A method and system for medical and nursing information processing based on big data.

Abstract

This invention discloses a method and system for medical and nursing information processing based on big data, belonging to the field of information processing technology. It retrieves patient nursing operation types, medication dosages, and patient physiological indicator information from the medical and nursing system; constructs a medical and nursing set; builds a three-dimensional patient nursing risk matrix; calculates nursing risk probabilities; calculates patient nursing risk weights; constructs a two-dimensional patient nursing risk image matrix; presets a nursing risk weight threshold; obtains nursing operation types and medication dosages with nursing risk weights greater than or equal to the threshold, and constructs high-risk combinations; constructs a high-risk combination set based on patients; calculates the risk similarity between different patients; presets a risk similarity threshold; and analyzes and performs comprehensive management. This invention can not only accurately quantify the nursing risk of a single patient but also further identify the risk similarity between multiple patients, forming a high-risk combination set and providing comprehensive management decision support.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, specifically to a method and system for processing medical and nursing information based on big data. Background Technology

[0002] With the rapid development of global healthcare informatization, big data technology has gradually penetrated the field of medical care, making the provision of more precise and efficient nursing services to patients an important research direction. Traditional medical care information processing methods largely rely on the experience and judgment of medical staff and manual recording. This approach is not only susceptible to human error, leading to incomplete and inaccurate data records, but also makes it difficult to quickly analyze and deeply mine massive amounts of patient data. In recent years, data acquisition technologies based on electronic medical records, monitoring equipment, and intelligent sensors have become increasingly widespread, resulting in an exponential increase in the amount of medical care information. This has made big data analysis and mining an important means to improve the quality of medical care. Researchers have explored various data analysis models, such as rule-matching-based risk assessment models and machine learning prediction models. These methods have improved the ability to identify nursing risks to some extent. However, because existing methods often focus only on changes in a single indicator and lack the ability to comprehensively analyze multi-dimensional and multi-level nursing data, they are unable to accurately characterize the patient risk features in complex nursing scenarios.

[0003] The shortcomings of existing technologies are mainly reflected in the following aspects: First, traditional methods often treat abnormalities in patients' physiological indicators as independent events, ignoring the interaction between nursing operation type, medication dosage, and physiological indicators, resulting in inaccurate risk assessment results; second, most existing models lack dynamic risk management mechanisms for individual patients, making it impossible to adjust nursing strategies in real time to cope with sudden abnormalities; third, existing technologies have limitations in multi-patient risk correlation analysis, failing to effectively uncover potential common risk characteristics among patients, thus limiting the optimal allocation of nursing resources. Summary of the Invention

[0004] The purpose of this invention is to provide a medical care information processing method and system based on big data, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A big data-based medical and nursing information processing method includes the following steps: Step S1: After patient authorization, retrieve the patient's nursing operation type, medication dosage, and physiological indicator information from the medical and nursing system; sort the medical and nursing information according to the nursing operation type to construct a medical and nursing set; Step S2: Based on the patient's medical and nursing set, construct a three-dimensional patient nursing risk matrix, and calculate the nursing risk probability that a single nursing operation type of a patient causes abnormal physiological indicator data due to a single medication dosage; Step S3: Based on the nursing risk probability, calculate the nursing risk weight that a single nursing operation type of a patient causes abnormal physiological indicator data of all patients due to a single medication dosage; construct a two-dimensional nursing risk image matrix of the patient based on the nursing risk weight; Step S4: Based on the two-dimensional nursing risk image matrix, preset a nursing risk weight threshold, obtain nursing operation types and medication dosages with nursing risk weights greater than or equal to the nursing risk weight threshold, and construct high-risk combinations; construct a high-risk combination set based on the patient, calculate the risk similarity between different patients, preset a risk similarity threshold, analyze and perform comprehensive management.

[0007] As a preferred embodiment of the big data-based medical and nursing information processing method described in this invention, after authorization by the patient, the patient's medical and nursing information is retrieved from the medical and nursing system. The medical and nursing information refers to the medical and nursing operation information, medication dosage, and patient physiological indicator information when the patient receives medical and nursing care; the medical and nursing operation information includes the type of nursing operation.

