Management cooperation system and method

Abstract

The invention provides a management cooperation system and method. The system comprises a data source layer used for collecting multi-source data; the processing layer is used for performing space-time label construction and preprocessing based on the multi-source data to obtain corresponding processing data; the decision-making layer is used for performing metabolic load calculation based on a pre-constructed four-dimensional model and the processing data to obtain a metabolic load value of the patient; and the application layer is used for making decisions and suggestions on patient prescriptions corresponding to the prescription medication data based on the metabolic load values. Therefore, the technical problems that in the prior art, the curative effect of prescription medication cannot be accurately predicted, and doctors cannot be assisted to make a prescription can be solved.

Description

Technical Field

[0001] This application relates to the field of medical technology, specifically to a management collaboration system and method. Background Technology

[0002] With increasing public awareness of health, the field of health treatment and management has developed rapidly. Based on this, existing technologies have designed corresponding management and coordination systems for health monitoring and management, but these systems have many shortcomings and limitations. For example, existing management and coordination systems mainly rely on manual recording and simple data statistical analysis, such as manually collecting single-dimensional data like patients' height, weight, and blood pressure. They use experience and simple calculation formulas to make preliminary assessments of patients' medication use, failing to comprehensively and accurately correlate the interaction between patients' physiological data and medications. Consequently, they cannot accurately predict the efficacy of prescribed medications and cannot assist doctors in prescribing / adjusting prescriptions for better efficacy.

[0003] Therefore, there is an urgent need to propose a better management and coordination system. Summary of the Invention

[0004] In view of this, the embodiments of this application are committed to providing a management collaboration system and method that can solve the technical problems in the prior art, such as the inability to accurately predict the efficacy of prescription drugs and the inability to assist doctors in issuing / adjusting prescriptions.

[0005] Firstly, this application provides a management collaboration system, comprising: The data source layer is used to collect multi-source data, which includes patient physiological data, prescription drug data, and environmental equipment data. The processing layer is used to construct and preprocess spatiotemporal labels based on the multi-source data to obtain the corresponding processed data. The decision layer is used to calculate the metabolic load based on the pre-built four-dimensional model and the processed data to obtain the metabolic load value of the patient. The application layer is used to make decision-making suggestions on patient prescriptions corresponding to the prescribed medication data based on the metabolic load value.

[0006] In some embodiments, the processing layer is used for: Based on the environmental equipment data, spatiotemporal labels of patient physiological data and prescription medication data in the multi-source data are constructed using four-tuples to obtain corresponding labeled multi-source data. The four-tuples include patient identifier, timestamp, geographic coordinates, and device identifier. The abnormal data is obtained by performing abnormal data processing based on the multi-source data of the tags, and the abnormal data processing includes at least data cleaning.

[0007] In some embodiments, the decision layer is used to: The processed data is input into the four-dimensional model for four-dimensional feature extraction and analysis to obtain corresponding key data. The four-dimensional model includes patient dimension, drug dimension, time dimension and risk dimension. The key data includes at least the patient's drug composition data, liver and kidney function indicators, daily drug dosage and drug half-life. Based on the aforementioned key data, drug risk matching and metabolic load value calculation are performed to obtain the corresponding risk level and metabolic load value.

[0008] In some embodiments, the decision layer is used to: Based on the aforementioned liver and kidney function indicators, the corresponding liver metabolic coefficient, glomerular filtration rate, and peak blood drug concentration were determined. The metabolic load value is calculated based on the daily dose of the drug, the drug half-life, the liver metabolic coefficient, the glomerular filtration rate, and the peak blood drug concentration.

[0009] In some embodiments, The decision-making layer is used to perform drug risk matching based on the drug component data in the key data to obtain the corresponding risk level. The application layer is also used to make decision-making suggestions on patient prescriptions corresponding to the prescription medication data based on the risk level.

[0010] In some embodiments, the application layer is configured to perform any of the following: When the risk level is greater than a preset level, a high-risk warning message for the patient's prescription is generated; When the metabolic load value exceeds a preset first threshold, a high-risk warning message for the patient's prescription is generated. When the risk level is less than or equal to a preset level, the patient's prescription is approved; The patient's prescription is approved when the metabolic load value is less than or equal to the preset first threshold.

