Risk early warning system for adjusting patient monitoring level

Abstract

The embodiment of the invention discloses a risk early warning system for adjusting a patient monitoring level. A specific embodiment of the system comprises the following steps: acquiring patient historical monitoring data and current monitoring data of a to-be-evaluated patient, and calling patient basic information of the to-be-evaluated patient; generating physical sign comparison data before and after an operation; selecting a complication structure tree matched with the to-be-evaluated patient to obtain a target complication structure tree, and correcting the target complication structure tree to obtain a corrected complication structure tree; acquiring case data matched with the sign comparison data before and after the operation from a pre-connected case data server, and performing data processing on the acquired case data through a second data processing device to obtain a processed case data set; generating a patient personalized prediction model; and performing risk grade evaluation on the patient to be evaluated, and controlling the man-machine interaction device to execute corresponding risk early warning operation. According to the embodiment, the safety of a patient can be improved.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the medical field and the computer technology field, and in particular to a risk warning system for adjusting a patient monitoring level. Background Art

[0002] Studies have shown that the probability of unplanned return to the ICU (Intensive Care Unit) is high for critically ill neurosurgery patients after they are transferred out of the ICU. Once an unplanned return to the ICU occurs, a series of problems will increase the patient's hospitalization costs, length of stay, increase delirium, and reduce the prognosis effect. Therefore, it is particularly important to identify high-risk groups of patients transferred out of neurological critical care early, so as to implement precise care for them and reduce the incidence of unplanned return to the ICU. At present, in clinical practice, the risk prediction of unplanned return to the ICU is mostly based on manual judgment. The prediction results are highly subjective and vary greatly due to the different professional abilities of each nurse. Therefore, there is an urgent need for a risk warning system that can accurately and objectively predict the high-risk groups of unplanned return to the ICU, improve the risk monitoring of patients, and thus improve patient safety.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0004] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0005] Some embodiments of the present disclosure propose a risk warning system for adjusting a patient monitoring level to solve the technical problems mentioned in the above background technology section.

[0006] In a first aspect, some embodiments of the present disclosure provide a risk warning system for adjusting a patient monitoring level, the system comprising: obtaining historical patient monitoring data and current patient monitoring data of a patient to be evaluated from a first data preprocessing device, and retrieving basic patient information of the patient to be evaluated from a multi-physiological parameter database; generating pre- and post-operative physical sign comparison data based on the current monitoring data, historical monitoring data and basic patient information, wherein the pre- and post-operative physical sign comparison data is used to characterize changes in physical sign data of the patient to be evaluated before and after the operation; selecting a complication structure tree matching the patient to be evaluated from a set of complication structure trees pre-established in the multi-physiological parameter database to obtain a target complication structure tree, and performing a comparison of the target complication structure tree based on the pre- and post-operative physical sign comparison data. The complication structure tree is modified to obtain a modified complication structure tree; case data matching the pre- and post-operative physical sign comparison data are obtained from a pre-connected case data server, and each obtained case data is processed by a second data processing device to obtain a processed case data set; the processed case data set is input into a risk assessment module to obtain a patient personalized prediction model, wherein the risk assessment module performs online additional training on the risk prediction basic model through the processed case data set to generate a patient personalized prediction model; risk level assessment is performed on the patient to be evaluated through the patient personalized prediction model and the modified complication structure tree to obtain risk level assessment information, and according to the risk level assessment information, the human-computer interaction device is controlled to perform corresponding risk warning operations.

[0007] In a second aspect, some embodiments of the present disclosure provide a risk warning device for adjusting a patient monitoring level, the device comprising: an acquisition unit, configured to acquire historical patient monitoring data and current monitoring data of a patient to be evaluated from a first data preprocessing device, and to retrieve basic patient information of the patient to be evaluated from a multi-physiological parameter database; a first generation unit, configured to generate pre- and post-operative physical sign comparison data based on the current monitoring data, historical monitoring data and basic patient information, wherein the pre- and post-operative physical sign comparison data is used to characterize changes in physical sign data of the patient to be evaluated before and after the operation; a selection and correction unit, configured to select a complication structure tree matching the patient to be evaluated from a set of complication structure trees pre-established in the multi-physiological parameter database, to obtain a target complication structure tree, and to perform a correction on the target complication structure tree based on the pre- and post-operative physical sign comparison data. The tree is modified to obtain a modified complication structure tree; the acquisition and processing unit is configured to obtain case data matching the pre- and post-operative physical sign comparison data from a pre-connected case data server, and to perform data processing on each acquired case data through a second data processing device to obtain a processed case data set; the model training unit is configured to input the processed case data set into a risk assessment module to obtain a patient personalized prediction model, wherein the risk assessment module performs online additional training on the risk prediction basic model through the processed case data set to generate a patient personalized prediction model; the assessment and risk warning unit is configured to perform risk level assessment on the patient to be assessed through the patient personalized prediction model and the modified complication structure tree to obtain risk level assessment information, and to control the human-computer interaction device to perform corresponding risk warning operations according to the risk level assessment information.

[0008] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the system described in any implementation method of the above-mentioned first aspect.

[0009] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the system described in any implementation of the first aspect is implemented.

