A neurology care assistance system

Abstract

The application relates to the technical field of medical assistance, and discloses a nursing assistance system for neurology departments, which realizes effective integration of medical multi-source data of patients through a data integration module, and realizes fusion analysis of the medical data after data integration through different neural network algorithms through a data analysis module, so that the accuracy of analysis results is improved. In addition, a convenient and efficient information sharing and cooperation platform is provided to improve the stability of nursing means and the nursing quality in the nursing process of medical staff to patients.

Description

Technical Field

[0001] This invention relates to the field of medical assistive technology, and in particular to a neurological nursing assistive system. Background Technology

[0002] Neurology is a branch of medicine that studies diseases of the nervous system, involving the diagnosis, treatment, and rehabilitation of diseases of the brain, spinal cord, and peripheral nervous system. Neurological nursing refers to the care provided to patients with neurological diseases, including monitoring changes in their condition, assisting doctors in diagnosis, and providing daily living assistance.

[0003] In neurological nursing, medical staff need to collect, integrate, and analyze a large amount of patient medical data. The comprehensive analysis of this data is crucial for developing scientific nursing plans and improving patient treatment outcomes. However, the necessary medical data is often distributed across different systems and databases, with inconsistent data formats and standards, making data collection, integration, and analysis difficult. Furthermore, traditional data integration and analysis methods typically require significant manpower and time, are prone to errors, leading to inefficiency and inaccurate results. Mechanisms for information sharing and collaboration among medical staff are also inadequate, hindering timely sharing of key information, discussion, and the development of nursing plans, which significantly impacts nursing effectiveness and patient treatment outcomes. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a neurological nursing assistance system to effectively integrate and analyze multi-source medical data of patients, providing effective data references for medical staff; and to provide a convenient and efficient information sharing and collaboration platform to improve the accuracy and efficiency of nursing care measures implemented by medical staff in the process of caring for patients.

[0005] This invention discloses a neurology nursing assistance system, which includes a data integration module, a data analysis module, and an information sharing and collaboration platform.

[0006] The data integration module is used to integrate patient medical data through a multi-source data integration algorithm to obtain a first dataset; wherein, the patient medical data includes the patient's clinical data, imaging record data, biomarker data, and data on the diagnosis and treatment process of the patient by medical staff;

[0007] The data analysis module is used to process and analyze the first dataset to obtain a first analysis result.

[0008] The information sharing and collaboration platform is used to assist medical staff in sharing patient medical data and the first analysis results, and to cooperate in various nursing procedures.

[0009] Furthermore, the data integration module includes a data preprocessing submodule, a creation submodule, and a data integration submodule;

[0010] The data integration module integrates patient medical data using a multi-source data integration algorithm to obtain the first dataset, which specifically includes:

[0011] The data preprocessing submodule performs data cleaning and preprocessing operations on patient medical data, and performs standardization and normalization on the data to obtain the first preprocessed data.

[0012] A patient identifier is created by creating a submodule; the patient identifier is used to associate a patient with the patient's medical data.

[0013] The data integration submodule integrates the first preprocessed data associated with patient identifiers using a multi-source data integration algorithm to form the first dataset.

[0014] Furthermore, the multi-source data integration algorithm includes an ensemble learning algorithm.

[0015] Furthermore, the process of integrating the first preprocessed data associated with the patient identifier using a multi-source data integration algorithm to form the first dataset specifically includes:

[0016] First preprocessed data associated with patient identifiers is obtained, and features are extracted from the first preprocessed data to obtain a first preprocessed dataset;

[0017] Different base learners are set according to the patient's medical data, and the base learners are integrated to construct a random forest model;

[0018] The first preprocessed dataset is input into the constructed random forest model, and the first dataset is output through the random forest model.

[0019] Furthermore, the data analysis module performs data processing and analysis on the first dataset, specifically including:

[0020] The data analysis module obtains the first dataset from the data integration module;

[0021] Based on the needs of medical staff, feature extraction and transformation operations are performed on the first dataset to obtain the second preprocessed dataset;

[0022] The second preprocessed dataset is processed and analyzed using a neural network algorithm to obtain the first analysis result.

[0023] Furthermore, the data processing and analysis of the second preprocessed dataset using a neural network algorithm specifically includes:

[0024] The clinical data and biomarker data in the second preprocessing dataset are represented by single-dimensional vectors, the image recording data are represented by multi-dimensional vectors, and the data of medical staff's diagnosis and treatment process are represented by sequences.

