Neurosurgery blood vessel nursing monitoring system based on medical information processing

Abstract

The invention discloses a neurosurgery vascular nursing monitoring system based on medical information processing. The neurosurgery vascular nursing monitoring system comprises a medical information acquisition module, a medical data optimization module, a neurosurgery vascular nursing evaluation model establishment module and a neurosurgery vascular nursing monitoring module. The invention belongs to the field of medical information processing, and particularly relates to a neurosurgical blood vessel nursing monitoring system based on medical information processing, which eliminates false anomalies through abnormal path length scoring, increases sensitivity to mild abnormal and serious abnormal nursing states based on a category weighted marginal contribution mechanism, and improves the accuracy of medical information processing. Historical medical care data are optimized through anomaly detection, screening and feature selection, and the care monitoring effect is improved. On the basis of efficient feature extraction of space-time dependence, the space interaction relation between dynamic physiological signals is captured, meanwhile, short-term fluctuation and long-term trend of the physiological signals are captured, dynamic attention weights are introduced according to the relation difference between the physiological signals, and then the nursing monitoring effect is improved.

Description

Technical Field

[0001] The present invention relates to the field of medical information processing, and in particular to a neurosurgery vascular care monitoring system based on medical information processing. Background Art

[0002] The neurosurgical vascular care monitoring system is a system that combines advanced medical equipment with data analysis, focusing on real-time monitoring and nursing assessment of the cerebrovascular status of neurosurgical patients. It integrates multimodal data and intelligent analysis technology to provide medical staff with accurate disease assessment and personalized nursing recommendations, helping to improve patient prognosis and optimize nursing resource allocation. However, general neurosurgical vascular care monitoring systems lack dynamic anomaly detection and insufficient feature screening for abnormal signal dependence, which leads to low accuracy of nursing status assessment and poor nursing monitoring effect. General neurosurgical vascular care monitoring systems have poor ability to capture dynamic physiological signals and insufficient collaborative analysis capabilities, resulting in inaccurate nursing status assessment, insufficient sensitivity to changes in nursing data distribution, and poor nursing monitoring effect. Summary of the invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a neurosurgery vascular care monitoring system based on medical information processing. In view of the lack of dynamic anomaly detection in general neurosurgery vascular care monitoring systems, insufficient feature screening of abnormal signal dependence, which leads to low accuracy of nursing status assessment and poor nursing monitoring effect, the present invention eliminates pseudo-anomalies caused by sensor failures and data fluctuations through a dynamic threshold mechanism of a sliding window and abnormal path length scoring, increases the sensitivity to mild and severe abnormal nursing states based on a category-weighted marginal contribution mechanism, introduces a combined indicator of mutual information and correlation redundancy regularization for feature selection, and uses anomaly detection, screening and feature selection to analyze historical medical data. The medical nursing data can be optimized to improve the accuracy of subsequent nursing status assessment and the nursing monitoring effect; the general neurosurgery vascular nursing monitoring system has poor ability to capture dynamic physiological signals and insufficient collaborative analysis capabilities, which leads to inaccurate nursing status assessment and insufficient sensitivity to changes in nursing data distribution, resulting in poor nursing monitoring effects. This solution is based on efficient feature extraction based on time and space dependence, captures the spatial interaction relationship between dynamic physiological signals, and captures the short-term fluctuations and long-term trends of physiological signals at the same time, improves the sensitivity of disease change trends, and introduces a multi-head attention mechanism based on dynamic attention weights to improve the prediction accuracy of nursing status and thus improve nursing monitoring effects.

[0004] The technical solution adopted by the present invention is as follows: the neurosurgery vascular care monitoring system based on medical information processing provided by the present invention includes a medical information acquisition module, a medical data optimization module, a neurosurgery vascular care evaluation model establishment module and a neurosurgery vascular care monitoring module;

[0005] The medical information collection module collects historical medical care data;

[0006] The medical data optimization module constructs data input for nursing status assessment through dynamic anomaly detection, feature screening and optimal feature selection;

[0007] The neurosurgery vascular care assessment model building module extracts and fuses time-dependent and spatial interaction features through residual networks, convolutional gated recurrent units, and multi-head attention mechanisms, and finally realizes nursing status prediction;

[0008] The neurosurgery vascular care monitoring module implements status evaluation of real-time collected data based on the established neurosurgery vascular care evaluation model, thereby realizing care monitoring.

