Clinical Nursing Data Management Method and System Based on Big Data

Abstract

The clinical nursing data management method and system based on big data provided by the present application optimizes a first data management thread based on the clinical nursing data to be processed to obtain a second data management thread; then obtains the nursing data to be managed, and inputs the nursing data to be managed into the second data management thread to obtain a data management result corresponding to the target category of clinical nursing event data in the nursing data to be managed. Thus, an efficient and accurate element data management process is achieved. By selecting attributes that are related to the target category and using the clinical nursing data to be processed that matches the target category and the supplementary attributes that are related to the target category to perform mining operations simultaneously, the mining weight of the target category matching attribute set is increased, the data management capability of the data management thread for the target category matching attribute set is guaranteed, and the accuracy of the clinical nursing event data data management is improved.

Description

Technical Field

[0001] The present application relates to the field of data management technology, and in particular, to a clinical nursing data management method and system based on big data. Background Art

[0002] Big data, or massive amounts of information, refers to datasets that are too large or complex for traditional data processing applications to handle. Big data can also be defined as large amounts of unstructured or structured data from a variety of sources. From an academic perspective, the emergence of big data has fostered novel research across a wide range of topics. This has also led to the development of various big data statistical methods.

[0003] At present, there are many kinds of clinical nursing projects, and the content of each patient's care is also different. As a result, there is a lot of relevant clinical nursing data. How to manage patient information is a problem that is difficult to solve at present. Summary of the Invention

[0004] In order to improve the technical problems existing in related technologies, this application provides a clinical nursing data management method and system based on big data.

[0005] In a first aspect, a clinical nursing data management method based on big data is provided, comprising:

[0006] Obtaining clinical care data to be processed, wherein the clinical care data to be processed matches clinical care event data of a target category;

[0007] Extracting a plurality of related attributes corresponding to the target category from the clinical nursing data to be processed based on a pending data management instruction, wherein the pending data management instruction is pre-set by combining a set of attributes in the clinical nursing data to be processed with nursing items in a target directory, and the target directory corresponds to the target category;

[0008] Calling the clinical nursing data to be processed and the plurality of related attributes to configure a preconfigured thread to obtain a first data management thread, wherein the configuration process of the preconfigured thread is performed based on a configuration instruction, and the mining operation indicated by the configuration instruction matches the plurality of related attributes;

[0009] Optimizing the first data management thread in combination with the clinical nursing data to be processed to obtain a second data management thread;

[0010] The nursing data to be managed is obtained, and the nursing data to be managed is input into the second data management thread to obtain a data management result corresponding to the target category of clinical nursing event data in the nursing data to be managed.

[0011] In an independently implemented embodiment, extracting a plurality of related attributes corresponding to the target category from the clinical care data to be processed based on the processing data management instruction includes:

[0012] extracting a plurality of pending attributes corresponding to the target category from the pending clinical nursing data in combination with the pending data management instruction;

[0013] Inputting a plurality of the pending attributes into the pending data management thread for scoring to obtain pending feature values;

[0014] The undetermined attributes are selected in combination with the undetermined feature values to obtain a plurality of the related attributes.

[0015] It can be understood that when extracting from the clinical nursing data to be processed based on the management instruction of the data to be processed, the problem of inaccurate extraction is improved, so that several related attributes of the target category can be accurately obtained.

[0016] In an independently implemented embodiment, extracting a plurality of pending attributes corresponding to the target category from the clinical care data to be processed in combination with the pending data management instruction includes:

[0017] Collecting statistics on key feature data of each attribute set in the clinical nursing data to be processed;

[0018] Determining a key attribute set in combination with the key feature data;

[0019] Determining the key attribute set and the nursing items of the target catalog in combination with the data management instruction to be processed;

[0020] A plurality of the undetermined attributes corresponding to the target category are determined through the care items.

[0021] It is understandable that when extracting from the clinical nursing data to be processed in combination with the management instruction for the data to be processed, the problem of inaccurate key feature data is improved, so that several pending attributes of the target category can be accurately obtained.

[0022] In an independent embodiment, the selecting of the undetermined attributes in combination with the undetermined feature values to obtain a plurality of the related attributes includes:

[0023] Selecting the attributes to be determined in combination with the feature values to be determined to obtain a feature attribute set;

[0024] Determining a set of characteristic attributes corresponding to the target type;

[0025] An attribute set matching the characteristic attribute set is called from the characteristic attribute set for detection to obtain a plurality of the related attributes.