[0011] In some embodiments, The application layer is also used to generate a monitoring task for the patient after the patient's prescription is approved. The monitoring task is at least used to instruct the data source layer to monitor and acquire new physiological data of the patient within a preset period. The decision-making layer is also used to perform efficacy evaluation calculations based on the new physiological data to determine the efficacy of the patient's prescription.

[0012] In some embodiments, the decision layer is used to: Based on the new physiological data, determine the actual and expected improvement rates of the target indicators; The efficacy coefficient is calculated based on the actual and expected improvement of the target indicators to obtain the corresponding efficacy coefficient. The efficacy of the patient's prescription is determined based on the efficacy coefficient.

[0013] In some embodiments, the decision layer is used to: When the efficacy coefficient is greater than a preset second threshold, the patient's prescription is determined to be effective; When the efficacy coefficient is less than or equal to the preset second threshold, the review and adjustment information of the patient's prescription is generated.

[0014] Secondly, this application provides a management collaboration method applied to the management collaboration system provided in the first aspect above, the method comprising: Multi-source data is collected through the data source layer, including patient physiological data, prescription medication data, and environmental equipment data. The processing layer constructs and preprocesses spatiotemporal labels based on the multi-source data to obtain the corresponding processed data. The metabolic load value of the patient is obtained by calculating the metabolic load based on the pre-built four-dimensional model and the processed data through the decision layer. The application layer provides decision-making suggestions for patient prescriptions corresponding to the prescribed medication data based on the metabolic load value.

[0015] For any content not introduced or described in the embodiments of this application, please refer to the relevant descriptions in the foregoing method embodiments; they will not be repeated here.

[0016] Thirdly, this application provides a vehicle, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the executable instructions to implement the steps of the above-described management coordination method.

[0017] Fourthly, this application provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the steps of the above-described management coordination method.

[0018] The technical solution provided in this application embodiment can include the following beneficial effects: This application provides a management collaboration system comprising: a data source layer for collecting multi-source data, including patient physiological data, prescription medication data, and environmental equipment data; a processing layer for constructing and preprocessing spatiotemporal labels based on the multi-source data to obtain corresponding processed data; a decision layer for calculating metabolic load based on a pre-constructed four-dimensional model and the processed data to obtain the patient's metabolic load value; and an application layer for providing decision suggestions for the patient's prescription corresponding to the prescription medication data based on the metabolic load value. In this way, by binding the patient's multi-source data and medication decisions, and analyzing the interaction between physiological data and drugs, it better assists doctors in prescribing patients with satisfactory therapeutic effects, thereby improving treatment outcomes. This improves the practicality and reliability of the management collaboration system. It also solves the technical problems in the prior art, such as the inability to accurately predict the efficacy of prescription medications and the inability to assist doctors in prescribing / adjusting prescriptions.

[0019] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0020] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0021] Figure 1 This is a schematic diagram of the structure of a management collaboration system provided in an embodiment of this application.

[0022] Figure 2 This is a schematic diagram illustrating the functional role of a four-dimensional model provided in an embodiment of this application.

[0023] Figure 3 This is a flowchart illustrating a management collaboration method provided in an embodiment of this application.

[0024] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] Unless otherwise defined, the technical or scientific terms used in the embodiments of this specification shall have the ordinary meaning understood by one of ordinary skill in the art to which this specification pertains. The terms "first," "second," and similar terms used in the embodiments of this specification do not indicate any order, quantity, or importance, but are merely used to avoid confusion of constituent elements.

[0027] Unless the context otherwise requires, throughout this specification, "a plurality of" means "at least two," and "including" is interpreted as open-ended or encompassing, that is, "including, but not limited to." In the description of this specification, terms such as "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples" are intended to indicate that a particular feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of this specification. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example.