[0010] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the risk warning system for adjusting the patient monitoring level of some embodiments of the present disclosure, the monitoring accuracy of patient monitoring can be improved and the patient safety can be improved. Specifically, the reason for the insufficient monitoring of patients and the high probability of unplanned return to the ICU is that manual judgment is often used, the prediction results are highly subjective, and the differences are large due to the different professional capabilities of each nursing staff. Based on this, the risk warning system for adjusting the patient monitoring level of some embodiments of the present disclosure, first, by setting the risk warning system and the first data preprocessing device, the multi-physiological parameter database, the risk assessment module, the second data processing device and the human-computer interaction device included, the automation of patient monitoring can be realized. Specifically, first, the patient historical monitoring data and the current monitoring data of the patient to be evaluated are obtained from the first data preprocessing device, and the patient basic information of the patient to be evaluated is retrieved from the multi-physiological parameter database. According to the above-mentioned current monitoring data, historical monitoring data and the above-mentioned patient basic information, the pre-operative and post-operative sign comparison data are generated, wherein the pre-operative and post-operative sign comparison data are used to characterize the changes in the sign data of the patient to be evaluated before and after the operation. Here, considering the impact of surgery on the patient's body, the multi-physiological parameter database is set to retain the patient's data before and after the surgery for comparison. Thus, it can be used to determine the patient's physical changes before and after the surgery. Then, the complication structure tree matching the above-mentioned patient to be evaluated is selected from the complication structure tree set pre-established in the multi-physiological parameter database to obtain the target complication structure tree, and the above-mentioned target complication structure tree is corrected according to the above-mentioned physical sign comparison data before and after the surgery to obtain the corrected complication structure tree. Here, considering that different surgeries correspond to different complications, and the degree and probability of complications are also different. Therefore, by setting a tree structure, the corresponding complications are associated with different surgeries. Thus, through the classification and refinement of the tree structure, the risk warning system can further improve its ability to predict the patient's postoperative condition. At the same time, because of the introduction of the tree structure, it is possible to quickly find the associated complications and the probability of occurrence in the numerous data. At the same time, considering the differences in the physical conditions of different patients, the physical sign comparison data before and after the surgery, that is, the changes in the patient's own physical condition are used to further adjust the complication structure tree to obtain the corrected complication structure tree. Thus, the corrected complication structure tree is more suitable for the patient to be evaluated. Then, case data matching the pre- and post-operative physical sign comparison data are obtained from the pre-connected case data server, and each case data obtained is processed by a second data processing device to obtain a processed case data set. The processed case data set is input into the risk assessment module to obtain a patient personalized prediction model, wherein the risk assessment module performs online additional training on the risk prediction basic model through the processed case data set to generate a patient personalized prediction model.Here, considering that there is still a large coupling when only referring to the changes in the patient's own physical signs, the pre-trained risk prediction basic model is trained online by introducing processed case data that matches the patient's physical signs and case data. Obtain a personalized patient prediction model. Here, because case data similar to the patient is introduced, not only can the changes in the patient itself be greatly retained in model training, but the model can also be trained by similar case data to reduce coupling. Finally, the risk level of the patient to be evaluated is evaluated through the above-mentioned patient personalized prediction model and the above-mentioned modified complication structure tree, and the risk level assessment information is obtained, and according to the above-mentioned risk level assessment information, the human-computer interaction device is controlled to perform the corresponding risk warning operation. Thereby, the patient monitoring level can be adjusted in time, the risk of unplanned return to the ICU can be reduced, and the patient's safety can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0012] Figure 1 is a flow chart of some embodiments of a risk warning system for adjusting a patient monitoring level according to the present disclosure; Figure 2 It is a schematic diagram of the complication tree structure; Figure 3 It is a partial schematic diagram of the revised complication structure tree; Figure 4 is a schematic diagram of the structure of some embodiments of the risk warning device for adjusting the patient monitoring level according to the present disclosure; Figure 5 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0013] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0014] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0015] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0016] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0017] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0018] With regard to the collection, storage, and use of user personal information (such as user vital signs data, user basic data, etc.) involved in this disclosure, before performing the corresponding operations, the relevant organizations or individuals shall fulfill their obligations, including conducting personal information security impact assessments, fulfilling the obligation to inform the personal information subject, and obtaining the authorization and consent of the personal information subject in advance. The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0019] Figure 1 A process 100 of some embodiments of a risk warning system for adjusting a patient monitoring level according to the present disclosure is shown. The above-mentioned risk warning system includes: a first data preprocessing device, a multi-physiological parameter database, a risk assessment module, a second data processing device, and a human-computer interaction device. Among them, the first data preprocessing device may be a device for processing personal data of a patient to be evaluated. The second data processing device may be a device for processing other case data. The multi-physiological parameter database may be used to store patient data. The risk assessment module may be a module for performing a risk assessment on a patient to be evaluated to determine whether he or she has an unplanned risk of returning to the ICU and adjusting the monitoring strategy. The human-computer interaction device may be a device for notifying a corresponding medical terminal to issue a risk warning.

[0020] The risk warning system for adjusting the patient monitoring level includes the following steps: Step 101 : acquiring historical patient monitoring data and current patient monitoring data of a patient to be evaluated from a first data preprocessing device, and retrieving basic patient information of the patient to be evaluated from a multi-physiological parameter database.

[0021] In some embodiments, the risk warning system (i.e., the executing entity) for adjusting the patient monitoring level can obtain the patient historical monitoring data and current monitoring data of the patient to be evaluated from the first data preprocessing device in a wired or wireless manner, and retrieve the patient basic information of the above-mentioned patient to be evaluated from the multi-physiological parameter database.

[0022] It should be noted that the above-mentioned computing device (executing subject) can be hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the computing device is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here. It should be understood that the number of computing devices can be any number according to the implementation needs.

[0023] In some optional implementations of some embodiments, the execution subject obtains the patient historical monitoring data and current monitoring data of the patient to be evaluated from the first data preprocessing device, and retrieves the patient basic information of the patient to be evaluated from the multi-physiological parameter database, including: The first step is to control the first data preprocessing device to obtain initial historical vital sign data and current monitoring data from the patient vital sign monitoring device. The current monitoring data includes: postoperative basic data, postoperative muscle strength test value, postoperative metabolic score value, postoperative mental cognition score value, disease identification, surgical identification, and indwelling drainage tube information.