[0026] It can be understood that when the undetermined attributes are selected in combination with the undetermined feature values, the problem of inaccurate feature attribute sets is improved, so that several related attributes can be accurately obtained.

[0027] In an independent implementation embodiment, the method further includes:

[0028] Determining a set of verification attributes in combination with the target type;

[0029] Sending the verification attribute set to a server so that the server builds a target attribute queue;

[0030] The target attribute queue is received, and several of the related attributes are debugged in conjunction with the target attribute queue.

[0031] It can be understood that by accurately obtaining the verification attribute set, the accuracy of attribute debugging is guaranteed.

[0032] In an independent implementation embodiment, the method further includes:

[0033] Obtaining important indicator information in response to determining the attributes that are associated;

[0034] The mining items corresponding to the mining operations in the configuration instructions are set in combination with the important indicator information to increase the mining weights of the plurality of related attributes.

[0035] It is understandable that accurate acquisition of important indicator information ensures the reliability of mining weights.

[0036] In an independently implemented embodiment, optimizing the first data management thread in combination with the clinical nursing data to be processed to obtain a second data management thread includes:

[0037] determining a target data management scenario in response to the generation of the first data management thread;

[0038] Invoking a feature configuration set in conjunction with the target data management scenario;

[0039] Determine a target configuration set by using the feature configuration set and the clinical care data to be processed;

[0040] The first data management thread is optimized in combination with the target configuration set to obtain the second data management thread.

[0041] It is understandable that when the first data management thread is optimized in combination with the clinical nursing data to be processed, the problem of inaccurate target configuration set is improved, so that the second data management thread can be accurately obtained.

[0042] In an independently implemented embodiment, obtaining the nursing data to be managed and inputting the nursing data to be managed into the second data management thread to obtain a data management result corresponding to the target category of clinical nursing event data in the nursing data to be managed includes:

[0043] obtaining the nursing data to be managed;

[0044] Determining the type of information corresponding to the nursing data to be managed;

[0045] Extracting elements from characteristic points in the nursing data to be managed by using the information types to obtain clinical nursing event data in the nursing data to be managed;

[0046] The clinical care event data is input into the second data management thread to obtain a data management result of the clinical care event data corresponding to the target category.

[0047] It can be understood that when obtaining the nursing data to be managed, the problem of inaccurate information type is improved, and the nursing data to be managed is input into the second data management thread, so that the clinical nursing event data in the nursing data to be managed can be accurately obtained corresponding to the data management results of the target type.

[0048] In an independently implemented embodiment, extracting elements from characteristic points in the nursing data to be managed by using the information type to obtain clinical nursing event data in the nursing data to be managed includes:

[0049] Extracting elements from characteristic points in the nursing data to be managed according to the information type to obtain extracted elements;

[0050] Determining description content corresponding to the information type;

[0051] The extracted elements are processed in combination with the description content to obtain clinical nursing event data in the nursing data to be managed.

[0052] It can be understood that when extracting elements from the characteristic points in the nursing data to be managed by using the information types, the problem of inaccurate extraction is improved, so that clinical nursing event data in the nursing data to be managed can be accurately obtained.

[0053] In an independent implementation embodiment, the method further includes:

[0054] Determining that the clinical nursing event data corresponds to a data management result of the target category;

[0055] Determining classification labels based on the data management results;

[0056] The nursing data to be managed is managed for target items through the classification tags.

[0057] It can be understood that by accurately determining the data management results, the accuracy of data management can be guaranteed.

[0058] In an independently implemented embodiment, the calling of the clinical nursing data to be processed and the plurality of related attributes to configure a preconfigured thread to obtain a first data management thread includes:

[0059] Calling the clinical nursing data to be processed and the plurality of related attributes to determine configuration data;

[0060] Randomly mining the configuration data in combination with the configuration instructions in the pre-configured thread to obtain a mining result;

[0061] Determining a distribution corresponding to the mining results to construct configuration example tuples;

[0062] The pre-configured thread is configured in combination with the configuration example tuple to obtain the first data management thread.

