Intelligent nursing system and method based on clinical big data

Abstract

The present invention relates to the technical field of clinical nursing management, and discloses an intelligent nursing system and method based on clinical big data, wherein the system comprises a clinical nursing node supervision module, a clinical nursing staff deployment module, and a clinical nursing execution module; the system accurately extracts the patient's clinical nursing project information according to the current nursing time point information in combination with an intelligent search algorithm and the clinical nursing plan information of the target patient, thereby realizing efficient customized supervision of clinical nursing projects for patients at different clinical nursing time nodes, thereby improving the service quality and efficiency of clinical nursing; the optimal clinical nursing staff is accurately and scientifically screened based on the clinical nursing project information of the target patient, the working status of the clinical nursing staff, and the spatial distance information between the clinical nursing staff and the patient, thereby realizing the intelligent deployment of clinical nursing staff, and improving the response speed and scientificity of clinical nursing.

Description

Technical Field

[0001] The present invention relates to the technical field of clinical nursing management, and specifically to an intelligent nursing system and method based on clinical big data. Background Art

[0002] Clinical nursing is an important branch of nursing science, referring to a series of medical care activities carried out by professional nurses based on the application of nursing theories and skills to clinical practice, focusing on patient needs. Its core content includes routine nursing operations, emergency and critical care, nursing management, and multi-departmental collaboration. The clinical nursing platform is a scientific and intelligent management of clinical nursing based on information technology. Existing clinical nursing platforms cannot realize customized supervision of patient clinical nursing projects, nor can they realize the efficient and scientific deployment of required nursing staff for patients.

[0003] A Chinese invention patent with publication number CN117457147B discloses a method and system for personalized nursing plans for rehabilitation patients. By conducting real-time data stream mining based on real-time monitoring equipment data and electronic health records, the system dynamically monitors health status and generates real-time health status reports, thereby achieving personalized and precise rehabilitation plans. The above technical solutions cannot achieve scientific supervision of the patient care plan process and scientific deployment of nursing staff. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In order to solve the problem that the above-mentioned existing clinical nursing platforms cannot realize customized supervision of patient clinical nursing projects, nor can they realize efficient and scientific allocation of required nursing staff for patients, the above-mentioned customized search for patient clinical nursing plan information, accurate supervision of patient clinical nursing time nodes, precise acquisition of patient clinical nursing project information at specific time, scientific allocation of optimal clinical nursing staff, and high-quality execution of clinical nursing tasks are achieved.

[0006] (2) Technical solution

[0007] The present invention is implemented through the following technical solution: an intelligent nursing method based on clinical big data, the method comprising the following steps:

[0008] S1, collect current nursing time point data and patient nursing characteristic text data;

[0009] S2. Searching and processing the patient's clinical nursing plan feature information based on the patient nursing feature text data and the hospital patient clinical nursing plan text data to generate target patient clinical nursing plan text data;

[0010] S3. Perform clinical nursing time node judgment processing on the patient based on the current nursing time point data and the clinical nursing plan text data of the target patient to generate clinical nursing time node judgment data for the target patient; if the time node has not been reached, directly end the current clinical nursing supervision operation;

[0011] S4. When a time node is reached, extracting and processing the patient's clinical nursing project information based on the current nursing time point data and the target patient's clinical nursing plan text data to generate the target patient's clinical nursing project text data;

[0012] S5. Collecting target clinical nursing staff nursing feature text data and performing spatial distance measurement processing on the patient nursing feature text data to construct target clinical nursing staff and patient distance data;

[0013] S6. Screening the optimal clinical nurse required by the patient based on the target clinical nurse's nursing feature text data and the target clinical nurse-patient distance data to generate the optimal clinical nurse's feature text data for the patient;

[0014] S7. Construct patient clinical care command data and perform patient clinical care tasks.

[0015] Preferably, the steps for collecting the current nursing time point data and the patient nursing feature text data are as follows:

[0016] S11. Collect the time point information of the target nursing patient's geographical location online through the time measurement module, and generate the current nursing time point data ,in The units of include year, month, day, hour and minute;

[0017] Collect the identity information and address information of target nursing patients online through the nursing management platform, and generate patient nursing feature text data The patient care feature text data includes the patient's name, ID number, contact number and nursing room address information.