[0024] Here, the postoperative basic data may be basic data characterizing the patient's body. For example, blood pressure, blood oxygen concentration, body temperature, heart rate, arterial pressure, respiratory rate, hematocrit, white blood cell count, etc. The postoperative muscle strength test value may be the muscle grade value of the patient after surgery that was pre-tested. For example, the muscle strength grade measured by the manual muscle strength test (Manual Muscle Testing, MMT). The postoperative metabolic score value may be used to characterize the patient's postoperative body metabolic capacity. For example, it may be measured by a mini-nutrition assessment or a (Nutrition Risk in Critically Ill Score) NUTRIC score. The postoperative mental cognition score may characterize the patient's mental state measured by a mental testing method. For example, the mental testing method may include: MMSE (Mini-Mental State Examination) simple mental state examination, MoCA (Montreal Cognitive Assessment) Montreal cognitive assessment, HAMD (Hamilton Depression Scale) Hamilton Depression Rating Scale or BPRS (Brief Psychiatric Rating Scale) brief mental illness rating scale. In addition, the disease identifier may be a unique identifier of the patient's condition. The surgical identification may be an identification of the surgical operation performed on the patient. The indwelling drainage tube information may include the number of indwelling drainage tubes and the drainage tube identification corresponding to each indwelling drainage tube. The indwelling drainage tube information represents the condition of the drainage tubes remaining in the patient's body after surgery.

[0025] The second step is to control the first data preprocessing device to perform data standardization on the initial historical vital sign data to obtain the patient historical monitoring data. The patient historical monitoring data includes: preoperative muscle strength test value, preoperative metabolic score value, preoperative basic data and preoperative vital sign ability value. Secondly, data standardization can be to arrange the various data in the initial historical vital sign data according to a preset field order to obtain the patient historical monitoring data. This facilitates subsequent data comparison. Here, the patient historical monitoring data can represent the various data of the patient to be evaluated before surgery.

[0026] The third step is to retrieve the basic information of the patient to be evaluated from the multi-physiological parameter database, wherein the basic information of the patient includes: past medical history information, smoking duration, and drinking duration.

[0027] Step 102, generating pre- and post-operative physical sign comparison data based on current monitoring data, historical monitoring data and basic patient information.

[0028] In some embodiments, the execution subject may generate pre- and post-operative physical sign comparison data based on the current monitoring data, historical monitoring data and the patient's basic information, wherein the pre- and post-operative physical sign comparison data is used to characterize the changes in physical sign data of the patient to be evaluated before and after the operation.

[0029] In practice, when predicting the risk of patients before and after surgery, the data before and after surgery can be used for comparison. In addition, for the two data to be compared with each other, it can also be: by obtaining the patient's physical sign data before being transferred out of the ICU and the data after being transferred out of the ICU, to predict the possibility of the patient's unplanned return to the ICU. Or: obtain the patient's physical sign data before being transferred out of the ICU and the physical sign data before the operation to predict whether the patient needs to return to the ICU after the operation. Therefore, no specific limitation is made.

[0030] In some optional implementations of some embodiments, the execution subject generates pre- and post-operative physical sign comparison data based on the current monitoring data, historical monitoring data and the basic information of the patient, including: In the first step, the postoperative basic data in the above-mentioned current monitoring data are evaluated by a preset scoring scale to generate a current basic data scoring value, and the current basic data scoring value is compared with the health scoring value in the above-mentioned preoperative basic data to generate an acute physiology and chronic health score. Among them, the scoring scale can be an Apache2 (Acute Physiology and Chronic Health) scoring scale. Among them, the postoperative basic data of the patient to be evaluated (for example, body temperature, mean arterial pressure, heart rate, respiratory rate, oxygenation, blood pH, hematocrit, white blood cell count, etc.) can be evaluated by the scoring scale to obtain the current basic data scoring value. Here, the current basic data scoring value can represent the overall scoring value of the patient's postoperative basic data. Then, the difference between the current basic data scoring value and the health scoring value in the preoperative basic data can be determined as the acute physiology and chronic health score.

[0031] The second step is to generate a muscle strength change grade according to the absolute value of the change between the preoperative muscle strength test value and the postoperative muscle strength test value, wherein the muscle strength change grade can be determined as the grade difference between the muscle strength grades corresponding to the preoperative muscle strength test value and the postoperative muscle strength test value.

[0032] For example, the absolute value of the difference in muscle strength change is two levels (from a level 5 muscle strength test value to a level 3 muscle strength test value after surgery). In practice, a decrease in muscle strength of two levels or more after surgery can be considered a significant change.

[0033] The third step is to generate a metabolic change coefficient according to the postoperative metabolic score value and the preoperative metabolic score value, wherein the metabolic difference between the postoperative metabolic score value and the preoperative metabolic score value can be determined as the metabolic change coefficient.

[0034] The fourth step is to generate a mental cognition change coefficient based on the postoperative mental cognition score and the preoperative mental cognition score in the preoperative basic data. The mental score difference between the postoperative mental cognition score and the preoperative mental cognition score in the preoperative basic data can be determined as the mental cognition change coefficient.

[0035] The fifth step is to determine the above-mentioned patient basic information, the above-mentioned acute physiological and chronic health scores, the above-mentioned muscle strength change levels, the above-mentioned metabolic change coefficients, the above-mentioned mental cognitive change coefficients, disease identification, surgical identification and indwelling drainage tube information as pre- and post-operative physical sign comparison data.

[0036] Step 103, selecting a complication structure tree matching the patient to be evaluated from a set of complication structure trees pre-established in the multi-physiological parameter database to obtain a target complication structure tree, and modifying the target complication structure tree based on the comparison data of physical signs before and after the operation to obtain a modified complication structure tree.

[0037] In some embodiments, the above-mentioned execution entity can select a complication structure tree matching the above-mentioned patient to be evaluated from a set of complication structure trees pre-established in a multi-physiological parameter database to obtain a target complication structure tree, and modify the above-mentioned target complication structure tree based on the above-mentioned pre- and post-operative physical sign comparison data to obtain a modified complication structure tree.

