TW202320085A - Postoperative condition evaluation and decision-making assisted system and method for spine surgery - Google Patents

Abstract

The present invention relates to a postoperative condition evaluation and decision-making assisted method for spine surgery, comprising: acquiring an evaluation information comprising a background health information and a surgical recordation information regarding an evaluatee; feeding the evaluation information into a postoperative condition ensemble learning decision-making and analysis model comprised in a remote-end server; computing respective incidence rates of a plurality of postoperative conditions for the evaluatee based on the evaluation information by performing the postoperative condition ensemble learning decision-making and analysis model; and showing the respective incidence rates of a plurality of postoperative conditions through a display interface for a user to read.

Description

Method and system for assisting decision-making in assessing postoperative conditions of spine surgery

The present invention relates to a decision-making assistance method and system for assessing the postoperative condition of spinal surgery, especially a decision-making assistance system and method for assessing the postoperative condition of patients undergoing spinal surgery through the implementation of an integrated learning decision-making analysis model, so as to assist medical staff to carry out Postoperative decisions.

According to the statistics of the World Health Organization, the highest incidence of compression fractures caused by osteoporosis is the spine, and it is more likely to occur in elderly patients over 65 years old, accounting for about 27% of all patients. Vertebral compression fractures caused by osteoporosis will cause vertebral collapse, spinal deformation, abdominal and thoracic space reduction and reduced lung function, resulting in limited mobility and psychological anxiety for patients.

In conventional techniques, spinal compression fractures caused by osteoporosis usually need to be treated with spine surgery. Spine surgery can be roughly distinguished from traditional vertebroplasty (vertebroplasty), which uses image guidance and injects bone cement into the fractured part. ), and a modified kyphoplasty (kyphoplasty) that first implants a balloon in the fracture affected area to create a filling space, and then injects bone cement into the filling space, also known as modified vertebroplasty and modified vertebroplasty. Bodyplasty or decompression vertebroplasty, etc., both of which can improve back pain in patients with vertebral compression fractures.

However, long-term clinical experience has also found that during the implementation of different spinal operations, the variation of physiological parameters and the decrease of oxygen saturation of patients will be related to the occurrence of surgical complications, or the speed and degree of postoperative recovery. , seems to be highly correlated. After collecting relevant factors, independent variables and dependent variables, it seems possible to use machine learning technology to establish a predictive model and method. The reference and basis for choosing a suitable surgical method.

For this reason, in view of the deficiencies of the conventional technology, the inventor, after careful trial and research, and a spirit of perseverance, finally conceived the case "postoperative condition evaluation and decision-making assistance method and system for spinal surgery", which can overcome the above shortcomings , the following is a brief description of the present invention.

In view of the fact that the results of traditional spine surgery are often related to various physiological data values of the patient during the spine surgery process and the background health information of the patient itself is implicitly related, the present invention learns from the big data of the surgery and anesthesia database by using an integrated learning method. , to find out the implicit correlation between the patient's background health status and different spinal surgery processes, relative to the patient's postoperative status, which can be used to predict postoperative high-risk patients and provide postoperative decision-making assistance for medical staff, and then Allow medical staff to prevent related complications early, and ensure the safety of patients during surgery, effectively reducing various uncertain risk factors.

Accordingly, the present invention proposes a method for assisting decision-making in assessment of postoperative spinal surgery conditions, which includes: obtaining assessment information about the subject including background health information and surgical record information; providing the assessment information to the remote server included in the postoperative a condition-integrated learning decision-making analysis model; estimating the probability of occurrence of multiple post-operative conditions of the subject based on the assessment information by implementing the post-operative condition-integrated learning decision-making analysis model; and calculating the probability of occurrence of the post-operative conditions It is provided to the user to read through the display interface.

The present invention further proposes a postoperative condition evaluation and decision support system for spinal surgery, which includes: a remote server, which is installed with a computer program product including an integrated learning decision analysis model for postoperative conditions; and user equipment, which is connected to the remote server. terminal server communication link, and provide a shortcut operation interface that includes multiple evaluation fields for the user to perform input operations, so that the user can input multiple evaluation information of the assessee, and the user device will input the evaluation information transmitting to the integrated learning decision analysis model for postoperative conditions, estimating the occurrence probability of multiple postoperative conditions of the subject based on the assessment information by executing the integrated learning decision analysis model for postoperative conditions, and calculating the probability of occurrence of multiple postoperative conditions of the subject The probability of occurrence is provided for the user to read through the shortcut operation interface.

The above summary of the invention is intended to provide a simplified summary of the disclosure to enable readers to have a basic understanding of the disclosure. This summary of the invention is not intended to disclose a complete description of the invention, and is not intended to point out important/key elements or components of the embodiments of the invention. define the scope of the invention.