[0025] Clinical record data and biomarker data are output through a fully connected layer neural network, image record data is output through a convolutional neural network, and medical staff's diagnosis and treatment process data is output through a recurrent neural network, ultimately obtaining feature vectors corresponding to different data.

[0026] By fusing the feature vectors corresponding to the different data, a global feature vector is obtained;

[0027] The global feature vector is input into a fully connected neural network, and the first analysis result is output.

[0028] Furthermore, the information sharing and collaboration platform also includes a user interface and a storage sub-module;

[0029] Specifically, the information sharing and collaboration platform assists medical staff in sharing patient medical data and the first analysis results, including:

[0030] The patient's medical data and the first analysis result are associated based on the patient identifier, and the patient's medical data and the first analysis result are stored in the storage submodule based on the patient identifier. In the storage module, the patient identifier, the patient's medical data and the first analysis result are in one-to-one correspondence.

[0031] Medical staff can input patient identifiers through a user interface, retrieve data associated with those patient identifiers, and display the data through the user interface.

[0032] Furthermore, the information sharing and collaboration platform also includes a real-time collaboration sub-module;

[0033] The information sharing and collaboration platform assists medical staff in coordinating and cooperating in various nursing procedures, specifically including:

[0034] The real-time collaboration submodule integrates real-time communication and collaboration tools, enabling medical staff to communicate in real time and conduct work handover during nursing procedures.

[0035] Furthermore, the information sharing and collaboration platform also includes a permission management submodule;

[0036] The permission management submodule is used to set access and operation permissions for medical staff on patient data.

[0037] Furthermore, the information sharing and collaboration platform also includes an intelligent reminder submodule;

[0038] The intelligent reminder submodule is used to set data indicator thresholds and monitor patients' data indicators. When it is determined that a patient's data is greater than or equal to the corresponding data indicator threshold, an alarm reminder is issued.

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

[0040] This invention achieves effective integration of multi-source patient medical data through a data integration module, and improves the accuracy of analysis results by using a data analysis module to fuse and analyze the integrated medical data through different neural network algorithms. Furthermore, it provides a convenient and efficient information sharing and collaboration platform to enhance the stability and quality of nursing care implemented by healthcare professionals during patient care. Attached Figure Description

[0041] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0042] Figure 1 This is a schematic diagram of the structure of a neurology nursing assistance system disclosed in an embodiment of the present invention. Detailed Implementation

[0043] To enable those skilled in the art to better understand the present invention, 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.

[0044] Example 1

[0045] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of a neurology nursing assistance system disclosed in an embodiment of the present invention. The assistance system includes a data integration module, a data analysis module, and an information sharing and collaboration platform.

[0046] The data integration module is used to integrate patient medical data through multi-source data integration algorithms to obtain the first dataset. Patient medical data includes the patient's clinical data, imaging records, biomarker data, and data on the diagnosis and treatment process by medical staff.

[0047] The data analysis module is used to process and analyze the first dataset to obtain the first analysis result.

[0048] The information sharing and collaboration platform is used to assist medical staff in sharing patient medical data and initial analysis results, and to collaborate in various nursing procedures.

[0049] Specifically, the clinical data for patients in this embodiment mainly includes their physiological parameters, symptom descriptions, medical history, and laboratory test results. In neurological nursing, clinical data may involve the patient's neurological functional status, such as their level of consciousness, motor abilities, and sensory abnormalities. In addition, it includes other clinical information related to the patient's neurological disease, such as the frequency of seizures and the degree of tremor in Parkinson's disease patients. This data is obtained through clinical assessment, physiological measurements, and laboratory tests, providing important references for medical staff to develop treatment plans and monitor disease progression.

[0050] Imaging data primarily refers to the results of neuroimaging examinations, such as MRI (Magnetic Resonance Imaging), CT (Computed Tomography), and PET (Positron Emission Tomography). These imaging examinations can provide detailed information on the structure and function of a patient's nervous system, helping medical staff in disease diagnosis and treatment monitoring. For example, neuroimaging examinations can reveal lesions such as brain tumors, cerebrovascular diseases, and neurodegenerative diseases, providing important information for doctors to develop treatment plans.