[0038] In some optional implementations of some embodiments, the execution subject selects a complication tree structure matching the patient to be evaluated from a set of complication tree structures pre-established in the multi-physiological parameter database to obtain a target complication tree structure, including: A complication tree structure that matches the current monitoring data including the surgical identification and the disease identification is selected from the multi-physiological parameter database as the target complication tree structure. In response to no match, a single match is performed again through the surgical identification or the disease identification. In response to a single match to a complication tree structure, it is used as the target complication tree structure. In response to a single match to two complication tree structures, the two complication tree structures are merged into a target complication tree structure, and the target complication tree structure includes the associated complication identifications and the corresponding concurrent probabilities.

[0039] In practice, it is considered that complications are too complicated and numerous. It is easy to ignore some items or the evaluation of a certain item is not accurate enough when manually confirmed. Therefore, by introducing the complication structure tree, a complication structure tree corresponding to each operation and disease can be established. In this way, the corresponding complications can be intuitively extracted and their corresponding probability of occurrence can be determined. Here, it is also considered that the construction of the complication structure tree is the construction of a single main node, that is, it is constructed according to each operation identifier as the main node or the disease identifier as the main node. Therefore, for matching the operation identifier and the disease identifier at the same time, it is easy to match two complication structure trees, and they correspond to some non-repeated complications respectively. Therefore, when matching two complication structure trees, the structure tree fusion can be used to merge the sub-nodes (i.e., the nodes of the complication identifier) ​​under the two main nodes of the operation identifier and the disease identifier into the same structure tree to obtain the target complication structure tree. Therefore, it can not only reduce the time and computing resources occupied by data matching, but also provide technical support for subsequent disease matching.

[0040] As an example, Figure 2 Schematic diagram of the complication structure tree shown. The main node is a neurosurgery operation (e.g., ventricular shunt surgery). Each corresponding complication category is a child node, namely intracranial complications, infection-related complications, metabolic / electrolyte disorders, and other complications. Each child node can also be divided into at least one complication as a leaf node. For example, the leaf nodes of intracranial complications include leaf nodes such as recurrence of hydrocephalus, subdural hematoma, and intracranial infection. Each leaf node also includes a corresponding concurrent probability. The concurrent probability of a child node is the sum of the leaf nodes under it.

[0041] In some optional implementations of some embodiments, the execution subject modifies the target complication structure tree according to the pre- and post-operative physical sign comparison data to obtain a modified complication structure tree, including: The first step is to determine the probability correction ratio and probability correction mark corresponding to the above-mentioned patient basic information, the above-mentioned acute physiological and chronic health score, the above-mentioned muscle strength change level, the above-mentioned metabolic change coefficient, the above-mentioned mental cognition change coefficient, the disease mark, the surgical mark, and the indwelling drainage tube information in the above-mentioned pre- and post-operative physical sign comparison data according to the pre-selected probability correction table.

[0042] Among them, the probability correction table may include various fields and corresponding probability correction ratios. Here, each field in the above probability correction table may correspond to multiple field value intervals, and each field value may correspond to a probability correction ratio. Specifically, each field may be a field value interval and a corresponding probability correction ratio pre-selected by a piecewise function. Thus, the corresponding probability correction ratio can be extracted for each parameter in the above patient basic information, the above acute physiology and chronic health score, the above muscle strength change level, the above metabolic change coefficient, the above mental cognitive change coefficient, the disease mark, the surgical mark, and the indwelling drainage tube information.

[0043] In the second step, through each probability correction ratio, the concurrent probability value corresponding to each probability correction mark in the above target complication structure tree is obtained to obtain the corrected complication structure tree.

[0044] As an example, for every five points increase in the acute physiology and chronic health score, the probability of the corresponding inflammatory reaction complications increases by 5% (upper limit 50%). If the muscle strength change level is greater than or equal to level 2, the probability of neuromuscular complications increases by 20%. Each drainage tube is indwelling for more than 7 days, and the corresponding probability of intracranial complications increases by 8%. If the body temperature is greater than 38.5 degrees for 3 days after surgery, the probability value of infection-related complications is ×1.5. In addition, the basic information of the patient includes: past medical history information, smoking duration, and drinking duration, and the probability value can also be modified accordingly. For example, if there is a history of cardiovascular disease, the risk of deep vein thrombosis increases by 25%. For example, if the (recent) smoking duration is less than six months, the risk of lung infection increases by 35%, and the risk of delayed wound healing increases by 25%. If you drink alcohol for a long time, the risk of electrolyte disorders increases by 42%. Drinking alcohol within three days before surgery increases the probability of abnormal coagulation function by 30%.

[0045] like Figure 3 As shown, a partial schematic diagram of the revised complication structure tree. Figure 2 For different patients, the probability values ​​after correction are all improved. Therefore, it can be further shown that for different patients, it is necessary to introduce the changes of personal physical characteristics before and after surgery to correct the complication structure tree so that it is more in line with the patient's personal disease situation.

[0046] Step 104, obtaining case data matching the pre- and post-operative physical sign comparison data from the pre-connected case data server, and performing data processing on each acquired case data by a second data processing device to obtain a processed case data set.

[0047] In some embodiments, the execution subject may obtain case data matching the pre- and post-operative physical sign comparison data from a pre-connected case data server, and process each acquired case data through a second data processing device to obtain a processed case data set.

[0048] In some optional implementations of some embodiments, the execution subject obtains case data matching the pre- and post-operative physical sign comparison data from a pre-connected case data server, and processes each acquired case data through a second data processing device to obtain a processed case data set, including: The first step is to obtain case data matching the pre- and post-operative physical sign comparison data and the modified complication structure tree from the case data server to obtain a case data set. The case data server may include data in a multi-physiological parameter database and case data from other shared platforms. Secondly, the matching may be that the surgical identification and the disease identification in the case data are the same. That is, case data corresponding to the same case and surgery are extracted.