[0018] Preferably, the steps of searching for the patient's clinical nursing plan feature information based on the patient nursing feature text data and the hospital patient clinical nursing plan text data to generate the target patient's clinical nursing plan text data are as follows:

[0019] S21. Establish a text data set of clinical nursing plans for hospital patients , ;in Indicates the Text data of hospital patient clinical care plans corresponding to each nursing patient, Indicates the maximum number of patients being cared for; the hospital patient clinical care plan text data indicates the clinical care plan information registered on the care management platform at different time points for each hospital patient being cared for; the clinical care plan information includes the patient's temperature monitoring care plan information, respiratory monitoring care plan information, blood pressure monitoring care plan information, medication monitoring care plan information, eating monitoring care plan information, excretion monitoring care plan information, dressing change monitoring care plan information, and blood sugar monitoring care plan information;

[0020] S22, the patient care feature text data A text data collection of clinical care plans for patients in the hospital Text data of clinical care plans for patients in the hospital Perform keyword matching on patient care features and search for text data on the patient care features Corresponding clinical care plan text data for the hospital patients , and generate target patient clinical care plan text data through data identification ; Execute to generate the target patient clinical care plan text data The specific steps are as follows:

[0021] S221, initialize algorithm parameters, population size N, maximum number of iterations T;

[0022] S222, initializing the population, calculating fitness, and determining a patient care plan search pathfinder and a patient care plan search follower;

[0023] S223, according to the position formula A text data set of clinical care plans for patients in the hospital Update the position of the patient care plan search pathfinder in the search space of , where t represents the current iteration number of the algorithm; represents the patient care plan search pathfinder after the tth iteration A text data set of clinical care plans for patients in the hospital The position in the search space, represents the patient care plan search pathfinder after the t-1th iteration A text data set of clinical care plans for patients in the hospital The position in the search space, Represents the patient care plan search pathfinder after the t+1th iteration A text data set of clinical care plans for patients in the hospital The position in the search space, represents the step size factor for the patient care plan search pathfinder movement, It takes the value [1,2] and obeys uniform distribution;

[0024] S224, according to the position formula Update patient care plan search follower clinical care plan text data set of patients in the hospital The position in the search space of represents the patient care plan search follower after the tth iteration A text data set of clinical care plans for patients in the hospital The position in the search space, represents the patient care plan search follower after the t+1th iteration A text data set of clinical care plans for patients in the hospital The position in the search space of Represents the search followers for other patient care plans after the tth iteration A text data set of clinical care plans for patients in the hospital The position in the search space of the patient care plan searches for the position of the follower Mobile not only with patient care plan search pathfinder position Related and influenced by other patient care programs Search follower locations The impact of Represents a patient care plan search among followers of the clinical care plan text data set of patients in the hospital The position distance parameter in the search space of Represents a collection of clinical care plan text data about patients in the hospital between a patient care plan search pathfinder and a patient care plan search follower. The position distance parameter in the search space of , ; represents the interaction coefficient between the patient care plan search followers, represents the attraction coefficient of the patient care plan search pathfinder to the patient care plan search follower, 、 All values ​​are [1,2] and obey uniform distribution; The step size factor for moving a patient care plan search follower relative to other patient care plan search followers, The step size factor for the movement of the Patient Care Plan Search Follower and Patient Care Plan Search Pathfinder, 、 All are random numbers in the range [0,1];

[0025] S225. Calculate the patient care feature text data Text data on clinical care plans for all of the hospital's patients The fitness value and clinical care plan text data set of patients in the hospital Update the search space to search for text data related to the patient care characteristics The most matching clinical care plan text data for the hospital patients Global optimal value;

[0026] S226: When the maximum number of iterations is met, output the patient care feature text data. The most matching clinical care plan text data for the hospital patients , and generate target patient clinical care plan text data through data identification The target patient clinical care plan text data represents clinical care plan information at different time points formulated for the target care patient.

[0027] Preferably, the clinical nursing time node judgment processing of the patient is performed based on the current nursing time point data and the clinical nursing plan text data of the target patient to generate the clinical nursing time node judgment data of the target patient; when the time node has not been reached, the operation steps of directly ending the clinical nursing supervision operation are as follows:

[0028] S31, the current nursing time point data Text data related to the target patient clinical care plan Compare the time values ​​of the time points in the experiment, and generate the target patient's clinical nursing time node judgment data based on the time point numerical comparison results. ;

[0029] when and If the time value comparison is successful, it means that the target patient has reached the specified clinical nursing time node, and the target patient clinical nursing time node judgment data is output. To the time node;

[0030] when and If the time value is not matched successfully, it means that the target patient has not reached the set clinical nursing time node, then the target patient clinical nursing time node judgment data is output. Since the time node has not been reached, the clinical nursing supervision task will be terminated directly at this time.

[0031] Preferably, when the time node is reached, the patient's clinical nursing item information is extracted and processed according to the current nursing time point data and the target patient's clinical nursing plan text data, and the operating steps for generating the target patient's clinical nursing item text data are as follows:

[0032] S41, when the target patient clinical nursing time node judgment data When the time node is reached, the BERT language model algorithm is used according to the current nursing time point data Corresponding time information from the target patient clinical care plan text data Search and extract the current nursing time point data Specific project information of the clinical nursing plan at the corresponding time node, and generate the target patient clinical nursing project text data through data identification The target patient clinical nursing project text data represents the specific nursing project information of the target nursing patient at the current nursing time node. The target patient clinical nursing project text data includes body temperature monitoring project information, respiratory monitoring project information, blood pressure monitoring project information, medication nursing project information, eating nursing project information, excretion nursing project information, dressing change nursing project information and blood sugar monitoring project information.