[0008] The medical and nursing information is sorted according to the nursing operation type to construct a medical and nursing set, denoted as . ,in, This represents the t-th type of nursing procedure. Indicates the type of nursing procedure The corresponding i-th dosage, Indicates dosage The corresponding physiological indicator data for the a-th patient, where T represents the total number of nursing operation types, I represents the total medication dosage, and A represents the total number of patient physiological indicator data. Let h represent the medical care set for the h-th patient.

[0009] As a preferred embodiment of the big data-based medical and nursing information processing method described in this invention, based on the medical and nursing data of the h-th patient... A three-dimensional patient care risk matrix is ​​constructed, wherein the row dimension, column dimension, and layer dimension of the three-dimensional patient care risk matrix are nursing operation type, medication dosage, and patient physiological index data, respectively. In the three-dimensional patient care risk matrix, each cell represents a nursing risk probability. The nursing risk probability that the t-th nursing operation type of the h-th patient causes abnormal physiological index data of the a-th patient due to the i-th medication dosage is denoted as . and the probability of nursing risks The calculation formula is as follows:

[0010] ;

[0011] in, This represents the pre-defined standard value of the physiological indicator data for the a-th patient.

[0012] As a preferred embodiment of the big data-based medical and nursing information processing method described in this invention, based on the t-th nursing operation type and the i-th medication dosage of the h-th patient, the corresponding row and column dimensions are matched in the three-dimensional patient nursing risk matrix. Based on the matched row and column dimensions, the nursing risk probability of abnormal physiological indicator data of all patients in the corresponding layer dimension is extracted, and the nursing risk weight of the t-th nursing operation type of the h-th patient due to the i-th medication dosage is calculated. The calculation formula is as follows:

[0013] ;

[0014] in, Let A represent the nursing risk weight of the t-th nursing operation type for the h-th patient due to the i-th medication dosage, and let A represent all patient physiological index data in the corresponding layer dimension.

[0015] Based on the nursing risk weight of the t-th nursing operation type for the h-th patient and the i-th medication dosage, Construct a two-dimensional nursing risk image matrix for the h-th patient, as follows:

[0016] ;

[0017] in, This represents the nursing risk weight of the T-type nursing procedure for the h-th patient due to the I-th dosage of medication.

[0018] As a preferred embodiment of the big data-based medical and nursing information processing method described in this invention, a nursing risk weight threshold is preset based on the two-dimensional nursing risk image matrix of the h-th patient. If the t-th nursing procedure type for the h-th patient is affected by the nursing risk weight of the i-th medication dosage... Greater than or equal to the nursing risk weight threshold Then, the t-th nursing operation type and the i-th medication dosage are recorded as a high-risk combination, denoted as . ;

[0019] Based on high-risk portfolio Construct a high-risk combination set based on the h-th patient, denoted as . ;

[0020] Based on the high-risk combination set of the h-th patient, the risk similarity between the h-th patient and the (h+1)-th patient is calculated using the following formula:

[0021] ;

[0022] in, This indicates the risk similarity between the h-th patient and the (h+1)-th patient. This represents the set of high-risk combinations for the (h+1)th patient;

[0023] A preset risk similarity threshold is set; if the risk similarity between patient h and patient h+1 is... If the risk similarity threshold is greater than or equal to the threshold, then an association is determined between the h-th patient and the (h+1)-th patient.

[0024] Comprehensive management should be implemented for different patients with related conditions, and relevant staff should be reminded of high-risk combinations. The types of nursing procedures and medication dosages were adjusted accordingly.

[0025] A big data-based medical and nursing information processing system, comprising: a data acquisition and set construction module, a three-dimensional matrix construction module, a weight calculation and matrix construction module, and a combination construction and analysis module;

[0026] The data acquisition and collection construction module: after patient authorization, retrieves the patient's nursing operation type, medication dosage, and patient physiological indicator information from the medical care system; sorts the medical care information according to the nursing operation type to construct a medical care set;

[0027] The three-dimensional matrix construction module: Based on the patient's medical care set, it constructs a three-dimensional patient care risk matrix and calculates the nursing risk probability that a single nursing operation type will cause abnormal physiological indicator data of a single patient due to a single medication dosage.

[0028] The weight calculation and matrix construction module calculates the nursing risk weight of a single nursing operation type that causes abnormalities in all patients' physiological indicators due to a single medication dosage, based on the nursing risk probability; and constructs a two-dimensional nursing risk image matrix of the patient based on the nursing risk weight.

