Intelligent platform management system carrying infusion port patient

Abstract

The invention relates to an intelligent platform management system carrying infusion port patients, and relates to the technical field of data management, and the system comprises a monitoring unit which is used for collecting impedance data, temperature data and medicine use time sequence data; the intelligent analysis unit is used for constructing a dynamic health baseline of the patient, calling a preset machine learning model based on the dynamic health baseline and outputting a complication risk level of the patient; the closed-loop execution unit is used for triggering a three-dimensional life behavior guidance scheme matched with the patient state based on the complication risk level, calculating a corresponding infusion port maintenance period based on the real-time smoothness score, and generating a medicine supply instruction in combination with medicine use time sequence data; and the hierarchical interaction platform is used for generating an early warning view containing the risk level of the patient and the key attention information based on the complication risk level, and pushing maintenance appointment suggestions and planning contents at the patient side based on the maintenance period of the infusion port. The system can improve the management efficiency and safety of the patients in the infusion port.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a smart platform management system for patients with a port for infusion, a method, an electronic device and a non-transitory computer readable storage medium. BACKGROUND

[0002] At present, the port for infusion has been widely used in tumor treatment and long-term intravenous therapy. After the implantation in the hospital, the patient mainly obtains the use and maintenance information through on-site education, paper materials or simple online communication. When problems occur, the patient needs to contact medical staff or go to the hospital for treatment, and the daily life guidance and maintenance appointment mainly depend on the patient's self-memory and arrangement.

[0003] However, the existing method has problems such as information dispersion, communication lag and management loss. After the patient goes home, there is a lack of continuous guidance, and the patient cannot obtain professional support in time when encountering abnormal conditions, the life behavior has misunderstanding, and the maintenance appointment is inconvenient, which easily leads to the increase of the incidence of complications related to the port for infusion. SUMMARY

[0004] The present application provides a smart platform management system for patients with a port for infusion, which can improve the management efficiency and safety of patients with a port for infusion.

[0005] The technical solution of the present application to solve the above technical problems is as follows: The present application provides a smart platform management system for patients with a port for infusion, which includes: A monitoring unit for collecting impedance data reflecting the patency state of the port for infusion by a port for infusion state monitoring terminal, collecting temperature data of the implantation site of the port for infusion by a vital sign sensor, and collecting drug use time sequence data of the patient by an intelligent drug management device; An intelligent analysis unit for generating a real-time patency score of the port for infusion based on the impedance data, fusing the temperature data and the drug use time sequence data, constructing a dynamic health baseline of the patient, calling a preset machine learning model based on the dynamic health baseline, and outputting a complication risk level of the patient; A closed-loop execution unit for triggering a three-dimensional life behavior guidance scheme matching the patient state based on the complication risk level, calculating a corresponding port for infusion maintenance period based on the real-time patency score, and generating a drug replenishment instruction in combination with the drug use time sequence data; A hierarchical interaction platform for generating an early warning view containing the patient risk level and key attention information on the medical side based on the complication risk level, and pushing a maintenance appointment suggestion and planning content on the patient side based on the port for infusion maintenance period.

[0006] Optionally, the monitoring unit is further configured to: The impedance phase difference data with a frequency range of 0.1 to 100 kHz is collected through a dual-frequency impedance detection circuit; An infrared temperature array was used to scan a 3 × 3 cm area at the site of the port implantation to obtain its temperature distribution. Synchronously record the number of times each heparin saline is pressed in the intelligent drug management device and the corresponding timestamp information.

[0007] Optionally, the intelligent analysis unit is further configured to: The continuously collected 10-minute impedance series was input into the long short-term memory neural network; Extracting the decay rate feature vector of the impedance waveform; Outputs a patency score ranging from 0 to 100 and marks abnormal detection points with a confidence level higher than 95%.

[0008] Optionally, the intelligent analysis unit is further configured to: Calculating the mutation rate of the patient's body temperature data within a 24-hour period; The infection coefficient was calculated based on the interval between the use of heparin saline; The patient's past medical history information is integrated to perform individualized corrections to the baseline health threshold.

[0009] Optionally, the closed-loop execution unit is further configured to: Match the preset database of contraindications based on the risk level of complications; Call the 3D skeleton binding animation module built on the Unity engine to generate interactive demonstration animation; For target patients at a high risk level, mandatory restrictions include demonstrations involving arm elevation greater than 90 degrees.

[0010] Optionally, the closed-loop execution unit is further configured to: Calculate baseline maintenance cycles; When the patency score drops by more than 20 points in a single day, the coefficient mechanism for shortening the cycle is activated; Adjust maintenance appointment dates based on comprehensive weather forecast data.

