Brain critical care analysis system and method based on multi-data fusion

Abstract

This invention discloses a multi-data fusion-based critical care nursing analysis system and method for brain diseases, belonging to the field of medical and nursing data analysis. The invention constructs a patient status assessment model, importing physiological monitoring parameters into the model for patient status assessment; a drug care effect assessment model, importing drug compatibility and cerebral hemodynamics into the model for drug care effect assessment; a complication care effect assessment model, importing the severity of complications and the correlation between complication care actions into the model for complication care effect assessment; and a comprehensive care effect assessment model, evaluating the overall nursing effect on the patient. The comprehensive assessment of patient status, drug care effect, and complication care effect yields the overall critical care nursing effect for brain diseases.

Description

Technical Field

[0001] This invention belongs to the field of medical and nursing data processing, specifically a brain critical care nursing analysis system and method based on multi-data fusion. Background Technology

[0002] With the continuous development of medical technology, the quality of nursing care and treatment outcomes for critically ill patients are crucial to their recovery. The nursing process for critically ill brain patients faces complex challenges, especially given the rapid changes in their condition, numerous and interconnected complications. Adjustments to nursing plans often rely on experience and manual judgment, lacking real-time and precise assessment tools. Existing nursing plans lack personalization, making it difficult to optimize nursing actions and treatment plans in real time based on the patient's condition. Feedback on nursing effectiveness lacks intelligent analysis and standardized judgment. Current systems typically overlook the synergistic effect between drug therapy and nursing interventions, failing to analyze the combined impact of drug therapy and nursing plans on the patient's physiological state in real time, and neglecting the relationship between the patient's complication risk and nursing interventions.

[0003] This invention proposes a multi-data fusion-based critical care nursing analysis system and method for brain diseases. The system assesses the nursing effect of critical care for brain diseases by evaluating patient status, drug nursing effect, and complication nursing effect. It integrates multi-dimensional factors in the dynamic monitoring of the nursing process to evaluate the nursing effect and proposes nursing improvement suggestions. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes a multi-data fusion-based critical care nursing analysis system and method for brain diseases. This invention constructs a patient status assessment model, importing physiological monitoring parameters into the model for patient status evaluation; a drug care effect assessment model, importing drug compatibility and cerebral hemodynamics into the model for drug care effect evaluation; a complication care effect assessment model, importing complication severity and the correlation between complication care actions into the model for complication care effect evaluation; and a comprehensive care effect assessment model, evaluating the overall nursing effect on the patient. The overall critical care nursing effect for brain diseases is assessed through a comprehensive evaluation of patient status, drug care effect, and complication care effect.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The method for analyzing critical care nursing care for brain patients based on multi-data fusion includes the following specific steps:

[0007] Obtain patient condition data and nursing data;

[0008] A patient status assessment model was constructed, and physiological monitoring parameters were imported into the patient status assessment model for patient status assessment.

[0009] A drug care efficacy evaluation model was constructed, and drug compatibility and cerebral hemodynamics were incorporated into the drug care efficacy evaluation model.

[0010] A complication care effectiveness assessment model was constructed, and the correlation between complication severity and complication care actions was incorporated into the complication care effectiveness assessment model for evaluating complication care effectiveness.

[0011] A comprehensive nursing effectiveness evaluation model was constructed to assess the overall nursing effectiveness for patients.

[0012] Preferably, the acquisition of patient condition data and nursing data includes the following specific steps:

[0013] S11. Obtain basic patient information and type of severe brain disease, and acquire real-time physiological monitoring parameters of the patient through monitoring instruments;

[0014] S12. Obtain information on the type and dosage of medications used by the patient, and information on the occurrence of complications.

[0015] S13. Obtain patient critical care scenarios.

[0016] Preferably, the construction of the patient status assessment model and the importation of physiological monitoring parameters into the patient status assessment model for patient status assessment includes the following specific steps:

[0017] S21. Substitute the patient's physiological monitoring parameters into the parameter standardization calculation formula to calculate the standardized parameters, where the calculation formula for the i-th standardized parameter is: Among them, A i H represents the real-time monitoring value of the i-th physiological parameter. i The standard value of the i-th physiological parameter;

[0018] S22. Substitute the standardized parameters into the patient monitoring status calculation formula to calculate the patient monitoring status, whereby the patient monitoring status calculation formula is: Among them, C i Let ω be the standardized parameter for the i-th term. i is the weight of the i-th standardized parameter, and n is the total number of physiological monitoring parameters.

