Nursing prediction method and device based on nursing data

Abstract

The invention relates to a nursing prediction method and device based on nursing data, and relates to the technical field of medical nursing, and the method comprises the following steps: obtaining initial disease monitoring data and real-time nursing monitoring data of a target user; based on the initial disease monitoring data and each piece of real-time nursing monitoring data, disassembling to obtain each biological index parameter curve; based on each biological index parameter curve, obtaining a biological index parameter curve slope, a biological index parameter curve maximum value and a biological index parameter curve height difference value; judging and obtaining disease deterioration early warning information in combination with a preset nursing prediction model; and based on the disease deterioration early warning information, issuing nursing alarm information. According to the invention, based on the nursing monitoring data of the patient, the future health risk of the patient is evaluated, and the nursing alarm information is issued when necessary, so that the medical staff is reminded of timely processing, and the monitoring work efficiency of the medical staff is greatly improved on the premise of guaranteeing daily nursing monitoring.

Description

Technical Field

[0001] This application relates to the technical field of medical care, and specifically relates to a nursing prediction method and device based on nursing data. Background Art

[0002] At present, in the daily nursing work in hospitals, it is necessary to monitor multiple patients in real time, and then, based on the monitoring data of the patients, provide a reference basis for the disease diagnosis of the patients and the subsequent nursing plan.

[0003] At present, when analyzing and judging the monitoring data of patients, it is mostly to manually compare and analyze the data itself. This method has a large subjectivity and poor real-time performance, and cannot effectively analyze and warn the monitoring data of patients, and cannot meet the current nursing needs.

[0004] Therefore, to meet the actual needs, a nursing prediction technology based on nursing data is provided now. Summary of the Invention

[0005] This application provides a nursing prediction method and device based on nursing data, which evaluates the future health risks of patients based on the nursing monitoring data of patients, and issues nursing alarm information when necessary to remind medical staff to deal with it in time, and greatly improves the monitoring work efficiency of medical staff on the premise of ensuring daily nursing monitoring.

[0006] In the first aspect, this application provides a nursing prediction method based on nursing data, and the method includes the following steps: Obtain the initial disease monitoring data of the target user and the real-time nursing monitoring data obtained by monitoring according to the set nursing monitoring period; Based on the initial disease monitoring data and each real-time nursing monitoring data, disassemble to obtain each biological index parameter curve; Based on each biological index parameter curve, obtain the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference of the biological index parameter curve; Based on the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference of the biological index parameter curve, combined with a preset nursing prediction model, determine and obtain a disease deterioration warning information; Based on the disease deterioration warning information, issue a nursing alarm information.

[0007] On the basis of the above technical solution, the method includes a model construction process, and the model construction process includes the following steps: Obtain the slope threshold, the rate of change threshold of the biological parameter curve, the highest value threshold of the biological parameter curve, and the high-low difference threshold of the biological parameter curve corresponding to different types of vital parameter curves; Construct the nursing prediction model based on the slope threshold of the biological parameter curve, the rate of change threshold of the biological parameter curve, the highest value threshold of the biological parameter curve, and the high-low difference threshold of the biological parameter curve corresponding to various types of vital parameter curves.

[0008] Based on the above technical solution, determining and obtaining the disease deterioration warning information by combining the preset nursing prediction model with the slope of the biological parameter curve, the highest value of the biological parameter curve, and the high-low difference of the biological parameter curve includes the following steps: Obtain the rate of change of the biological parameter curve slope based on the slope of the biological parameter curve. Perform data comparison by combining the preset nursing prediction model with the slope of the biological parameter curve, the rate of change of the biological parameter curve slope, the highest value of the biological parameter curve, and the high-low difference of the biological parameter curve. When the slope of the biological parameter curve exceeds the slope threshold of the biological parameter curve, or the rate of change of the biological parameter curve slope exceeds the rate of change threshold of the biological parameter curve, or the highest value of the biological parameter curve exceeds the highest value threshold of the biological parameter curve, or the high-low difference of the biological parameter curve exceeds the high-low difference threshold of the biological parameter curve, it is determined that the disease deterioration warning information is obtained.

[0009] Based on the above technical solution, disassembling to obtain each biological parameter curve based on the initial disease monitoring data and each real-time nursing monitoring data includes the following steps: Disassemble to obtain different types of vital parameter curves and their corresponding monitoring times based on the initial disease monitoring data and each real-time nursing monitoring data. Construct the biological parameter curves corresponding to different types of vital parameter curves based on different types of vital parameter curves and their corresponding monitoring times.

[0010] Based on the above technical solution, the method further includes the following steps: Obtain the nursing monitoring cycle adjustment coefficient by combining the preset nursing prediction model with the slope of the biological parameter curve, the highest value of the biological parameter curve, and the high-low difference of the biological parameter curve. Adjust the nursing monitoring cycle based on the nursing monitoring cycle adjustment coefficient to obtain the adjusted nursing monitoring cycle.

[0011] On the basis of the above technical solution, the nursing prediction model is configured with safety values of the slope of the biological index parameter curve, the change rate safety value of the slope of the biological index parameter curve, the highest value safety value of the biological index parameter curve, and the high-low difference safety value corresponding to different types of vital index parameters.