[0051] Biomarker data in neurological nursing involves patients' biological samples, such as blood and cerebrospinal fluid. These samples contain a range of biomarkers that can reflect a patient's disease status, disease progression, and treatment effectiveness. Commonly used biomarkers in neurological nursing include serum neuron-specific proteins (such as neuron-specific enolase), inflammatory markers (such as C-reactive protein and white blood cell count), neurotransmitters, and metabolites. Monitoring changes in these biomarkers helps healthcare professionals promptly assess changes in a patient's condition and guide adjustments to treatment plans.

[0052] Furthermore, the patient medical data in this embodiment also includes data on the diagnosis and treatment process of patients handled by medical staff. This data includes operational records, observation records, and communication records of doctors and nurses during the diagnosis, treatment, and nursing care of patients. In neurological nursing, the data on the diagnosis and treatment process of medical staff typically includes medical history taking, clinical examination, medical imaging examination, laboratory examination, drug treatment, surgical treatment, and rehabilitation nursing. This data records the patient's treatment process and nursing care, providing important evidence for information sharing and collaboration among medical staff, and helping to optimize treatment plans and improve the quality of nursing care.

[0053] In this embodiment of the invention, by integrating the above-mentioned different source data, the comprehensiveness of the basic data used for analysis is improved, and the accuracy of subsequent data analysis is further improved.

[0054] Furthermore, the data integration module includes a data preprocessing submodule, a creation submodule, and a data integration submodule;

[0055] The data integration module integrates patient medical data using a multi-source data integration algorithm to obtain the first dataset, which specifically includes:

[0056] The data preprocessing submodule performs data cleaning and preprocessing operations on patient medical data, and performs standardization and normalization on the data to obtain the first preprocessed data.

[0057] Specifically, the data preprocessing submodule cleans patient medical data, removing outliers and missing values ​​to ensure data quality. It also standardizes and normalizes the data to ensure that data from different data sources have similar scale and distribution, thus facilitating better integration and analysis.

[0058] A patient identifier is created by creating a submodule, which is used to associate the patient with their medical data. Specifically, in this embodiment, a unique identifier is generated for each patient to associate the patient with their medical data. This identifier can be generated based on the patient's personal information (such as name, date of birth, etc.) or it can be a unique code automatically generated by the system. The specific form of the identifier is not limited in this embodiment of the invention.

[0059] The data integration submodule integrates the first preprocessed data associated with patient identifiers using a multi-source data integration algorithm to form the first dataset.

[0060] Furthermore, multi-source data integration algorithms include ensemble learning algorithms.

[0061] Furthermore, the first preprocessed data associated with patient identifiers is integrated using a multi-source data integration algorithm to form the first dataset, which specifically includes:

[0062] First preprocessed data associated with patient identifiers is acquired, and feature extraction is performed on this first preprocessed data to obtain a first preprocessed dataset. Specifically, based on the patient identifier, first preprocessed data associated with it is acquired from various data sources. This data includes cleaned and preprocessed clinical data, imaging record data, biomarker data, and data on the diagnosis and treatment process of patients by medical staff. Feature extraction is performed on the first preprocessed data to convert the raw data into feature vectors for subsequent data integration and analysis.

[0063] Different base learners are set according to the patient's medical data, and the base learners are integrated to build a random forest model.

[0064] The first preprocessed dataset is input into the constructed random forest model, and the first dataset is output through the random forest model.

[0065] Specifically, in this embodiment of the invention, the random forest model used to output the integrated first dataset of patient medical data is:

[0066]

[0067] Where RF(X) represents the output of the random forest model, X represents the input data; T represents the number of decision trees in the random forest, t is the t-th decision tree, and the value of t ranges from 1 to T; N represents the number of data sources, i is the i-th data source, and the value of i ranges from 1 to N; This represents the output of the t-th decision tree to the i-th data source. This represents a specific parameter associated with the i-th data source.

[0068] In this embodiment of the invention, a random forest model is trained by setting test and validation sets of data from different data sources in the patient's medical care process. The random forest model then integrates information from these different data sources to improve the accuracy and robustness of the integrated data. A first preprocessed dataset is input into the constructed random forest model, and the model outputs the integrated first dataset, which contains comprehensive medical information about the patient and can be used for subsequent data analysis and information sharing.

[0069] Furthermore, the first dataset is processed and analyzed through the data analysis module, specifically including:

[0070] The data analysis module obtains the first dataset from the data integration module;

[0071] Based on the needs of medical staff, feature extraction and transformation operations were performed on the first dataset to obtain the second preprocessed dataset.