[0019] Preferably, the construction of the drug care effect evaluation model, which incorporates drug compatibility and cerebral hemodynamics into the drug care effect evaluation model, includes the following specific steps:

[0020] S31. Substitute the patient's medication dosage into the drug suitability assessment formula to calculate the drug-patient suitability. The formula for assessing the suitability of the patient's medication with the i-th physiological parameter is: Among them, F j, where B is the amount of drug j added. j λ is the standard addition amount of the j-th drug. j Let A be the weight of the effect of drug j on physiological parameter i. i Let A be the real-time monitoring value of the i-th physiological parameter. i ′ represents the monitored value of the first physiological parameter when no medication was used, H i Let be the standard value of the i-th physiological parameter, and o be a number that infinitely approaches 0 but is not zero;

[0021] S32. Substitute the patient's intracranial pressure into the formula for calculating the degree of change in intracranial pressure to calculate the degree of change in intracranial pressure. The formula for calculating the degree of change in intracranial pressure is as follows: Among them, ICP k This refers to intracranial pressure after fluid resuscitation, ICP z The normal value for intracranial pressure is denoted as , and the patient's cerebral perfusion pressure is substituted into the formula for calculating the degree of change in cerebral perfusion pressure. The formula for calculating the degree of change in cerebral perfusion pressure is as follows: Among them, CPP k CPP is the cerebral perfusion pressure after fluid resuscitation. z The normal value for cerebral perfusion pressure is used. The changes in intracranial pressure and cerebral perfusion pressure are substituted into the cerebral hemodynamic assessment formula to evaluate cerebral hemodynamics after fluid resuscitation. The cerebral hemodynamic assessment formula is: S = S ICP +S CPP ;

[0022] S33. Substitute drug compatibility and cerebral hemodynamics into the patient drug care effect assessment formula to evaluate the patient drug care effect. The patient drug care effect assessment formula is as follows: Among them, R i , represents the fit between the drug and the i-th physiological parameter, S represents cerebral hemodynamics, Y represents the probability of developing severe brain disease in the patient's age group, and GCS represents the Glasgow Coma Scale.

[0023] Preferably, the construction of the complication nursing effect assessment model, which incorporates the correlation between complication severity and complication nursing actions into the complication nursing effect assessment model for complication nursing effect assessment, includes the following specific steps:

[0024] S41. Substitute the severity level of the patient's complication as defined by the doctor into the complication severity calculation formula to calculate the severity of the complication. The complication severity calculation formula is as follows: Among them, G α α represents the severity level of the patient's α-th complication, β represents the number of complications the patient experienced, and μ represents the number of complications. α Let G be the probability of the occurrence of the αth complication. maxThis represents the highest severity level of the complication.

[0025] S42. Substitute the nursing actions that affect complications into the nursing action correlation calculation formula to calculate the nursing action correlation degree. The formula for calculating the nursing action correlation degree for the α-th complication is as follows: Among them, G α θ represents the severity level of the patient's αth complication. σ,α Let σ represent the degree of influence of the σth nursing action on the αth complication. , where is the frequency of the execution of the first nursing action;

[0026] S43. Substitute the severity of the complication and the correlation between complication nursing actions into the formula for calculating the complication nursing effect. The formula for calculating the complication nursing effect is as follows: Where G′ represents the severity of the complication, and Q... α , where α is the correlation between nursing actions for the αth complication and β is the number of complications occurring in the patient.

[0027] Preferably, the construction of a comprehensive nursing effect evaluation model to evaluate the overall nursing effect on patients includes the following specific steps:

[0028] S51. Substitute the patient's monitoring status, medication effect, and complication care effect into the comprehensive nursing effect assessment formula to evaluate the comprehensive nursing effect. The comprehensive nursing effect assessment formula is: Z = D × (L + P) × ζ, where ζ is the patient's critical care scenario level.

[0029] S52. Import the comprehensive nursing effect evaluation value into the pre-trained deep learning model to obtain the threshold. Based on the nursing effect threshold output by the model, compare it with the current comprehensive nursing effect evaluation value, and classify the comprehensive nursing effect evaluation value. The higher the comprehensive nursing effect evaluation value, the better the nursing effect.