[0012] On the basis of the above technical solution, obtaining a nursing monitoring cycle adjustment coefficient based on the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the high-low difference of the biological index parameter curve, in combination with a preset nursing prediction model, includes the following steps: When the slope of the biological index parameter curve is greater than the safety value of the slope of the biological index parameter curve, a corresponding first nursing monitoring cycle adjustment coefficient is obtained based on the ratio of the safety value of the slope of the biological index parameter curve to the slope of the biological index parameter curve; When the highest value of the biological index parameter curve is greater than the safety value of the highest value of the biological index parameter curve, a corresponding second nursing monitoring cycle adjustment coefficient is obtained based on the ratio of the safety value of the highest value of the biological index parameter curve to the highest value of the biological index parameter curve; When the high-low difference of the biological index parameter curve is greater than the high-low difference safety value of the biological index parameter curve, a corresponding third nursing monitoring cycle adjustment coefficient is obtained based on the ratio of the high-low difference safety value of the biological index parameter curve to the high-low difference of the biological index parameter curve; When the high-low difference of the biological index parameter curve is greater than the high-low difference safety value of the biological index parameter curve, a corresponding fourth nursing monitoring cycle adjustment coefficient is obtained based on the ratio of the high-low difference safety value of the biological index parameter curve to the high-low difference of the biological index parameter curve; The nursing monitoring cycle adjustment coefficient is obtained based on the first nursing monitoring cycle adjustment coefficient, the second nursing monitoring cycle adjustment coefficient, the third nursing monitoring cycle adjustment coefficient, and the fourth nursing monitoring cycle adjustment coefficient.

[0013] On the basis of the above technical solution, when the value of the nursing monitoring cycle adjustment coefficient is smaller, the adjusted nursing monitoring cycle is shorter.

[0014] On the basis of the above technical solution, obtaining the nursing monitoring cycle adjustment coefficient based on the first nursing monitoring cycle adjustment coefficient, the second nursing monitoring cycle adjustment coefficient, the third nursing monitoring cycle adjustment coefficient, and the fourth nursing monitoring cycle adjustment coefficient, includes the following steps: Based on the first nursing monitoring cycle adjustment coefficient, the second nursing monitoring cycle adjustment coefficient, the third nursing monitoring cycle adjustment coefficient, and the fourth nursing monitoring cycle adjustment coefficient, obtain the corresponding average value, denoted as the nursing monitoring cycle adjustment coefficient.

[0015] In a second aspect, the present application provides a nursing prediction device based on nursing data, the device includes: A monitoring information acquisition module, which is used to acquire the initial disease monitoring data of the target user and the real-time nursing monitoring data obtained by monitoring according to a set nursing monitoring cycle; An index parameter curve acquisition module, which is used to disassemble and obtain each biological index parameter curve based on the initial disease monitoring data and each real-time nursing monitoring data; An index parameter curve analysis module, which is used to obtain the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference of the biological index parameter curve based on each biological index parameter curve; An index parameter curve prediction module, which is used to determine and obtain disease deterioration warning information by combining a preset nursing prediction model based on the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference of the biological index parameter curve; A nursing alarm publishing module, which is used to publish nursing alarm information based on the disease deterioration warning information.

[0016] Based on the above technical solution, the device further includes a model construction module; The model construction module is used to obtain the threshold of the slope of the biological index parameter curve, the threshold of the change rate of the slope of the biological index parameter curve, the threshold of the highest value of the biological index parameter curve, and the threshold of the height difference of the biological index parameter curve corresponding to different types of vital index parameters; The model construction module is used to construct the nursing prediction model based on the threshold of the slope of the biological index parameter curve, the threshold of the change rate of the slope of the biological index parameter curve, the threshold of the highest value of the biological index parameter curve, and the threshold of the height difference of the biological index parameter curve corresponding to various types of vital index parameters.

[0017] Based on the above technical solution, the index parameter curve prediction module is further used to obtain the change rate of the slope of the biological index parameter curve based on the slope of the biological index parameter curve; The index parameter curve prediction module is further used to perform data comparison by combining a preset nursing prediction model based on the slope of the biological index parameter curve, the change rate of the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference of the biological index parameter curve; The index parameter curve prediction module is further configured to determine that the disease deterioration warning information is obtained when the slope of the biological index parameter curve exceeds the slope threshold of the biological index parameter curve, or the change rate of the slope of the biological index parameter curve exceeds the change rate threshold of the biological index parameter curve, or the highest value of the biological index parameter curve exceeds the highest value threshold of the biological index parameter curve, or the height difference of the biological index parameter curve exceeds the height difference threshold of the biological index parameter curve.

[0018] Based on the above technical solution, the index parameter curve acquisition module is further configured to disassemble different types of vital index parameters and corresponding monitoring times based on the initial disease monitoring data and each piece of real-time nursing monitoring data; The index parameter curve acquisition module is further configured to construct a biological index parameter curve corresponding to each different type of vital index parameter based on different types of the vital index parameters and the corresponding monitoring times.