[0009] Furthermore, in the medical information collection module, the historical medical care data includes dynamic physiological signals, chemical metabolism data, time and care status; the care status includes normal, mild abnormality and severe abnormality; and the care status is used as a data label.

[0010] Furthermore, the medical data optimization module specifically includes the following contents:

[0011] Preliminary anomaly detection: For dynamic physiological signal data, detect anomalies caused by sensor failures and introduce a dynamic threshold mechanism based on sliding windows, expressed as: ; ; ; Construct historical sequence features for historical medical care data after preliminary anomaly removal; where IF(·) is the anomaly detection score; x is the data point; mT is the number of time steps, t is the time step index; E(·) is the anomaly path length; n is the number of samples; c is the normalization coefficient; is the dynamic anomaly weight; is the data point value at time step t; k is the neighborhood size, ik is the neighbor index; d(·) is the Euclidean distance; is the nearest neighbor point; is the maximum neighbor distance; is a sliding window[ ] is the median value within ; and are the standard deviation and median absolute deviation, respectively;

[0012] Preliminary feature screening: In medical data, different features contribute differently to cerebrovascular abnormalities, so the marginal contribution of each feature is quantified, and the category-weighted marginal contribution is introduced to perform preliminary feature screening; expressed as: ; ; ;in, is the contribution value of the i1th feature; is the size of the feature subset S; is the size of the feature set N; is the category weight; and They are the weights of abnormal states; f1, f2 and f3 are normal state, mild abnormal state and severe abnormal state respectively; is a performance metric, obtained based on a pre-trained regression model; is the sample weight; i1 is the feature index; i is the data point index; is the number of data points contained in the category; is the category to which the data point belongs;

[0013] Optimal feature selection: For the correlation features of the preliminary feature screening, further select the optimal feature subset, which is expressed as: ; ; ; ; ;in, is the balance weight; bZ is the final result of feature selection; m is the number of selected features; and is the feature weight; is the current feature subset; is the true nursing status label of the i-th sample; is the predicted label of the ith sample based on the feature subset; f is a single feature; c is the target care status; I(·) is the mutual information; f i1 and f j1 are two features in the feature subset; Var(·) is the variance; y and They are the true label and the predicted label respectively; is the KL divergence; is the normalization constant; is the balancing factor.

[0014] Furthermore, the neurosurgery vascular care evaluation model establishment module is based on the historical medical care data processed by the medical data optimization module, and specifically includes the following contents:

[0015] Feature extraction: Physiological signals and chemical data have obvious time dependence. Abnormal short-term fluctuations may be a precursor to impaired cerebrovascular regulation, while long-term trend changes reflect worsening of the disease. Therefore, the spatial correlation between physiological signals is extracted through the residual network to capture the interactive relationship between different monitoring signals, which is expressed as: ; Dynamic time features are extracted based on the convolution operation through the convolution gated recurrent unit to capture the time dependency of physiological signals, which is expressed as: ; ;in, is the historical medical care data input at time step ti; W a is the input weight matrix; is the bias vector; Softmax(·) is the Softmax function; is the residual network output; It is a spatial correlation feature, reflecting the interactive relationship between different physiological signals; It is the update gate; and They are the convolution weight matrix and the circulation weight matrix respectively; is the convolutional gated recurrent unit bias vector; is the Sigmoid activation function; is the candidate state for the current time step; is the final hidden state; is a hidden state;