[0011] Optionally, the closed-loop execution unit is further configured to: Compare current medication levels to maintenance requirements; When the current remaining amount of the medicine is less than the required maintenance amount by a preset multiple, generating a medicine replenishment order including replenishment content; Automatically associate the pharmacy inventory data corresponding to the patient's geographic location, generate the optimal delivery route and method, and obtain the drug refill instruction.

[0012] Optionally, the warning view includes: Map the complication risk levels to three color codes: red, yellow, and blue for visual display; Rendering the patient's position in an electronic map interface to form a thermal distribution map; In response to the user clicking on the high-risk patient location, a pop-up display shows the patient's recent temperature trend graph and patency score historical data.

[0013] Optionally, the maintenance appointment suggestion includes: Analyze the nursing resource availability information in the hospital information system; Planning the optimal transportation route to the target hospital based on the patient's real-time geographic location; A departure reminder and a checklist of required items are pushed to the patient 2 hours before the appointment time.

[0014] Optionally, a feedback optimization module is further included, wherein the feedback optimization module is configured to: Collect records of modifications made by medical staff to the risk levels generated by the system; Adjusting feature weight parameters in the machine learning model based on the correction record; The threshold for determining the dynamic health baseline is automatically updated every 24 hours.

[0015] The present invention also provides a smart platform management method for patients with infusion ports, the method comprising: The infusion port status monitoring terminal collects impedance data to reflect the patency of the infusion port, the vital signs sensor collects temperature data of the infusion port implantation site, and the intelligent drug management device collects the patient's drug usage time series data; generating a real-time patency score for the infusion port based on the impedance data, fusing the temperature data with the medication usage time series data to construct a dynamic health baseline for the patient, and, based on the dynamic health baseline, invoking a preset machine learning model to output a complication risk level for the patient; Based on the complication risk level, a three-dimensional lifestyle behavior guidance program matching the patient's condition is triggered. Based on the real-time patency score, the corresponding infusion port maintenance cycle is calculated. Combined with the medication usage time series data, medication refill instructions are generated. Based on the complication risk level, a warning view containing the patient's risk level and key information is generated on the medical side. Based on the infusion port maintenance cycle, maintenance appointment recommendations and planning details are pushed to the patient side. Furthermore, to achieve the above objectives, the present invention also proposes an electronic device comprising: a memory for storing a computer software program; and a processor for reading and executing the computer software program, thereby implementing the aforementioned intelligent platform management method for patients with infusion ports.

[0016] In addition, to achieve the above-mentioned purpose, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, it implements the smart platform management method for patients with infusion ports as described above.

[0017] The beneficial effects of the present invention are: (1) The present invention establishes a patient's individualized dynamic health baseline by integrating multi-dimensional data such as impedance phase difference, temperature distribution at the implant site, and drug use timing. This can significantly improve the ability to identify complications such as infection and tube blockage in the early stages, avoiding the delay and subjective misjudgment caused by traditional reliance on manual observation.

[0018] (2) The present invention introduces a machine learning model to quantitatively evaluate patency and infection risk, and triggers personalized three-dimensional behavioral guidance plans and drug replenishment instructions based on the graded risk levels. It also realizes information linkage between the medical and patient ends, and builds a full-process closed-loop management system from perception to decision-making to intervention.

[0019] (3) The present invention dynamically calculates individualized maintenance cycles through patency scores and usage frequency, and makes appointment recommendations based on weather, geographic location, and hospital nursing resources. This can significantly improve patients' maintenance compliance and medical resource allocation efficiency, and reduce tube blockage rates and maintenance delays.

[0020] In summary, the present invention establishes an intelligent, personalized, full-cycle infusion port patient management platform through the four core capabilities of multi-dimensional perception, intelligent analysis, closed-loop execution and adaptive feedback, which significantly improves management efficiency and patient safety, and has broad clinical application prospects and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a scenario diagram of a smart platform management method for patients with infusion ports provided by the present invention; Figure 2 This is a schematic diagram of the structure of a smart platform management system for patients with infusion ports provided by the present invention; Figure 3 This is a flow chart of a smart platform management method for patients with infusion ports provided by the present invention; Figure 4 A possible hardware structure schematic diagram of an electronic device provided by the present application is shown in the following figure; Figure 5 A possible hardware structure schematic diagram of a computer readable storage medium provided by the present application is shown in the following figure. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0023] In the description of the present application, the terms "first", "second" are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0024] In the description of the present application, the term "for example" is used to indicate "as an example, illustration or explanation". Any embodiment described as "for example" in the present application is not necessarily interpreted as more preferred or more advantageous than other embodiments. The following description is given in order to enable any person skilled in the art to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that those skilled in the art can realize the present application without using these specific details. In other examples, well-known structures and processes will not be described in detail in order to avoid unnecessary details making the description of the present application obscure. Therefore, the present application is not intended to be limited to the shown embodiments, but is consistent with the broadest scope of the principles and characteristics disclosed.

