parameters, the GRU incorporates a gating mechanism to address the vanishing gradient issue of traditional RNNs and simplify the complex LSTM structure, further improving the model's computational efficiency and shortening iteration time. The GRU network leverages the multidimensional data of patients undergoing peritoneal dialysis over a certain period of time, rather than relying solely on data from a single historical moment. This allows for consistent and accurate identification and prediction of sudden illnesses during peritoneal dialysis, such as heart failure, peritonitis, renal anemia, and electrolyte imbalances. The data processed by the Transformer layer is fully utilized in the GRU layer, and these features are then determined to be remembered or forgotten through the GRU's gating structure. This allows for modeling long-term dependencies between feature sequences at multiple scales, addressing the Transformer's difficulty capturing local dependencies. Since most of the physiological factors of sudden illnesses during peritoneal dialysis overlap, different combinations of physiological factors will identify different sudden illnesses. Therefore, in order to better distinguish different actual scenarios, an attention mechanism is added after the GRU layer. The hidden features of the physiological factors of different sudden illnesses are given different weight coefficients, thereby reflecting their different degrees of influence on the prediction of different sudden illnesses.Finally, the outputs obtained from these calculations are mapped through the Softmax function to obtain the final output values of the different tasks. Based on these final output values, the prediction results and warning information are obtained. The prediction results are used to reflect the probability of occurrence of different sudden illnesses. In this embodiment, the maximum output value is the model's recognition result of whether a sudden illness will occur or what type of sudden illness will occur. Based on this prediction result, warning information can be obtained.

[0032] In addition, the remote server of this embodiment can also formulate personalized peritoneal dialysis treatment plans based on multi-dimensional data, such as the patient's daily treatment parameters and physiological indicators, and timely adjust the treatment parameters of the fully automatic peritoneal dialysis machine (such as the number of exchanges, cycle time, and dialysate concentration). The technical route is as follows Figure 3 As shown, this embodiment utilizes a convolutional neural network (CNN) architecture combined with fully connected layers for personalized peritoneal dialysis treatment plan development. Specifically, the input layer contains at least five key parameters: dry weight, ultrafiltration volume, total body fluid volume, biochemical indicators (creatinine, urea, electrolytes), and the patient's subjective experience (using binary classification, with 0 representing good and 1 representing poor). This data is processed sequentially through CNN layers, including one-dimensional convolution (Convolution1D) feature extraction, batch normalization (BatchNormalization) data standardization, and max pooling (MaxPooling) downsampling. The features are then converted into one-dimensional vectors through a flattening layer. Finally, a fully connected layer (Dense) outputs three treatment parameters: daily exchanges, dialysis cycle time, and dialysate concentration. This enables the generation of precise treatment plans based on multi-dimensional data. The data plan generated in this embodiment can be pushed to the physician for review via a mobile device. Upon confirmation, it is automatically synchronized to the peritoneal dialysis machine for parameter updates. The system also establishes a closed-loop feedback mechanism, feeding the treatment plan's effectiveness and patient data back to the model to continuously optimize decision-making accuracy. This solution significantly improves the timeliness and accuracy of prescription adjustments by integrating data-driven intelligent analysis with clinical experience, reduces the risk of complications and prolongs the patient's stable renal function.

[0033] Furthermore, the APP function diagrams of the patient mobile terminal and the doctor mobile terminal of this embodiment are as follows: Figure 4As shown, the mobile APP of this embodiment serves as a human-computer interaction interface, realizing lightweight function deployment and efficient doctor-patient collaboration. The patient's mobile terminal obtains health reports and adverse event warnings pushed by the cloud in real time through the APP, so that they can pay attention to their own health status in a timely manner; the doctor's mobile terminal APP provides a visual data dashboard, dynamically displays the patient's multi-dimensional health indicator trends, marks abnormal data and triggers early warning prompts, and integrates an intelligent consultation platform to quickly formulate or adjust prescriptions based on the treatment plan pushed by the remote server, and realizes collaborative decision-making for operations such as dialysis machine parameter settings through remote tools. The mobile terminal software realizes a closed-loop design of "centralized analysis in the cloud and efficient application at the terminal" through efficient cooperation with the remote server, significantly improving the complication prevention ability and medical management efficiency of home dialysis patients. In actual application, this embodiment can also send relevant control instructions to the patient-side device based on the treatment plan pushed by the remote server, adjust the monitoring frequency of the corresponding device, so as to obtain the user's multi-dimensional data in a timely manner.