[0033] Preferably, the steps of collecting the target clinical nursing staff nursing feature text data and performing clinical nursing staff and patient spatial distance measurement processing with the patient nursing feature text data to construct the target clinical nursing staff and patient distance data are as follows:

[0034] S51, the target patient clinical nursing project text data Import the data input dialog box of the nursing management platform, and the nursing management platform will import the data of the clinical nursing project of the target patient into the data input dialog box of the nursing management platform. The corresponding clinical nursing project information searches for the nursing characteristic information of the nursing staff that matches the clinical nursing project required by the target nursing patient, and generates the nursing characteristic text data set of the target clinical nursing staff through data identification. , ;in Indicates the collected The target clinical nurses’ nursing characteristic text data corresponding to each clinical nurse, Indicates the maximum number of clinical nursing staff; the target clinical nursing staff nursing feature text data includes the clinical nursing staff's identity feature information, contact information, current nursing work status information and the nursing room address information where the current nursing work is located, and the current nursing work status includes being in the nursing work state and not in the nursing work state;

[0035] S52, respectively, the target clinical nursing staff nursing feature text data set Text data on nursing characteristics of target clinical nursing staff Import the departure dialog box of the map navigation software in order according to the nursing staff number, as well as the patient nursing feature text data Import the destination dialog box of the map navigation software separately. The map navigation software measures the spatial distance between different clinical nurses and target patients online, and constructs a data set of the distance between target clinical nurses and patients. ,in Indicates the The target clinical nurse and patient distance data corresponding to each clinical nurse and target nursing patient, The unit is meter.

[0036] Preferably, the optimal clinical nurse required by the patient is screened based on the target clinical nurse nursing feature text data and the target clinical nurse-patient distance data, and the steps for generating the optimal clinical nurse feature text data for the patient are as follows:

[0037] S61, using the Boyer-Moore search algorithm to search the target clinical nursing staff nursing feature text data set according to the nursing work status keywords and spatial spacing values. Text data on nursing characteristics of target clinical nursing staff and the target clinical nursing staff-patient distance data collection Target clinical staff-patient distance data as described in Search for the nursing feature information of clinical nurses who are not in the nursing work state and have the smallest spatial distance value with the target nursing patient, and generate the optimal clinical nurse feature text data of the patient through data identification. .

[0038] Preferably, the steps of constructing the patient clinical nursing command data and executing the patient clinical nursing tasks are as follows:

[0039] S71, the current nursing time point data , the patient care feature text data , the target patient's clinical care plan text data , clinical nursing time node judgment data of the target patient , the target patient clinical nursing project text data , the patient's optimal clinical nursing staff feature text data After data combination, patient clinical care command data is constructed ,in ;

[0040] S72, the nursing management platform sends the patient clinical nursing command data The information is pushed to the mobile terminal of the target clinical nurse through the Internet of Things communication network, and the target clinical nurse is prompted to perform clinical nursing tasks for the patient. The mobile terminal includes any one of a smart phone, a smart bracelet and a smart tablet.

[0041] An intelligent nursing system based on clinical big data, used for the intelligent nursing method based on clinical big data, the system includes a clinical nursing node supervision module, a clinical nursing staff deployment module, and a clinical nursing execution module;

[0042] The clinical nursing node supervision module includes a current nursing time point information collection unit, a patient nursing feature information collection unit, a hospital patient clinical nursing plan information storage unit, a patient clinical nursing plan information search unit, a patient clinical nursing time node judgment unit, and a patient clinical nursing project information extraction unit;

[0043] The current nursing time point information acquisition unit acquires the current nursing time point data through the time measurement module; the patient nursing feature information acquisition unit acquires the patient nursing feature text data through the nursing management platform; the hospital patient clinical nursing plan information storage unit is used to store the hospital patient clinical nursing plan text data; the patient clinical nursing plan information search unit searches for the patient's clinical nursing plan feature information based on the patient nursing feature text data and the hospital patient clinical nursing plan text data stored based on clinical big data, and generates the target patient clinical nursing plan text data; the patient clinical nursing time node judgment unit performs the patient's clinical nursing time node judgment based on the current nursing time point data and the target patient clinical nursing plan text data, and generates the target patient clinical nursing time node judgment data; the patient clinical nursing item information extraction unit extracts the patient's clinical nursing item information based on the current nursing time point data and the target patient clinical nursing plan text data, and generates the target patient clinical nursing item text data;

[0044] The clinical nursing staff deployment module includes a target clinical nursing staff nursing feature information collection unit, a clinical nursing staff and patient spatial distance measurement unit, and a patient optimal clinical nursing staff object screening unit;