[0072] Specifically, in this embodiment of the invention, before performing feature extraction on the first dataset through the data analysis module, it is necessary to determine the medical staff's data analysis needs, determine the features to be extracted based on the analysis needs, and select appropriate feature extraction methods, such as statistical features, morphological features, etc.

[0073] Based on the results of feature extraction, the raw data is converted into a format suitable for input to neural network algorithms, such as normalization, standardization, dimensionality reduction, and other operations, as well as data format conversion, such as image data processing.

[0074] This embodiment performs selective feature extraction and transformation operations on the first dataset according to the needs of medical staff, so that the data can be processed and analyzed more effectively and in a more targeted manner by the neural network algorithm, thereby obtaining accurate and useful analysis results and providing effective auxiliary decision support for medical staff.

[0075] Furthermore, the second preprocessed dataset is processed and analyzed using a neural network algorithm to obtain the first analysis result.

[0076] Specifically, the data processing and analysis of the second preprocessed dataset using neural network algorithms includes:

[0077] The clinical data and biomarker data in the second preprocessing dataset are represented by single-dimensional vectors, the image recording data are represented by multi-dimensional vectors, and the data of medical staff's diagnosis and treatment process are represented by sequences.

[0078] Clinical record data and biomarker data are output through a fully connected layer neural network, image record data is output through a convolutional neural network, and medical staff's diagnosis and treatment process data is output through a recurrent neural network, ultimately obtaining feature vectors corresponding to different data.

[0079] By fusing the feature vectors corresponding to the different data, a global feature vector is obtained;

[0080] The global feature vector is input into a fully connected neural network, and the first analysis result is output.

[0081] Specifically, in this embodiment of the invention, the data analysis module processes and analyzes the second preprocessed dataset using a neural network algorithm, and the mathematical process for outputting the first analysis result is as follows:

[0082]

[0083] in, The output of the neural network model represents the first analytical result obtained after processing the input data X. (·) represents a fully connected neural network layer used to process fused patient medical data; It is a convolutional neural network used to process image recording data; For medical staff's diagnosis and treatment process data, (·) represents a recurrent neural network used to process data from the diagnosis and treatment process of medical staff; , These are clinical record data and biomarker data, respectively. , These are fully connected neural networks, used to process clinical record data and biomarker data, respectively. These are undetermined hyperparameters used to adjust the influence of the fully connected layer; (·) is a modified linear unit activation function, used to increase the nonlinear characteristics of the network; For is (·) Weight parameter matrix in a fully connected neural network; This is a bias term used to offset the output of the fully connected layer; These are undetermined hyperparameters used to adjust the influence of the feature fusion term; (·) is the hyperbolic tangent activation function, used to add additional nonlinear characteristics; Let be the parameter matrix of the linear transformation after feature fusion operation. This is the bias term, used in the result of feature fusion.

[0084] In this embodiment, the process of data processing and analysis of the second preprocessed dataset involves the processing and analysis of multimodal data. In the field of neurological nursing, patient medical data typically encompasses multiple data types with different sources and properties, thus requiring different processing methods for analysis.

[0085] Clinical data and biomarker data are typically single numerical values ​​or features, which can be processed by fully connected neural networks to output a single-dimensional vector representation.

[0086] Image recording data is usually multidimensional, such as two-dimensional image data, so it can be processed by convolutional neural networks to output multidimensional vector representations.

[0087] Data from the diagnosis and treatment process of medical staff may be sequential data, containing time sequence information. Therefore, it can be processed by recurrent neural networks to output a sequence representation.

[0088] Then, the feature vectors corresponding to different types of data are fused to obtain a global feature vector. Patient medical data may be correlated and complementary. By fusing data from different modalities into a unified feature vector, a more comprehensive description of the patient's condition can be obtained. Different modalities of data provide information at different levels and from different perspectives. For example, clinical data provides basic patient information and disease diagnosis; imaging data provides the location and morphology of lesions; biomarker data provides physiological indicators and metabolic status; and data from healthcare professionals' treatment processes provide treatment plans and process management information. By integrating and analyzing this data, a more accurate assessment of the patient's health status and disease risk can be achieved.