[0030] The brain intensive care nursing analysis system based on multi-data fusion is implemented based on the aforementioned brain intensive care nursing analysis method based on multi-data fusion, and specifically includes:

[0031] The data acquisition module is used to acquire patient condition data and nursing data;

[0032] The patient status assessment module is used to assess the patient's status through physiological monitoring parameters;

[0033] The drug care efficacy assessment module is used to evaluate the drug care efficacy through drug compatibility and cerebral hemodynamics.

[0034] The complication care effectiveness assessment module is used to evaluate the effectiveness of complication care by assessing the correlation between the severity of the complication and the complication care actions.

[0035] The comprehensive nursing effectiveness assessment module is used to evaluate the overall nursing effectiveness of patients.

[0036] An electronic device includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0037] The processor executes the aforementioned method for analyzing critical brain care based on multi-data fusion by calling the computer program stored in the memory.

[0038] A computer-readable storage medium is characterized by storing instructions that, when executed on a computer, cause the computer to perform the aforementioned method for analyzing critical brain care based on multi-data fusion.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] This invention constructs a patient status assessment model, importing physiological monitoring parameters into the model for patient status assessment; it also constructs a drug care effect assessment model, importing drug compatibility and cerebral hemodynamics into the model for drug care effect assessment; it further constructs a complication care effect assessment model, importing the severity of complications and the correlation between complication care actions into the model for complication care effect assessment; and finally, it constructs a comprehensive nursing effect assessment model to evaluate the overall nursing effect on patients. The comprehensive assessment of patient status, drug care effect, and complication care effect results in the overall nursing effect for critically ill patients with brain conditions. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the overall process of the brain critical care nursing analysis method based on multi-data fusion of the present invention;

[0042] Figure 2 A flowchart for comprehensive nursing effectiveness evaluation;

[0043] Figure 3 This is a schematic diagram of the overall framework of the brain critical care nursing analysis system based on multi-data fusion of the present invention. Detailed Implementation

[0044] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0045] Example 1

[0046] Please see Figures 1-2 The present invention provides an embodiment of a brain critical care nursing analysis method based on multi-data fusion, which includes the following specific steps:

[0047] Obtain patient condition data and nursing data;

[0048] A patient status assessment model was constructed, and physiological monitoring parameters were imported into the patient status assessment model for patient status assessment.

[0049] A drug care efficacy evaluation model was constructed, and drug compatibility and cerebral hemodynamics were incorporated into the drug care efficacy evaluation model.

[0050] A complication care effectiveness assessment model was constructed, and the correlation between complication severity and complication care actions was incorporated into the complication care effectiveness assessment model for evaluating complication care effectiveness.

[0051] A comprehensive nursing effectiveness evaluation model was constructed to assess the overall nursing effectiveness for patients.

[0052] In this embodiment, it should be specifically explained that obtaining patient condition data and nursing data includes the following specific steps:

[0053] S11. Obtain basic patient information and type of severe brain disease, and acquire real-time physiological monitoring parameters of the patient through monitoring instruments;

[0054] S12. Obtain information on the type and dosage of medications used by the patient, and information on the occurrence of complications.

[0055] S13. Obtain patient critical care scenarios.

[0056] In this embodiment, it should be specifically explained that constructing a patient status assessment model and importing physiological monitoring parameters into the patient status assessment model includes the following specific steps:

[0057] S21. Substitute the patient's physiological monitoring parameters into the parameter standardization calculation formula to calculate the standardized parameters, where the calculation formula for the i-th standardized parameter is: Among them, A i H represents the real-time monitoring value of the i-th physiological parameter. i Let i be the standard value of the i-th physiological parameter. Standardized parameter calculation ensures that physiological monitoring parameters are in the same dimension.

[0058] S22. Substitute the standardized parameters into the patient monitoring status calculation formula to calculate the patient monitoring status, whereby the patient monitoring status calculation formula is: Among them, C i Let ω be the standardized parameter for the i-th term. i Let be the weight of the i-th standardized parameter, representing the importance of the i-th physiological monitoring parameter in the physiological process, obtained from experts and papers, and n be the total number of physiological monitoring parameters.