[0016] Feature fusion: The changes of different physiological signals in the time dimension have different importance for the prediction of abnormal cerebral blood flow. The relationship between features is captured by the parallel attention operation of the multi-head attention mechanism, which is expressed as: ; ; Global pooling based on adaptive pooling is expressed as: ; Add feature regularization term, expressed as: ; Finally, the nursing status is predicted through the fully connected layer; the model uses the cross entropy loss function and the gradient descent method to optimize the parameters; where MHd(·) is the output of the multi-head attention mechanism; Q, K and V are the query matrix, key matrix and value matrix respectively; , and They are the outputs of the 1st, 2nd, and n2th attention heads respectively; , and is the corresponding weight; is the linear mapping weight; It is a splicing operation; and is the i2th linear transformation result; and is the j2th linear transformation result; Sim(·) is; is the feature after pooling; is an adaptive pooling operation; is the size of the output feature; x is the input feature matrix, including short-term fluctuations and long-term trend features; is the feature regularization term of the fully connected layer; is the weight matrix of the fully connected layer; is the regularization strength; is the L2 norm.

[0017] Furthermore, the neurosurgery vascular care monitoring module is based on an established neurosurgery vascular care evaluation model, collects medical care data in real time, and uses the model output as the evaluation result of the collected medical care data. When the evaluation result is seriously abnormal, the nursing staff is warned.

[0018] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0019] (1) In view of the lack of dynamic anomaly detection and insufficient feature screening of abnormal signal dependence in general neurosurgery vascular care monitoring systems, which leads to low accuracy of nursing status assessment and poor nursing monitoring effect, this scheme uses a dynamic threshold mechanism of sliding windows and abnormal path length scoring to eliminate pseudo-anomalies caused by sensor failures and data fluctuations, increases the sensitivity to mild and severe abnormal nursing states based on a category-weighted marginal contribution mechanism, introduces a combined indicator of mutual information and correlation redundancy regularization for feature selection, and optimizes historical medical care data through anomaly detection, screening, and feature selection, thereby improving the accuracy of subsequent nursing status assessment and the nursing monitoring effect.

[0020] (2) In view of the problems that general neurosurgery vascular care monitoring systems have poor ability to capture dynamic physiological signals and insufficient collaborative analysis capabilities, which lead to inaccurate nursing status assessment and insufficient sensitivity to changes in nursing data distribution, resulting in poor nursing monitoring effects, this solution is based on efficient feature extraction based on time and space dependencies to capture the spatial interaction relationship between dynamic physiological signals, while capturing the short-term fluctuations and long-term trends of physiological signals, thereby improving the sensitivity to disease change trends. In view of the relationship differences between physiological signals, a multi-head attention mechanism based on dynamic attention weights is introduced to improve the prediction accuracy of nursing status, thereby improving the nursing monitoring effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of the process flow of the neurosurgery vascular care monitoring system based on medical information processing provided by the present invention;

[0022] Figure 2Schematic diagram of the process flow of the medical data optimization module.

[0023] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0025] In the description of the present invention, it should be understood that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0026] Example 1, see Figure 1 The neurosurgery vascular care monitoring system based on medical information processing provided by the present invention includes a medical information acquisition module, a medical data optimization module, a neurosurgery vascular care evaluation model establishment module and a neurosurgery vascular care monitoring module;

[0027] The data collection module collects historical medical care data; and sends the data to the medical data optimization module;

[0028] The medical data optimization module constructs data input for nursing status assessment through dynamic anomaly detection, feature screening and optimal feature selection; and sends the data to the neurosurgery vascular nursing assessment model establishment module;

[0029] The neurosurgery vascular care evaluation model establishment module extracts and fuses time-dependent and spatial interaction features through residual networks, convolutional gated recurrent units, and multi-head attention mechanisms, and finally realizes the nursing status prediction; and sends the data to the neurosurgery vascular care monitoring module;

[0030] The neurosurgery vascular care monitoring module implements status evaluation of real-time collected data based on the established neurosurgery vascular care evaluation model, thereby realizing care monitoring.

[0031] Example 2, see Figure 1This embodiment is based on the above embodiment. In the medical information acquisition module, the historical medical care data includes dynamic physiological signals, chemical metabolic data, time and care status; the care status includes normal, mild abnormality and severe abnormality; the care status is used as a data label; the dynamic physiological signals include intracranial pressure, cerebral perfusion pressure, cerebral blood flow velocity, cerebral oxygen saturation, heart rate, blood pressure, cardiac output, patient position and activity status; the chemical metabolic data includes cerebrospinal fluid detection data and blood detection data.