[0029] The combination construction and analysis module: based on a two-dimensional nursing risk image matrix, preset nursing risk weight thresholds, obtain nursing operation types and medication dosages with nursing risk weights greater than or equal to the nursing risk weight thresholds, and construct high-risk combinations; construct a set of high-risk combinations based on patients, calculate the risk similarity between different patients, preset risk similarity thresholds, analyze and perform comprehensive management.

[0030] Furthermore, the data acquisition and collection construction module includes a data acquisition unit and a collection construction unit;

[0031] The data acquisition unit, after authorization by the patient, retrieves the patient's medical and nursing information from the medical and nursing system. The medical and nursing information refers to the patient's medical and nursing operation information, medication dosage, and patient physiological indicator information when receiving medical and nursing care. The medical and nursing operation information includes the type of nursing operation.

[0032] The set construction unit sorts the medical and nursing information according to the nursing operation type to construct a medical and nursing set.

[0033] Furthermore, the three-dimensional matrix construction module includes a three-dimensional matrix construction unit;

[0034] The three-dimensional matrix construction unit: Based on the patient's medical care set, a three-dimensional patient care risk matrix is ​​constructed. The row dimension, column dimension, and layer dimension of the three-dimensional patient care risk matrix are the nursing operation type, medication dosage, and patient physiological index data, respectively. In the three-dimensional patient care risk matrix, each cell represents a nursing risk probability.

[0035] Furthermore, the weight calculation and matrix construction module includes a weight calculation unit and a matrix construction unit;

[0036] The weight calculation unit: Based on the individual nursing operation type and individual medication dosage of a single patient, matches the corresponding row and column dimensions in the three-dimensional patient nursing risk matrix; based on the matched row and column dimensions, extracts the nursing risk probability of abnormal physiological indicator data of all patients in the corresponding layer dimension; and calculates the nursing risk weight of a single nursing operation type of a single patient due to a single medication dosage.

[0037] The matrix construction unit constructs a two-dimensional nursing risk image matrix for a single patient based on the nursing risk weight of a single nursing operation type and a single medication dosage.

[0038] Furthermore, the combined construction and analysis module includes a combined construction unit and an analysis unit;

[0039] The combined construction unit is based on a two-dimensional nursing risk image matrix of a single patient and a preset nursing risk weight threshold. If the nursing risk weight of a single nursing operation type of a single patient due to a single medication dosage is greater than or equal to the nursing risk weight threshold, then the single nursing operation type and the single medication dosage are recorded as a high-risk combination.

[0040] The analysis unit: constructs a set of high-risk combinations based on individual patients; calculates the risk similarity between different patients based on the set of high-risk combinations for individual patients; presets a risk similarity threshold, and if the risk similarity between different patients is greater than or equal to the risk similarity threshold, it determines that there is a correlation between the different patients; performs comprehensive management on different patients with correlation, and reminds relevant staff to adjust the nursing operation type and medication dosage in the high-risk combinations.

[0041] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention provides a big data-based medical and nursing information processing method and system. By constructing a three-dimensional patient nursing risk matrix and a two-dimensional nursing risk image matrix, it achieves multi-dimensional correlation analysis of nursing operation types, medication dosages, and patient physiological indicator data. This not only accurately quantifies the nursing risk of individual patients but also further identifies the risk similarity among multiple patients, forming a high-risk combination set and providing comprehensive management decision support. This invention solves the problem of how to achieve multi-dimensional nursing risk quantification and comprehensive management of multi-patient correlations based on big data. Attached Figure Description

[0042] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0043] Figure 1 This is a schematic diagram illustrating the steps of a big data-based medical and nursing information processing method according to the present invention.

[0044] Figure 2 This is a schematic diagram of the structure of a medical care information processing system based on big data according to the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Please see Figure 1In this first embodiment: a method for processing medical and nursing information based on big data is provided, which includes the following steps:

[0047] Step S1: After obtaining authorization from the patient, retrieve the patient's nursing operation type, medication dosage, and patient physiological indicator information from the medical care system; sort the medical care information according to the nursing operation type to construct a medical care set.

[0048] Specifically, with the patient's authorization, the patient's medical and nursing information is retrieved from the medical and nursing system. This medical and nursing information refers to the patient's medical and nursing operation information, medication dosage, and physiological indicator information when receiving medical and nursing care. The medical and nursing operation information includes the type of nursing operation.