[0028] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0029] In the process of filing this application, the applicant also discovered that existing technologies use independent databases to store patient records. For example, the data stored in chronic disease management systems, medication decision-making systems, and internet hospital systems are independent of each other, making it impossible to drive medication decisions in real time. Furthermore, traditional medication decision-making cannot dynamically link real-time patient physiological data with the interaction between drugs. The hospital prescription process lacks closed-loop management, and prescription behavior is decoupled from the actual therapeutic effect on patients. To address these problems, this application proposes a management collaboration system and method.

[0030] Please see Figure 1 This is a schematic diagram of the structure of a management collaboration system provided in an embodiment of this application. For example... Figure 1The system shown can be applied to electronic devices or computer devices. The system may include a data source layer 101, a processing layer 102 (also known as a fusion processing layer), a decision layer 103 (also known as a medication decision layer), and an application layer 104. The functions involved in each layer are described below.

[0031] The aforementioned data source layer 101 is used to collect multi-source data, which includes patient physiological data, prescription drug data, and environmental equipment data. The aforementioned processing layer 102 is used to construct and preprocess spatiotemporal labels based on the multi-source data to obtain corresponding processed data; The aforementioned decision layer 103 is used to calculate the metabolic load based on the pre-constructed four-dimensional model and the processed data, thereby obtaining the metabolic load value of the patient. The aforementioned application layer 104 is used to make decision-making suggestions on patient prescriptions corresponding to the prescription medication data based on the metabolic load value.

[0032] In the aforementioned data source layer 101, data sources may include, for example, wearable devices, Hospital Information Systems (HIS), and drug knowledge bases, which are used to collect and obtain corresponding multi-source data from their respective sources. The aforementioned multi-source data may include patient physiological data, prescription medication data, environmental device data (also referred to as environmental and contextual data), or other custom data, which are not further limited in this application. The aforementioned patient physiological data may refer to data related to patient physiology, which may include, but is not limited to, patient blood glucose, blood pressure, heart rate, blood oxygen saturation, body temperature, or other physiological data. The aforementioned prescription medication data may refer to data related to patient prescriptions issued by doctors, which may include, but is not limited to, data such as medication time, dosage / daily dosage, drug name, and medication box opening records. This data can serve as a bridge to establish a causal relationship between prescription execution and patient physiological data. The aforementioned environmental equipment data may include data related to the construction of the following spatiotemporal labels, which may include, but are not limited to, device geographic location or geographic coordinates collected by positioning technologies (such as Wi-Fi or GPS), device motion status collected by accelerometers, timestamps, device IDs, or other custom data.

[0033] In the aforementioned processing layer 102, a data fusion engine can be designed to perform preprocessing such as spatiotemporal label construction and abnormal data processing. Specifically, this application can construct spatiotemporal labels for each data point (such as patient physiological data and prescription medication data) from the aforementioned environmental equipment data, generating corresponding four-tuple labels to obtain the corresponding tagged multi-source data. The aforementioned four-tuples or four-tuple labels include patient identifier, timestamp, geographic coordinates, and device identifier, such as PT2024001-202406201030-116.40E39.90N-DEVICE_G001. Furthermore, this application can preprocess the tagged multi-source data after spatiotemporal label construction, such as performing abnormal data processing, to obtain the corresponding processed data. This application does not limit the specific implementation of the aforementioned abnormal data processing; for example, this application can perform differentiated data cleaning and other abnormal processing based on the different types of each data point, thereby improving data quality and data processing efficiency.

[0034] In some optional embodiments, in the processing layer 102 described above, this application can collect continuous data according to a preset sliding window and calculate the standard deviation of the continuous data in the sliding window. If the data at any time point in the multi-source data exceeds a preset threshold, a device calibration command can be automatically triggered to recalibrate or correct the device, thereby avoiding abnormal acquisition of multi-source data due to device malfunction or inaccuracy, which could affect the accuracy or precision of subsequent data processing. The preset threshold is determined based on the standard deviation, and for example, it can be the sum of three times the standard deviation and the mean of the data within the sliding window. This application does not impose further limitations on this.