[0045] The target clinical nursing staff nursing feature information collection unit collects the target clinical nursing staff nursing feature text data through the nursing management platform; the clinical nursing staff and patient spatial distance measurement unit performs clinical nursing staff and patient spatial distance measurement processing based on the target clinical nursing staff nursing feature text data and the patient nursing feature text data, and constructs target clinical nursing staff and patient distance data; the patient optimal clinical nursing staff object screening unit performs optimal clinical nursing staff screening processing required by the patient based on the target clinical nursing staff nursing feature text data and the target clinical nursing staff and patient distance data, and generates patient optimal clinical nursing staff feature text data;

[0046] The clinical nursing execution module includes a patient clinical nursing command information construction unit and a patient clinical nursing operation execution unit;

[0047] The patient clinical nursing command information construction unit constructs the patient clinical nursing command data based on the current nursing time point, patient nursing characteristic information, target patient clinical nursing plan information, target patient clinical nursing time node judgment information, target patient clinical nursing project information, and patient optimal clinical nursing staff characteristic information in combination with data processing; the patient clinical nursing operation execution unit, the nursing management platform pushes the patient clinical nursing command data to the mobile terminal of the target clinical nursing staff through the Internet of Things communication network, and prompts the target clinical nursing staff to execute the patient clinical nursing operation.

[0048] (3) Beneficial effects

[0049] The present invention provides an intelligent nursing system and method based on clinical big data. It has the following beneficial effects:

[0050] 1. Accurately collect current nursing time point information and patient nursing feature information through the time measurement module and nursing management platform to provide reliable data support for scientific supervision of patient clinical nursing operations; based on patient nursing feature information combined with intelligent recognition algorithm and hospital patient clinical nursing plan information based on clinical big data storage, perform efficient search for patient clinical nursing plan feature information, and realize customized search for patient clinical nursing plan information; accurately judge patient clinical nursing time nodes based on current nursing time point information combined with intelligent search algorithm and target patient clinical nursing plan information, realize dynamic supervision of patient clinical nursing time points, and improve the reliability of patient clinical nursing; accurately extract patient clinical nursing project information based on current nursing time point information combined with intelligent search algorithm and target patient clinical nursing plan information, realize efficient customized supervision of clinical nursing projects for patients at different clinical nursing time nodes, and improve the service quality and efficiency of clinical nursing.

[0051] 2. Collect nursing characteristic information of target clinical nurses through the nursing management platform to provide real data support for the scientific deployment of clinical nurses; accurately and scientifically screen out the best clinical nurses based on the clinical nursing project information of target patients, the working status of clinical nurses, and the spatial distance information between clinical nurses and patients, realize the intelligent deployment of clinical nurses, and improve the response speed and scientific nature of clinical nursing.

[0052] 3. By scientifically constructing patient clinical nursing command data based on the current nursing time point, patient nursing characteristic information, target patient clinical nursing plan information, target patient clinical nursing time node judgment information, target patient clinical nursing project information, and patient optimal clinical nursing staff characteristic information, and combining the nursing management platform and mobile terminals to autonomously and efficiently execute patient clinical nursing tasks, the accurate and efficient collection of clinical nursing information and the precise execution of clinical nursing tasks can be achieved, thereby improving the satisfaction and applicability of clinical nursing. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a module diagram of the intelligent nursing system based on clinical big data provided by the present invention;

[0054] Figure 2 This is a flowchart of the intelligent nursing method based on clinical big data provided by the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] The embodiments of the intelligent nursing system and method based on clinical big data are as follows:

[0057] Example 1:

[0058] See also Figure 1 - Figure 2 , an intelligent nursing method based on clinical big data, the method comprises the following steps:

[0059] S1, collect current nursing time point data and patient nursing characteristic text data;

[0060] S2. Search and process the patient's clinical nursing plan feature information based on the patient's nursing feature text data and the hospital's patient clinical nursing plan text data to generate the target patient's clinical nursing plan text data;

[0061] S3. Determine the clinical nursing time node of the patient based on the current nursing time point data and the clinical nursing plan text data of the target patient, and generate clinical nursing time node determination data for the target patient; if the time node has not been reached, directly terminate the current clinical nursing supervision operation;

[0062] S4. When the time node is reached, the patient's clinical nursing project information is extracted and processed based on the current nursing time point data and the target patient's clinical nursing plan text data to generate the target patient's clinical nursing project text data;

[0063] S5. Collect the target clinical nursing staff's nursing feature text data and perform spatial distance measurement between the clinical nursing staff and the patient with the patient's nursing feature text data to construct the target clinical nursing staff and patient distance data;

[0064] S6. Screening the optimal clinical nurse required by the patient based on the target clinical nurse's nursing characteristic text data and the distance data between the target clinical nurse and the patient, and generating the optimal clinical nurse's characteristic text data for the patient;

[0065] S7. Construct patient clinical care command data and perform patient clinical care tasks.