[0034] Specifically, the patient's mobile terminal in this embodiment can also access the user's multi-dimensional data from a cloud server via a network interface. This cross-platform application, implemented using ReactNative (an open-source, cross-platform mobile application development framework), displays real-time body composition data, such as total body water, muscle mass, extracellular to intracellular fluid ratio, hemoglobin values, and dialysis parameters, such as ultrafiltration volume and dialysate composition. Furthermore, the data visualization library can be used to create trend charts, displaying weight change curves from 7 to 30 days, hemoglobin fluctuation line charts, and fluid balance trend charts, visually reflecting dynamic data changes. The doctor's mobile terminal, based on the backend management framework, can pull various data from the remote server and apply data analysis algorithms to perform multi-dimensional data processing. Patients can be grouped and statistically analyzed based on specific criteria, such as identifying a high-incidence group for anemia. Complication rate heat maps can be generated using Geographic Information System (GIS) technologies to visually display complication distribution. Comparative views can also be created to compare and analyze current patient data with historical data, such as analyzing peritoneal failure risk trends, and generate professional statistical reports to assist physicians in decision-making.

[0035] In this embodiment, after analysis, the remote server recommends a treatment plan or prescription adjustment. The doctor can then modify the prescription based on the recommendation, recording the reason for the modification (e.g., "The patient is overhydrated and needs to increase dehydration"). Upon confirmation, the prescription is automatically synchronized to the cloud-based historical prescription database. After confirming the adjusted prescription with the patient, the prescription parameters are sent to the peritoneal dialysis machine via the Internet of Things. The dialysis machine then returns a confirmation status (e.g., "Parameters updated"), and the app records the operation log. The newly updated prescription is then used on the peritoneal dialysis machine.

[0036] After receiving the warning information pushed by the remote server, the patient's mobile terminal can perform a graded warning. The graded warning rules of this embodiment are as follows: Level 1 warning (low risk): A single or short-term data abnormality (such as a slight decrease in hemoglobin or fluid overload) will trigger a pop-up window prompt on the patient's APP and push health advice (such as dietary adjustments).

[0037] Level 2 warning (medium risk): Continuous data abnormalities or critical key indicators (such as insufficient ultrafiltration for three consecutive days, hemoglobin <10g / dL) will be sent to the patient through pop-up windows and text messages, and a doctor-side analysis report will be generated, with remote evaluation recommended.

[0038] Level 3 Warning (High Risk): Critical values (such as extremely high blood creatinine, electrolyte imbalance, or a sudden drop in hemoglobin) or equipment failure. This embodiment triggers an emergency pop-up window, SMS, and voice call reminder. The system automatically contacts the hospital and generates a follow-up consultation recommendation (e.g., "Emergency treatment is required within 2 hours"), thus implementing a "pop-up window → SMS notification → emergency alarm" warning model.

[0039] Because peritoneal dialysis patients' lifestyles (diet, exercise) require more refined management to ensure the stability of various physical indicators, this embodiment can read the patient's physiological indicators from a remote server in real time based on the automated decision-making module of the rule engine (for example, when the hemoglobin level is lower than 10g / dL, it will automatically trigger dietary adjustment suggestions, recommending increased intake of iron-rich foods such as red meat and animal liver).

[0040] The remote intelligent home peritoneal dialysis system of the present invention has at least the following advantages: 1. More comprehensive data collection for peritoneal dialysis patients For the first time, this platform integrates automated peritoneal dialysis machines, body composition analyzers, non-invasive hemoglobin analyzers, and biochemical analyzers into a unified platform, enabling the monitoring of multi-dimensional health data such as fluid balance, anemia, and treatment parameters. This overcomes the limitations of existing single-function devices and systematically monitors the health status of peritoneal dialysis patients from multiple dimensions. Furthermore, these devices are mostly non-invasive, significantly reducing the frequency of hospital visits and alleviating the pain of invasive testing.

[0041] 2. Automatic data upload It replaces the process of patients recording various data by hand, reducing the inaccuracy and timeliness of the data.

[0042] 3. AI-driven data analysis (1) Dynamic prediction model for complications: Based on multimodal deep learning, combined with real-time data and historical trends, dynamic risk warnings for complications such as peritonitis and heart failure are achieved instead of relying on static thresholds. This greatly reduces the probability of complications in peritoneal dialysis patients and significantly extends the patient's life cycle.

[0043] (2) Personalized treatment plan generation: Combining a large number of prescription cases, using AI to evaluate each patient's current treatment plan based on daily treatment conditions and physical data, and assist in making prescription modification recommendations. This provides strong support for doctors to better formulate more professional and effective dialysis plans for each dialysis patient.