[0059] In this embodiment, it should be specifically explained that constructing a drug care effect evaluation model and incorporating drug compatibility and cerebral hemodynamics into the model for drug care effect evaluation includes the following specific steps:

[0060] S31. Substitute the patient's medication dosage into the drug suitability assessment formula to calculate the drug-patient suitability. The formula for assessing the suitability of the patient's medication with the i-th physiological parameter is: Among them, F j , where is the dosage of the j-th drug. The dosage of the same drug varies depending on individual patient differences. B j λ is the standard addition amount of the j-th drug. j The weight of the effect of drug j on physiological parameter i is obtained from clinical trial data. i Let A be the real-time monitoring value of the i-th physiological parameter. i ′, where H is the monitored value of the first physiological parameter without the use of medication. i Let be the standard value of the i-th physiological parameter, and o be a number that infinitely approaches 0 but is not zero, where The weights of the actual and predicted changes in the patient's physiological parameters are used to measure the effectiveness of drug use.

[0061] S32. Substitute the patient's intracranial pressure into the formula for calculating the degree of change in intracranial pressure to calculate the degree of change in intracranial pressure. The formula for calculating the degree of change in intracranial pressure is as follows: Among them, ICP k This refers to intracranial pressure after fluid resuscitation, ICP z The normal value for intracranial pressure is denoted as , and the patient's cerebral perfusion pressure is substituted into the formula for calculating the degree of change in cerebral perfusion pressure. The formula for calculating the degree of change in cerebral perfusion pressure is as follows: Among them, CPP k CPP is the cerebral perfusion pressure after fluid resuscitation. z The normal value for cerebral perfusion pressure is used. The changes in intracranial pressure and cerebral perfusion pressure are substituted into the cerebral hemodynamic assessment formula to evaluate cerebral hemodynamics after fluid resuscitation. The cerebral hemodynamic assessment formula is: S = S ICP +S CPP ;

[0062] S33. Substitute drug compatibility and cerebral hemodynamics into the patient drug care effect assessment formula to evaluate the patient drug care effect. The patient drug care effect assessment formula is as follows: Among them, R i, where represents the fit between the medication and the i-th physiological parameter; S represents cerebral hemodynamics; Y represents the probability of developing severe brain disease within the patient's age group; and GCS represents the Glasgow Coma Scale, obtained through the GCS coma scale. A higher GCS level indicates a better patient condition. The overall fit between the drug and all physiological parameters.

[0063] In this embodiment, it is necessary to specifically explain that constructing a complication nursing effectiveness assessment model and importing the correlation between complication severity and complication nursing actions into the complication nursing effectiveness assessment model includes the following specific steps:

[0064] S41. Substitute the severity level of the patient's complication as defined by the doctor into the complication severity calculation formula to calculate the severity of the complication. The complication severity calculation formula is as follows: Among them, G α α represents the severity grade of the patient's complication α, obtained using the Clarke-Dindo complication grading system. A lower complication grade indicates a better outcome. β represents the number of complications experienced by the patient. α Let G be the probability of the occurrence of the αth complication. max The highest severity level of the complication;

[0065] S42. Substitute the nursing actions that affect complications into the nursing action correlation calculation formula to calculate the nursing action correlation degree. The formula for calculating the nursing action correlation degree for the α-th complication is as follows: Among them, G α θ represents the severity level of the patient's αth complication. σ,α Let σ represent the degree of influence of the σth nursing action on the αth complication. , where is the frequency of the execution of the first nursing action;

[0066] S43. Substitute the severity of the complication and the correlation between complication nursing actions into the formula for calculating the complication nursing effect. The formula for calculating the complication nursing effect is as follows: Where G′ represents the severity of the complication, and Q... α , where α is the correlation between nursing actions for the αth complication and β is the number of complications occurring in the patient.

[0067] In this embodiment, it should be specifically explained that constructing a comprehensive nursing effect evaluation model to evaluate the overall nursing effect on patients includes the following specific steps:

[0068] S51. Substitute the patient's monitoring status, medication effect, and complication care effect into the comprehensive nursing effect assessment formula to evaluate the comprehensive nursing effect. The comprehensive nursing effect assessment formula is: Z = D × (L + P) × ζ, where ζ is the patient's critical care scenario level, which is the level of monitoring determined by medical staff based on the patient's location.