[0025] Please refer to Figure 1 , Figure 1 A scene diagram of a smart platform management method for patients carrying infusion ports provided by the present application is shown in the following figure. As shown in Figure 1 , the terminal and the server are connected through a network, such as wired or wireless network connection, etc. Among them, the terminal can include but is not limited to mobile phones, tablets and other portable terminals installed with various network platform applications, as well as computers, inquiry machines, advertising machines and other fixed terminals. Among them, the server provides various service services for users, including service push server, user recommendation server, etc.

[0026] It should be noted that,Figure 1 The scenario diagram of a smart platform management method for patients with infusion ports shown is only an example. The terminal, server, and application scenario described in the embodiment of the present invention are intended to more clearly illustrate the technical solution of the embodiment of the present invention, and do not generate any limitation on the technical solution provided by the embodiment of the present invention. Ordinary technicians in this field can know that with the evolution of the system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0027] Among them, the terminal can be used to: The infusion port status monitoring terminal collects impedance data to reflect the patency of the infusion port, the vital signs sensor collects temperature data of the infusion port implantation site, and the intelligent drug management device collects the patient's drug usage time series data; Generate a real-time patency score for the infusion port based on impedance data, fuse temperature data with medication usage time series data to build a patient's dynamic health baseline. Based on the dynamic health baseline, call a preset machine learning model to output the patient's complication risk level; Based on the complication risk level, a three-dimensional lifestyle behavior guidance plan that matches the patient's condition is triggered. Based on the real-time patency score, the corresponding infusion port maintenance cycle is calculated. Combined with the time series data of drug use, drug refill instructions are generated. Based on the complication risk level, an early warning view containing the patient's risk level and key attention information is generated on the medical side. Based on the infusion port maintenance cycle, maintenance appointment suggestions and planning content are pushed to the patient side.

[0028] See also Figure 2 , Figure 2 This is a structural diagram of an intelligent platform management system for patients with infusion ports provided by the present invention.

[0029] like Figure 2 As shown, an intelligent platform management system for patients with infusion ports proposed in an embodiment of the present invention includes a monitoring unit 201, an intelligent analysis unit 202, a closed-loop execution unit 203, and a hierarchical interactive platform 204, specifically as follows: The monitoring unit 201 is used to collect impedance data reflecting the patency of the infusion port through the infusion port status monitoring terminal, collect temperature data of the infusion port implantation site through the vital sign sensor, and collect the patient's drug usage time series data through the intelligent drug management device.

[0030] In the specific implementation, the monitoring unit 201 serves as the basic data acquisition module of the system. Its main function is to obtain and integrate multi-source information on the operation status of the infusion port and related physiological indicators, including the following three aspects: Impedance data collection (via the infusion port status monitoring terminal) utilizes a dedicated impedance detection circuit (such as a dual-frequency impedance measurement module) to collect impedance phase difference signals within the 0.1–100 kHz frequency range, enabling real-time monitoring of the fluid flow path within the infusion port. This impedance data can reveal abnormalities in the infusion channel, such as blockage, cuff formation, or thrombosis, providing a quantitative basis for subsequent patency scoring.

[0031] Temperature data collection (via vital sign sensors) can continuously monitor the temperature distribution of the skin surface or subcutaneous area at the port implant site using an infrared thermometer array or adhesive temperature sensor. Elevated temperatures can indicate local inflammation or infection risk, making this temperature data an important indicator for early detection of complications (such as infection and leakage).

[0032] Medication usage data collection (via smart medication management devices) can be combined with smart pillbox devices equipped with lid opening detection, weight sensing, or press recording capabilities to record patients' use of maintenance medications such as heparin saline, including usage time, frequency, and dosage, generating time-series data on medication use. This data can be used to analyze patients' maintenance adherence and treatment behavior patterns, and also provide behavioral input for dynamic health baseline modeling.

[0033] In summary, the monitoring unit 201 provides comprehensive data support for the subsequent intelligent analysis unit to establish an accurate and dynamic individual health model by synchronously acquiring physiological signals (impedance and temperature) and behavioral data (drug use), thereby realizing full-time and full-dimensional monitoring of the status of patients with infusion ports.

[0034] The intelligent analysis unit 202 is used to generate a real-time patency score of the infusion port based on the impedance data, fuse the temperature data with the drug usage time series data, build a dynamic health baseline for the patient, and based on the dynamic health baseline, call a preset machine learning model to output the patient's complication risk level.