[0089] Furthermore, by employing different neural network architectures to process different types of data, their respective strengths can be better leveraged. Fully connected neural networks are suitable for processing structured data, convolutional neural networks are suitable for processing image data, and recurrent neural networks are suitable for processing sequence data. This flexibility allows the model to better adapt to different types of input data.

[0090] Finally, by inputting the global feature vector into a fully connected neural network, further data processing and analysis are performed to obtain the final analysis results. The fully connected layer can comprehensively process various features and output higher-level feature representations, thereby improving the understanding and analysis capabilities of patient medical data.

[0091] This embodiment comprehensively utilizes information from different data types, enabling the model to gain a more holistic understanding of the patient's medical condition and to make more accurate predictions and analyses. Through neural network processing, it can automatically learn the relationships and patterns between different data types, thereby improving the accuracy of understanding and diagnosing the patient's condition.

[0092] Furthermore, the information sharing and collaboration platform also includes a user interface and a storage sub-module.

[0093] Specifically, the information sharing and collaboration platform assists medical staff in sharing patient medical data and initial analysis results, including:

[0094] The patient's medical data and the first analysis result are associated based on the patient identifier, and the patient's medical data and the first analysis result are stored in the storage submodule based on the patient identifier. In the storage module, the patient identifier, the patient's medical data and the first analysis result are in one-to-one correspondence.

[0095] Medical staff can input patient identifiers through a user interface, retrieve data associated with those patient identifiers, and display the data through the user interface.

[0096] Specifically, in this embodiment of the invention, the user interface is used for medical staff to interact with the system, retrieve relevant data by inputting a patient identifier, and display the patient's medical data and the first analysis result through the interface. The storage submodule is responsible for associating and storing the patient identifier, medical data, and analysis results in the database for subsequent retrieval and use.

[0097] Furthermore, the information sharing and collaboration platform also includes a real-time collaboration sub-module;

[0098] Specifically, assisting medical staff in collaborating across various nursing procedures through an information sharing and collaboration platform includes:

[0099] By integrating real-time communication and collaboration tools through the real-time collaboration submodule, medical staff can communicate in real time through real-time communication tools and hand over work in nursing procedures through collaboration tools.

[0100] Specifically, by integrating real-time communication tools, such as instant messaging, voice calls, and video conferencing, into the patient personal care data center corresponding to specific patient identifiers, rapid communication between medical staff can be achieved. In this way, whether in the nursing field or in a remote work environment, medical staff can communicate and consult in a timely manner to jointly develop and adjust nursing plans, thereby improving work efficiency and the quality of care.

[0101] Collaboration tools enable healthcare professionals to seamlessly hand over their responsibilities during the nursing process. This includes recording and transmitting patient information, nursing plans, and medical orders, ensuring that incoming staff understand the patient's condition and the necessary interventions, thereby maintaining continuity and stability in nursing care.

[0102] This embodiment provides a real-time collaborative environment where healthcare professionals can jointly edit and update patient care records, assessment results, treatment plans, and other information. This collaborative environment can promote communication and collaboration between teams, reduce errors and delays in information transmission, and improve the quality and efficiency of healthcare services.

[0103] Furthermore, the information sharing and collaboration platform also includes a permissions management submodule.

[0104] The access control submodule is used to set access and operation permissions for medical staff on patient data.

[0105] Specifically, firstly, the system needs to authenticate medical staff to ensure they have the authority to access and manipulate patient data, such as through username and password verification, biometric technology, etc. Once the medical staff are authenticated, the system administrator can assign appropriate data access and manipulation permissions based on their roles and responsibilities. These permissions can be further subdivided into different levels such as read, edit, and delete, and can be customized according to specific needs.

[0106] As a preferred embodiment of this example, the access control submodule also records and monitors the access and operation behavior of medical staff on patient data in order to promptly detect and handle abnormal situations, including recording the time, content, operator and other information of each operation, and generating audit reports for analysis and review.

[0107] Furthermore, the information sharing and collaboration platform also includes an intelligent reminder sub-module;

[0108] The intelligent reminder submodule is used to set data indicator thresholds and monitor patients' data indicators. When it is determined that a patient's data is greater than or equal to the corresponding data indicator threshold, an alarm reminder is issued.