[0063] It is understandable that when the to-be-processed clinical nursing data and the plurality of related attributes are called to configure the pre-configured thread, the problem of inaccurate mining results is improved, so that the first data management thread can be accurately obtained.

[0064] In a second aspect, a clinical nursing data management system based on big data is provided, comprising a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute it to implement the above method.

[0065] The clinical nursing data management method and system based on big data provided by the embodiment of the present application obtains clinical nursing data to be processed, matches the clinical nursing data to be processed with clinical nursing event data of a target category; then extracts several related attributes corresponding to the target category from the clinical nursing data to be processed based on the management instruction of the data to be processed, the management instruction of the data to be processed is pre-set based on the attribute set in the clinical nursing data to be processed and the nursing items of the target directory, and the target directory corresponds to the target category. Further, the pre-configured thread is configured based on the clinical nursing data to be processed and the several related attributes to obtain a first data management thread, the configuration process of the pre-configured thread is performed based on the configuration instruction, and the mining operation indicated by the configuration instruction matches the several related attributes; and the first data management thread is optimized based on the clinical nursing data to be processed to obtain a second data management thread; then the nursing data to be managed is obtained, and the nursing data to be managed is input into the second data management thread to obtain a data management result that the clinical nursing event data in the nursing data to be managed corresponds to the target category. This enables an efficient and accurate factor data management process. Since the attributes that are related to the target category are selected, and the mining operation is performed simultaneously with the pending clinical nursing data that matches the target category and the supplementary related attributes, the mining weight of the target category matching attribute set is improved, and the data management capability of the data management thread for the target category matching attribute set is guaranteed, thereby improving the accuracy of clinical nursing event data management. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0067] Figure 1 A flowchart of a clinical nursing data management method based on big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0068] In order to better understand the above technical solution, the technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0069] See also Figure 1, shows a clinical nursing data management method based on big data, which may include the technical solutions described in the following steps 301-305.

[0070] 301. Obtain clinical nursing data to be processed.

[0071] In this embodiment, the clinical nursing data to be processed is matched with clinical nursing event data of the target category.

[0072] 302. Extract several related attributes corresponding to the target category from the clinical nursing data to be processed based on the data management instruction to be processed.

[0073] In this embodiment, the data management instruction to be processed is pre-set based on the attribute set in the clinical nursing data to be processed and the nursing items of the target directory, and the target directory corresponds to the target category.

[0074] Optionally, the process of determining the related attributes may also include a further detection process, namely, first selecting the attributes to be determined based on the feature values to be determined to obtain a feature attribute set; then determining the feature attribute set corresponding to the target category, such as a representative vulgar attribute set; further calling an attribute set that matches the feature attribute set from the feature attribute set for detection to obtain several related attributes, thereby improving the accuracy of the related attributes.

[0075] Optionally, you can also call an external attribute set to debug the related attributes, that is, first determine the verification attribute set based on the target type; then send the verification attribute set to the server so that the server builds a target attribute queue; then receive the target attribute queue, and debug several related attributes based on the target attribute queue, thereby ensuring the comprehensiveness of the related attributes.

[0076] 303. Call the clinical nursing data to be processed and several related attributes to configure the pre-configured thread to obtain a first data management thread.

[0077] In this embodiment, the configuration process of the pre-configured thread is performed based on the configuration instruction, and the mining operation indicated by the configuration instruction is matched with several related attributes; wherein, the target category is vulgar category information, the pre-configured thread is a compression and derivation unit, the configuration instruction is a self-supervision instruction, and the self-supervision instruction is performed based on the mining language thread.

[0078] Specifically, for the execution process of the mining instruction, first call the clinical nursing data to be processed and several related attributes to determine the configuration data; then randomly mine the configuration data based on the configuration instructions in the pre-configured thread to obtain the mining results; then determine the distribution corresponding to the mining results to build a configuration example tuple; and thus configure the pre-configured thread based on the configuration example tuple to obtain the first data management thread.

[0079] Optionally, the mining process can be biased towards related attributes, that is, important indicator information is obtained in response to the determination of related attributes; then the mining items corresponding to the mining operation in the configuration instruction are set based on the important indicator information to increase the mining weights of several related attributes.