[0044] 4. Multifunctional mobile management platform This professional peritoneal dialysis management platform, designed for peritoneal dialysis patients, integrates key components such as data dashboards, report generation, alarm functions, and information push notifications. It provides a platform for doctors to remotely monitor patients' conditions in real time and provide guidance.

[0045] Based on the above embodiment, the present invention also provides a peritoneal dialysis control method based on the remote intelligent home peritoneal dialysis system in the above embodiment. The method of this embodiment can be applied to a terminal, which can be a smart product terminal such as a mobile phone or a computer. Figure 5 As shown, the method of this embodiment includes the following steps: Step S100: collecting multi-dimensional data based on the patient-side device; Step S200: Collect multidimensional data collected by the patient-side device, analyze and process the multidimensional data using an AI model, and output warning information, prediction results, and treatment plans; Step S300: Obtain the warning information, prediction results, and treatment plans pushed by the remote server, and generate a visual data dashboard.

[0046] In this embodiment, the patient-side device includes any one or more of a fully automatic peritoneal dialysis machine, a body composition analyzer, a non-invasive hemoglobin detector, a home biochemical analyzer, a weight scale, and a blood pressure monitor. The multidimensional data includes: the ultrafiltration volume and drainage time recorded by the fully automatic peritoneal dialysis machine, the total body fluid volume, fat mass, and muscle mass assessed by the body composition analyzer, the hemoglobin volume detected by the non-invasive hemoglobin detector, the electrolytes, creatinine, and urea analyzed by the home biochemical analyzer, the daily pre- and post-dialysis weight recorded by the weight scale, and the daily blood pressure recorded by the blood pressure monitor. The mobile terminal includes: a patient mobile terminal and a doctor mobile terminal. The patient mobile terminal obtains health reports and adverse event warnings pushed by the remote server in real time through the APP; the doctor mobile terminal is used to provide a visual data dashboard, dynamically display the patient's multi-dimensional health indicator trends, mark abnormal data, and trigger warning prompts.

[0047] The remote server of this embodiment can perform multi-task prediction during peritoneal dialysis based on the CNN-Transformer-GRU-Attention joint model to obtain the prediction results and warning information. Furthermore, the remote server can output a treatment plan through an architecture combining a convolutional neural network with a fully connected layer.

[0048] The various steps in the peritoneal dialysis control method of the remote intelligent home peritoneal dialysis system of this embodiment are the same as the principles of the various modules in the above system embodiments and will not be repeated here.

[0049] Based on the above embodiment, the present invention further provides a terminal, the principle block diagram of the terminal can be as follows: Figure 6 The terminal may include one or more processors 100 ( Figure 6 (only one is shown in the figure), memory 101, and computer program 102 stored in memory 101 and executable on one or more processors 100. For example, a peritoneal dialysis control program for a remote intelligent home peritoneal dialysis system. When one or more processors 100 execute computer program 102, each step of an embodiment of a peritoneal dialysis control method for a remote intelligent home peritoneal dialysis system can be implemented. Alternatively, when one or more processors 100 execute computer program 102, each module / unit in an embodiment of a peritoneal dialysis control system for a remote intelligent home peritoneal dialysis system can be implemented, without limitation.

[0050] In one embodiment, the processor 100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0051] In one embodiment, memory 101 may be an internal storage unit of an electronic device, such as a hard drive or memory. Memory 101 may also be an external storage device of the electronic device, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash memory card, etc. Furthermore, memory 101 may include both an internal storage unit of the electronic device and an external storage device. Memory 101 is used to store computer programs and other programs and data required by the terminal. Memory 101 may also be used to temporarily store data that has been output or is about to be output.

[0052] Those skilled in the art will understand that Figure 6 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the solution of the present invention, and does not constitute a limitation on the terminal to which the solution of the present invention is applied. The specific terminal may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0053] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, operation database or other media used in the embodiments provided by the present invention may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAM bus dynamic RAM (RDRAM), etc.

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A remote intelligent home peritoneal dialysis system, characterized in that: The system comprises: Patient-side devices, which are used for collecting and transmitting multidimensional data, and include any one or more of a fully automatic peritoneal dialysis machine, a body composition analyzer, a non-invasive hemoglobin detector, a home biochemical analyzer, a weight scale, and a blood pressure monitor; A remote server, configured to collect the multidimensional data collected by the patient-side device, analyze and process the multidimensional data using an AI model, and output warning information, prediction results, and treatment plans; The mobile terminal is used to obtain the early warning information, prediction results and treatment plans pushed by the remote server, and generate a visual data dashboard.