[0066] For further information, see Figure 1 - Figure 2 The steps for collecting current nursing time point data and patient nursing feature text data are as follows:

[0067] S11. Collect the time point information of the target nursing patient's geographical location online through the time measurement module, and generate the current nursing time point data ,in The units of include year, month, day, hour and minute;

[0068] Collect the identity information and address information of target nursing patients online through the nursing management platform, and generate patient nursing feature text data ,The patient care feature text data includes the patient’s name, ID number, contact number and ,nursing room address information.

[0069] The steps for searching and processing the patient's clinical nursing plan feature information based on the patient nursing feature text data and the hospital patient clinical nursing plan text data to generate the target patient's clinical nursing plan text data are as follows:

[0070] S21. Establish a text data set of clinical nursing plans for hospital patients , ;in Indicates the Text data of hospital patient clinical care plans corresponding to each nursing patient, Indicates the maximum number of patients being cared for; hospital patient clinical care plan text data indicates the clinical care plan information registered on the care management platform at different time points for each hospital patient; clinical care plan information includes the patient's temperature monitoring care plan information, respiratory monitoring care plan information, blood pressure monitoring care plan information, medication monitoring care plan information, eating monitoring care plan information, excretion monitoring care plan information, dressing change monitoring care plan information, and blood sugar monitoring care plan information;

[0071] S22. Patient care feature text data A collection of clinical care plan text data for hospital patients Text data of clinical nursing plans for patients in traditional Chinese medicine hospitals Perform keyword matching on patient care characteristics and search for text data on patient care characteristics Corresponding hospital patient clinical care plan text data , and generate target patient clinical care plan text data through data identification ; Execute to generate clinical care plan text data for target patients The specific steps are as follows:

[0072] S221, initialize algorithm parameters, population size N, maximum number of iterations T;

[0073] S222, initializing the population, calculating fitness, and determining a patient care plan search pathfinder and a patient care plan search follower;

[0074] S223, according to the position formula Collection of clinical care plan text data for hospital patients Update the position of the patient care plan search pathfinder in the search space of , where t represents the current iteration number of the algorithm; represents the patient care plan search pathfinder after the tth iteration Collection of clinical care plan text data for hospital patients The position in the search space, represents the patient care plan search pathfinder after the t-1th iteration Collection of clinical care plan text data for hospital patients The position in the search space, Represents the patient care plan search pathfinder after the t+1th iteration Collection of clinical care plan text data for hospital patients The position in the search space, represents the step size factor for the patient care plan search pathfinder movement, It takes the value [1,2] and obeys uniform distribution;

[0075] S224, according to the position formula Update patient care plan search followers in hospital patient clinical care plan text data collection The position in the search space of represents the patient care plan search follower after the tth iteration Collection of clinical care plan text data for hospital patients The position in the search space, represents the patient care plan search follower after the t+1th iteration Collection of clinical care plan text data for hospital patients The position in the search space of Represents the search followers for other patient care plans after the tth iteration Collection of clinical care plan text data for hospital patients The position in the search space of the patient care plan searches for the position of the follower Mobile not only with patient care plan search pathfinder position Related and influenced by other patient care programs Search follower locations The impact of Represents a collection of clinical care plan text data for hospital patients among followers of patient care plan search The position distance parameter in the search space of Represents a collection of clinical care plan text data between patient care plan search pathfinders and patient care plan search followers in hospitals The position distance parameter in the search space of , ; represents the interaction coefficient between the patient care plan search followers, represents the attraction coefficient of the patient care plan search pathfinder to the patient care plan search follower, 、 All values ​​are [1,2] and obey uniform distribution; The step size factor for moving a patient care plan search follower relative to other patient care plan search followers, The step size factor for the movement of the Patient Care Plan Search Follower and Patient Care Plan Search Pathfinder, 、 All are random numbers in the range [0,1];

[0076] S225. Calculate patient care feature text data Clinical care plan text data for all hospital patients The fitness value and clinical care plan text data set of hospital patients Update the search space to search for text data related to patient care characteristics The most matching hospital patient clinical care plan text data Global optimal value;

[0077] S226. When the maximum number of iterations is met, output the patient care feature text data The most matching hospital patient clinical care plan text data , and generate target patient clinical care plan text data through data identification ,The target patient clinical care plan text data represents the ,clinical care plan information at different time points formulated for the target ,care patients.

[0078] The clinical nursing time node judgment processing of the patient is performed based on the current nursing time point data and the target patient's clinical nursing plan text data to generate the target patient's clinical nursing time node judgment data; if the time node has not been reached, the operation steps for directly ending this clinical nursing supervision operation are as follows:

[0079] S31, the current nursing time point data Text data related to clinical care plans for target patients Compare the time values ​​of the time points in the experiment, and generate the target patient's clinical nursing time node judgment data based on the time point numerical comparison results. ;

[0080] when and If the time value comparison is successful, it means that the target nursing patient has reached the specified clinical nursing time node, and the target patient's clinical nursing time node judgment data is output. To the time node;

[0081] when and If the time value is not matched successfully, it means that the target patient has not reached the designated clinical care time node, then the target patient clinical care time node judgment data will be output. Since the time node has not been reached, the clinical nursing supervision task will be terminated directly at this time.