[0019] Based on the above technical solution, the device further includes a nursing monitoring period adjustment module, which is configured to obtain a nursing monitoring period adjustment coefficient based on the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference of the biological index parameter curve, in combination with a preset nursing prediction model; The nursing monitoring period adjustment module is further configured to adjust the nursing monitoring period based on the nursing monitoring period adjustment coefficient to obtain an adjusted nursing monitoring period.

[0020] Based on the above technical solution, the nursing prediction model is configured with a slope safety value of the biological index parameter curve, a change rate safety value of the slope of the biological index parameter curve, a highest value safety value of the biological index parameter curve, and a height difference safety value of the biological index parameter curve corresponding to different types of vital index parameters.

[0021] Based on the above technical solution, the nursing monitoring period adjustment module is further configured to obtain a corresponding first nursing monitoring period adjustment coefficient based on the ratio of the slope safety value of the biological index parameter curve to the slope of the biological index parameter curve when the slope of the biological index parameter curve is greater than the slope safety value of the biological index parameter curve; The nursing monitoring period adjustment module is further configured to obtain a corresponding second nursing monitoring period adjustment coefficient based on the ratio of the highest value safety value of the biological index parameter curve to the highest value of the biological index parameter curve when the highest value of the biological index parameter curve is greater than the highest value safety value of the biological index parameter curve; The nursing monitoring period adjustment module is further configured to, when the difference between the maximum and minimum values of the biological index parameter curve is greater than the safety value of the difference between the maximum and minimum values of the biological index parameter curve, obtain a corresponding third nursing monitoring period adjustment coefficient based on the ratio of the safety value of the difference between the maximum and minimum values of the biological index parameter curve to the difference between the maximum and minimum values of the biological index parameter curve; The nursing monitoring period adjustment module is further configured to, when the difference between the maximum and minimum values of the biological index parameter curve is greater than the safety value of the difference between the maximum and minimum values of the biological index parameter curve, obtain a corresponding fourth nursing monitoring period adjustment coefficient based on the ratio of the safety value of the difference between the maximum and minimum values of the biological index parameter curve to the difference between the maximum and minimum values of the biological index parameter curve; The nursing monitoring period adjustment module is further configured to obtain the nursing monitoring period adjustment coefficient based on the first nursing monitoring period adjustment coefficient, the second nursing monitoring period adjustment coefficient, the third nursing monitoring period adjustment coefficient, and the fourth nursing monitoring period adjustment coefficient.

[0022] Based on the above technical solution, when the value of the nursing monitoring period adjustment coefficient is smaller, the adjusted nursing monitoring period is shorter.

[0023] Based on the above technical solution, the nursing monitoring period adjustment module is further configured to obtain a corresponding average value based on the first nursing monitoring period adjustment coefficient, the second nursing monitoring period adjustment coefficient, the third nursing monitoring period adjustment coefficient, and the fourth nursing monitoring period adjustment coefficient, and record it as the nursing monitoring period adjustment coefficient.

[0024] The beneficial effects brought by the technical solution provided in this application include: Based on the nursing monitoring data of the patient, evaluate the future health risks of the patient, and issue a nursing alarm message when necessary to remind the medical staff to handle it in time. On the premise of ensuring daily nursing monitoring, the monitoring work efficiency of the medical staff is greatly improved.

[0025] Adjust the daily nursing monitoring period according to the real-time nursing monitoring data, so as to monitor the physical condition of the patient in real time and carry out early warning work in time. Brief Description of the Drawings

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0027] Figure 1 It is a step flow chart of the nursing prediction method based on nursing data provided in the embodiment of the present application; Figure 2 Flow chart of the operation steps of the data mining model for the nursing prediction method based on nursing data provided in the embodiments of the present application; Figure 3 SPIRIT conceptual framework diagram of the nursing prediction method based on nursing data provided in the embodiments of the present application; Figure 4 Schematic diagram of the working mode of the nursing prediction method based on nursing data provided in the embodiments of the present application; Figure 5 Block diagram of the structure of the nursing prediction device based on nursing data provided in the embodiments of the present application. Detailed implementation manners

[0028] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0029] The embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0030] The embodiments of the present application provide a nursing prediction method and device based on nursing data, which evaluate the future health risks of patients based on the nursing monitoring data of the patients, and issue nursing alarm information when necessary to remind medical staff to handle it in time, so as to greatly improve the monitoring work efficiency of medical staff on the premise of ensuring daily nursing monitoring.

[0031] To achieve the above technical effects, the general idea of the present application is as follows: A nursing prediction method based on nursing data, the method includes the following steps: S1. Obtain the initial disease monitoring data of the target user and the real-time nursing monitoring data obtained by monitoring according to the set nursing monitoring period; S2. Based on the initial disease monitoring data and each real-time nursing monitoring data, disassemble to obtain each biological index parameter curve; S3. Based on each biological index parameter curve, obtain the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference value of the biological index parameter curve; S4. Based on the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference value of the biological index parameter curve, combined with the preset nursing prediction model, determine and obtain the disease deterioration warning information; S5. Based on the disease deterioration warning information, issue nursing alarm information.