[0035] In a specific implementation, a real-time patency score reflecting the patency status of the infusion port can be generated based on the impedance data collected by the monitoring unit 201. The score can be achieved by extracting features of the continuous impedance waveform (such as phase difference, fluctuation amplitude, waveform attenuation rate) and inputting it into a preset deep learning model (such as an LSTM network). The infusion channel status is quantified and a patency score with a numerical range of 0 to 100 is output to represent the current catheter patency level.

[0036] Furthermore, the intelligent analysis unit 202 is also used to perform multimodal fusion of the collected port implant temperature data and medication usage time series data to construct a dynamic health baseline for the individual patient. This baseline reflects the patient's physical characteristics and behavioral patterns under specific physiological conditions and can be dynamically adjusted over time. For example, the infection trend coefficient K = ΔT / ΔD can be calculated based on the temperature mutation rate ΔT and the medication usage interval ΔD within 24 hours, and the patient's medical history parameters can be incorporated for personalized correction.

[0037] Based on the dynamic health baseline, the intelligent analysis unit 202 calls a preset machine learning model, which may include a neural network, random forest, support vector machine or other classification models, to comprehensively judge the patient's current health status and complication development trend, and then output the corresponding complication risk level. The risk level may include "low risk", "medium risk" or "high risk" classification labels, which serve as an important basis for the subsequent closed-loop execution unit to trigger the intervention strategy.

[0038] The closed-loop execution unit 203 is used to trigger a three-dimensional life behavior guidance plan that matches the patient's status based on the complication risk level, calculate the corresponding infusion port maintenance cycle based on the real-time patency score, and generate drug resupply instructions based on the drug usage time series data.

[0039] In a specific implementation, the complication risk level output by the intelligent analysis unit 202 can automatically match the patient's current health status with a lifestyle intervention plan, triggering the generation and delivery of 3D behavioral guidance content. The 3D behavioral guidance plan can include interactive animations generated by a 3D modeling engine (such as Unity or Unreal Engine) to demonstrate actions that patients should avoid in scenarios such as bathing, exercising, and dressing (such as raising their arms too high or compressing the implant site). The guidance plan dynamically adjusts the displayed content based on the risk level, ensuring that high-risk patients receive more stringent behavioral restrictions.

[0040] Furthermore, the closed-loop execution unit 203 calculates an individualized port maintenance cycle based on the real-time patency score generated by the intelligent analysis unit through a set function or decision model. For example, this can be calculated using the formula T = α × (100 − S) / F (where S is the score, F is the frequency of use, and α is an adjustment parameter). If the score fluctuates significantly or drops by more than a preset threshold in a single day, the system automatically triggers a cycle shortening mechanism to improve maintenance timeliness.

[0041] Furthermore, the closed-loop execution unit 203 combines medication usage time series data collected by the intelligent medication management device to determine whether the patient's current medication balance matches maintenance requirements. If insufficient to cover the next maintenance cycle, it automatically generates a medication refill instruction. This instruction includes medication type, dosage requirements, and recommended refill time. It can also be linked to inventory information from the medication supply platform or hospital pharmacy to recommend delivery routes or initiate orders.

[0042] The hierarchical interactive platform 204 is used to generate an early warning view containing patient risk levels and key focus information on the medical side based on the complication risk level, and push maintenance appointment suggestions and planning content on the patient side based on the infusion port maintenance cycle.

[0043] In specific implementations, intelligent tiered management can be implemented for each patient connected to the system's infusion port based on their complication risk level, generating a visual early warning view on the medical side. This view includes information such as the patient's risk level label, abnormal vital sign trends, and key attention points. To enhance intuitiveness, risk levels are color-coded with red, yellow, and blue, and a heat map is supported to render the patient's geographic distribution. Medical staff can click on high-risk individual nodes to view detailed information such as their recent temperature change curve, patency score trends, and key maintenance records.

[0044] At the same time, the hierarchical interactive platform 204 is also configured to automatically generate and push maintenance appointment suggestions in the patient-side application based on the infusion port maintenance cycle calculated by the closed-loop execution unit. The appointment suggestions comprehensively consider factors such as hospital nursing resource scheduling, patient location and transportation routes, automatically plan the optimal maintenance period, and simultaneously provide transportation suggestions, preparation lists and departure reminders to improve patient compliance and medical efficiency.

[0045] Through collaborative display between doctors and patients, the hierarchical interactive platform 204 realizes risk-driven intelligent prompts, task planning and resource optimization, effectively improving the hierarchical management capabilities and service response speed of infusion port patients.

[0046] In some embodiments, the monitoring unit 201 is further configured to: The impedance phase difference data with a frequency range of 0.1 to 100 kHz is collected through a dual-frequency impedance detection circuit; An infrared temperature array was used to scan a 3 × 3 cm area at the site of the port implantation to obtain its temperature distribution. Synchronously record the number of times each heparin saline is pressed in the intelligent drug management device and the corresponding timestamp information.