[0035] In the aforementioned decision-making layer 103, a medication decision-making center can be deployed. This center can perform tasks such as feature extraction and analysis of a four-dimensional model, and metabolic load calculation. Specifically, this application can input the processed data into a pre-constructed four-dimensional model for four-dimensional feature extraction and analysis to obtain the corresponding key data. Please refer to [further details omitted]. Figure 2This is a schematic diagram illustrating the functional role of an exemplary four-dimensional model provided in this application embodiment. The four-dimensional model can include the following four dimensions: patient dimension, drug dimension, time dimension, and risk dimension. The patient dimension is used to extract patient-related data, such as describing the patient's static profile and dynamic real-time data. This may include, but is not limited to, basic patient profile data (e.g., age, gender, medical history), real-time physiological data (e.g., blood glucose, blood pressure), metabolic characteristic data (e.g., liver and kidney function indicators), or other patient-related characteristic data. The drug dimension is used to extract the properties of the drug itself and its interactions. This may include, but is not limited to, data such as the drug's chemical composition, pharmacological effects, and drug-drug interactions (e.g., the combined use of warfarin and aspirin increases the risk of bleeding). The time dimension is used to analyze the sequence and trend of data changes over time. This may include, but is not limited to, data such as the time series of medication records (when and what medication was taken), the trend of physiological indicator changes (e.g., blood glucose fluctuations within a week), and the metabolic half-life of the drug (e.g., determining the duration of drug effect and dosing intervals). The aforementioned risk dimensions are used to analyze and evaluate the positive and negative indicators of drug efficacy, which may include, but are not limited to, data such as efficacy indicators (whether blood glucose is within the target range, whether blood pressure is controlled), and adverse reaction indicators (whether events such as hypoglycemia, bleeding, and rash occur).

[0036] Furthermore, this application performs drug risk matching and metabolic load value calculation based on the aforementioned key data to obtain the corresponding risk level and metabolic load value. In specific implementation, this application can analyze the drug component data in the patient's prescription, and then perform drug risk matching based on the drug component data in the aforementioned key data. For example, it can match the interaction matrix from a drug knowledge base (also known as a drug risk relationship knowledge base) to obtain the corresponding risk level and risk content. For instance, this application matches the corresponding risk level (e.g., high-risk, medium-risk, or low-risk) and risk content (e.g., bleeding, hepatitis virus, or abnormal blood sugar) from the interaction matrix based on the respective chemical components of drug A and drug B.

[0037] This application can determine the patient's liver metabolic coefficient, glomerular filtration rate, and peak blood drug concentration based on liver and kidney function indicators; then, based on the above key data, the above liver metabolic coefficient, the above glomerular filtration rate, and the above peak blood drug concentration, the metabolic load value is calculated, thereby obtaining the patient's metabolic load value. The specific calculation of the above metabolic load value is shown in the following formula (1): Formula (1) in, This represents the daily dosage of the i-th drug. This represents the half-life of the i-th drug. This represents the liver metabolic coefficient. This indicates the glomerular filtration rate. This indicates the peak blood drug concentration.

[0038] In the application layer 104 described above, this application can make decision-making recommendations for patient prescriptions corresponding to the above-mentioned prescription medication data based on the above-mentioned risk level and / or the above-mentioned metabolic load value. Several possible implementation methods are illustrated below.

[0039] In one implementation, when the aforementioned risk level exceeds a preset level, the system can automatically generate high-risk warning information for the patient's prescription, such as a high-risk warning or a suggestion to adjust or replace the patient's prescription. The preset level is a level or level value pre-defined by the system based on actual conditions, such as a medium-risk level value, and this application does not impose further limitations on it.

[0040] In another implementation, when the aforementioned risk level is less than or equal to a preset level, the patient's prescription can be automatically approved.

[0041] In another embodiment, when the aforementioned metabolic load value exceeds a preset first threshold, the system can automatically generate a high-risk warning message for the patient's prescription, prompting the doctor / patient to adjust or change the patient's prescription, etc. The aforementioned preset first threshold is a metabolic load threshold that the system pre-defined according to the actual situation. It can be an empirical value set based on user experience, or a statistical value calculated based on a series of experimental data, etc., and this application does not impose any further limitations on it.