100: Post-surgery condition assessment and decision-making assistance system for spinal surgery of the present invention

101: Notebook computer

103:Desktop computer

105:Smartphone

107: Tablet device

109:Remote server

111:Internet

130:Back-end management platform

140: Web browser

150:Front-end application

161: Operation interface

163: Quick operation interface

165: display interface

200: Postoperative condition assessment and decision-making assistance method for spinal surgery of the present invention

201-205: Implementation steps

Figure 1 is a schematic diagram showing the system architecture of the postoperative condition assessment and decision-making assistance system for spinal surgery of the present invention;

Figure 2 shows the importance network diagram generated during the analysis process by implementing the Bayesian network sub-module for the analysis of the modified kyphoplasty by the integrated learning decision-making analysis model of the postoperative situation of the present invention;

Figure 3 reveals the importance analysis results obtained by implementing the Bayesian network sub-module for the analysis of the modified kyphoplasty using the integrated learning decision analysis model for postoperative conditions of the present invention fruit histogram;

Figure 4 is a network diagram of a single hidden layer that reveals the integrated learning decision analysis model of the present invention through the implementation of the neural network sub-module for the analysis of the improved kyphoplasty;

Figure 5 is a histogram showing the importance analysis results of the improved kyphoplasty obtained through the implementation of the integrated learning decision-making analysis model of the postoperative situation of the present invention through the neural network sub-module;

Figure 6 is a histogram showing the importance analysis results of the traditional vertebroplasty obtained through the implementation of the discriminant analysis sub-module of the integrated learning decision-making analysis model for the postoperative situation of the present invention;

Figure 7 is a histogram showing the importance analysis results obtained by each sub-module of the integrated learning decision-making analysis model for postoperative status of the present invention for traditional vertebroplasty; and

Fig. 8 is a flow chart showing the operational steps of the method for assisting decision-making in assessment of postoperative spinal surgery according to the present invention.

The implementation of the present invention will be fully understood through the following description, so that those who are familiar with the technical field of the present invention can complete it, but the implementation of the present invention is not limited to the following description; Limitation of scale, the size, dimensions and scale of the present invention are not limited by the drawings when it is actually implemented. Any steps described or recorded in the specification or claims can be performed in any order, and are not limited to the order described or recorded in the specification or claims. The scope of the present invention should be determined only by the claims and their equivalents, not by the embodiments described in the specification.

The word "preferably" in this article is non-exclusive and should be understood as "preferably but not limited to". The word "for example" in this article is non-exclusive and should be understood as "such as but not limited to". When the term "comprising" and its variations appear in the specification and claims, it is an open term without restrictive meaning, and does not exclude the addition of other features or steps.

Based on the fact that the results of spine surgery are often related to the various physiological data values of the patient during the spine surgery and the background health information of the patient itself is implicitly related, the present invention uses an ensemble learning method to learn from the surgery and anesthesia database In the big data and a large amount of patient background health information, discover the hidden correlation between different types of spinal surgery and heart rate variability and oxygen saturation, as well as the patient's background health status and different types of spinal surgery process conditions, compared with patients after surgery The implicit correlation between conditions can be used to predict high-risk patients after surgery, and further provide decision-making assistance for medical staff, so that medical staff can prevent related complications early, ensure the safety of patients during surgery, and effectively reduce Various uncertain risk factors.

The present invention constitutes an integrated learning decision analysis model by combining at least three machine learning algorithms of Bayesian Network (BN), Artificial Neural Network (ANN) and Discriminant Analysis (DA). According to the analysis results of ANN, the area under the curve (AUC) index can reach AUC>0.7, the overall important factors are mainly the symptoms of lower limb numbness and immobility before operation, and the vital sign parameters are heartbeat, diastolic blood pressure, The oxygen saturation in blood before anesthesia and the oxygen saturation in blood after bone cement infusion during operation were the most important significant factors.

The present invention further integrates combined Bayesian classifier, deep neural network (DNN), recursive neural network (RNN), long-term short-term memory model (LSTM), multi-layer perception model (MLP), weak learning classifier, strong learning one of a classifier, a strong voting classifier, a weak voting classifier, a support vector machine (SVM), a decision tree, an unsupervised learning classifier, a supervised learning classifier, a semi-supervised learning classifier, and combinations thereof, and An integrated learning decision-making analysis model for postoperative conditions type. Through the implementation of the postoperative status integrated learning decision analysis model constructed by the present invention, the original implicit and non-obvious associations can be found, and whether there is a clear association between different surgical methods and whether the vital signs are abnormal, and based on this, the subsequent hospitalization of patients can be evaluated. The number of days can be provided to the medical staff to assist in the decision-making of the surgical method.