[0032] The embodiments of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0033] In a first aspect, as shown in Figures 1 to 4 , the embodiments of the present application provide a nursing prediction method based on nursing data, and the method includes the following steps: S1. Obtain the initial disease monitoring data of the target user and the real-time nursing monitoring data obtained by monitoring according to a set nursing monitoring cycle; S2. Based on the initial disease monitoring data and each real-time nursing monitoring data, disassemble to obtain each biological index parameter curve; S3. Based on each biological index parameter curve, obtain the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference value of the biological index parameter curve; S4. Based on the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference value of the biological index parameter curve, combine with a preset nursing prediction model to determine and obtain disease deterioration warning information; S5. Based on the disease deterioration warning information, issue a nursing alarm message.

[0034] It should be noted that the biological index parameters may specifically be blood glucose parameters, blood oxygen parameters, heart rate parameters, blood pressure parameters, and other health parameters related to health monitoring.

[0035] In the embodiments of the present application, based on the nursing monitoring data of the patient, the future health risks of the patient are evaluated, and a nursing alarm message is issued when necessary to remind the medical staff to handle it in time, which greatly improves the monitoring work efficiency of the medical staff on the premise of ensuring daily nursing monitoring.

[0036] Furthermore, the method includes a model construction process, and the model construction process includes the following steps: Obtain the slope threshold of the biological index parameter curve, the slope change rate threshold of the biological index parameter curve, the highest value threshold of the biological index parameter curve, and the height difference threshold of the biological index parameter curve corresponding to different types of life index parameters; Based on the slope threshold of the biological index parameter curve, the slope change rate threshold of the biological index parameter curve, the highest value threshold of the biological index parameter curve, and the height difference threshold of the biological index parameter curve corresponding to various types of life index parameters, construct the nursing prediction model.

[0037] Furthermore, the determining and obtaining disease deterioration warning information based on the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference value of the biological index parameter curve, and combining with a preset nursing prediction model includes the following steps: Based on the slope of the biological index parameter curve, obtain the change rate of the slope of the biological index parameter curve; Based on the slope of the biological index parameter curve, the change rate of the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference of the biological index parameter curve, perform data comparison in combination with a preset nursing prediction model; When the slope of the biological index parameter curve exceeds the slope threshold of the biological index parameter curve, or the change rate of the slope of the biological index parameter curve exceeds the change rate threshold of the biological index parameter curve, or the highest value of the biological index parameter curve exceeds the highest value threshold of the biological index parameter curve, or the height difference of the biological index parameter curve exceeds the height difference threshold of the biological index parameter curve, it is determined that the disease deterioration warning information is obtained.

[0038] Further, the disassembling the initial disease monitoring data and each real-time nursing monitoring data to obtain each biological index parameter curve includes the following steps: Based on the initial disease monitoring data and each real-time nursing monitoring data, disassemble to obtain different types of vital index parameters and corresponding monitoring times; Based on different types of the vital index parameters and the corresponding monitoring times, construct biological index parameter curves corresponding to different types of vital index parameters.

[0039] Further, the method further includes the following steps: Based on the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference of the biological index parameter curve, in combination with a preset nursing prediction model, obtain a nursing monitoring cycle adjustment coefficient; Based on the nursing monitoring cycle adjustment coefficient, adjust the nursing monitoring cycle to obtain an adjusted nursing monitoring cycle.

[0040] Further, the nursing prediction model is configured with safety values of the slope of the biological index parameter curve, change rate safety values of the slope of the biological index parameter curve, highest value safety values of the biological index parameter curve, and height difference safety values of the biological index parameter curve corresponding to different types of vital index parameters.

[0041] Further, the obtaining the nursing monitoring cycle adjustment coefficient based on the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference of the biological index parameter curve, in combination with a preset nursing prediction model, includes the following steps: When the slope of the biological index parameter curve is greater than the safety value of the slope of the biological index parameter curve, based on the ratio of the safety value of the slope of the biological index parameter curve to the slope of the biological index parameter curve, obtain a corresponding first nursing monitoring cycle adjustment coefficient; When the highest value of the biological index parameter curve is greater than the safety value of the highest value of the biological index parameter curve, a corresponding second nursing monitoring period adjustment coefficient is obtained based on the ratio of the safety value of the highest value of the biological index parameter curve to the highest value of the biological index parameter curve; When the high-low difference value of the biological index parameter curve is greater than the safety value of the high-low difference value of the biological index parameter curve, a corresponding third nursing monitoring period adjustment coefficient is obtained based on the ratio of the safety value of the high-low difference value of the biological index parameter curve to the high-low difference value of the biological index parameter curve; When the high-low difference value of the biological index parameter curve is greater than the safety value of the high-low difference value of the biological index parameter curve, a corresponding fourth nursing monitoring period adjustment coefficient is obtained based on the ratio of the safety value of the high-low difference value of the biological index parameter curve to the high-low difference value of the biological index parameter curve; Based on the first nursing monitoring period adjustment coefficient, the second nursing monitoring period adjustment coefficient, the third nursing monitoring period adjustment coefficient, and the fourth nursing monitoring period adjustment coefficient, the nursing monitoring period adjustment coefficient is obtained.