[0042] In another embodiment, when the metabolic load value is less than or equal to a preset first threshold, the system can automatically approve the patient's prescription, etc.

[0043] It should be noted that the above-mentioned implementation methods can be implemented individually or in combination with one or more of them. The specific implementation method can be determined according to the actual situation, and this application does not limit it.

[0044] In some optional embodiments, after approving the patient's prescription, the application layer 104 can generate a corresponding monitoring task for the patient. This monitoring task is at least used to instruct the data source layer 101 to monitor and acquire new physiological data of the patient within a preset period. For example, for hypoglycemic drugs, the monitoring task can be generated periodically, such as collecting data on fasting blood glucose and hypoglycemic events on the 1st, 3rd, and 10th days after the patient takes the medication. For antihypertensive drugs, data such as systolic or diastolic blood pressure can be monitored every two days. For anticoagulant drugs, data such as the patient's international normalized INR value and bleeding symptoms can be monitored weekly; this application is not limited in this regard. Accordingly, the decision layer 103 can perform efficacy evaluation calculations based on the new physiological data to determine the efficacy of the patient's prescription. Specifically, after generating the monitoring task, new multi-source data can be collected again, including at least the patient's new physiological data. The processing layer can also perform spatiotemporal label construction and data cleaning on the collected new multi-source data before transmitting it to the decision layer 103 for efficacy evaluation calculations. This application does not limit the specific implementation method of the above-mentioned efficacy evaluation calculation. For example, this application can input the above-mentioned new physiological data into the efficacy evaluation model to first determine the actual improvement range and expected improvement range of the target indicator; and then calculate the efficacy coefficient based on the actual improvement range and expected improvement range of the target indicator to obtain the corresponding efficacy coefficient. Among them, the efficacy coefficient calculation involved in the above-mentioned efficacy evaluation model is shown in the following formula (2): Formula (2) in, This represents the efficacy coefficient. This indicates the actual extent of improvement. This indicates the expected level of improvement.

[0045] Finally, the efficacy of the treatment for the above patients is determined based on the efficacy coefficient. Specifically, for example, if the efficacy coefficient is greater than a preset second threshold, it can be determined that the treatment effect of the above patient's prescription is satisfactory / effective, and the original prescription can be maintained. Conversely, if the efficacy coefficient is less than or equal to the preset second threshold, it can be determined that the efficacy of the above patient's prescription is unsatisfactory. In this case, review and adjustment information for the above patient's prescription can be automatically generated to proactively remind the doctor or patient to adjust / change the patient's prescription in a timely manner for better treatment. The preset second threshold is a pre-defined efficacy threshold set by the system according to the actual situation. It can be an empirical value set based on user experience, or a statistical value calculated based on a series of experimental data. For example, the preset second threshold could be 70%.

[0046] To help better understand the embodiments of this application, examples are given below. Data source layer 101 can obtain the following multi-source data from the hospital management information system: [Patient ID: P123, Diagnosis: Grade 2 hypertension, Prescription: Nifedipine controlled-release tablets 30mg once daily]. It can obtain the following multi-source data from the wearable device: [..., (timestamp 1, blood pressure 160 / 95), (timestamp 2, blood pressure 155 / 92), (timestamp 3, blood pressure 178 / 100 after exercise)...]. It can obtain the following multi-source data from the laboratory system: eGFR: 75mL / min / 1.73m², Alanine aminotransferase (ALT): 28U / L, etc.

[0047] Processing layer 102 can create corresponding quadruple labels for each of the above data. For example, a blood pressure data can be labeled as: P123-20240621-116.40E39.90N-DEVICE_BP001, etc. Then, the multi-source data carrying the quadruple labels (i.e., the multi-source data with the above labels) is subjected to differentiated data cleaning. For example, if the blood pressure data of 178 / 100 is identified as being accompanied by exercise, it can be determined as a normal physiological phenomenon, and no abnormality is marked. This data is retained for subsequent data processing, etc.