[0047] Specifically, an impedance detection circuit with an integrated dual-frequency drive signal can be used to inject and collect responses to electrical signals in the frequency range of 0.1 kHz to 100 kHz, acquiring impedance phase difference data caused by the fluid medium and tissue structure in the infusion port channel. This phase difference information can reflect the presence of structural abnormalities within the catheter, such as blockage or sediment formation, and thus serve as an important physical quantity basis for subsequent patency assessment.

[0048] An infrared thermometer array can be used to obtain the temperature distribution of the implanted area. Monitoring unit 201 further includes an infrared thermometer array component configured to scan a subcutaneous or skin surface area approximately 3 x 3 cm² centered at the implanted port, capturing a temperature distribution image of that area. Pixel-level temperature statistics and trend analysis can assist in identifying localized inflammation, infection, or other tissue reactions.

[0049] The monitoring unit 201 can simultaneously record medication usage data from the intelligent medication management device. The monitoring unit 201 also communicates with the intelligent medication management device to synchronously collect behavioral data on patients using maintenance medications such as heparinized saline at home or in outpatient settings. Specifically, this data includes the number of times each medication is pressed and the corresponding timestamp information, thereby forming a complete time series record of medication use, providing input support for modeling by the intelligent analysis unit.

[0050] In some embodiments, the intelligent analysis unit 202 is further configured to: The continuously collected 10-minute impedance series was input into the long short-term memory neural network; Extracting the decay rate feature vector of the impedance waveform; Outputs a patency score ranging from 0 to 100 and marks abnormal detection points with a confidence level higher than 95%.

[0051] Specifically, the 10-minute impedance data sequence continuously collected by the monitoring unit 201 can be input into a preset long short-term memory neural network (LSTM). This model has the ability to learn long-range dependencies and trend changes in time series, and can accurately identify the dynamic characteristics of the conduction state of the infusion port.

[0052] During the neural network modeling process, the system extracts multiple feature vectors such as waveform attenuation rate, peak-to-valley change amplitude, phase jitter amplitude, etc. from the impedance waveform as key dimensions to assist in patency judgment, thereby enhancing the judgment accuracy and generalization ability of the model.

[0053] Based on the model output results, the intelligent analysis unit 202 generates a patency score value ranging from 0 to 100, which is used to quantitatively evaluate the patency of the current infusion port; at the same time, the system performs confidence interval assessment on the time points where abnormal signal fluctuations occur, and uniquely marks abnormal detection points with a confidence level exceeding 95% for subsequent intervention triggering and warning generation.

[0054] In some embodiments, the intelligent analysis unit 202 is further configured to: Calculate the mutation rate of the patient's body temperature data within a 24-hour period; The infection coefficient was calculated based on the interval between the use of heparin saline; The patient's medical history information is integrated to make individualized corrections to the baseline health threshold.

[0055] Specifically, the temperature data sequence of the infusion port implantation site within a continuous 24-hour period can be collected, the temperature difference between adjacent sampling points can be calculated, and the significant mutation amplitude and frequency can be extracted based on the sliding window algorithm to obtain the patient's body temperature mutation rate ΔT within 24 hours, which is used to reflect whether there is a sharp fluctuation in the local tissue state, indicating potential inflammation or infection.

[0056] The analysis unit further obtains the heparin saline usage record from the intelligent drug management device and extracts the time interval ΔD between two consecutive medications; the ratio of this parameter to the aforementioned temperature mutation rate ΔT is calculated to obtain the infection coefficient K=ΔT / ΔD, which is used to quantify the relationship between the patient's inflammatory response rate and medication control, as an auxiliary indicator for evaluating infection trends.

[0057] The intelligent analysis unit 202 can also access the patient's medical history database to extract key information parameters including infection history, allergic reactions, immune status, tumor treatment plan, etc., and personalize the risk judgment threshold involved in the dynamic health baseline based on the rule engine or weight adjustment mechanism to make the baseline model more in line with the specific patient's actual physiological state and disease background, thereby improving the accuracy and robustness of subsequent risk predictions.

[0058] In some embodiments, the closed-loop execution unit 203 is further configured to: Match the preset database of contraindications based on the risk level of complications; Call the 3D skeleton binding animation module built on the Unity engine to generate interactive demonstration animation; For target patients at a high risk level, mandatory restrictions include demonstrations involving arm elevation greater than 90 degrees.