[0082] When the time node is reached, the patient's clinical nursing project information is extracted and processed based on the current nursing time point data and the target patient's clinical nursing plan text data. The steps for generating the target patient's clinical nursing project text data are as follows:

[0083] S41, when the target patient clinical care time node judgment data When the time node is reached, the BERT language model algorithm is used according to the current nursing time point data. The corresponding time information is obtained from the target patient clinical care plan text data Search and extract current nursing time point data Specific project information of the clinical nursing plan at the corresponding time node, and generate the target patient clinical nursing project text data through data identification The target patient clinical nursing project text data represents the specific nursing project information of the target nursing patient at the current nursing time node. The target patient clinical nursing project text data includes body temperature monitoring project information, respiratory monitoring project information, blood pressure monitoring project information, medication nursing project information, eating nursing project information, excretion nursing project information, dressing change nursing project information and blood glucose monitoring project information.

[0084] Through the mutual cooperation of the current nursing time point information collection unit and the patient nursing feature information collection unit, the time measurement module and the nursing management platform are used to accurately collect the current nursing time point information and patient nursing feature information, providing reliable data support for the scientific supervision of patient clinical nursing operations; the patient clinical nursing plan information search unit, based on the patient nursing feature information combined with the intelligent recognition algorithm and the hospital patient clinical nursing plan information based on clinical big data storage, efficiently searches for the patient clinical nursing plan feature information, and realizes customized search of the patient clinical nursing plan information; the patient clinical nursing time node judgment unit, based on the current nursing time point information combined with the intelligent search algorithm and the target patient clinical nursing plan information, accurately judges the patient clinical nursing time node, realizes dynamic supervision of the patient clinical nursing time point, and improves the reliability of the patient's clinical nursing; the patient clinical nursing project information extraction unit, based on the current nursing time point information combined with the intelligent search algorithm and the target patient clinical nursing plan information, accurately extracts the patient clinical nursing project information, realizes efficient customized supervision of clinical nursing projects for patients at different clinical nursing time nodes, and improves the service quality and efficiency of clinical nursing.

[0085] For further information, see Figure 1 - Figure 2 , collect the target clinical nursing staff nursing feature text data and the patient nursing feature text data to measure the clinical nursing staff and patient spatial distance, and construct the target clinical nursing staff and patient distance data as follows:

[0086] S51. Text data of clinical nursing items of target patients Import the data input dialog box of the nursing management platform, the nursing management platform is based on the clinical nursing project text data of the target patient The corresponding clinical nursing project information searches for the nursing characteristic information of the nursing staff that matches the clinical nursing project required by the target nursing patient, and generates the nursing characteristic text data set of the target clinical nursing staff through data identification. , ;in Indicates the collected The target clinical nurses’ nursing characteristic text data corresponding to each clinical nurse, Indicates the maximum number of clinical nursing staff; the target clinical nursing staff nursing feature text data includes the clinical nursing staff's identity feature information, contact information, current nursing work status information and the address information of the nursing room where the current nursing work is located. The current nursing work status includes being in the nursing work state and not being in the nursing work state;

[0087] S52, respectively collect the target clinical nursing staff nursing feature text data Nursing characteristics text data of target clinical nurses Import the departure dialog box of the map navigation software and the patient care feature text data in order according to the nursing staff number Import the destination dialog box of the map navigation software separately. The map navigation software measures the spatial distance between different clinical nurses and target patients online, and constructs a data set of the distance between target clinical nurses and patients. ,in Indicates the The target clinical nurse and patient distance data corresponding to each clinical nurse and target nursing patient, The unit is meter.

[0088] The optimal clinical nurse required by the patient is screened based on the target clinical nurse's nursing feature text data and the target clinical nurse's and patient's distance data. The steps for generating the optimal clinical nurse's feature text data for the patient are as follows:

[0089] S61. Use the Boyer-Moore search algorithm to search the target clinical nursing staff nursing feature text data set according to the nursing work status keywords and spatial spacing values. Nursing characteristics text data of target clinical nurses and target clinical nurse-patient distance data collection Target clinical nursing staff and patient distance data Search for the nursing feature information of clinical nurses who are not in the nursing work state and have the smallest spatial distance value with the target nursing patient, and generate the optimal clinical nurse feature text data of the patient through data identification. .

[0090] Through the target clinical nursing staff nursing characteristic information collection unit, the nursing management platform is used to collect the target clinical nursing staff nursing characteristic information, providing real data support for the scientific deployment of clinical nursing staff; the clinical nursing staff and patient spatial distance measurement unit and the patient optimal clinical nursing staff object screening unit cooperate with each other to accurately and scientifically screen the optimal clinical nursing staff based on the clinical nursing project information of the target patient, the working status of the clinical nursing staff and the spatial distance information between the clinical nursing staff and the patient, realize the intelligent deployment of clinical nursing staff, and improve the response speed and scientific nature of clinical nursing.