[0032] Example 3, see Figure 1 and Figure 2 This embodiment is based on the above embodiment, and the medical data optimization module specifically includes the following contents:

[0033] Preliminary anomaly detection: For dynamic physiological signal data, detect anomalies caused by sensor failures and introduce a dynamic threshold mechanism based on sliding windows, expressed as: ; ; ; Construct historical sequence features for historical medical care data after preliminary anomaly removal; where IF(·) is the anomaly detection score; x is the data point; mT is the number of time steps, t is the time step index; E(·) is the anomaly path length; n is the number of samples; c is the normalization coefficient; is the dynamic anomaly weight; is the data point value at time step t; k is the neighborhood size, ik is the neighbor index; d(·) is the Euclidean distance; is the nearest neighbor point; is the maximum neighbor distance; is a sliding window[ ] is the median value within ; and are the standard deviation and median absolute deviation, respectively;

[0034] Preliminary feature screening: In medical data, different features contribute differently to cerebrovascular abnormalities, so the marginal contribution of each feature is quantified, and the category-weighted marginal contribution is introduced to increase the sensitivity to abnormal states; thus, preliminary feature screening is performed; expressed as: ; ; ;in, is the contribution value of the i1th feature; is the size of the feature subset S; is the size of the feature set N; is the category weight; and They are the weights of abnormal states; f1, f2 and f3 are normal state, mild abnormal state and severe abnormal state respectively; is a performance metric, obtained based on a pre-trained regression model; is the sample weight; i1 is the feature index; i is the data point index; is the number of data points contained in the category; is the category to which the data point belongs;

[0035] Optimal feature selection: for the correlation features of the preliminary feature screening, further select the optimal feature subset to enhance the combined analysis capability of dynamic physiological signals and chemical metabolism data; expressed as: ; ; ; ; ;in, is the balance weight; bZ is the final result of feature selection; m is the number of selected features; and is the feature weight; is the current feature subset; is the true nursing status label of the i-th sample; is the predicted label of the ith sample based on the feature subset; f is a single feature; c is the target care status; I(·) is the mutual information; f i1 and f j1 are two features in the feature subset; Var(·) is the variance; y and They are the true label and the predicted label respectively; is the KL divergence; is the normalization constant; is the balancing factor.

[0036] By performing the above operations, in view of the lack of dynamic anomaly detection and insufficient feature screening of abnormal signal dependence in general neurosurgery vascular care monitoring systems, which leads to low accuracy of nursing status assessment and poor nursing monitoring effect, this scheme uses the dynamic threshold mechanism of the sliding window and the abnormal path length scoring to eliminate pseudo anomalies caused by sensor failures and data fluctuations, increases the sensitivity to mild and severe abnormal nursing states based on the category-weighted marginal contribution mechanism, introduces a combined indicator of mutual information and correlation redundancy regularization for feature selection, and optimizes historical medical care data through anomaly detection, screening and feature selection, thereby improving the accuracy of subsequent nursing status assessment and improving nursing monitoring effects.

[0037] Example 4, see Figure 1 This embodiment is based on the above embodiment. The neurosurgery vascular care evaluation model establishment module is based on the historical medical care data processed by the medical data optimization module, and specifically includes the following contents:

[0038] Feature extraction: Physiological signals and chemical data have obvious time dependence. Abnormal short-term fluctuations may be a precursor to impaired cerebrovascular regulation, while long-term trend changes reflect worsening of the disease. Therefore, the spatial correlation between physiological signals is extracted through the residual network to capture the interactive relationship between different monitoring signals, which is expressed as: ; Dynamic time features are extracted based on the convolution operation through the convolution gated recurrent unit to capture the time dependency of physiological signals, which is expressed as: ; ;in, is the historical medical care data input at time step ti; W a is the input weight matrix; is the bias vector; Softmax(·) is the Softmax function; is the residual network output; It is a spatial correlation feature, reflecting the interactive relationship between different physiological signals; It is the update gate; and They are the convolution weight matrix and the circulation weight matrix respectively; is the convolutional gated recurrent unit bias vector; is the Sigmoid activation function; is the candidate state for the current time step; is the final hidden state; is a hidden state;