2. The remote intelligent home peritoneal dialysis system according to claim 1, characterized in that: The multidimensional data includes any one or more of the following: ultrafiltration volume and drainage time recorded by the fully automatic peritoneal dialysis machine, total body fluid volume, fat mass and muscle mass assessed by the body composition analyzer, hemoglobin volume detected by the non-invasive hemoglobin detector, electrolytes, creatinine, urea analyzed by a home biochemical analyzer, daily pre- and post-dialysis weight recorded by a weight scale, and daily blood pressure recorded by a sphygmomanometer.

3. The remote intelligent home peritoneal dialysis system according to claim 1, characterized in that: The mobile terminal includes: a patient mobile terminal and a doctor mobile terminal. The patient mobile terminal obtains health reports and adverse event warnings pushed by a remote server in real time through the APP; the doctor mobile terminal is used to provide a visual data dashboard, dynamically display the patient's multi-dimensional health indicator trends, mark abnormal data and trigger warning prompts.

4. A peritoneal dialysis control method based on the remote intelligent home peritoneal dialysis system according to any one of claims 1 to 3, characterized in that: The method comprises: Collect multi-dimensional data based on patient-side devices; Collect multidimensional data collected by the patient-side device, analyze and process the multidimensional data using an AI model, and output warning information, prediction results, and treatment plans; Obtain the warning information, prediction results and treatment plans pushed by the remote server, and generate a visual data dashboard.

5. The peritoneal dialysis control method of the remote intelligent home peritoneal dialysis system according to claim 4, characterized in that: The AI model is used to analyze and process the multidimensional data and output warning information, prediction results, and treatment plans, including: Perform multi-task prediction during peritoneal dialysis based on a CNN-Transformer-GRU-Attention joint model to obtain the prediction result and the warning information; The treatment plan is output through an architecture that combines convolutional neural networks with fully connected layers.

6. The peritoneal dialysis control method of the remote intelligent home peritoneal dialysis system according to claim 5, characterized in that: Multi-task prediction during peritoneal dialysis is performed based on a CNN-Transformer-GRU-Attention joint model to obtain the prediction results and the warning information, including: Extracting local features and time series information of the multidimensional data based on a one-dimensional convolutional layer in a convolutional neural network, and obtaining important diagnostic information contained in the local features and dynamic change features in the time series information; The Transformer model reconstructs the time series data, extracts the hidden features of different sudden illnesses, and couples the Transformer model input with the GRU layer. An attention mechanism is added after the GRU layer, and different weight coefficients are assigned to the hidden features of different sudden illnesses; The final output values of different tasks are obtained through Softmax function mapping, and the prediction results and the warning information are obtained according to the final output values. The prediction results are used to reflect the probability of occurrence of different sudden diseases.

7. The peritoneal dialysis control method of the remote intelligent home peritoneal dialysis system according to claim 5, characterized in that: The architecture combining convolutional neural network and fully connected layer outputs the treatment plan, including: The multidimensional data is sequentially subjected to one-dimensional convolution to extract features, batch normalization to standardize the data, maximum pooling layer to downsample, and flattening layer to convert the features into one-dimensional vectors; Three treatment parameters are output through the fully connected layer: number of exchanges per day, dialysis cycle time, and dialysate concentration; The treatment plan is generated based on the number of daily exchanges, dialysis cycle time, and dialysate concentration.

8. The peritoneal dialysis control method of the remote intelligent home peritoneal dialysis system according to claim 4, characterized in that: The method further comprises: An integrated intelligent consultation platform is used to formulate or adjust prescriptions based on the treatment plan and set parameters through remote tools.

9. A terminal, characterized in that: The terminal includes a memory, a processor, and a peritoneal dialysis control program for a remote intelligent home peritoneal dialysis system stored in the memory and executable on the processor. When the processor executes the peritoneal dialysis control program for the remote intelligent home peritoneal dialysis system, the steps of the peritoneal dialysis control method for a remote intelligent home peritoneal dialysis system as described in any one of claims 4 to 8 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a peritoneal dialysis control program for a remote intelligent home peritoneal dialysis system, and the peritoneal dialysis control program for the remote intelligent home peritoneal dialysis system implements the steps of the peritoneal dialysis control method for a remote intelligent home peritoneal dialysis system as described in any one of claims 4 to 8 on the computer-readable storage medium.

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