[0069] S52. Import the comprehensive nursing effect evaluation value into the pre-trained deep learning model to obtain the threshold. Based on the nursing effect threshold output by the model, compare it with the current comprehensive nursing effect evaluation value, and classify the comprehensive nursing effect evaluation value into excellent nursing effect, good nursing effect, moderate nursing effect and low nursing effect.

[0070] S521. Excellent nursing outcomes indicate ideal nursing outcomes, with good patient condition, medication management, and complication control. The current nursing strategy should be maintained, and the patient's condition should be monitored regularly.

[0071] S522. Good nursing effect means that the nursing effect is relatively good, but there is room for improvement. Analyze the shortcomings in the monitoring status, drug nursing effect and complication nursing effect and improve them.

[0072] S523, Moderate nursing effect indicates that the nursing effect is moderate and there are many aspects that need improvement. Detailed analysis of patient status, medication care and complication sub-scores is required to carry out nursing adjustment.

[0073] S524. Low nursing effectiveness indicates poor nursing outcomes and serious problems. Immediately notify the healthcare team and recommend a reassessment of the nursing plan, including patient monitoring, medication use, and complication control.

[0074] Example 2

[0075] like Figure 3 As shown, the brain critical care nursing analysis system based on multi-data fusion is implemented based on the aforementioned multi-data fusion-based brain critical care nursing analysis method. Specifically, it includes a data acquisition module, a patient status assessment module, a medication and nursing effect assessment module, a complication nursing effect assessment module, and a comprehensive nursing effect assessment module. The data acquisition module is used to acquire patient condition data and nursing data; the patient status assessment module is used to assess patient status through physiological monitoring parameters; the medication and nursing effect assessment module is used to assess the medication and nursing effect through drug compatibility and cerebral hemodynamics; the complication nursing effect assessment module is used to assess the complication nursing effect through the severity of complications and the correlation between complication nursing actions; and the comprehensive nursing effect assessment module is used to assess the overall nursing effect on the patient.

[0076] Example 3

[0077] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0078] The processor executes the aforementioned brain intensive care analysis method based on multi-data fusion by calling computer programs stored in memory.

[0079] The electronic device can vary considerably depending on its configuration or performance. It may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the brain intensive care analysis method based on multi-data fusion provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Details will not be elaborated upon in this embodiment.

[0080] Example 4

[0081] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored.

[0082] When the computer program runs on a computer device, it causes the device to execute the aforementioned multi-data fusion-based brain intensive care analysis method.