[0049] Furthermore, the medical and nursing information is sorted according to the nursing operation type to construct a medical and nursing set, denoted as... ,in, This represents the t-th type of nursing procedure. Indicates the type of nursing procedure The corresponding i-th dosage, Indicates dosage The corresponding physiological indicator data for the a-th patient, where T represents the total number of nursing operation types, I represents the total medication dosage, and A represents the total number of patient physiological indicator data. Let h represent the medical care set for the h-th patient.

[0050] It's important to note that this step creates a structured nursing dataset, providing a unified data foundation for subsequent analysis. Specifically, by authorizing patient access to medical and nursing information, the legality and completeness of the data are ensured, laying the groundwork for precision nursing analysis. Sorting by nursing procedure type categorizes and organizes the previously disorganized nursing data, improving its orderliness and facilitating subsequent extraction and analysis. Constructing a medical and nursing dataset links nursing procedure type, medication dosage, and patient physiological indicators, establishing personalized nursing records for each patient. This achieves data structuring and provides multi-dimensional data support for further risk assessment. This step also enables standardized modeling of patient nursing data, laying the data foundation for the subsequent construction of a risk matrix and avoiding misjudgments caused by incomplete or non-standardized data in traditional analysis, thereby improving the accuracy of nursing decisions.

[0051] Step S2: Based on the patient's medical care set, construct a three-dimensional patient care risk matrix and calculate the nursing risk probability that a single nursing operation type will cause abnormal physiological indicator data of a single patient due to a single medication dosage.

[0052] Specifically, based on the medical care set of the h-th patient A three-dimensional patient care risk matrix is ​​constructed, wherein the row dimension, column dimension, and layer dimension of the three-dimensional patient care risk matrix are nursing operation type, medication dosage, and patient physiological index data, respectively. In the three-dimensional patient care risk matrix, each cell represents a nursing risk probability. The nursing risk probability that the t-th nursing operation type of the h-th patient causes abnormal physiological index data of the a-th patient due to the i-th medication dosage is denoted as . and the probability of nursing risks The calculation formula is as follows:

[0053] ;

[0054] in, This represents the pre-defined standard value of the physiological indicator data for the a-th patient.

[0055] It's important to note that this step transforms complex nursing relationships into a three-dimensional matrix, establishing a specific risk probability mapping for each procedure, medication, and physiological indicator. This quantifies nursing risk. Specifically, a three-dimensional nursing risk matrix is ​​constructed, binding nursing procedure types, medication dosages, and patient physiological indicators together to form a data cube. This facilitates layer-by-layer analysis of risk changes in each dimension. Each cell corresponds to a specific risk probability, precisely quantifying the risk level of each nursing procedure and medication on a specific patient's physiological indicator. This achieves a refined expression of risk, avoiding errors caused by experience-based judgment. The introduction of standard value comparison compares the patient's real-time physiological indicators with preset standard values, dynamically assessing nursing risk and enhancing the real-time nature and scientific rigor of risk assessment. This step achieves a refined quantitative expression of nursing risk, enabling the risk of each nursing procedure and medication to be captured at a fine-grained level. This provides strong data support for subsequent comprehensive analysis of risk weights and improves the sensitivity of risk identification.

[0056] Step S3: Based on the nursing risk probability, calculate the nursing risk weight of a single nursing operation type causing abnormalities in all patients' physiological indicators due to a single medication dosage; based on the nursing risk weight, construct a two-dimensional nursing risk image matrix for the patient.

[0057] Specifically, based on the t-th nursing operation type and the i-th medication dosage for the h-th patient, the corresponding row and column dimensions are matched in the three-dimensional patient nursing risk matrix. Based on the matched row and column dimensions, the nursing risk probability of abnormal physiological indicator data for all patients in the corresponding layer dimension is extracted, and the nursing risk weight of the t-th nursing operation type for the h-th patient due to the i-th medication dosage is calculated. The calculation formula is as follows:

[0058] ;

[0059] in, Let A represent the nursing risk weight of the t-th nursing operation type for the h-th patient due to the i-th medication dosage, and let A represent all patient physiological index data in the corresponding layer dimension.