[0109] Finally, it should be noted that the neurological nursing assistance system disclosed in the embodiments of the present invention is only a preferred embodiment of the present invention and is only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A neurological nursing support system, characterized in that, The auxiliary system includes a data integration module, a data analysis module, and an information sharing and collaboration platform; The data integration module is used to integrate patient medical data through a multi-source data integration algorithm to obtain a first dataset; wherein, the patient medical data includes the patient's clinical data, imaging record data, biomarker data, and data on the diagnosis and treatment process of the patient by medical staff; The data analysis module is used to process and analyze the first dataset to obtain a first analysis result. The information sharing and collaboration platform is used to assist medical staff in sharing patient medical data and the first analysis results, and to cooperate in various nursing procedures.

2. The neurological nursing assistance system according to claim 1, characterized in that, The data integration module includes a data preprocessing submodule, a creation submodule, and a data integration submodule. The data integration module integrates patient medical data using a multi-source data integration algorithm to obtain the first dataset, which specifically includes: The data preprocessing submodule performs data cleaning and preprocessing operations on patient medical data, and performs standardization and normalization on the data to obtain the first preprocessed data. A patient identifier is created by creating a submodule; the patient identifier is used to associate a patient with the patient's medical data. The data integration submodule integrates the first preprocessed data associated with patient identifiers using a multi-source data integration algorithm to form the first dataset.

3. The neurological nursing support system according to claim 2, characterized in that, The multi-source data integration algorithm includes an ensemble learning algorithm.

4. The neurological nursing support system according to claim 3, characterized in that, The process of integrating the first preprocessed data associated with patient identifiers using a multi-source data integration algorithm to form the first dataset specifically includes: First preprocessed data associated with patient identifiers is obtained, and features are extracted from the first preprocessed data to obtain a first preprocessed dataset; Different base learners are set according to the patient's medical data, and the base learners are integrated to construct a random forest model; The first preprocessed dataset is input into the constructed random forest model, and the first dataset is output through the random forest model.

5. The neurological nursing support system according to any one of claims 1-4, characterized in that, The data analysis module performs data processing and analysis on the first dataset, specifically including: The data analysis module obtains the first dataset from the data integration module; Based on the needs of medical staff, feature extraction and transformation operations are performed on the first dataset to obtain the second preprocessed dataset; The second preprocessed dataset is processed and analyzed using a neural network algorithm to obtain the first analysis result.

6. The neurological nursing assistance system according to claim 5, characterized in that, The data processing and analysis of the second preprocessed dataset using a neural network algorithm specifically includes: The clinical data and biomarker data in the second preprocessing dataset are represented by single-dimensional vectors, the image recording data are represented by multi-dimensional vectors, and the data of medical staff's diagnosis and treatment process are represented by sequences. Clinical record data and biomarker data are output through a fully connected layer neural network, image record data is output through a convolutional neural network, and medical staff's diagnosis and treatment process data is output through a recurrent neural network, ultimately obtaining feature vectors corresponding to different data. By fusing the feature vectors corresponding to the different data, a global feature vector is obtained; The global feature vector is input into a fully connected neural network, and the first analysis result is output.

7. The neurological nursing support system according to claim 6, characterized in that, The information sharing and collaboration platform also includes a user interface and a storage sub-module; Specifically, the information sharing and collaboration platform assists medical staff in sharing patient medical data and the first analysis results, including: The patient's medical data and the first analysis result are associated based on the patient identifier, and the patient's medical data and the first analysis result are stored in the storage submodule based on the patient identifier. In the storage module, the patient identifier, the patient's medical data and the first analysis result are in one-to-one correspondence. Medical staff can input patient identifiers through a user interface, retrieve data associated with those patient identifiers, and display the data through the user interface.

8. The neurological nursing assistance system according to claim 7, characterized in that, The information sharing and collaboration platform also includes a real-time collaboration sub-module; The information sharing and collaboration platform assists medical staff in coordinating and cooperating in various nursing procedures, specifically including: The real-time collaboration submodule integrates real-time communication and collaboration tools, enabling medical staff to communicate in real time and conduct work handover during nursing procedures.

9. The neurological nursing assistance system according to claim 8, characterized in that, The information sharing and collaboration platform also includes a permission management sub-module; The permission management submodule is used to set access and operation permissions for medical staff on patient data.

10. The neurological nursing assistance system according to claim 9, characterized in that, The information sharing and collaboration platform also includes an intelligent reminder sub-module; The intelligent reminder submodule is used to set data indicator thresholds and monitor patients' data indicators. When it is determined that a patient's data is greater than or equal to the corresponding data indicator threshold, an alarm reminder is issued.

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