[0048] The aforementioned decision layer 103 can perform decision calculations on the cleaned multi-source data from four dimensions based on a four-dimensional model. For example, it can substitute the patient's eGFR, ALT, and drug dosage into the metabolic load value calculation formula to calculate the patient's metabolic load value ML=0.6. Simultaneously, matching in the interaction matrix reveals that "nifedipine" has no high-risk interaction with other drugs currently taken by the patient. In this case, corresponding risk information can be output, such as [Metabolic load value (ML): 0.6, Risk matching: None, Decision suggestion: Approve patient's prescription], etc.

[0049] Optionally, after approving the patient's prescription, the decision layer 103 can automatically create and generate corresponding monitoring tasks based on the monitoring task rules of the corresponding drug. For example, based on the antihypertensive drug, the monitoring task could be blood pressure monitoring; frequency: once in the morning and once in the evening; key indicators: systolic blood pressure / diastolic blood pressure / heart rate; duration: 7 days, etc. The patient takes the medication on time and measures their blood pressure with a blood pressure monitor, transmitting the data back to the data source layer 101. Accordingly, the data source layer 101 collects new multi-source data (e.g., the patient's new blood pressure data). After preprocessing such as labeling and data cleaning by the processing layer 102, the decision layer 103 initiates the efficacy evaluation model to calculate the efficacy coefficient. For example, based on the patient's new blood pressure data on day 1 and day 7 after data cleaning, the actual improvement in blood pressure ΔQactual = baseline blood pressure 160 / 95 mmHg - blood pressure on day 7 142 / 88 mmHg = 18 / 7 mmHg. The expected improvement ΔQexpected = 25 / 10 mmHg. The efficacy coefficient TE = (18 / 25 + 7 / 10) / 2 × 100% ≈ 71%. Since this efficacy coefficient 71% > 70%, it indicates that the efficacy target has been achieved, and the original prescription can be maintained. Optionally, the system can also automatically generate corresponding prompts, such as the patient's response to nifedipine is acceptable, but the optimal efficacy has not been achieved, and it is recommended to pay attention during the follow-up visit, to help doctors better treat patients.

[0050] By implementing the embodiments of this application, this application provides a management collaboration system comprising: a data source layer for collecting multi-source data, including patient physiological data, prescription medication data, and environmental equipment data; a processing layer for constructing and preprocessing spatiotemporal labels based on the multi-source data to obtain corresponding processed data; a decision layer for calculating metabolic load based on a pre-constructed four-dimensional model and the processed data to obtain the patient's metabolic load value; and an application layer for providing decision suggestions for the patient's prescription corresponding to the prescription medication data based on the metabolic load value. In this way, the patient's multi-source data and medication decisions are bound together, and the interaction between physiological data and drugs is analyzed, better assisting doctors in prescribing patients with satisfactory therapeutic effects and improving treatment outcomes. This improves the practicality and reliability of the management collaboration system. It also solves the technical problems in the prior art, such as the inability to accurately predict the efficacy of prescription medications and the inability to assist doctors in prescribing / adjusting prescriptions.

[0051] Based on the foregoing embodiments, please refer to Figure 3 This is a flowchart illustrating a management collaboration method provided in an embodiment of this application. Figure 3 The method shown can be applied to the aforementioned Figure 1 In the management and coordination system shown, the method may include the following implementation steps: S301. Collect multi-source data through the data source layer, including patient physiological data, prescription medication data, and environmental equipment data; S302. The processing layer constructs and preprocesses spatiotemporal labels based on the multi-source data to obtain corresponding processed data. S303. The metabolic load of the patient is obtained by the decision layer calculating the metabolic load based on the pre-built four-dimensional model and the processed data. S304. The application layer provides decision-making suggestions for patient prescriptions corresponding to the prescription medication data based on the metabolic load value.

[0052] For any content not described or introduced in the embodiments of this application, please refer to the foregoing. Figures 1-2 The relevant descriptions in the embodiments will not be repeated here.