[0049] The second step is to screen each case data in the case data set through the second data processing device to obtain a processed case data set. Among them, each processed case data in the processed case data set matches the age group, gender, surgical location identifier, surgical identifier and disease identifier of the patient to be evaluated. Here, the second data processing device can be used to screen case data. The processed case data is matched with the age group, gender, surgical location identifier, surgical identifier and disease identifier of the patient to be evaluated. In this way, similar case data can be selected. Thus, the model prediction error caused by differences in age group and gender can be avoided. Furthermore, the prediction accuracy of the model for the probability of complications is improved.

[0050] Step 105, input the processed case data set into the risk assessment module to obtain a patient personalized prediction model.

[0051] In some embodiments, the execution subject may input the processed case data set into a risk assessment module to obtain a patient personalized prediction model. The risk assessment module performs online additional training on the risk prediction basic model through the processed case data set to generate a patient personalized prediction model.

[0052] In practice, the above-mentioned basic risk prediction model can be a preset teacher model. Here, the structure of the teacher model may include: an input layer, a first hidden layer, a second hidden layer, and an output layer. Specifically, the input dimension of the input layer may be N×D, that is, the number of input batches × the feature dimension. For example, 1×20. The output is N×128. The first hidden layer can use a bidirectional LSTM (Long Short-Term Memory) and a fully connected layer for feature processing. The second hidden layer can perform feature fusion according to the feature dimension through feature concatenation and attention mechanism. The output layer can output a probability prediction value sequence of the same length as the preset complication sequence through a fully connected layer and a Softmax normalization layer as a prediction output. Thus, the basic risk prediction model is obtained by pre-training the teacher model. In addition, in the pre-training process, the data set used includes data of various cases. Thus, the teacher model can have the basic ability to predict the probability of various complications according to the patient's physical condition.

[0053] Here, the above-mentioned risk prediction basic model can be used as a teacher model and the above-mentioned processed case data set can be used as a training sample by means of knowledge distillation to train the corresponding student model and obtain a personalized prediction model for patients. Specifically, the model structure of the student model may include: a personalized input layer (for example, the processed case data is processed into a dimension of N×5 as input, and an N×64-dimensional data matrix is ​​output), a nonlinear activation layer, a regularization layer, a fully connected layer, a splicing layer, a fusion fully connected layer, and an output layer. When training the student model, the weight of the teacher model can be frozen. In this way, not only can a personalized complication prediction model be adapted for each patient individually, but it can also rely on the teacher model to reduce coupling. In addition, different student models can be trained at different time periods after the patient's surgery, or the same student model can be cumulatively trained.

[0054] Step 106, performing risk level assessment on the patient to be assessed through the patient personalized prediction model and the modified complication structure tree, obtaining risk level assessment information, and controlling the human-computer interaction device to perform corresponding risk warning operations according to the risk level assessment information.

[0055] In some embodiments, the above-mentioned execution entity can perform risk level assessment on the patient to be evaluated through the above-mentioned patient personalized prediction model and the above-mentioned revised complication structure tree, obtain risk level assessment information, and control the human-computer interaction device to perform corresponding risk warning operations based on the above-mentioned risk level assessment information.

[0056] In some optional implementations of some embodiments, the execution subject performs risk level assessment on the patient to be evaluated through the patient personalized prediction model and the modified complication structure tree to obtain risk level assessment information, and controls the human-computer interaction device to perform corresponding risk warning operations according to the risk level assessment information, including: The first step is to evaluate the risk level of the patient to be evaluated through the above-mentioned patient personalized prediction model to obtain a risk level evaluation probability value sequence. Among them, each risk level evaluation probability value in the above-mentioned risk level evaluation probability value sequence corresponds to a node in the above-mentioned revised complication structure tree. Here, the current monitoring data, historical monitoring data and the above-mentioned patient basic information of the patient to be evaluated can be combined into the input data of the model according to the preset field order, so as to be input into the above-mentioned patient personalized prediction model to obtain the risk level evaluation probability value sequence.

[0057] The second step is to make a secondary adjustment to the above-mentioned revised complication structure tree through the above-mentioned risk level assessment probability value sequence to obtain a secondary adjusted complication structure tree. Among them, for each risk level assessment probability value in the risk level assessment probability value sequence and the probability value of the corresponding node in the revised complication structure tree, the weighted sum of the two probability values ​​can be used as the secondary adjusted probability value. In this way, the secondary adjusted complication structure tree can be obtained.

[0058] The third step is to generate maintenance strategy information according to the above-mentioned complication structure tree after the second adjustment in response to the fact that the complication structure tree after the second adjustment meets the preset first maintenance adjustment condition, and send the above-mentioned maintenance strategy information to the maintenance processing terminal through the above-mentioned human-computer interaction device. Among them, the first maintenance adjustment condition can be that there is no node in the complication structure tree after the second adjustment that exceeds the warning probability value (that is, the complication occurrence probability value that does not exceed the corresponding warning probability value). Therefore, the patient data corresponding to the complication identification whose complication occurrence probability value exceeds the low-risk warning probability value can be used as the maintenance target, and the corresponding maintenance strategy information is generated. For example, the maintenance target is the postoperative muscle strength test value. Then the maintenance strategy information is the information for muscle strength training recovery. For another example, if the maintenance target is the postoperative metabolic score value, then the maintenance strategy information is the information for metabolic recovery. Here, the maintenance processing terminal can be a medical terminal.