Accordingly, the present invention proposes and constructs a decision-making assistance method and a corresponding system for assessing postoperative conditions of spinal surgery, using cloud computing technology of software as a service (Software as a Service, SaaS) and platform as a service (Platform as a Service, PaaS) technology, and built as a cloud-like service platform, through the Internet and a web browser (internet browser) to provide users with access, operation and use, users only need to connect to the network environment Internet access, operation and use can be performed through a browser running on the user's equipment. The system of the present invention can be provided to users in an off-premises manner. Users only need to subscribe online to obtain access and use the system and platform. The system of the present invention can also selectively apply API technology to construct a mobile phone application program, and provide users to install it on their own user equipment for operation. The user is preferably a medical practitioner such as but not limited to a medical staff, a physician, a medical staff, a nurse, a nurse or a nursing staff.

The post-surgery situation evaluation and decision-making assistance system of the present invention is composed of various hardware in the physical equipment layer and a program platform (computer program product) in the application program layer, according to the international open system interconnection communication reference model The definition of (OSI/RM) architecture, the community-based learning creation program platform of the present invention, and the multiple constituent modules contained therein are software application services executed and operated on the seventh layer (application layer) of the OSI/RM architecture , the software application service at layer 7 can independently select various communication protocols in the transport layer at layer 4, form data packets at the network layer at layer 3 and determine the transmission path, and add logical links through the data link layer at layer 2 Control (LLC) and Media Access Control (MAC) After that, establish the required two-way communication link (upload communication link, download communication link) with various devices on the physical layer of the first layer, such as but not limited to: servers, equipment, user equipment and components, etc. .

Figure 1 is a schematic diagram showing the system architecture of the post-surgery condition assessment and decision-making support system of the present invention; the post-spine surgery condition assessment and decision-making support system 100 of the present invention disclosed in Figure 1 is composed of multiple hardware and program platforms. A plurality of devices at the device layer, including but not limited to: a plurality of user devices at the local end (local end), including a notebook computer 101, a desktop computer 103, a smart phone 105 and a tablet device 107, and Server devices at the remote end, including remote server 109; local devices such as notebook computers 101, desktop computers 103, smart phones 105 and tablet devices 107, are connected through the Internet 111 And link to the remote server 109, communicate with the remote server 109 two-way and access data, the Internet 111 is composed of local area network (LAN), wide area network (WAN), GSM network, 4G network, 5G network, 6G network, Wi-Fi network, Bluetooth network and their combination.

The user only needs to establish a communication link with the remote server 109 through the Internet 111 through the user equipment such as the notebook computer 101, the desktop computer 103, the smart phone 105 or the tablet device 107, and then use it. The web browser 140 executed on the device is used to directly access, operate and use a computer program product platform installed on the remote server 109 that includes an integrated learning decision-making analysis model for postoperative conditions.

The computer program product platform includes a back-end management platform 130 installed on a remote server 109, and a front-end application program 150 provided to the user to be installed on the user's device. Install a front-end application program 150 on a notebook computer 101, a desktop computer 103, a smart phone 105 or a tablet device 107, and The back-end management platform 130 on the remote server 109 is accessed, operated, and used through the front-end application program 150 . The front-end application program 150 can generate an operation interface 161, a shortcut operation interface 163, or a display interface on a display unit of the notebook computer 101, the desktop computer 103, the smart phone 105, or the tablet device 107, such as but not limited to a monitor or a touch screen. 165 and other interfaces for users to operate.

Bayesian network, also known as belief network (belief network) or directed acyclic graphical model (directed acyclic graphical model), is a probabilistic graphical model. Know the properties of a set of random variables and their n sets of conditional probability distributions (CPDs).

The Bayesian network can be used to represent the probability relationship between a disease and its related symptoms. For example, if a certain symptom is known, the Bayesian network can be used to calculate the probability of various possible diseases. The Bayesian network Nodes in a DAG represent random variables, which can be observable variables, latent variables, unknown parameters, etc. The arrows connecting two nodes represent whether the two random variables are causally related or non-conditionally independent; and if there is no arrow connecting the two nodes, the random variables are said to be conditionally independent from each other, if two nodes They are connected together with a single arrow, indicating that one of the nodes is "parents" and the other is "children", and the two nodes will generate a conditional probability value.

The neural network is used to estimate or approximate the function. The neural network is an adaptive system with a learning function. It is also a nonlinear statistical data modeling tool, usually through a type of learning based on mathematical statistics. The method can be optimized. In the field of artificial perception of artificial intelligence, we can make simple decisions and simple judgments similar to humans through the application of mathematics and statistics. Compared with formal logical reasoning and calculation, this method more advantageous.