[0060] Furthermore, the nursing risk weight based on the t-th nursing operation type for the h-th patient and the i-th medication dosage... Construct a two-dimensional nursing risk image matrix for the h-th patient, as follows:

[0061] ;

[0062] in, This represents the nursing risk weight of the T-type nursing procedure for the h-th patient due to the I-th dosage of medication.

[0063] It should be noted that this step expands the single-point risk probability to a comprehensive risk weight, overcoming the limitations of single indicators and achieving a comprehensive risk assessment from one-dimensional to multi-dimensional anomalies. Specifically, it extracts the risk of abnormalities from all physiological indicator data, no longer limited to a single indicator, thus expanding the coverage of risk assessment and improving the comprehensiveness of risk identification. The comprehensive risk weight of nursing operation type and medication is calculated, integrating multiple indicator anomalies into a single weight value, facilitating subsequent screening and management of high-risk combinations and improving the intuitiveness and efficiency of risk assessment. A two-dimensional nursing risk image matrix is ​​constructed, visually representing nursing operation type and medication in matrix form, facilitating rapid identification of risk clusters and helping medical personnel more intuitively determine high-risk points. This step achieves a weighted comprehensive assessment of multi-indicator risks, forming an intuitive risk image matrix, breaking through the reliance of traditional nursing protocols on single-point indicators, improving the overall comprehensiveness and ease of use of risk assessment, and providing a more accurate basis for subsequent screening of high-risk combinations.

[0064] Step S4: Based on the two-dimensional nursing risk image matrix, preset the nursing risk weight threshold, obtain the nursing operation type and medication dosage with nursing risk weight greater than or equal to the nursing risk weight threshold, and construct high-risk combinations; construct a high-risk combination set based on patients, calculate the risk similarity between different patients, preset the risk similarity threshold, analyze and perform comprehensive management.

[0065] Specifically, based on the two-dimensional nursing risk image matrix of the h-th patient, a preset nursing risk weight threshold is established. If the t-th nursing procedure type for the h-th patient is affected by the nursing risk weight of the i-th medication dosage... Greater than or equal to the nursing risk weight threshold Then, the t-th nursing operation type and the i-th medication dosage are recorded as a high-risk combination, denoted as . ;

[0066] Furthermore, based on high-risk portfolios Construct a high-risk combination set based on the h-th patient, denoted as . ;

[0067] Based on the high-risk combination set of the h-th patient, the risk similarity between the h-th patient and the (h+1)-th patient is calculated using the following formula:

[0068] ;

[0069] in, This indicates the risk similarity between the h-th patient and the (h+1)-th patient. This represents the set of high-risk combinations for the (h+1)th patient;

[0070] Furthermore, a preset risk similarity threshold is set; if the risk similarity between the h-th patient and the (h+1)-th patient is... If the risk similarity threshold is greater than or equal to the threshold, then an association is determined between the h-th patient and the (h+1)-th patient.

[0071] Comprehensive management should be implemented for different patients with related conditions, and relevant staff should be reminded of high-risk combinations. The types of nursing procedures and medication dosages were adjusted accordingly.

[0072] It's important to note that this step introduces multi-patient risk similarity assessment, forming a cross-patient risk association network to achieve collaborative risk management for patient groups. Specifically, it screens high-risk combinations, quickly identifies high-risk procedures and medications, proactively identifies potential nursing risks, and effectively prevents risk spread. A high-risk combination set is constructed, packaging and managing high-risk nursing procedures and medication combinations to facilitate rapid identification and response by the medical team in actual operations, improving emergency response speed. Risk similarity calculation is introduced to compare high-risk combinations from different patients, quantifying the degree of risk association between patients and helping the medical team identify patient groups with similar risk patterns, enabling early detection and intervention. Comprehensive management and adjustment are implemented, reminding medical staff to adjust nursing plans promptly for patient groups with high-risk associations to prevent risk spread and achieve proactive risk prevention management. This step breaks through the traditional single-patient management model, realizing group-based management of cross-patient risks, further enhancing the early warning capability of medical and nursing risks, and providing a more systematic risk control solution for large-scale medical and nursing scenarios.

[0073] Please see Figure 2 In this second embodiment: a medical care information processing system based on big data is provided. The system includes: a data acquisition and set construction module, a three-dimensional matrix construction module, a weight calculation and matrix construction module, and a combination construction and analysis module.