[0059] In the fourth step, in response to the above-mentioned secondary adjusted complication structure tree satisfying the preset second maintenance adjustment condition, the human-computer interaction device is controlled to perform the corresponding risk warning operation to adjust the patient monitoring level. Among them, the second maintenance adjustment condition can be that there is a node in the complication structure tree after the secondary adjustment that exceeds the warning probability value (that is, the complication occurrence probability value that exceeds the corresponding warning probability value). For example, the probability of intracranial complications exceeds the warning probability value. Thus, the human-computer interaction device can be controlled to issue a warning prompt to the medical terminal. In order to adjust the patient monitoring level, such as returning to the ICU monitoring room.

[0060] In practice, the revised complication structure tree adjusted for a single disease or surgery still has a high degree of coupling. Therefore, by introducing the model prediction method, the risk prediction basic model is trained from a large number of cases. This makes the model's prediction of complication probability more universal. However, considering that there are hidden differences between different surgeries or cases, it is difficult to accurately locate the individual's situation if the same model is used for prediction. Therefore, the knowledge distillation model is introduced to train the student model using similar case data. This further improves the accuracy of the student model's prediction of the probability of complications associated with a single surgery or case. Finally, by making a secondary adjustment to the revised complication structure tree, the results of the two methods can be merged. As a result, the prediction results are not only universal, but also more suitable for the actual situation of the above-mentioned patients to be evaluated. Therefore, it can be used to accurately and objectively predict whether the patient needs to return to the ICU. This improves the risk monitoring of patients and ensures patient safety.

[0061] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the risk warning system for adjusting the patient monitoring level of some embodiments of the present disclosure, the monitoring accuracy of patient monitoring can be improved and the patient safety can be improved. Specifically, the reason for the insufficient monitoring of patients and the high probability of unplanned return to the ICU is that manual judgment is often used, the prediction results are highly subjective, and the differences are large due to the different professional capabilities of each nursing staff. Based on this, the risk warning system for adjusting the patient monitoring level of some embodiments of the present disclosure, first, by setting the risk warning system and the first data preprocessing device, the multi-physiological parameter database, the risk assessment module, the second data processing device and the human-computer interaction device included, the automation of patient monitoring can be realized. Specifically, first, the patient historical monitoring data and the current monitoring data of the patient to be evaluated are obtained from the first data preprocessing device, and the patient basic information of the patient to be evaluated is retrieved from the multi-physiological parameter database. According to the above-mentioned current monitoring data, historical monitoring data and the above-mentioned patient basic information, the pre-operative and post-operative sign comparison data are generated, wherein the pre-operative and post-operative sign comparison data are used to characterize the changes in the sign data of the patient to be evaluated before and after the operation. Here, considering the impact of surgery on the patient's body, the multi-physiological parameter database is set to retain the patient's data before and after the surgery for comparison. Thus, it can be used to determine the patient's physical changes before and after the surgery. Then, the complication structure tree matching the above-mentioned patient to be evaluated is selected from the complication structure tree set pre-established in the multi-physiological parameter database to obtain the target complication structure tree, and the above-mentioned target complication structure tree is corrected according to the above-mentioned physical sign comparison data before and after the surgery to obtain the corrected complication structure tree. Here, considering that different surgeries correspond to different complications, and the degree and probability of complications are also different. Therefore, by setting a tree structure, the corresponding complications are associated with different surgeries. Thus, through the classification and refinement of the tree structure, the risk warning system can further improve its ability to predict the patient's postoperative condition. At the same time, because of the introduction of the tree structure, it is possible to quickly find the associated complications and the probability of occurrence in the numerous data. At the same time, considering the differences in the physical conditions of different patients, the physical sign comparison data before and after the surgery, that is, the changes in the patient's own physical condition are used to further adjust the complication structure tree to obtain the corrected complication structure tree. Thus, the corrected complication structure tree is more suitable for the patient to be evaluated. Then, case data matching the pre- and post-operative physical sign comparison data are obtained from the pre-connected case data server, and each case data obtained is processed by a second data processing device to obtain a processed case data set. The processed case data set is input into the risk assessment module to obtain a patient personalized prediction model, wherein the risk assessment module performs online additional training on the risk prediction basic model through the processed case data set to generate a patient personalized prediction model.Here, considering that there is still a large coupling when only referring to the changes in the patient's own physical signs, the pre-trained risk prediction basic model is trained online by introducing processed case data that matches the patient's physical signs and case data. Obtain a personalized patient prediction model. Here, because case data similar to the patient is introduced, not only can the changes in the patient itself be greatly retained in model training, but the model can also be trained by similar case data to reduce coupling. Finally, the risk level of the patient to be evaluated is evaluated through the above-mentioned patient personalized prediction model and the above-mentioned modified complication structure tree, and the risk level assessment information is obtained, and according to the above-mentioned risk level assessment information, the human-computer interaction device is controlled to perform the corresponding risk warning operation. Thereby, the patient monitoring level can be adjusted in time, the risk of unplanned return to the ICU can be reduced, and the patient's safety can be improved. Further references Figure 4 As an implementation of the systems shown in the above figures, the present disclosure provides some embodiments of a risk warning device for adjusting a patient monitoring level. These device embodiments are similar to Figure 1 Corresponding to the system embodiments shown, the risk warning device for adjusting the patient monitoring level can be specifically applied to various electronic devices.