[0042] Furthermore, the smaller the value of the nursing monitoring period adjustment coefficient, the shorter the adjusted nursing monitoring period.

[0043] Furthermore, the obtaining of the nursing monitoring period adjustment coefficient based on the first nursing monitoring period adjustment coefficient, the second nursing monitoring period adjustment coefficient, the third nursing monitoring period adjustment coefficient, and the fourth nursing monitoring period adjustment coefficient includes the following steps: Based on the first nursing monitoring period adjustment coefficient, the second nursing monitoring period adjustment coefficient, the third nursing monitoring period adjustment coefficient, and the fourth nursing monitoring period adjustment coefficient, a corresponding average value is obtained and denoted as the nursing monitoring period adjustment coefficient.

[0044] It should be noted that the ratio of the safety value of the slope of the biological index parameter curve to the slope of the biological index parameter curve can specifically be the safety value of the slope of the biological index parameter curve divided by the slope of the biological index parameter curve; The ratio of the safety value of the highest value of the biological index parameter curve to the highest value of the biological index parameter curve can specifically be the safety value of the highest value of the biological index parameter curve divided by the highest value of the biological index parameter curve; The ratio of the safety value of the high-low difference value of the biological index parameter curve to the high-low difference value of the biological index parameter curve can specifically be the safety value of the high-low difference value of the biological index parameter curve divided by the high-low difference value of the biological index parameter curve; The ratio of the safety value of the difference in height of the biological index parameter curve to the difference in height of the biological index parameter curve can specifically be the safety value of the difference in height of the biological index parameter curve divided by the difference in height of the biological index parameter curve.

[0045] It should be noted that based on the technical solution of the embodiment of the present application, when analyzing and warning data, a data mining model can be built. The data mining model can include six steps: business understanding, data understanding, data preparation, model building, model evaluation, and result deployment. The operation steps of the data mining model are as shown in the Figure 2 accompanying drawings of the specification.

[0046] Among them, in the business understanding stage, it is mainly to analyze business problems and then determine the goals of data mining; in the data understanding stage, it is mainly to collect original business data according to business goals and fully understand the data in combination with business knowledge; in the data preparation stage, it is mainly a process of screening, cleaning, and standardizing the original data to make the data meet the requirements of mining methods; the model building stage is the core stage of the entire data mining. In this stage, it is necessary to determine the model type, implementation method, and model construction; in the model evaluation stage, it is mainly to conduct business understanding and evaluation on the results obtained by the data mining model. If the mining results do not meet the requirements, it is necessary to return to the business understanding stage and conduct business analysis again; finally, it is the result deployment stage, that is, to apply the mined results to actual management work to realize the value of data mining.

[0047] Modify the technical framework level of this project with reference to the SPIRIT (Systematic Planning of Intelligent Reuse of Integrated Clinical Routine Data) model proposed by Werner O. Hackl on "reuse of clinical routine data" (Hackl WO, Ammenwerth E. 2016). The SPIRIT conceptual framework diagram is as shown in the Figure 3 accompanying drawings of the specification. This model includes three stages: "formulating strategies - analyzing and classifying - integrating and analyzing", realizing the intelligent reuse of clinical routine data, and forming a closed-loop management of safety and quality.

[0048] When conducting data mining, the specific situation is as follows: The data sources for data mining are various nursing electronic medical records, including temperature sheets, doctor's order sheets, nursing record sheets, etc. Design a data mining program according to the steps in the CRISP-DM model, and embed the safety indicators formulated in the early stage. In this project, the following three functions in data mining are planned to be implemented and developed: concept description, association knowledge mining, and predictive knowledge mining. The specific descriptions and implementation examples of each function are as follows.

[0049] Concept description: It is a summary of the data in the database, enabling an overall understanding of the data. Concept description is usually achieved through methods of mathematical statistics, such as calculating the sum, mean, maximum, minimum, variance, etc. of each data item, or through online analytical processing to achieve multi-dimensional query and operation of data. For example, the daily regular statistics of the use of antibiotics in a department (administration time, administration speed, etc.) are used to judge whether the application of antibiotics is standardized.

[0050] Associated knowledge mining: It reflects the dependence or interaction relationship between an object and other objects in the database. Usually, the associations among a large amount of data in the database are implicit and cannot be directly manifested. Through association rule mining, the temporal relationship, quantitative relationship, or causal relationship, etc. existing between different objects can be found. Such association rules usually have great practical value and can judge the future development trend of the associated object based on the state of the known object. The algorithm used is the Apriori algorithm. For example, when the postoperative care record of a patient undergoing hepatobiliary surgery is entered as "the patient is restless due to the influence of sedative drugs", through association, the system automatically evaluates the risk factors related to the patient's unplanned extubation and fall / falling out of bed in the data, and prompts the nurse to pay key attention to and prevent the occurrence of these two types of nursing safety (adverse) events.