[0074] The data acquisition and collection construction module: after patient authorization, retrieves the patient's nursing operation type, medication dosage, and patient physiological indicator information from the medical care system; sorts the medical care information according to the nursing operation type to construct a medical care set;

[0075] The three-dimensional matrix construction module: Based on the patient's medical care set, it constructs a three-dimensional patient care risk matrix and calculates the nursing risk probability that a single nursing operation type will cause abnormal physiological indicator data of a single patient due to a single medication dosage.

[0076] The weight calculation and matrix construction module calculates the nursing risk weight of a single nursing operation type that causes abnormalities in all patients' physiological indicators due to a single medication dosage, based on the nursing risk probability; and constructs a two-dimensional nursing risk image matrix of the patient based on the nursing risk weight.

[0077] The combination construction and analysis module: based on a two-dimensional nursing risk image matrix, preset nursing risk weight thresholds, obtain nursing operation types and medication dosages with nursing risk weights greater than or equal to the nursing risk weight thresholds, and construct high-risk combinations; construct a set of high-risk combinations based on patients, calculate the risk similarity between different patients, preset risk similarity thresholds, analyze and perform comprehensive management.

[0078] Furthermore, the data acquisition and collection construction module includes a data acquisition unit and a collection construction unit;

[0079] The data acquisition unit, after authorization by the patient, retrieves the patient's medical and nursing information from the medical and nursing system. The medical and nursing information refers to the patient's medical and nursing operation information, medication dosage, and patient physiological indicator information when receiving medical and nursing care. The medical and nursing operation information includes the type of nursing operation.

[0080] The set construction unit sorts the medical and nursing information according to the nursing operation type to construct a medical and nursing set.

[0081] Furthermore, the three-dimensional matrix construction module includes a three-dimensional matrix construction unit;

[0082] The three-dimensional matrix construction unit: Based on the patient's medical care set, a three-dimensional patient care risk matrix is ​​constructed. The row dimension, column dimension, and layer dimension of the three-dimensional patient care risk matrix are the nursing operation type, medication dosage, and patient physiological index data, respectively. In the three-dimensional patient care risk matrix, each cell represents a nursing risk probability.

[0083] Furthermore, the weight calculation and matrix construction module includes a weight calculation unit and a matrix construction unit;

[0084] The weight calculation unit: Based on the individual nursing operation type and individual medication dosage of a single patient, matches the corresponding row and column dimensions in the three-dimensional patient nursing risk matrix; based on the matched row and column dimensions, extracts the nursing risk probability of abnormal physiological indicator data of all patients in the corresponding layer dimension; and calculates the nursing risk weight of a single nursing operation type of a single patient due to a single medication dosage.

[0085] The matrix construction unit constructs a two-dimensional nursing risk image matrix for a single patient based on the nursing risk weight of a single nursing operation type and a single medication dosage.

[0086] Furthermore, the combined construction and analysis module includes a combined construction unit and an analysis unit;

[0087] The combined construction unit is based on a two-dimensional nursing risk image matrix of a single patient and a preset nursing risk weight threshold. If the nursing risk weight of a single nursing operation type of a single patient due to a single medication dosage is greater than or equal to the nursing risk weight threshold, then the single nursing operation type and the single medication dosage are recorded as a high-risk combination.

[0088] The analysis unit: constructs a set of high-risk combinations based on individual patients; calculates the risk similarity between different patients based on the set of high-risk combinations for individual patients; presets a risk similarity threshold, and if the risk similarity between different patients is greater than or equal to the risk similarity threshold, it determines that there is a correlation between the different patients; performs comprehensive management on different patients with correlation, and reminds relevant staff to adjust the nursing operation type and medication dosage in the high-risk combinations.