[0059] Specifically, based on the complication risk level output by the intelligent analysis unit 202, a contraindicated action database associated with each risk level can be called, which contains potential dangerous action items in various daily life scenarios and their medical explanation information, such as lifting the upper limbs, continuous pressure, and turning the body, etc. The execution unit automatically filters out a set of key actions that need to be avoided according to the current risk level of the patient.

[0060] The execution unit can further call an animation module developed based on Unity 3D engine to convert the filtered action data into three-dimensional skeletal model driving signals, generate corresponding interactive behavior demonstration animations, and display them in a visual manner in the patient terminal application. The animation model supports user-selective browsing, action amplification, angle rotation, and pause playback functions, enhancing patient understanding and imitation effect.

[0061] For target patients under the label of "high risk level", the closed-loop execution unit 203 enables a forced content screening mechanism to automatically shield or restrict demonstration action modules with high-risk amplitude such as lifting arms more than 90 degrees, lifting heavy objects, and stretching the back, to avoid potential dangers when patients imitate and ensure the safety and applicability of the intervention content.

[0062] In some embodiments, the closed-loop execution unit 203 is further configured to: calculate a reference maintenance period; when the patency score decreases by more than 20 points in a single day, activate a coefficient mechanism to shorten the period; adjust the maintenance appointment date based on weather forecast data.

[0063] Specifically, the closed-loop execution unit calculates the reference maintenance period T of the current patient according to the following formula based on the patency score S output by the intelligent analysis unit 202 and the patient's weekly infusion frequency F: T = a x (100-S) / F. Wherein a is a preset period adjustment coefficient, S is the patency score (range 0-100), and F is the number of infusions per week. The calculation result is used to determine the recommended next infusion port maintenance time node.

[0064] When the continuous monitoring data shows that the patency score decreases by more than 20 points in a day, i.e. ΔS ≥ 20, the closed-loop execution unit automatically activates the shortened period mechanism to introduce a risk factor β to the original maintenance period T based on the reference period calculation to reduce the original maintenance period T, so as to move the maintenance time forward in time to cope with the potential risk of pipe blockage or functional decline.

[0065] The closed-loop execution unit can also access an external weather service module to obtain weather forecast information for the next few days. If there is extreme weather such as heavy rain, cold wave, or high temperature, the maintenance appointment date will be automatically moved forward or delayed to ensure patient safety and improve the experience of medical treatment.

[0066] In some embodiments, the closed-loop execution unit 203 is further configured to: Compare current medication levels to maintenance requirements; When the current remaining amount of the drug is less than the preset multiple of the required maintenance amount, a drug replenishment order containing replenishment content is generated; Automatically associate the pharmacy inventory data corresponding to the patient's geographic location, generate the optimal delivery route and method, and obtain drug refill instructions.

[0067] Specifically, the closed-loop execution unit can execute the balance-demand ratio judgment logic based on the real-time drug balance V collected by the intelligent drug management device and the drug dosage Q required in the current maintenance cycle (such as heparin saline dosage).

[0068] When it is judged that V<1.2Q (that is, the current balance is less than 1.2 times the preset maintenance demand), the system automatically generates a drug replenishment order. The order content includes the drug name, specifications, quantity, recommended replenishment time, and is marked as "recommended for immediate processing" status.

[0069] The closed-loop execution unit further links the inventory database of the pharmacy system in the region based on the patient's current geographic location information, screens the pharmacy nodes with sufficient inventory and the nearest distance, and plans the optimal drug delivery route and method (such as express delivery, self-pickup, in-hospital collection, etc.) based on this, and finally generates drug supply instructions and pushes them to the patient-side application and pharmacy-side management system.

[0070] In some embodiments, the alert view includes: Map the complication risk levels to three color codes: red, yellow, and blue for visual display; Render the patient's location in an electronic map interface to form a heat distribution map; In response to the user clicking on the high-risk patient location, a pop-up display shows the patient's recent temperature trend graph and patency score historical data.

[0071] Specifically, the complication risk level output by the intelligent analysis unit can be mapped into three color labels: red (high risk), yellow (medium risk), and blue (low risk), and displayed intuitively in the form of labels, cards or icons on the medical interface to assist medical staff in quickly identifying key areas of concern.

[0072] Combined with the patient's real-time geographic location information, a heat distribution map is generated on the electronic map interface, where hot spots represent high-density, high-risk patient clusters, which is used to support regional prevention and control, resource allocation, and emergency response.

[0073] When medical staff click on a high-risk patient icon node, the system automatically pops up an interactive window containing the patient's recent temperature trend graph and patency score historical curve, graphically assisting in judging the trend of disease changes and improving the interpretability and scientific nature of decision-making.