[0080] 304. Optimize the first data management thread based on the clinical nursing data to be processed to obtain a second data management thread.

[0081] 305. Obtain the nursing data to be managed, and input the nursing data to be managed into the second data management thread to obtain a data management result corresponding to the target category of clinical nursing event data in the nursing data to be managed.

[0082] Specifically, for the process of information data management, different clinical nursing event data can be extracted based on different information types, that is, first obtain the nursing data to be managed; then determine the information type corresponding to the nursing data to be managed; and then extract elements from the characteristic points in the nursing data to be managed according to the information type to obtain the clinical nursing event data in the nursing data to be managed; then input the clinical nursing event data into the second data management thread to obtain the data management results of the clinical nursing event data corresponding to the target type, so as to extract different types of elements in a targeted manner.

[0083] It should be understood that after obtaining the data management results, classification labels can be determined based on the data management results; then data management can be performed on the nursing data to be managed for the target items according to the classification labels, thereby realizing the process of classified data management.

[0084] In combination with the above embodiments, it can be seen that by obtaining clinical nursing data to be processed, the clinical nursing data to be processed is matched with clinical nursing event data of the target category; then, based on the management instruction of the data to be processed, several related attributes corresponding to the target category are extracted from the clinical nursing data to be processed, and the management instruction of the data to be processed is pre-set based on the attribute set in the clinical nursing data to be processed and the nursing items of the target directory, and the target directory corresponds to the target category. Further, the pre-configured thread is configured based on the clinical nursing data to be processed and several related attributes to obtain a first data management thread. The configuration process of the pre-configured thread is based on the configuration instruction, and the mining operation indicated by the configuration instruction is matched with several related attributes; and the first data management thread is optimized based on the clinical nursing data to be processed to obtain a second data management thread; then, the nursing data to be managed is obtained, and the nursing data to be managed is input into the second data management thread to obtain a data management result that the clinical nursing event data in the nursing data to be managed corresponds to the target category. This enables an efficient and accurate factor data management process. Since the attributes that are related to the target category are selected, and the mining operation is performed simultaneously with the pending clinical nursing data that matches the target category and the supplementary related attributes, the mining weight of the target category matching attribute set is improved, and the data management capability of the data management thread for the target category matching attribute set is guaranteed, thereby improving the accuracy of clinical nursing event data management.

[0085] In a possible implementation embodiment, the specific description content of extracting several related attributes corresponding to the target category from the clinical care data to be processed based on the data management instruction to be processed includes the content described in the following steps a1-a3.

[0086] a1, extracting a plurality of pending attributes corresponding to the target category from the clinical nursing data to be processed in combination with the pending data management instruction;

[0087] For example, attributes can be understood as descriptive factors in the target category. For example, the attributes for post-operative care include wound cleaning, wound dressing change, and wound recovery.

[0088] a2, inputting several of the pending attributes into the pending data management thread for scoring to obtain pending feature values;

[0089] Exemplarily, in this application, scoring is performed on pending attributes, so that the weight of each attribute can be determined.

[0090] A3, selecting the attributes to be determined in combination with the feature values to be determined, to obtain a plurality of related attributes.

[0091] It can be understood that when extracting from the clinical nursing data to be processed based on the management instruction of the data to be processed, the problem of inaccurate extraction is improved, so that several related attributes of the target category can be accurately obtained.

[0092] In a possible implementation embodiment, the step of extracting specific descriptions of several pending attributes corresponding to the target category from the pending clinical care data in combination with the pending data management instruction includes the contents described in the following steps b1-b4.

[0093] b1, counting key feature data of each attribute set in the clinical nursing data to be processed;

[0094] For example, key feature data in this application can be understood as the key care points corresponding to each type of care. For example, in a hospital, a doctor will explain the condition of each patient and the matters that the patient needs to pay attention to in order to avoid worsening of the condition.

[0095] b2, determining a key attribute set based on the key feature data;

[0096] b3, determining the key attribute set and the nursing items of the target catalog in combination with the data management instruction to be processed;

[0097] b4. Determine a number of pending attributes corresponding to the target category through the nursing items.

[0098] It is understandable that when extracting from the clinical nursing data to be processed in combination with the management instruction for the data to be processed, the problem of inaccurate key feature data is improved, so that several pending attributes of the target category can be accurately obtained.