[0091] For further information, see Figure 1 - Figure 2 The steps to construct patient clinical nursing command data and perform patient clinical nursing tasks are as follows:

[0092] S71, the current nursing time point data , patient care feature text data , target patient clinical care plan text data , clinical nursing time node judgment data for target patients , target patient clinical nursing project text data , patient optimal clinical nursing staff characteristic text data After data combination, patient clinical care command data is constructed ,in ;

[0093] S72, the nursing management platform will be the patient's clinical nursing command data The information is pushed to the mobile terminal of the target clinical nurse through the Internet of Things communication network, and the target clinical nurse is prompted to perform clinical nursing tasks for the patient. The mobile terminal includes any one of a smart phone, a smart bracelet and a smart tablet.

[0094] Through the cooperation between the patient clinical nursing command information construction unit and the patient clinical nursing operation execution unit, the patient clinical nursing command data is scientifically constructed based on the current nursing time point, patient nursing characteristic information, target patient clinical nursing plan information, target patient clinical nursing time node judgment information, target patient clinical nursing project information, and patient optimal clinical nursing staff characteristic information. At the same time, combined with the nursing management platform and mobile terminals, the patient clinical nursing operations are autonomously and efficiently executed, realizing the accurate and efficient collection of clinical nursing information and the precise execution of clinical nursing operations, thereby improving the satisfaction and applicability of clinical nursing.

[0095] Example 2:

[0096] See also Figure 1 - Figure 2, intelligent nursing system based on clinical big data, used for intelligent nursing methods based on clinical big data, the system includes clinical nursing node supervision module, clinical nursing staff deployment module, and clinical nursing execution module;

[0097] The clinical nursing node supervision module includes a current nursing time point information collection unit, a patient nursing feature information collection unit, a hospital patient clinical nursing plan information storage unit, a patient clinical nursing plan information search unit, a patient clinical nursing time node judgment unit, and a patient clinical nursing project information extraction unit;

[0098] The current nursing time point information collection unit collects the current nursing time point data through the time measurement module; the patient nursing feature information collection unit collects the patient nursing feature text data through the nursing management platform; the hospital patient clinical nursing plan information storage unit is used to store the hospital patient clinical nursing plan text data; the patient clinical nursing plan information search unit searches for the patient's clinical nursing plan feature information based on the patient nursing feature text data and the hospital patient clinical nursing plan text data stored based on clinical big data, and generates the target patient clinical nursing plan text data; the patient clinical nursing time node judgment unit judges the patient's clinical nursing time node based on the current nursing time point data and the target patient clinical nursing plan text data, and generates the target patient clinical nursing time node judgment data; the patient clinical nursing project information extraction unit extracts the patient's clinical nursing project information based on the current nursing time point data and the target patient clinical nursing plan text data, and generates the target patient clinical nursing project text data;

[0099] The clinical nursing staff deployment module includes a target clinical nursing staff nursing characteristic information collection unit, a clinical nursing staff and patient spatial distance measurement unit, and a patient optimal clinical nursing staff object screening unit;

[0100] The target clinical nursing staff nursing feature information collection unit collects the target clinical nursing staff nursing feature text data through the nursing management platform; the clinical nursing staff and patient spatial distance measurement unit measures the clinical nursing staff and patient spatial distance based on the target clinical nursing staff nursing feature text data and the patient nursing feature text data, and constructs the target clinical nursing staff and patient distance data; the patient optimal clinical nursing staff object screening unit screens the optimal clinical nursing staff required by the patient based on the target clinical nursing staff nursing feature text data and the target clinical nursing staff and patient distance data, and generates the patient optimal clinical nursing staff feature text data;

[0101] The clinical nursing execution module includes a patient clinical nursing command information construction unit and a patient clinical nursing operation execution unit;

[0102] The patient clinical nursing command information construction unit constructs the patient clinical nursing command data based on the current nursing time point, patient nursing characteristic information, target patient clinical nursing plan information, target patient clinical nursing time node judgment information, target patient clinical nursing project information, and patient optimal clinical nursing staff characteristic information combined with data processing; the patient clinical nursing operation execution unit, the nursing management platform pushes the patient clinical nursing command data to the mobile terminal of the target clinical nursing staff through the Internet of Things communication network, and prompts the target clinical nursing staff to perform the patient clinical nursing operation.