[0074] In some embodiments, the maintenance appointment suggestion includes: Analyze the nursing resource availability information in the hospital information system; Plan the optimal transportation route to the target hospital based on the patient's real-time geographic location; A departure reminder and a checklist of required items will be sent to the patient 2 hours before the appointment time.

[0075] Specifically, it can access and analyze the nursing scheduling data and nursing resource idle status information in the hospital information system (HIS), automatically identify the reception capacity of each nursing period, and provide patients with optional appointment time periods.

[0076] Specifically, the system can combine the patient's real-time location information with the hospital's geographical location, call the path planning module, comprehensively consider road conditions, travel methods and time costs, generate the optimal transportation route recommendation to the target hospital, and display it on the patient side in the form of graphics or voice.

[0077] Within 2 hours before the maintenance appointment time, the system can automatically push a "departure reminder notification" to the patient and generate a matching item checklist at the same time. The checklist lists the items that need to be brought to the clinic, such as medicines, identity documents, test records, etc., to ensure that the patient maintenance process is efficient and without omissions.

[0078] See also Figure 3 , provides a flowchart of a smart platform management method for patients with infusion ports of the present invention, comprising the following steps: Step 301: The impedance data reflecting the patency of the infusion port is collected through the infusion port status monitoring terminal, the temperature data of the infusion port implantation site is collected through the vital sign sensor, and the patient's drug use time series data is collected through the intelligent drug management device.

[0079] Step 302: Generate a real-time patency score for the infusion port based on the impedance data, fuse the temperature data with the medication usage time series data, build a dynamic health baseline for the patient, and based on the dynamic health baseline, call a preset machine learning model to output the patient's complication risk level.

[0080] Step 303: Based on the complication risk level, trigger a three-dimensional life behavior guidance plan that matches the patient's status. Based on the real-time patency score, calculate the corresponding infusion port maintenance cycle, and combine the drug usage time series data to generate drug resupply instructions.

[0081] Step 304: Based on the complication risk level, an early warning view including the patient risk level and key attention information is generated on the medical side, and based on the infusion port maintenance cycle, maintenance appointment suggestions and planning content are pushed to the patient side.

[0082] It should be noted that, for the specific embodiments and beneficial effects of the above steps 301 - 304 , please refer to the above detailed description of modules 201 - 204 , which will not be repeated here.

[0083] See also Figure 4 , Figure 4 Schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented: The infusion port status monitoring terminal collects impedance data to reflect the patency of the infusion port, the vital signs sensor collects temperature data of the infusion port implantation site, and the intelligent drug management device collects the patient's drug usage time series data; Generate a real-time patency score for the infusion port based on impedance data, fuse temperature data with medication usage time series data to build a patient's dynamic health baseline. Based on the dynamic health baseline, call a preset machine learning model to output the patient's complication risk level; Based on the complication risk level, a three-dimensional lifestyle behavior guidance plan that matches the patient's condition is triggered. Based on the real-time patency score, the corresponding infusion port maintenance cycle is calculated. Combined with the time series data of drug use, drug refill instructions are generated. Based on the complication risk level, an early warning view containing the patient's risk level and key attention information is generated on the medical side. Based on the infusion port maintenance cycle, maintenance appointment suggestions and planning content are pushed to the patient side.

[0084] See also Figure 5 , Figure 5 Schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 5 As shown, this embodiment provides a computer-readable storage medium 500 on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented: The infusion port status monitoring terminal collects impedance data to reflect the patency of the infusion port, the vital signs sensor collects temperature data of the infusion port implantation site, and the intelligent drug management device collects the patient's drug usage time series data; Generate a real-time patency score for the infusion port based on impedance data, fuse temperature data with medication usage time series data to build a patient's dynamic health baseline. Based on the dynamic health baseline, call a preset machine learning model to output the patient's complication risk level; Based on the complication risk level, a three-dimensional lifestyle behavior guidance plan that matches the patient's condition is triggered. Based on the real-time patency score, the corresponding infusion port maintenance cycle is calculated. Combined with the time series data of drug use, drug refill instructions are generated. Based on the complication risk level, an early warning view containing the patient's risk level and key attention information is generated on the medical side. Based on the infusion port maintenance cycle, maintenance appointment suggestions and planning content are pushed to the patient side.

[0085] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0086] Those skilled in the art will appreciate that embodiments of the present invention may be provided as systems, methods, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0087] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0088] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system that is implemented in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0089] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operations steps are performed on the computer or other programmable devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable devices provide the function of realizing the processes specified in the flowcharts Figure 1 one flowchart or multiple flowcharts and / or blocks Figure 1 one block or multiple blocks.

[0090] Although the preferred embodiments of the application have been described, those skilled in the art will be able to make additional modifications and variations to these embodiments without departing from the spirit and scope of the application. Accordingly, it is intended that the appended claims be construed to include all such modifications and variations as fall within the scope of the application.