[0099] In a possible implementation example, the selecting of the to-be-determined attributes in combination with the to-be-determined feature values to obtain specific descriptions of several of the related attributes includes the contents described in the following steps c1 to c3.

[0100] c1, selecting the attributes to be determined in combination with the feature values to be determined to obtain a feature attribute set;

[0101] c2, determining a set of characteristic attributes corresponding to the target type;

[0102] c3. Calling an attribute set that matches the characteristic attribute set from the characteristic attribute set for detection to obtain a plurality of the related attributes.

[0103] It can be understood that when the undetermined attributes are selected in combination with the undetermined feature values, the problem of inaccurate feature attribute sets is improved, so that several related attributes can be accurately obtained.

[0104] In one possible implementation, the method further includes:

[0105] Determining a set of verification attributes in combination with the target type;

[0106] Sending the verification attribute set to a server so that the server builds a target attribute queue;

[0107] The target attribute queue is received, and several of the related attributes are debugged in conjunction with the target attribute queue.

[0108] It can be understood that by accurately obtaining the verification attribute set, the accuracy of attribute debugging is guaranteed.

[0109] In one possible implementation, the method further includes:

[0110] Obtaining important indicator information in response to determining the attributes that are associated;

[0111] The mining items corresponding to the mining operations in the configuration instructions are set in combination with the important indicator information to increase the mining weights of the plurality of related attributes.

[0112] It is understandable that accurate acquisition of important indicator information ensures the reliability of mining weights.

[0113] In a possible implementation embodiment, the first data management thread is optimized in combination with the clinical nursing data to be processed to obtain a specific description of the second data management thread, including the contents described in the following steps d1-d4.

[0114] d1, determining a target data management scenario in response to generation of the first data management thread;

[0115] For example, the target data management scenario can be understood as an application scenario for different nursing data, such as: xxx patient, requiring a sterile scenario, etc.

[0116] d2, calling the feature configuration set in combination with the target data management scenario;

[0117] d3, determining a target configuration set by using the feature configuration set and the clinical nursing data to be processed;

[0118] d4. Optimize the first data management thread in combination with the target configuration set to obtain the second data management thread.

[0119] It is understandable that when the first data management thread is optimized in combination with the clinical nursing data to be processed, the problem of inaccurate target configuration set is improved, so that the second data management thread can be accurately obtained.

[0120] In one possible implementation embodiment, the process of obtaining the nursing data to be managed and inputting the nursing data to be managed into the second data management thread to obtain the clinical nursing event data in the nursing data to be managed corresponding to the data management results of the target category includes the contents described in the following steps e1-e4.

[0121] e1, obtaining the nursing data to be managed;

[0122] e2, determining the information type corresponding to the nursing data to be managed;

[0123] e3, extracting elements from characteristic points in the nursing data to be managed according to the information type to obtain clinical nursing event data in the nursing data to be managed;

[0124] e4. Input the clinical nursing event data into the second data management thread to obtain a data management result of the clinical nursing event data corresponding to the target category.

[0125] It can be understood that when obtaining the nursing data to be managed, the problem of inaccurate information type is improved, and the nursing data to be managed is input into the second data management thread, so that the clinical nursing event data in the nursing data to be managed can be accurately obtained corresponding to the data management results of the target type.

[0126] In a possible implementation embodiment, the feature points in the nursing data to be managed are extracted using the information types to obtain specific descriptions of clinical nursing event data in the nursing data to be managed, including the contents described in the following steps f1-f3.

[0127] f1, extracting elements from characteristic points in the nursing data to be managed according to the information type to obtain extracted elements;

[0128] f2, determining the description content corresponding to the information type;

[0129] f3. Process the extracted elements in combination with the description content to obtain clinical nursing event data in the nursing data to be managed.

[0130] It can be understood that when extracting elements from the characteristic points in the nursing data to be managed by using the information types, the problem of inaccurate extraction is improved, so that clinical nursing event data in the nursing data to be managed can be accurately obtained.

[0131] In one possible implementation, the method further includes:

[0132] Determining that the clinical nursing event data corresponds to a data management result of the target category;

[0133] Determining classification labels based on the data management results;

[0134] The nursing data to be managed is managed for target items through the classification tags.