[0089] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0090] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for processing medical and nursing information based on big data, characterized in that, The method includes the following steps: Step S1: After obtaining authorization from the patient, retrieve the patient's nursing operation type, medication dosage, and patient physiological indicator information from the medical care system; sort the medical care information according to the nursing operation type to construct a medical care set; Step S2: Based on the patient's medical care set, construct a three-dimensional patient care risk matrix and calculate the nursing risk probability that a single nursing operation type will cause abnormal physiological indicator data of a single patient due to a single medication dosage. Step S3: Based on the nursing risk probability, calculate the nursing risk weight of a single nursing operation type causing abnormalities in all patients' physiological indicators due to a single medication dosage; based on the nursing risk weight, construct a two-dimensional nursing risk image matrix for the patient; Step S4: Based on the two-dimensional nursing risk image matrix, preset the nursing risk weight threshold, obtain the nursing operation type and medication dosage with nursing risk weight greater than or equal to the nursing risk weight threshold, and construct high-risk combinations; construct a high-risk combination set based on patients, calculate the risk similarity between different patients, preset the risk similarity threshold, analyze and perform comprehensive management; The specific implementation process of step S1 includes: With the patient's authorization, the patient's medical and nursing information is retrieved from the medical and nursing system. This medical and nursing information refers to the medical and nursing operation information, medication dosage, and patient physiological index information when the patient receives medical and nursing care. The medical and nursing operation information includes the type of nursing operation. The medical and nursing information is sorted according to the nursing operation type to construct a medical and nursing set, denoted as MTC. h ={TNO t ,DI i (TNO t ),PI a [DI i (TNO t )]|t∈[1,T],i∈[1,I],a∈[1,A]}, where TNO t DI represents the t-th type of nursing procedure. i (TNO t ) indicates the nursing procedure type TNO t The corresponding i-th dosage, PI a [DI i (TNO t )] indicates the dosage DI i (TNO t The corresponding physiological indicator data for patient a, where T represents the total number of nursing operation types, I represents the total medication dosage, A represents the total number of patient physiological indicator data, and MTC h This represents the medical care set for the h-th patient; The specific implementation process of step S2 includes: Based on the medical care set MTC of the h-th patient h A three-dimensional patient care risk matrix is ​​constructed, wherein the row dimension, column dimension, and layer dimension of the three-dimensional patient care risk matrix are nursing operation type, medication dosage, and patient physiological index data, respectively. In the three-dimensional patient care risk matrix, each cell represents a nursing risk probability. The nursing risk probability that the t-th nursing operation type of the h-th patient causes abnormal physiological index data of the a-th patient due to the i-th medication dosage is denoted as R. t,i,a (h), and the probability of nursing risk R t,i,a The formula for calculating (h) is as follows: Among them, PI a_sta This represents the pre-defined standard value of the physiological indicator data for the a-th patient; The specific implementation process of step S3 includes: Based on the t-th nursing operation type and i-th medication dosage for the h-th patient, the corresponding row and column dimensions are matched in the three-dimensional patient nursing risk matrix. Based on the matched row and column dimensions, the nursing risk probability of abnormal physiological indicator data for all patients in the corresponding layer dimension is extracted, and the nursing risk weight of the t-th nursing operation type for the h-th patient due to the i-th medication dosage is calculated. The calculation formula is as follows: Among them, WNR t,i (h) represents the nursing risk weight of the t-th nursing operation type for the h-th patient due to the i-th medication dosage, and A represents all patient physiological index data in the corresponding layer dimension; Based on the nursing procedure type t for the h-th patient and the nursing risk weight WNR due to the i-th medication dosage, t,i (h), construct the two-dimensional nursing risk image matrix for the h-th patient, as follows: Among them, WNR T,I (h) represents the nursing risk weight of the T-type nursing operation for the h-th patient due to the I-th dosage of medication; The specific implementation process of step S4 includes: Based on the two-dimensional nursing risk image matrix of the h-th patient, a preset nursing risk weight threshold γ is used. If the t-th nursing operation type of the h-th patient is affected by the nursing risk weight WNR of the i-th medication dosage... t,i (h) If the nursing risk weight threshold γ is greater than or equal to the t-th nursing operation type and the i-th medication dosage, then the t-th nursing operation type and the i-th medication dosage are recorded as a high-risk combination, denoted as [TNO]. t ,DI i (TNO t )]; Based on high-risk portfolio [TNO] t ,DI i (TNO t Construct a high-risk combination set based on the h-th patient, denoted as HF. h ={[TNO t ,DI i (TNO t )]|WNR t,i (h)≥γ}; Based on the high-risk combination set of the h-th patient, the risk similarity between the h-th patient and the (h+1)-th patient is calculated using the following formula: Where Sim(h,h+1) represents the risk similarity between the h-th patient and the h+1-th patient, HF h+1 This represents the set of high-risk combinations for the (h+1)th patient; A risk similarity threshold is preset. If the risk similarity Sim(h,h+1) between the h-th patient and the h+1-th patient is greater than or equal to the risk similarity threshold, then it is determined that there is an association between the h-th patient and the h+1-th patient. Comprehensive management of different patients with related conditions, and reminders to relevant staff regarding high-risk combinations [TNO] t ,DI i (TNO t Adjust the nursing procedure type and medication dosage in the [ )] ] .