[0091] Obviously, various modifications and changes are possible in the present application without departing from the spirit and scope of the application. It is to be understood that the application includes any such modifications and changes only insofar as they come within the scope of the appended claims and their equivalents.

Claims

1. An intelligent platform management system for patients with infusion ports, characterized by: The system comprises: A monitoring unit, configured to collect impedance data reflecting the patency of the infusion port through an infusion port status monitoring terminal, collect temperature data of the infusion port implantation site through a vital sign sensor, and collect time series data of the patient's medication use through an intelligent medication management device; an intelligent analysis unit, configured to generate a real-time patency score for the infusion port based on the impedance data, fuse the temperature data with the medication usage time series data to construct a dynamic health baseline for the patient, and based on the dynamic health baseline, invoke a preset machine learning model to output a complication risk level for the patient; a closed-loop execution unit, configured to trigger a three-dimensional lifestyle behavior guidance program that matches the patient's condition based on the complication risk level, calculate a corresponding port maintenance cycle based on the real-time patency score, and generate a medication resupply instruction based on the medication usage time series data; A hierarchical interactive platform is used to generate an early warning view containing the patient's risk level and key attention information on the medical side based on the complication risk level, and to push maintenance appointment suggestions and planning content on the patient side based on the infusion port maintenance cycle.

2. The intelligent platform management system for patients with infusion ports according to claim 1 is characterized in that: The monitoring unit is further configured to: The impedance phase difference data with a frequency range of 0.1 to 100 kHz is collected through a dual-frequency impedance detection circuit; An infrared temperature array was used to scan a 3 × 3 cm area at the site of the port implantation to obtain its temperature distribution. Synchronously record the number of times each heparin saline is pressed in the intelligent drug management device and the corresponding timestamp information.

3. The intelligent platform management system for patients with infusion ports according to claim 2, characterized in that: The intelligent analysis unit is also used for: The continuously collected 10-minute impedance series was input into the long short-term memory neural network; Extracting the decay rate feature vector of the impedance waveform; Outputs a patency score ranging from 0 to 100 and marks abnormal detection points with a confidence level higher than 95%.

4. The intelligent platform management system for patients with infusion ports according to claim 3, characterized in that: The intelligent analysis unit is also used for: Calculating the mutation rate of the patient's body temperature data within a 24-hour period; The infection coefficient was calculated based on the interval between the use of heparin saline; The patient's past medical history information is integrated to perform individualized correction of the baseline health threshold.

5. The intelligent platform management system for patients with infusion ports according to claim 4 is characterized in that: The closed-loop execution unit is further configured to: Match the preset database of contraindications based on the risk level of complications; Call the 3D skeleton binding animation module built on the Unity engine to generate interactive demonstration animation; For target patients at a high risk level, mandatory restrictions include demonstrations involving arm elevation greater than 90 degrees.

6. The intelligent platform management system for patients with infusion ports according to claim 5, characterized in that: The closed-loop execution unit is further configured to: Calculate baseline maintenance cycles; When the patency score drops by more than 20 points in a single day, the coefficient mechanism for shortening the cycle is activated; Adjust maintenance appointment dates based on comprehensive weather forecast data.

7. The intelligent platform management system for patients with infusion ports according to claim 6, characterized in that: The closed-loop execution unit is further configured to: Compare current medication levels to maintenance requirements; When the current remaining amount of the medicine is less than the required maintenance amount by a preset multiple, generating a medicine replenishment order including replenishment content; Automatically associate the pharmacy inventory data corresponding to the patient's geographic location, generate the optimal delivery route and method, and obtain the drug refill instruction.

8. The intelligent platform management system for patients with infusion ports according to claim 7, characterized in that: The warning view includes: Map the complication risk levels to three color codes: red, yellow, and blue for visual display; Rendering the patient's position in an electronic map interface to form a thermal distribution map; In response to the user clicking on the high-risk patient location, a pop-up display shows the patient's recent temperature trend graph and patency score historical data.

9. The intelligent platform management system for patients with infusion ports according to claim 8, characterized in that: The maintenance appointment recommendations include: Analyze nursing resource availability information in hospital information systems; Planning the optimal transportation route to the target hospital based on the patient's real-time geographic location; A departure reminder and a checklist of required items are pushed to the patient 2 hours before the appointment time.

10. The intelligent platform management system for patients with infusion ports according to claim 9, characterized in that: It also includes a feedback optimization module, which is used to: Collect records of modifications made by medical staff to the risk levels generated by the system; Adjusting feature weight parameters in the machine learning model based on the correction record; The threshold for determining the dynamic health baseline is automatically updated every 24 hours.

Images