[0051] Predictive knowledge mining: It is to construct a prediction model based on the past and current data values in the database. Through the prediction model, the future development trend of the object can be determined. The method used is the regression analysis method in mathematical statistics and its various variants such as the log-linear model. For example, for the indicators proposed clinically, through historical data analysis, the situation in the past 3 years and the incidence rate of medical safety events are investigated, and the mean value is used as the baseline. Combining clinical experience, management requirements, and health economics references, the early warning trigger conditions are determined. Another example is to embed some existing or newly developed risk early warning models in the system, such as the modified early warning score system and the delirium prediction model. The system automatically captures the data corresponding to the patient and the model, and based on the established rules, warns of the possibility of the patient developing a certain disease / complication, prompting medical staff to intervene early.

[0052] Taking safe blood transfusion as an example, the existing clinical information system and nursing information system can record each time point of the whole process of blood transfusion, and there are also clear regulations for each link of infusion in the relevant infusion operation industry guidelines. The original blood transfusion safety management is evaluated by asking nurses. The "Safety" module can automatically compare the compliance rate between the two. If there is a gap, first, it gives a warning prompt to the nurse, and second, it notifies the quality and safety manager to come to the scene for supervision, reflecting timeliness and scientificity. The schematic diagram of the working mode of the application embodiment itself is as shown in the Figure 4 accompanying drawings of the specification. This module is a prototype of the informatization management of nursing safety. Next, this module will be embedded in the existing system and put into trial operation.

[0053] In a second aspect, referring to Figure 5 as shown, an embodiment of the present application provides a nursing prediction device based on nursing data, and the device includes: A monitoring information acquisition module, which is used to acquire the initial disease monitoring data of the target user and the real-time nursing monitoring data obtained by monitoring according to a set nursing monitoring period; An index parameter curve acquisition module, which is used to disassemble and obtain each biological index parameter curve based on the initial disease monitoring data and each real-time nursing monitoring data; An index parameter curve analysis module, which is used to obtain the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference of the biological index parameter curve based on each biological index parameter curve; An index parameter curve prediction module, which is used to determine and obtain a disease deterioration warning message based on the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference of the biological index parameter curve, in combination with a preset nursing prediction model; A nursing alarm publishing module, which is used to publish a nursing alarm message based on the disease deterioration warning message.

[0054] It should be noted that the biological index parameters may specifically be blood glucose parameters, blood oxygen parameters, heart rate parameters, blood pressure parameters, and other health parameters related to health monitoring.

[0055] In the embodiment of the present application, based on the nursing monitoring data of the patient, the future health risks of the patient are evaluated, and a nursing alarm message is published when necessary to remind the medical staff to handle it in time, which greatly improves the monitoring work efficiency of the medical staff on the premise of ensuring daily nursing monitoring.

[0056] Further, the device further includes a model construction module; The model construction module is used to obtain the threshold of the slope of the biological index parameter curve, the threshold of the change rate of the slope of the biological index parameter curve, the threshold of the highest value of the biological index parameter curve, and the threshold of the height difference of the biological index parameter curve corresponding to different types of vital index parameters; The model construction module is used to construct the nursing prediction model based on the threshold of the slope of the biological index parameter curve, the threshold of the change rate of the slope of the biological index parameter curve, the threshold of the highest value of the biological index parameter curve, and the threshold of the height difference of the biological index parameter curve corresponding to various types of vital index parameters.

[0057] Further, the index parameter curve prediction module is further used to obtain the change rate of the slope of the biological index parameter curve based on the slope of the biological index parameter curve; The index parameter curve prediction module is further configured to perform data comparison based on the slope of the biological index parameter curve, the change rate of the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the high-low difference of the biological index parameter curve, in combination with a preset nursing prediction model; The index parameter curve prediction module is further configured to determine that the disease deterioration warning information is obtained when the slope of the biological index parameter curve exceeds the slope threshold of the biological index parameter curve, or the change rate of the slope of the biological index parameter curve exceeds the change rate threshold of the biological index parameter curve, or the highest value of the biological index parameter curve exceeds the highest value threshold of the biological index parameter curve, or the high-low difference of the biological index parameter curve exceeds the high-low difference threshold of the biological index parameter curve.

[0058] Further, the index parameter curve acquisition module is further configured to disassemble different types of life index parameters and corresponding monitoring times based on the initial disease monitoring data and each of the real-time nursing monitoring data; The index parameter curve acquisition module is further configured to construct a biological index parameter curve corresponding to each different type of life index parameter based on the different types of life index parameters and the corresponding monitoring times.

[0059] Further, the device further includes a nursing monitoring cycle adjustment module, which is configured to obtain a nursing monitoring cycle adjustment coefficient based on the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the high-low difference of the biological index parameter curve, in combination with a preset nursing prediction model; The nursing monitoring cycle adjustment module is further configured to adjust the nursing monitoring cycle based on the nursing monitoring cycle adjustment coefficient to obtain an adjusted nursing monitoring cycle.

[0060] Further, the nursing prediction model is configured with a slope safety value of the biological index parameter curve, a change rate safety value of the slope of the biological index parameter curve, a highest value safety value of the biological index parameter curve, and a high-low difference safety value of the biological index parameter curve corresponding to different types of life index parameters.