[0062] like Figure 4As shown, some embodiments of the risk warning device 400 for adjusting the patient monitoring level include: an acquisition unit 401, a first generation unit 402, a selection and correction unit 403, an acquisition and processing unit 404, a model training unit 405 and an estimation and risk warning unit 406. The acquisition unit 401 is configured to acquire the patient's historical monitoring data and current monitoring data of the patient to be evaluated from the first data preprocessing device, and retrieve the patient's basic information of the patient to be evaluated from the multi-physiological parameter database; the first generation unit 402 is configured to generate before and after physical sign comparison data based on the current monitoring data, historical monitoring data and the patient's basic information, wherein the before and after physical sign comparison data is used to characterize the change of physical sign data of the patient to be evaluated before and after the operation; the selection and correction unit 403 is configured to select a complication structure tree matching the patient to be evaluated from the complication structure tree set pre-established in the multi-physiological parameter database, obtain a target complication structure tree, and correct the target complication structure tree based on the before and after physical sign comparison data to obtain a corrected complication structure tree; the acquisition and The processing unit 404 is configured to obtain case data matching the above-mentioned before-and-after physical sign comparison data from a pre-connected case data server, and to perform data processing on each case data obtained through a second data processing device to obtain a processed case data set; the model training unit 405 is configured to input the above-mentioned processed case data set into the risk assessment module to obtain a patient personalized prediction model, wherein the above-mentioned risk assessment module performs online additional training on the risk prediction basic model through the above-mentioned processed case data set to generate a patient personalized prediction model; the assessment and risk warning unit 406 is configured to perform risk level assessment on the patient to be assessed through the above-mentioned patient personalized prediction model and the above-mentioned revised complication structure tree to obtain risk level assessment information, and control the human-computer interaction device to perform corresponding risk warning operations according to the above-mentioned risk level assessment information.

[0063] It is understandable that the various units recorded in the risk warning device 400 for adjusting the patient monitoring level are similar to those in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the system are also applicable to the device 400 and the units included therein, and will not be described in detail here. Reference below Figure 5 , which shows a schematic diagram of the structure of an electronic device (such as a risk warning system) suitable for implementing some embodiments of the present disclosure. Figure 5 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure. Figure 5As shown, the computer device includes a processor, a memory and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions, which, when executed, enable the processor to execute any risk warning system for adjusting the patient monitoring level. The processor is used to provide computing and control capabilities to support the operation of the entire computer device. The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium, which, when executed by the processor, enables the processor to execute any risk warning system for adjusting the patient monitoring level. The network interface is used for network communications, such as sending assigned tasks, etc. Those skilled in the art will appreciate that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present disclosure, and does not constitute a limitation on the computer device to which the scheme of the present disclosure is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0064] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0065] In one embodiment, the processor is used to run a computer program stored in a memory to implement the following steps: obtaining historical patient monitoring data and current patient monitoring data of the patient to be evaluated from a first data preprocessing device, and retrieving basic patient information of the patient to be evaluated from a multi-physiological parameter database; generating before-and-after physical sign comparison data based on the current monitoring data, historical monitoring data and basic patient information, wherein the before-and-after physical sign comparison data is used to characterize changes in physical sign data of the patient to be evaluated before and after surgery; selecting a complication structure tree matching the patient to be evaluated from a set of complication structure trees pre-established in the multi-physiological parameter database to obtain a target complication structure tree, and performing a comparison of the target complication structure tree based on the before-and-after physical sign comparison data. The tree is modified to obtain a modified complication structure tree; case data matching the before-and-after physical sign comparison data are obtained from a pre-connected case data server, and each case data obtained is processed by a second data processing device to obtain a processed case data set; the processed case data set is input into a risk assessment module to obtain a patient personalized prediction model, wherein the risk assessment module performs online additional training on the risk prediction basic model through the processed case data set to generate a patient personalized prediction model; risk level assessment is performed on the patient to be evaluated through the patient personalized prediction model and the modified complication structure tree to obtain risk level assessment information, and according to the risk level assessment information, the human-computer interaction device is controlled to perform corresponding risk warning operations.

[0066] An embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions. The method implemented when the program instructions are executed can refer to the various embodiments of the risk warning system for adjusting the patient monitoring level disclosed in the present disclosure.

[0067] The computer-readable storage medium may be an internal storage unit of the computer device in the aforementioned embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., provided on the computer device.

[0068] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system 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 system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0069] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A risk warning system for adjusting patient monitoring levels, characterized in that: The risk warning system comprises: a first data preprocessing device, a multi-physiological parameter database, a risk assessment module, a second data processing device and a human-computer interaction device, wherein: Acquire historical patient monitoring data and current patient monitoring data of the patient to be evaluated from the first data preprocessing device, and retrieve basic patient information of the patient to be evaluated from the multi-physiological parameter database; Generate pre- and post-operative physical sign comparison data based on the current monitoring data, historical monitoring data and the patient's basic information, wherein the pre- and post-operative physical sign comparison data is used to characterize changes in physical sign data of the patient to be evaluated before and after the operation; Selecting a complication structure tree matching the patient to be evaluated from a set of complication structure trees pre-established in a multi-physiological parameter database to obtain a target complication structure tree, and modifying the target complication structure tree according to the pre- and post-operative physical sign comparison data to obtain a modified complication structure tree; Acquire case data matching the pre- and post-operative physical sign comparison data from a pre-connected case data server, and perform data processing on each acquired case data by a second data processing device to obtain a processed case data set; Inputting the processed case data set into a risk assessment module to obtain a patient personalized prediction model, wherein the risk assessment module performs online additional training on the risk prediction basic model through the processed case data set to generate a patient personalized prediction model; The risk level of the patient to be evaluated is assessed through the patient personalized prediction model and the modified complication structure tree to obtain risk level assessment information, and according to the risk level assessment information, the human-computer interaction device is controlled to execute corresponding risk warning operations.

2. The system according to claim 1, characterized in that The step of acquiring the patient historical monitoring data and current monitoring data of the patient to be evaluated from the first data preprocessing device, and retrieving the patient basic information of the patient to be evaluated from the multi-physiological parameter database, includes: Controlling the first data preprocessing device to obtain initial historical vital sign data and current monitoring data from the patient vital sign monitoring device, wherein the current monitoring data includes: postoperative basic data, postoperative muscle strength test value, postoperative metabolic score value, postoperative mental cognition score value, disease identification, surgical identification, and indwelling drainage tube information; Controlling the first data preprocessing device to perform data standardization on the initial historical vital sign data to obtain the patient historical monitoring data, wherein the patient historical monitoring data includes: preoperative muscle strength test value, preoperative metabolic score value and preoperative basic data; The basic patient information of the patient to be evaluated is retrieved from the multi-physiological parameter database, wherein the basic patient information includes: past medical history information, smoking duration, and drinking duration.