[0103] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. Intelligent nursing method based on clinical big data, characterized by: The method comprises the following steps: S1, collect current nursing time point data and patient nursing characteristic text data; S2. Search and process the patient's clinical nursing plan feature information to generate clinical nursing plan text data for the target patient; S3. Perform clinical nursing time node judgment processing on the patient and generate clinical nursing time node judgment data for the target patient; if the time node has not been reached, directly end the clinical nursing supervision operation; S4. When the time node is reached, extract and process the patient's clinical nursing project information to generate clinical nursing project text data for the target patient; S5. Collecting target clinical nursing staff nursing feature text data and performing spatial distance measurement processing on the patient nursing feature text data to construct target clinical nursing staff and patient distance data; S6. Screening the optimal clinical nursing staff required by the patient and generating characteristic text data of the optimal clinical nursing staff for the patient; S7. Construct patient clinical care command data and execute patient clinical care tasks; S2 includes the following steps: S21. Establish a text data set of clinical nursing plans for hospital patients , ;in Indicates the Text data of hospital patient clinical care plans corresponding to each nursing patient, Indicates the maximum number of patients being cared for; Said S1 comprises the following steps: S11. Collect the time point information of the target nursing patient's geographical location online through the time measurement module, and generate the current nursing time point data ,in The units of include year, month, day, hour and minute; Collect the identity information and address information of target nursing patients online through the nursing management platform, and generate patient nursing feature text data , the patient care feature text data includes the patient's name, ID number, contact number and nursing room address information; S22, the With the As stated in Perform keyword matching of patient care characteristics and search for the The corresponding , and generate target patient clinical care plan text data through data identification ; Execute to generate the target patient clinical care plan text data The specific steps are as follows: S221, initialize algorithm parameters, population size N, maximum number of iterations T; S222, initializing the population, calculating fitness, and determining a patient care plan search pathfinder and a patient care plan search follower; S223, in the Update the position of the patient care plan search pathfinder in the search space; S224, update the patient care plan search follower in the The position in the search space of S225, calculate the With all the above The fitness value, and in the Update the search space to find the The best match Global optimal value; S226, when the maximum number of iterations is met, output The best match , and generate target patient clinical care plan text data through data identification .

2. The intelligent nursing method based on clinical big data according to claim 1, characterized in that: The S3 includes the following steps: S31, the With the Compare the time values ​​of the time points in the experiment, and generate the target patient's clinical nursing time node judgment data based on the time point numerical comparison results. ; when and If the time value comparison is successful, the output is To the time node; when and If the time value is not successfully compared, the output is Since the time node has not been reached, the clinical nursing supervision task will be terminated directly at this time.

3. The intelligent nursing method based on clinical big data according to claim 2, characterized in that: The S4 comprises the following steps: S41, when the When the time node is reached, the BERT language model algorithm is used as described The corresponding time information is from the Search for the extracted Specific project information of the clinical nursing plan at the corresponding time node, and generate the target patient clinical nursing project text data through data identification .

4. The intelligent nursing method based on clinical big data according to claim 3 is characterized in that: The S5 comprises the following steps: S51, the Import the data input dialog box of the nursing management platform. The nursing management platform The corresponding clinical nursing project information searches for the nursing characteristic information of the nursing staff that matches the clinical nursing project required by the target nursing patient, and generates the nursing characteristic text data set of the target clinical nursing staff through data identification. , ;in Indicates the collected The target clinical nurses’ nursing characteristic text data corresponding to each clinical nurse, Indicates the maximum number of clinical nursing staff; the target clinical nursing staff nursing feature text data includes the clinical nursing staff's identity feature information, contact information, current nursing work status information and the nursing room address information where the current nursing work is located, and the current nursing work status includes being in the nursing work state and not in the nursing work state; S52, respectively As stated in Import the departure dialog box of the map navigation software in order according to the nursing staff number, as well as the Import the destination dialog box of the map navigation software separately. The map navigation software measures the spatial distance between different clinical nurses and target patients online, and constructs a data set of the distance between target clinical nurses and patients. ,in Indicates the The target clinical nurse and patient distance data corresponding to each clinical nurse and target nursing patient, The unit is meter.

5. The intelligent nursing method based on clinical big data according to claim 4 is characterized in that: The S6 comprises the following steps: S61, using the Boyer-Moore search algorithm according to the nursing work status keywords and spatial spacing values ​​in the As stated in and stated As stated in Search for the nursing feature information of clinical nurses who are not in the nursing work state and have the smallest spatial distance value with the target nursing patient, and generate the optimal clinical nurse feature text data of the patient through data identification. .

6. The intelligent nursing method based on clinical big data according to claim 5, characterized in that: The S7 comprises the following steps: S71, the 、 、 、 、 、 After data combination, patient clinical care command data is constructed ; S72, the nursing management platform will The information is pushed to the mobile terminal of the target clinical nurse through the Internet of Things communication network, and the target clinical nurse is prompted to perform clinical nursing tasks for the patient.

7. An intelligent nursing system based on clinical big data, for implementing the intelligent nursing method based on clinical big data according to any one of claims 1 to 6, characterized in that: The system includes a clinical nursing node supervision module, a clinical nursing staff deployment module, and a clinical nursing execution module.

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