[0135] It can be understood that by accurately determining the data management results, the accuracy of data management can be guaranteed.

[0136] In a possible implementation, the calling of the clinical care data to be processed and the plurality of related attributes to configure the preconfigured thread to obtain a specific description of the first data management thread includes the contents described in the following steps g1-g4.

[0137] g1, calling the clinical nursing data to be processed and the plurality of related attributes to determine configuration data;

[0138] g2, randomly mining the configuration data in combination with the configuration instructions in the pre-configured thread to obtain a mining result;

[0139] g3, determining the distribution of the mining results to construct configuration example tuples;

[0140] g4. Configure the pre-configured thread in combination with the configuration example tuple to obtain the first data management thread.

[0141] It is understandable that when the to-be-processed clinical nursing data and the plurality of related attributes are called to configure the pre-configured thread, the problem of inaccurate mining results is improved, so that the first data management thread can be accurately obtained.

[0142] Based on the above, a clinical nursing data management device based on big data is provided, which includes:

[0143] A data acquisition module, configured to acquire clinical nursing data to be processed, wherein the clinical nursing data to be processed matches clinical nursing event data of a target type;

[0144] an attribute extraction module for extracting a plurality of related attributes corresponding to the target category from the pending clinical nursing data based on a pending data management instruction, wherein the pending data management instruction is pre-set based on a set of attributes in the pending clinical nursing data and nursing items in a target directory, wherein the target directory corresponds to the target category;

[0145] a thread configuration module, configured to call the clinical nursing data to be processed and the plurality of related attributes to configure a preconfigured thread to obtain a first data management thread, wherein the configuration process of the preconfigured thread is performed based on a configuration instruction, and the mining operation indicated by the configuration instruction matches the plurality of related attributes;

[0146] a thread optimization module, configured to optimize the first data management thread in combination with the clinical nursing data to be processed to obtain a second data management thread;

[0147] The result management module is used to obtain the nursing data to be managed and input the nursing data to be managed into the second data management thread to obtain the data management result corresponding to the target category of clinical nursing event data in the nursing data to be managed.

[0148] Based on the above, a clinical nursing data management system based on big data is shown, which includes a processor and a memory that communicate with each other, and the processor is used to read a computer program from the memory and execute it to implement the above method.

[0149] Based on the above, a computer-readable storage medium is also provided, on which a computer program stored implements the above method when running.

[0150] In summary, based on the above scheme, by obtaining the clinical nursing data to be processed, the clinical nursing data to be processed is matched with the clinical nursing event data of the target category; then, based on the management instruction of the data to be processed, several related attributes corresponding to the target category are extracted from the clinical nursing data to be processed, and the management instruction of the data to be processed is pre-set based on the attribute set in the clinical nursing data to be processed and the nursing items of the target directory, and the target directory corresponds to the target category. Further, the pre-configured thread is configured based on the clinical nursing data to be processed and several related attributes to obtain a first data management thread, and the configuration process of the pre-configured thread is performed based on the configuration instruction, and the mining operation indicated by the configuration instruction is matched with several related attributes; and the first data management thread is optimized based on the clinical nursing data to be processed to obtain a second data management thread; then, the nursing data to be managed is obtained, and the nursing data to be managed is input into the second data management thread to obtain a data management result that the clinical nursing event data in the nursing data to be managed corresponds to the target category. This enables an efficient and accurate factor data management process. Since the attributes that are related to the target category are selected, and the mining operation is performed simultaneously with the pending clinical nursing data that matches the target category and the supplementary related attributes, the mining weight of the target category matching attribute set is improved, and the data management capability of the data management thread for the target category matching attribute set is guaranteed, thereby improving the accuracy of clinical nursing event data management.

[0151] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated hardware. Those skilled in the art will understand that the above-mentioned methods and systems can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the system and its modules of the present application. Not only can hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc. be implemented, they can also be implemented using software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software (for example, firmware).

[0152] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other possible beneficial effects.