[0061] Further, when the slope of the biological index parameter curve is greater than the slope safety value of the biological index parameter curve, the nursing monitoring cycle adjustment module is further configured to obtain a corresponding first nursing monitoring cycle adjustment coefficient based on the ratio of the slope safety value of the biological index parameter curve to the slope of the biological index parameter curve; When the highest value of the biological index parameter curve is greater than the highest value safety value of the biological index parameter curve, the nursing monitoring cycle adjustment module is further configured to obtain a corresponding second nursing monitoring cycle adjustment coefficient based on the ratio of the highest value safety value of the biological index parameter curve to the highest value of the biological index parameter curve; The nursing monitoring period adjustment module is further configured to, when the difference between the highest and lowest values of the biological index parameter curve is greater than the safety value of the difference between the highest and lowest values of the biological index parameter curve, obtain a corresponding third nursing monitoring period adjustment coefficient based on the ratio of the safety value of the difference between the highest and lowest values of the biological index parameter curve to the difference between the highest and lowest values of the biological index parameter curve; The nursing monitoring period adjustment module is further configured to, when the difference between the highest and lowest values of the biological index parameter curve is greater than the safety value of the difference between the highest and lowest values of the biological index parameter curve, obtain a corresponding fourth nursing monitoring period adjustment coefficient based on the ratio of the safety value of the difference between the highest and lowest values of the biological index parameter curve to the difference between the highest and lowest values of the biological index parameter curve; The nursing monitoring period adjustment module is further configured to obtain the nursing monitoring period adjustment coefficient based on the first nursing monitoring period adjustment coefficient, the second nursing monitoring period adjustment coefficient, the third nursing monitoring period adjustment coefficient, and the fourth nursing monitoring period adjustment coefficient.

[0062] Further, the smaller the value of the nursing monitoring period adjustment coefficient, the shorter the adjusted nursing monitoring period.

[0063] Further, the nursing monitoring period adjustment module is further configured to obtain a corresponding average value based on the first nursing monitoring period adjustment coefficient, the second nursing monitoring period adjustment coefficient, the third nursing monitoring period adjustment coefficient, and the fourth nursing monitoring period adjustment coefficient, and record it as the nursing monitoring period adjustment coefficient.

[0064] It should be noted that the ratio of the safety value of the slope of the biological index parameter curve to the slope of the biological index parameter curve may specifically be the safety value of the slope of the biological index parameter curve divided by the slope of the biological index parameter curve; The ratio of the safety value of the highest value of the biological index parameter curve to the highest value of the biological index parameter curve may specifically be the safety value of the highest value of the biological index parameter curve divided by the highest value of the biological index parameter curve; The ratio of the safety value of the difference between the highest and lowest values of the biological index parameter curve to the difference between the highest and lowest values of the biological index parameter curve may specifically be the safety value of the difference between the highest and lowest values of the biological index parameter curve divided by the difference between the highest and lowest values of the biological index parameter curve; The ratio of the safety value of the difference between the highest and lowest values of the biological index parameter curve to the difference between the highest and lowest values of the biological index parameter curve may specifically be the safety value of the difference between the highest and lowest values of the biological index parameter curve divided by the difference between the highest and lowest values of the biological index parameter curve.

[0065] It should be noted that in this application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0066] The above are only specific embodiments of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features claimed herein.

Claims

1. A nursing prediction method based on nursing data, characterized in that, The method includes the following steps: Obtain the initial disease monitoring data of the target user and the real-time nursing monitoring data obtained by monitoring according to the set nursing monitoring cycle; Based on the initial disease monitoring data and each of the real-time nursing monitoring data, disassemble to obtain each biological index parameter curve; Based on each of the biological index parameter curves, obtain the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference value of the biological index parameter curve; Based on the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference value of the biological index parameter curve, combine with the preset nursing prediction model to determine and obtain the disease deterioration warning information; Based on the disease deterioration warning information, issue a nursing alarm information.

2. The nursing prediction method based on nursing data according to claim 1, wherein The method includes a model construction process, and the model construction process includes the following steps: Obtain the slope threshold of the biological index parameter curve, the slope change rate threshold of the biological index parameter curve, the highest value threshold of the biological index parameter curve, and the height difference value threshold of the biological index parameter curve corresponding to different types of vital index parameters; Based on the slope threshold of the biological index parameter curve, the slope change rate threshold of the biological index parameter curve, the highest value threshold of the biological index parameter curve, and the height difference value threshold of the biological index parameter curve corresponding to various types of vital index parameters, construct the nursing prediction model.

3. The nursing prediction method based on nursing data according to claim 2, wherein The determining and obtaining the disease deterioration warning information based on the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference value of the biological index parameter curve, and combining with the preset nursing prediction model includes the following steps: Based on the slope of the biological index parameter curve, obtain the slope change rate of the biological index parameter curve; Based on the slope of the biological index parameter curve, the slope change rate of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference value of the biological index parameter curve, combine with the preset nursing prediction model to perform data comparison; When the slope of the biological index parameter curve exceeds the slope threshold of the biological index parameter curve, or the slope change rate of the biological index parameter curve exceeds the slope change rate threshold of the biological index parameter curve, or the highest value of the biological index parameter curve exceeds the highest value threshold of the biological index parameter curve, or the height difference value of the biological index parameter curve exceeds the height difference value threshold of the biological index parameter curve, then determine and obtain the disease deterioration warning information.