[0039] Feature fusion: The changes of different physiological signals in the time dimension have different importance for the prediction of abnormal cerebral blood flow. The relationship between features is captured by the parallel attention operation of the multi-head attention mechanism, which is expressed as: ; ; Global pooling based on adaptive pooling is expressed as: ; Add feature regularization term, expressed as: ; Finally, the nursing status is predicted through the fully connected layer; the model uses the cross entropy loss function and the gradient descent method to optimize the parameters; where MHd(·) is the output of the multi-head attention mechanism; Q, K and V are the query matrix, key matrix and value matrix respectively; , and They are the outputs of the 1st, 2nd, and n2th attention heads respectively; , and is the corresponding weight; is the linear mapping weight; It is a splicing operation; and is the i2th linear transformation result; and is the j2th linear transformation result; Sim(·) is; is the feature after pooling; is an adaptive pooling operation; is the size of the output feature; x is the input feature matrix, including short-term fluctuations and long-term trend features; is the feature regularization term of the fully connected layer; is the weight matrix of the fully connected layer; is the regularization strength; is the L2 norm.

[0040] By performing the above operations, the general neurosurgery vascular care monitoring system has poor ability to capture dynamic physiological signals, insufficient collaborative analysis capabilities, resulting in inaccurate nursing status assessment, and insufficient sensitivity to changes in nursing data distribution, resulting in poor nursing monitoring effects. This solution is based on efficient feature extraction based on time and space dependencies to capture the spatial interaction relationship between dynamic physiological signals, while capturing short-term fluctuations and long-term trends of physiological signals, improving the sensitivity to disease change trends, and introducing a multi-head attention mechanism based on dynamic attention weights to improve the prediction accuracy of nursing status, thereby improving nursing monitoring effects.

[0041] Example 5, see Figure 1 This embodiment is based on the above embodiment. The neurosurgery vascular care monitoring module is based on the established neurosurgery vascular care evaluation model, collects medical care data in real time, and uses the model output as the evaluation result of the collected medical care data. When the evaluation result is seriously abnormal, the nursing staff is warned.

[0042] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0043] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

[0044] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. Neurosurgery vascular care monitoring system based on medical information processing, characterized by: The system includes a medical information collection module, a medical data optimization module, a neurosurgery vascular care evaluation model establishment module, and a neurosurgery vascular care monitoring module; The medical information collection module collects historical medical care data; The medical data optimization module constructs data input for nursing status assessment through dynamic anomaly detection, feature screening and optimal feature selection; The neurosurgery vascular care assessment model building module extracts and fuses time-dependent and spatial interaction features through residual networks, convolutional gated recurrent units, and multi-head attention mechanisms, and finally realizes nursing status prediction; The neurosurgery vascular care monitoring module implements status evaluation of real-time collected data based on the established neurosurgery vascular care evaluation model, thereby realizing care monitoring; The medical data optimization module includes the following contents: preliminary anomaly detection; for dynamic physiological signal data, detecting anomalies caused by sensor failures, and introducing a dynamic threshold mechanism based on a sliding window, which is expressed as: ; ; ; Construct historical sequence features for historical medical care data after preliminary anomaly removal; where IF(·) is the anomaly detection score; x is the data point; mT is the number of time steps, t is the time step index; E(·) is the anomaly path length; n is the number of samples; c is the normalization coefficient; is the dynamic anomaly weight; is the data point value at time step t; k is the neighborhood size, ik is the neighbor index; d(·) is the Euclidean distance; is the nearest neighbor point; is the maximum neighbor distance; is a sliding window[ ] is the median value within ; and are the standard deviation and median absolute deviation respectively.