Claims

1. A clinical nursing data management method based on big data, characterized in that: The method comprises: Obtaining clinical care data to be processed, wherein the clinical care data to be processed matches clinical care event data of a target category; Extracting a plurality of related attributes corresponding to the target category from the clinical nursing data to be processed based on the pending data management instruction; wherein the pending data management instruction is pre-set based on the attribute set in the clinical nursing data to be processed and the nursing items in the target directory, and the target directory corresponds to the target category; Calling the clinical nursing data to be processed and the plurality of related attributes to configure a preconfigured thread to obtain a first data management thread, wherein the configuration process of the preconfigured thread is performed based on a configuration instruction, and the mining operation indicated by the configuration instruction matches the plurality of related attributes; Optimizing the first data management thread in combination with the clinical nursing data to be processed to obtain a second data management thread; The nursing data to be managed is obtained, and the nursing data to be managed is input into the second data management thread to obtain a data management result corresponding to the target category of clinical nursing event data in the nursing data to be managed.

2. The method according to claim 1, characterized in that The extracting, based on the pending data management instruction, a plurality of related attributes corresponding to the target category from the pending clinical nursing data includes: extracting a plurality of pending attributes corresponding to the target category from the pending clinical nursing data in combination with the pending data management instruction; Inputting a plurality of the pending attributes into the pending data management thread for scoring to obtain pending feature values; The undetermined attributes are selected in combination with the undetermined feature values to obtain a plurality of the related attributes.

3. The method according to claim 2, characterized in that The extracting of a plurality of pending attributes corresponding to the target category from the clinical nursing data to be processed in combination with the pending data management instruction includes: Collecting statistics on key feature data of each attribute set in the clinical nursing data to be processed; Determining a key attribute set in combination with the key feature data; Determining the key attribute set and the nursing items of the target catalog in combination with the data management instruction to be processed; A plurality of the undetermined attributes corresponding to the target category are determined through the care items.

4. The method according to claim 2, characterized in that The method further comprises: Determining a set of verification attributes in combination with the target type; Sending the verification attribute set to a server so that the server builds a target attribute queue; The target attribute queue is received, and several of the related attributes are debugged in conjunction with the target attribute queue.

5. The method according to claim 1, wherein The method further comprises: Obtaining important indicator information in response to determining the attributes that are associated; The mining items corresponding to the mining operations in the configuration instructions are set in combination with the important indicator information to increase the mining weights of the plurality of related attributes.

6. The method according to claim 1, characterized in that The optimizing the first data management thread in combination with the clinical nursing data to be processed to obtain a second data management thread includes: determining a target data management scenario in response to the generation of the first data management thread; Invoking a feature configuration set in conjunction with the target data management scenario; Determine a target configuration set by using the feature configuration set and the clinical care data to be processed; The first data management thread is optimized in combination with the target configuration set to obtain the second data management thread.

7. The method according to claim 1, characterized in that The obtaining of the nursing data to be managed and inputting the nursing data to be managed into the second data management thread to obtain a data management result corresponding to the target category of clinical nursing event data in the nursing data to be managed includes: obtaining the nursing data to be managed; Determining the type of information corresponding to the nursing data to be managed; Extracting elements from characteristic points in the nursing data to be managed by using the information types to obtain clinical nursing event data in the nursing data to be managed; Inputting the clinical nursing event data into the second data management thread to obtain a data management result of the clinical nursing event data corresponding to the target category; The extracting of features from the nursing data to be managed by using the information type to obtain clinical nursing event data from the nursing data to be managed includes: Extracting elements from characteristic points in the nursing data to be managed according to the information type to obtain extracted elements; Determining description content corresponding to the information type; Processing the extracted elements in combination with the description content to obtain clinical nursing event data in the nursing data to be managed; The method further comprises: Determining that the clinical care event data corresponds to a data management result of the target category; Determining classification labels based on the data management results; The nursing data to be managed is managed for target items through the classification tags.

8. The method according to claim 1, characterized in that The calling of the clinical nursing data to be processed and the plurality of related attributes to configure a preconfigured thread to obtain a first data management thread includes: Calling the clinical nursing data to be processed and the plurality of related attributes to determine configuration data; Randomly mining the configuration data in combination with the configuration instructions in the pre-configured thread to obtain a mining result; Determining a distribution corresponding to the mining results to construct configuration example tuples; The pre-configured thread is configured in combination with the configuration example tuple to obtain the first data management thread.

9. A clinical nursing data management system based on big data, characterized in that: The method comprises a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the method according to any one of claims 1 to 8.

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