[0053] By implementing the embodiments of this application, this application collects multi-source data through a data source layer, including patient physiological data, prescription medication data, and environmental equipment data; a processing layer constructs and preprocesses spatiotemporal labels based on the multi-source data to obtain corresponding processed data; a decision layer calculates metabolic load based on a pre-constructed four-dimensional model and the processed data to obtain the patient's metabolic load value; and an application layer provides decision suggestions for the patient's prescription corresponding to the prescription medication data based on the metabolic load value. In this way, the patient's multi-source data and medication decisions are linked, and the interaction between physiological data and drugs is analyzed, better assisting doctors in prescribing medications that meet therapeutic goals and improving treatment outcomes. This improves the practicality and reliability of the management collaboration system. It also solves the technical problems in existing technologies, such as the inability to accurately predict the efficacy of prescription medications and the inability to assist doctors in prescribing / adjusting prescriptions.

[0054] Please see Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. For example... Figure 4 The electronic devices shown can be mobile phones, computers, digital broadcasting terminals, messaging devices, game consoles, tablets, medical devices, fitness equipment, personal digital assistants, etc. The aforementioned management and collaboration system can be applied to... Figure 4 In the electronic device shown.

[0055] Reference Figure 4 The electronic device 400 may include one or more of the following components: processing component 402, memory 404, power supply component 406, multimedia component 408, audio component 410, input / output interface 412, sensor component 414, and communication component 416.

[0056] Processing component 402 typically controls the overall operation of electronic device 400, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 402 may include one or more processors 420 to execute instructions to complete all or part of the steps of the aforementioned management coordination method. Furthermore, processing component 402 may include one or more modules to facilitate interaction between processing component 402 and other components. For example, processing component 402 may include a multimedia module to facilitate interaction between multimedia component 408 and processing component 402.

[0057] Memory 404 is configured to store various types of data to support the operation of electronic device 400. Examples of such data include instructions for any application or method operating on electronic device 400, contact data, phonebook data, messages, pictures, videos, etc. Memory 404 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0058] Power supply component 406 provides power to various components of electronic device 400. Power supply component 406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 400.

[0059] Multimedia component 408 includes a screen that provides an output interface between the electronic device 400 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 408 includes a front-facing camera and / or a rear-facing camera. When the electronic device 400 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0060] Audio component 410 is configured to output and / or input audio signals. For example, audio component 410 includes a microphone (MIC) configured to receive external audio signals when electronic device 400 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 404 or transmitted via communication component 416. In some embodiments, audio component 410 also includes a speaker for outputting audio signals.

[0061] Input / output interface 412 provides an interface between processing component 402 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, start buttons, and lock buttons.

[0062] Sensor assembly 414 includes one or more sensors for providing state assessments of various aspects of electronic device 400. For example, sensor assembly 414 may detect the on / off state of electronic device 400, the relative positioning of components such as the display and keypad of electronic device 400, changes in position of electronic device 400 or a component of electronic device 400, the presence or absence of user contact with electronic device 400, orientation or acceleration / deceleration of electronic device 400, and temperature changes of electronic device 400. Sensor assembly 414 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 414 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 414 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0063] Communication component 416 is configured to facilitate wired or wireless communication between electronic device 400 and other devices. Electronic device 400 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 416 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 416 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0064] In an exemplary embodiment, the electronic device 400 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described management coordination method.

[0065] Understandably, the processor 420 in this application embodiment can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiment can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor described above can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0066] Understandably, the memory 404 in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0067] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 404 including instructions, which can be executed by a processor 420 of an electronic device 400 to complete the aforementioned upper-level management and coordination method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0068] It should be noted that all relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here. The vehicle provided in this embodiment is used to execute the above-described management and collaboration method, and therefore can achieve the same effect as the above-described implementation method.

[0069] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the above-described management collaboration method when executed by the programmable device.