2. The neurosurgery vascular care monitoring system based on medical information processing according to claim 1 is characterized in that: The medical data optimization module specifically includes the following contents: Preliminary anomaly detection; Preliminary feature screening: In medical data, different features contribute differently to cerebrovascular abnormalities, so the marginal contribution of each feature is quantified, and the category-weighted marginal contribution is introduced to perform preliminary feature screening; expressed as: ; ; ;in, is the contribution value of the i1th feature; is the size of the feature subset S; is the size of the feature set N; is the category weight; and They are the weights of abnormal states; f1, f2 and f3 are normal state, mild abnormal state and severe abnormal state respectively; is a performance metric, obtained based on a pre-trained regression model; is the sample weight; i1 is the feature index; i is the data point index; is the number of data points contained in the category; is the category to which the data point belongs; Optimal feature selection: For the correlation features of the preliminary feature screening, further select the optimal feature subset, which is expressed as: ; ; ; ; ;in, is the balance weight; bZ is the final result of feature selection; m is the number of selected features; and is the feature weight; is the current feature subset; is the true nursing status label of the i-th sample; is the predicted label of the ith sample based on the feature subset; f is a single feature; c is the target care status; I(·) is the mutual information; f i1 and f j1 are two features in the feature subset; Var(·) is the variance; y and They are the true label and the predicted label respectively; is the KL divergence; is the normalization constant; is the balancing factor.

3. The neurosurgery vascular care monitoring system based on medical information processing according to claim 2 is characterized in that: The neurosurgery vascular care evaluation model establishment module is based on the historical medical care data processed by the medical data optimization module and specifically includes the following contents: Feature extraction; Physiological signals and chemical data have obvious time dependence. Abnormal short-term fluctuations may be a precursor to impaired cerebrovascular regulation, while long-term trend changes reflect worsening of the disease. Therefore, the spatial correlation between physiological signals is extracted through the residual network to capture the interactive relationship between different monitoring signals, which is expressed as: ; Dynamic time features are extracted based on the convolution operation through the convolution gated recurrent unit to capture the time dependency of physiological signals, which is expressed as: ; ;in, is the historical medical care data input at time step ti; W a is the input weight matrix; is the bias vector; Softmax(·) is the Softmax function; is the residual network output; It is a spatial correlation feature, reflecting the interactive relationship between different physiological signals; It is the update gate; and They are the convolution weight matrix and the circulation weight matrix respectively; is the convolutional gated recurrent unit bias vector; is the Sigmoid activation function; is the candidate state for the current time step; is the final hidden state; is a hidden state; Feature fusion: The changes of different physiological signals in the time dimension have different importance for the prediction of abnormal cerebral blood flow. The relationship between features is captured by the parallel attention operation of the multi-head attention mechanism, which is expressed as: ; ; Global pooling based on adaptive pooling is expressed as: ; Add feature regularization term, expressed as: ; Finally, the nursing status is predicted through the fully connected layer; the model uses the cross entropy loss function and the gradient descent method to optimize the parameters; where MHd(·) is the output of the multi-head attention mechanism; Q, K and V are the query matrix, key matrix and value matrix respectively; , and They are the outputs of the 1st, 2nd, and n2th attention heads respectively; , and is the corresponding weight; is the linear mapping weight; It is a splicing operation; and is the i2th linear transformation result; and is the j2th linear transformation result; Sim(·) is; is the feature after pooling; is an adaptive pooling operation; is the size of the output feature; x is the input feature matrix, including short-term fluctuations and long-term trend features; is the feature regularization term of the fully connected layer; is the weight matrix of the fully connected layer; is the regularization strength; is the L2 norm.

4. The neurosurgery vascular care monitoring system based on medical information processing according to claim 3 is characterized in that: In the medical information collection module, the historical medical care data includes dynamic physiological signals, chemical metabolism data, time and care status; the care status includes normal, mild abnormality and severe abnormality; and the care status is used as a data label.

5. The neurosurgery vascular care monitoring system based on medical information processing according to claim 4 is characterized in that: The neurosurgery vascular care monitoring module is based on an established neurosurgery vascular care evaluation model, collects medical care data in real time, and uses the model output as the evaluation result of the collected medical care data. When the evaluation result is seriously abnormal, the nursing staff is warned.

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