4. The nursing prediction method based on nursing data according to claim 1, wherein The disassembling to obtain each biological index parameter curve based on the initial disease monitoring data and each of the real-time nursing monitoring data includes the following steps: Based on the initial disease monitoring data and each of the real-time nursing monitoring data, disassemble to obtain different types of vital index parameters and the corresponding monitoring time; Based on different types of the vital index parameters and the corresponding monitoring time, construct the biological index parameter curves corresponding to each different type of vital index parameter.

5. The nursing prediction method based on nursing data according to claim 1, wherein The method further includes the following steps: Based on the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the difference between the highest and lowest values of the biological index parameter curve, combined with a preset nursing prediction model, obtain a nursing monitoring cycle adjustment coefficient; Based on the nursing monitoring cycle adjustment coefficient, adjust the nursing monitoring cycle to obtain an adjusted nursing monitoring cycle.

6. The nursing prediction method based on nursing data according to claim 5, characterized in that: The nursing prediction model is configured with safety values of the slope of the biological index parameter curve, the change rate safety value of the slope of the biological index parameter curve, the safety value of the highest value of the biological index parameter curve, and the safety value of the difference between the highest and lowest values of the biological index parameter curve corresponding to different types of vital index parameters.

7. The nursing prediction method based on nursing data according to claim 6, wherein, The step of obtaining the nursing monitoring cycle adjustment coefficient based on the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the difference between the highest and lowest values of the biological index parameter curve, combined with a preset nursing prediction model, includes the following steps: When the slope of the biological index parameter curve is greater than the safety value of the slope of the biological index parameter curve, obtain a corresponding first nursing monitoring cycle adjustment coefficient based on the ratio of the safety value of the slope of the biological index parameter curve to the slope of the biological index parameter curve; When the highest value of the biological index parameter curve is greater than the safety value of the highest value of the biological index parameter curve, obtain a corresponding second nursing monitoring cycle adjustment coefficient based on the ratio of the safety value of the highest value of the biological index parameter curve to the highest value of the biological index parameter curve; When the difference between the highest and lowest values of the biological index parameter curve is greater than the safety value of the difference between the highest and lowest values of the biological index parameter curve, obtain a corresponding third nursing monitoring cycle adjustment coefficient based on the ratio of the safety value of the difference between the highest and lowest values of the biological index parameter curve to the difference between the highest and lowest values of the biological index parameter curve; When the difference between the highest and lowest values of the biological index parameter curve is greater than the safety value of the difference between the highest and lowest values of the biological index parameter curve, obtain a corresponding fourth nursing monitoring cycle adjustment coefficient based on the ratio of the safety value of the difference between the highest and lowest values of the biological index parameter curve to the difference between the highest and lowest values of the biological index parameter curve; Based on the first nursing monitoring cycle adjustment coefficient, the second nursing monitoring cycle adjustment coefficient, the third nursing monitoring cycle adjustment coefficient, and the fourth nursing monitoring cycle adjustment coefficient, obtain the nursing monitoring cycle adjustment coefficient.

8. The nursing prediction method based on nursing data according to claim 6, characterized in that: The smaller the value of the nursing monitoring cycle adjustment coefficient, the shorter the adjusted nursing monitoring cycle.

9. The nursing prediction method based on nursing data according to claim 7, wherein The step of obtaining the nursing monitoring cycle adjustment coefficient based on the first nursing monitoring cycle adjustment coefficient, the second nursing monitoring cycle adjustment coefficient, the third nursing monitoring cycle adjustment coefficient, and the fourth nursing monitoring cycle adjustment coefficient, includes the following steps: Based on the first nursing monitoring cycle adjustment coefficient, the second nursing monitoring cycle adjustment coefficient, the third nursing monitoring cycle adjustment coefficient, and the fourth nursing monitoring cycle adjustment coefficient, obtain the corresponding average value, denoted as the nursing monitoring cycle adjustment coefficient.

10. A nursing prediction device based on nursing data, characterized in that, The device includes: A monitoring information acquisition module, which is used to acquire the initial disease monitoring data of the target user and the real-time nursing monitoring data obtained by monitoring according to the set nursing monitoring cycle; An index parameter curve acquisition module, which is used to disassemble and obtain each biological index parameter curve based on the initial disease monitoring data and each real-time nursing monitoring data; An index parameter curve analysis module, which is used to obtain the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference of the biological index parameter curve based on each biological index parameter curve; An index parameter curve prediction module, which is used to determine and obtain the disease deterioration warning information by combining the preset nursing prediction model based on the slope of the biological index parameter curve, the highest value of the biological index parameter curve, and the height difference of the biological index parameter curve; A nursing alarm publishing module, which is used to publish nursing alarm information based on the disease deterioration warning information.

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