2. A big data-based medical and nursing information processing system, executing the big data-based medical and nursing information processing method as described in claim 1, characterized in that, The system includes: a data acquisition and set construction module, a three-dimensional matrix construction module, a weight calculation and matrix construction module, and a combination construction and analysis module; The data acquisition and collection construction module: after patient authorization, retrieves the patient's nursing operation type, medication dosage, and patient physiological indicator information from the medical care system; sorts the medical care information according to the nursing operation type to construct a medical care set; The three-dimensional matrix construction module: Based on the patient's medical care set, it constructs a three-dimensional patient care risk matrix and calculates the nursing risk probability that a single nursing operation type will cause abnormal physiological indicator data of a single patient due to a single medication dosage. The weight calculation and matrix construction module calculates the nursing risk weight of a single nursing operation type that causes abnormalities in all patients' physiological indicators due to a single medication dosage, based on the nursing risk probability; and constructs a two-dimensional nursing risk image matrix of the patient based on the nursing risk weight. The combination construction and analysis module: based on a two-dimensional nursing risk image matrix, preset nursing risk weight thresholds, obtain nursing operation types and medication dosages with nursing risk weights greater than or equal to the nursing risk weight thresholds, and construct high-risk combinations; construct a set of high-risk combinations based on patients, calculate the risk similarity between different patients, preset risk similarity thresholds, analyze and perform comprehensive management.

3. The medical and nursing information processing system based on big data according to claim 2, characterized in that: The data acquisition and collection construction module includes a data acquisition unit and a collection construction unit; The data acquisition unit, after authorization by the patient, retrieves the patient's medical and nursing information from the medical and nursing system. The medical and nursing information refers to the patient's medical and nursing operation information, medication dosage, and patient physiological indicator information when receiving medical and nursing care. The medical and nursing operation information includes the type of nursing operation. The set construction unit: sorts the medical and nursing information according to the nursing operation type to construct a medical and nursing set.

4. The medical and nursing information processing system based on big data according to claim 3, characterized in that: The three-dimensional matrix construction module includes a three-dimensional matrix construction unit; The three-dimensional matrix construction unit: Based on the patient's medical care set, a three-dimensional patient care risk matrix is ​​constructed. The row dimension, column dimension, and layer dimension of the three-dimensional patient care risk matrix are the nursing operation type, medication dosage, and patient physiological index data, respectively. In the three-dimensional patient care risk matrix, each cell represents a nursing risk probability.

5. A medical care information processing system based on big data according to claim 4, characterized in that: The weight calculation and matrix construction module includes a weight calculation unit and a matrix construction unit; The weight calculation unit: Based on the individual nursing operation type and individual medication dosage of a single patient, matches the corresponding row and column dimensions in the three-dimensional patient nursing risk matrix; based on the matched row and column dimensions, extracts the nursing risk probability of abnormal physiological indicator data of all patients in the corresponding layer dimension; and calculates the nursing risk weight of a single nursing operation type of a single patient due to a single medication dosage. The matrix construction unit constructs a two-dimensional nursing risk image matrix for a single patient based on the nursing risk weight of a single nursing operation type and a single medication dosage.

6. A medical care information processing system based on big data according to claim 5, characterized in that: The combined construction and analysis module includes a combined construction unit and an analysis unit; The combined construction unit is based on a two-dimensional nursing risk image matrix of a single patient and a preset nursing risk weight threshold. If the nursing risk weight of a single nursing operation type of a single patient due to a single medication dosage is greater than or equal to the nursing risk weight threshold, then the single nursing operation type and the single medication dosage are recorded as a high-risk combination. The analysis unit: Based on high-risk combinations, constructs a set of high-risk combinations for a single patient; based on the set of high-risk combinations for a single patient, calculates the risk similarity between different patients; A risk similarity threshold is preset. If the risk similarity between different patients is greater than or equal to the risk similarity threshold, it is determined that there is a correlation between the different patients. Different patients with correlation are managed in a comprehensive manner, and relevant staff are reminded to adjust the nursing operation type and medication dosage in high-risk combinations.

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