[0070] It should be noted that the descriptions of the above embodiments of storage media, devices, and equipment are similar to the descriptions of the above method embodiments, and have similar beneficial effects. For technical details not disclosed in the embodiments of storage media, devices, and equipment of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0071] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of this application. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed in this application. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0072] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A management collaboration system, characterized in that, include: The data source layer is used to collect multi-source data, which includes patient physiological data, prescription drug data, and environmental equipment data. The processing layer is used to construct and preprocess spatiotemporal labels based on the multi-source data to obtain the corresponding processed data. The decision layer is used to calculate the metabolic load based on the pre-built four-dimensional model and the processed data to obtain the metabolic load value of the patient. The application layer is used to make decision-making suggestions on patient prescriptions corresponding to the prescribed medication data based on the metabolic load value.

2. The system according to claim 1, characterized in that, The processing layer is used for: Based on the environmental equipment data, spatiotemporal labels of patient physiological data and prescription medication data in the multi-source data are constructed using four-tuples to obtain corresponding labeled multi-source data. The four-tuples include patient identifier, timestamp, geographic coordinates, and device identifier. The abnormal data is obtained by performing abnormal data processing based on the multi-source data of the tags, and the abnormal data processing includes at least data cleaning.

3. The system according to claim 1, characterized in that, The decision-making layer is used for: The processed data is input into the four-dimensional model for four-dimensional feature extraction and analysis to obtain corresponding key data. The four-dimensional model includes patient dimension, drug dimension, time dimension and risk dimension. The key data includes at least the patient's drug composition data, liver and kidney function indicators, daily drug dosage and drug half-life. Based on the aforementioned key data, drug risk matching and metabolic load value calculation are performed to obtain the corresponding risk level and metabolic load value.

4. The method according to claim 3, characterized in that, The decision-making layer is used for: Based on the aforementioned liver and kidney function indicators, the corresponding liver metabolic coefficient, glomerular filtration rate, and peak blood drug concentration were determined. The metabolic load value is calculated based on the daily dose of the drug, the drug half-life, the liver metabolic coefficient, the glomerular filtration rate, and the peak blood drug concentration.

5. The method according to claim 3, characterized in that, The decision-making layer is used to perform drug risk matching based on the drug component data in the key data to obtain the corresponding risk level. The application layer is also used to make decision-making suggestions on patient prescriptions corresponding to the prescription medication data based on the risk level.

6. The system according to claim 1 or 5, characterized in that, The application layer is used to perform any of the following: When the risk level is greater than a preset level, a high-risk warning message for the patient's prescription is generated; When the metabolic load value exceeds a preset first threshold, a high-risk warning message for the patient's prescription is generated. When the risk level is less than or equal to a preset level, the patient's prescription is approved; The patient's prescription is approved when the metabolic load value is less than or equal to the preset first threshold.

7. The system according to any one of claims 1-5, characterized in that, The application layer is also used to generate a monitoring task for the patient after the patient's prescription is approved. The monitoring task is at least used to instruct the data source layer to monitor and acquire new physiological data of the patient within a preset period. The decision-making layer is also used to perform efficacy evaluation calculations based on the new physiological data to determine the efficacy of the patient's prescription.

8. The system according to claim 7, characterized in that, The decision-making layer is used for: Based on the new physiological data, determine the actual and expected improvement rates of the target indicators; The efficacy coefficient is calculated based on the actual and expected improvement of the target indicators to obtain the corresponding efficacy coefficient. The efficacy of the patient's prescription is determined based on the efficacy coefficient.

9. The system according to claim 8, characterized in that, The decision-making layer is used for: When the efficacy coefficient is greater than a preset second threshold, the patient's prescription is determined to be effective; When the efficacy coefficient is less than or equal to the preset second threshold, the review and adjustment information of the patient's prescription is generated.

10. A management collaboration method, characterized in that, Applied to any one of the management collaboration systems described in claims 1-9 above, the method comprises: Multi-source data is collected through the data source layer, including patient physiological data, prescription medication data, and environmental equipment data. The processing layer constructs and preprocesses spatiotemporal labels based on the multi-source data to obtain the corresponding processed data. The metabolic load value of the patient is obtained by calculating the metabolic load based on the pre-built four-dimensional model and the processed data through the decision layer. The application layer provides decision-making suggestions for patient prescriptions corresponding to the prescribed medication data based on the metabolic load value.

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