3. The system according to claim 2, characterized in that The generating of pre- and post-operative physical sign comparison data according to the current monitoring data, the historical monitoring data and the basic information of the patient includes: The postoperative basic data in the current monitoring data is evaluated by a preset scoring scale to generate a current basic data scoring value, and the current basic data scoring value is compared with the health scoring value in the preoperative basic data to generate an acute physiological and chronic health score; generating a muscle strength change grade according to an absolute value of a change between the preoperative muscle strength test value and the postoperative muscle strength test value; generating a metabolic change coefficient according to the postoperative metabolic score value and the preoperative metabolic score value; Generate a mental cognition change coefficient according to the postoperative mental cognition score value and the preoperative mental cognition score value in the preoperative basic data; The patient's basic information, the acute physiological and chronic health scores, the muscle strength change level, the metabolic change coefficient, the mental cognitive change coefficient, the disease mark, the surgery mark and the indwelling drainage tube information are determined as the pre- and post-operative physical sign comparison data.

4. The system according to claim 3, characterized in that The step of selecting a complication tree structure matching the patient to be evaluated from a set of complication tree structures pre-established in the multi-physiological parameter database to obtain a target complication tree structure includes: A complication tree structure that matches the current monitoring data including the surgical identifier and the disease identifier is selected from the multi-physiological parameter database as the target complication tree structure, wherein, in response to no match, a single match is re-performed using the surgical identifier or the disease identifier, and in response to a single match to one complication tree structure, it is used as the target complication tree structure, and in response to a single match to two complication tree structures, the two complication tree structures are merged into a target complication tree structure, wherein the target complication tree structure includes the associated complication identifiers and the corresponding concurrency probabilities.

5. The system according to claim 4, characterized in that The target complication structure tree is modified according to the comparison data of physical signs before and after the operation to obtain a modified complication structure tree, including: According to the pre-selected probability correction table, the probability correction ratio and probability correction mark corresponding to the patient's basic information, the acute physiological and chronic health score, the muscle strength change level, the metabolic change coefficient, the mental cognition change coefficient, the disease mark, the surgery mark, and the indwelling drainage tube information in the pre- and post-operative physical sign comparison data are determined respectively; Through each probability correction ratio, the concurrent probability value corresponding to each probability correction mark in the target complication structure tree is obtained to obtain a corrected complication structure tree.

6. The system according to claim 5, characterized in that The method of acquiring case data matching the pre- and post-operative physical sign comparison data from a pre-connected case data server, and performing data processing on each acquired case data by a second data processing device to obtain a processed case data set includes: Acquire case data matching the pre- and post-operative physical sign comparison data and the corrected complication structure tree from the case data server to obtain a case data set; The second data processing device is used to screen each case data in the case data set to obtain a processed case data set, wherein each processed case data in the processed case data set matches the age group, gender, surgical location identifier, surgical identifier and disease identifier of the patient to be evaluated.

7. The system according to claim 6, characterized in that The step of performing risk level assessment on the patient to be assessed by using the patient personalized prediction model and the modified complication structure tree to obtain risk level assessment information, and controlling the human-computer interaction device to perform corresponding risk warning operations according to the risk level assessment information, includes: Performing risk level assessment on the patient to be assessed by using the patient personalized prediction model to obtain a risk level assessment probability value sequence, wherein each risk level assessment probability value in the risk level assessment probability value sequence corresponds to a node in the modified complication structure tree; Performing a secondary adjustment on the revised complication structure tree by using the risk level assessment probability value sequence to obtain a secondary adjusted complication structure tree; In response to the second-adjusted complication structure tree satisfying a preset first maintenance adjustment condition, generating maintenance strategy information according to the second-adjusted complication structure tree, and sending the maintenance strategy information to a maintenance processing terminal through the human-computer interaction device; In response to the complication structure tree satisfying the preset second maintenance adjustment condition after the secondary adjustment, the human-computer interaction device is controlled to perform a corresponding risk warning operation to adjust the patient monitoring level.

8. A risk warning device for adjusting a patient monitoring level, comprising: an acquisition unit, configured to acquire patient historical monitoring data and current monitoring data of the patient to be evaluated from the first data preprocessing device, and retrieve patient basic information of the patient to be evaluated from the multi-physiological parameter database; A first generating unit is configured to generate pre- and post-operative physical sign comparison data according to the current monitoring data, the historical monitoring data and the basic information of the patient, wherein the pre- and post-operative physical sign comparison data is used to characterize the change of physical sign data of the patient to be evaluated before and after the operation; The selection and correction unit is configured to select a complication structure tree matching the patient to be evaluated from a set of complication structure trees pre-established in the multi-physiological parameter database to obtain a target complication structure tree, and to correct the target complication structure tree according to the pre- and post-operative physical sign comparison data to obtain a corrected complication structure tree; an acquisition and processing unit, configured to acquire case data matching the pre- and post-operative physical sign comparison data from a pre-connected case data server, and to perform data processing on each acquired case data through a second data processing device to obtain a processed case data set; A model training unit is configured to input the processed case data set into a risk assessment module to obtain a patient personalized prediction model, wherein the risk assessment module performs online additional training on the risk prediction basic model through the processed case data set to generate a patient personalized prediction model; The evaluation and risk warning unit is configured to perform risk level evaluation on the patient to be evaluated through the patient personalized prediction model and the modified complication structure tree, obtain risk level evaluation information, and control the human-computer interaction device to perform corresponding risk warning operations according to the risk level evaluation information.

9. An electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the system according to any one of claims 1 to 7.

10. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the system according to any one of claims 1 to 7 is implemented.

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