[0083] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact optical disc (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0084] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0085] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0086] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0087] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0088] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0089] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one possible method, and in actual implementation, other division methods may exist. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0090] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0091] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0092] In the description of this specification, the references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0093] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementations. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for analyzing critical care nursing care in patients with brain disorders based on multi-data fusion, characterized in that: It includes the following specific steps: Obtain patient condition data and nursing data; A patient status assessment model was constructed, and physiological monitoring parameters were imported into the patient status assessment model for patient status assessment. The specific steps include the following: S21. Substitute the patient's physiological monitoring parameters into the parameter standardization calculation formula to calculate the standardized parameters, where the calculation formula for the i-th standardized parameter is: ,in, Let be the real-time monitoring value of the i-th physiological parameter. The standard value of the i-th physiological parameter; S22. Substitute the standardized parameters into the patient monitoring status calculation formula to calculate the patient monitoring status, whereby the patient monitoring status calculation formula is: ,in, For the i-th standardized parameter, is the weight of the i-th standardized parameter, and n is the total number of physiological monitoring parameters; A drug care efficacy evaluation model was constructed, incorporating drug compatibility and cerebral hemodynamics into the model for evaluating drug care efficacy. This included the following specific steps: S31. Substitute the patient's medication dosage into the drug suitability assessment formula to calculate the drug-patient suitability. The formula for assessing the suitability of the patient's medication with the i-th physiological parameter is: ,in, Let j be the amount of drug added. The standard dosage of drug j is... Let j be the weight of the effect of drug j on physiological parameter i. Let be the real-time monitoring value of the i-th physiological parameter. The monitored value for the first physiological parameter is the value when no medication is used. Let be the standard value of the i-th physiological parameter, and o be a number that infinitely approaches 0 but is not zero; S32. Substitute the patient's intracranial pressure into the formula for calculating the degree of change in intracranial pressure to calculate the degree of change in intracranial pressure. The formula for calculating the degree of change in intracranial pressure is as follows: ,in, This refers to intracranial pressure after fluid resuscitation. Assuming normal intracranial pressure, the patient's cerebral perfusion pressure is substituted into the formula for calculating the degree of change in cerebral perfusion pressure. The formula for calculating the degree of change in cerebral perfusion pressure is as follows: ,in, This refers to the cerebral perfusion pressure after fluid resuscitation. The normal value for cerebral perfusion pressure is used as a reference. The changes in intracranial pressure and cerebral perfusion pressure are substituted into the cerebral hemodynamic assessment formula to evaluate cerebral hemodynamics after fluid resuscitation. The cerebral hemodynamic assessment formula is as follows: ; S33. Substitute drug compatibility and cerebral hemodynamics into the patient drug care effect assessment formula to evaluate the patient drug care effect. The patient drug care effect assessment formula is as follows: ,in, To determine the fit between the drug and the i-th physiological parameter, For cerebral blood flow dynamics, The probability of developing severe brain disease within a patient's age group. Glasgow Coma Scale; A complication care effectiveness assessment model was constructed, incorporating the correlation between complication severity and nursing actions to evaluate the effectiveness of complication care. This included the following specific steps: S41. Substitute the severity level of the patient's complication as defined by the doctor into the complication severity calculation formula to calculate the severity of the complication. The complication severity calculation formula is as follows: ,in, For the patient's first The severity level of each complication, The number of complications occurring in patients, For the first The probability of occurrence of each complication, The highest severity level of the complication; S42. Substitute the nursing actions that affect complications into the nursing action correlation calculation formula to calculate the nursing action correlation, where the first... The formula for calculating the correlation between nursing actions for a complication is as follows: ,in, For the patient's first The severity level of each complication, For the The nursing action is for the first The impact of each complication The frequency of execution of the first nursing action; S43. Substitute the severity of the complication and the correlation between complication nursing actions into the formula for calculating the complication nursing effect. The formula for calculating the complication nursing effect is as follows: ,in, As the severity of complications, For the first The correlation between nursing actions for each complication The number of complications occurring in the patient; A comprehensive nursing effectiveness evaluation model was constructed to assess the overall nursing effectiveness for patients.

2. The brain intensive care nursing analysis method based on multi-data fusion as described in claim 1, characterized in that, The construction of a comprehensive nursing effectiveness evaluation model to assess the overall nursing effectiveness for patients includes the following specific steps: S51. Substitute patient monitoring status, medication effectiveness, and complication management effectiveness into the comprehensive nursing effectiveness assessment formula to evaluate the overall nursing effectiveness. The comprehensive nursing effectiveness assessment formula is as follows: ,in, Classification of patient intensive care scenarios; S52. Import the comprehensive nursing effect evaluation value into the pre-trained deep learning model to obtain the threshold. Based on the nursing effect threshold output by the model, compare it with the current comprehensive nursing effect evaluation value, and classify the comprehensive nursing effect evaluation value. The higher the comprehensive nursing effect evaluation value, the better the nursing effect.

3. A brain intensive care nursing analysis system based on multi-data fusion, which is implemented based on the brain intensive care nursing analysis method based on multi-data fusion as described in any one of claims 1-2, characterized in that, Specifically, it includes: The data acquisition module is used to acquire patient condition data and nursing data; The patient status assessment module is used to assess the patient's status through physiological monitoring parameters; The drug care efficacy assessment module is used to evaluate the drug care efficacy through drug compatibility and cerebral hemodynamics. The complication care effectiveness assessment module is used to evaluate the effectiveness of complication care by assessing the correlation between the severity of the complication and the complication care actions. The comprehensive nursing effectiveness assessment module is used to evaluate the overall nursing effectiveness of patients.

4. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor is characterized in that it executes a brain intensive care nursing analysis method based on multi-data fusion as described in any one of claims 1-2 by calling a computer program stored in the memory.

5. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed on a computer, cause the computer to perform a multi-data fusion-based brain intensive care analysis method as described in any one of claims 1-2.

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