Discriminant analysis is a method used in multivariate statistical analysis to distinguish the type (group) of samples (respondents), classify similar samples (respondents) into one category (group), and deduce one or one group according to the sample data. Group discriminant (distinguishing) function, and at the same time specify a discriminant rule, which is used to determine the category of the sample to be discriminated, so as to minimize the misjudgment rate.

Preferably, discriminant analysis is a generalization of Fisher's linear discriminant method, which uses statistics, pattern recognition and machine learning methods to try to find a linear combination of features of two classes of objects or events to be able to characterize Or distinguish them, and the resulting combination can be used as a linear classifier for dimensionality reduction for subsequent classification.

Discriminant analysis is closely related to analysis of variance (ANOVA) and regression analysis, both of which also attempt to represent a dependent variable by a linear combination of some features or measurements, whereas ANOVA uses categorical independent variables and continuous dependent variables, whereas Discriminant analysis of continuous independent variables and categorical dependent variables.

Discriminant analysis is also similar to logistic regression and probability regression than analysis of variance, because they also use continuous independent variables to explain category dependent variables, but the basic assumption of discriminant analysis is that independent variables are normally distributed. When this assumption cannot be satisfied, In practical applications, the other methods mentioned above are more likely to be used.

Discriminant analysis is also closely related to principal component analysis (PCA) and factor analysis, both of which look for the linear combination of variables that best explains the data, but discriminant analysis explicitly tries to model between different data classes, while PCA does not consider the class Any difference is just to reduce the dimensionality on the premise of retaining most of the information. Factor analysis is to establish feature combinations based on differences rather than the same points.

The parent source of the samples in the present invention is based on the medical records of patients who have undergone vertebroplasty between January 01, 2011, and December 31, 2018 in the North District Hospital of Taiwan. source, the age distribution of the patients is between 20 years old and under 80 years old, and their health information includes at least age, gender, height, weight, symptoms, etc. The operation record information includes at least the name of the operation (about 8 attributes) and anesthesia records. The anesthesia records at least include vital signs, operation time, bone cement infusion to completion time, blood oxygen saturation concentration, end-tidal carbon dioxide concentration (End tidal CO2, etCO2), days from operation to discharge, complications, degree of postoperative recovery, and postoperative recovery speed, etc.

The collected fields include at least the original continuous value, and use the fields classified as normal and abnormal values, as follows: (1) SBP>140 abnormal; (2) DBP<60mmh abnormal; (3) SaO2<95% Abnormal; (4) PR>100/mins abnormal; and (5) RR≧28/misn abnormal.

Table 1 and Table 2 series show the statistical characteristics of all samples included in the matrix of the present invention, as can be known from Table 1 and Table 2, the average age of the total 1136 cases collected in the data collection stage of the present invention is 55.89 years old, with Most of the cases are female (65.6%), and the symptoms are characterized by pain (91.7%) and weakness (66.5%). For etiological analysis, the most common active postures are standing and sitting. As for the severity of pain , measured by the standard pain scale, the average pain score of most patients was 6.5, and the degree was moderate.

In the model building stage, data preprocessing needs to be performed in advance. In the data collection stage, a large number of spinal surgery records of patients who have undergone spinal surgery, as well as background health information of patients who have undergone vertebroplasty, etc. are collected. Raw data is preferably stored and recorded in the cloud database in the form of digital files, so it is necessary to delete outliers, delete erroneous data, delete incomplete records, identify fields, and Text recognition, semantic recognition, format conversion, standardization or formatting, etc., leaving Correct and reliable records, remove incorrect and missing records, etc., and normalize the correct data, and convert these raw data into data types that can be read by the integrated learning decision-making analysis model of postoperative conditions.

Then use the pre-processed data as a data set, and cut it into a training set and a test set, and input the training set into the integrated learning decision analysis model to read, identify, learn and Training to find out the implicit correlation between different types of spinal surgery and heart rate variability and oxygen saturation, which can be used to predict high-risk patients after surgery, and to identify patients' preoperative health information and intraoperative surgical record information , and the correlation, implicit correlation and incidence rate between the postoperative status of the patient and the patient.

Figure 2 reveals the importance network diagram generated during the analysis process of the integrated learning decision-making analysis model for the postoperative status of the present invention through the implementation of the Bayesian network sub-module for the analysis of the modified kyphoplasty; Figure 3 discloses the present invention The histogram of the importance analysis results obtained after the implementation of the Bayesian network sub-module for the analysis of the modified kyphoplasty by the postoperative status integrated learning decision analysis model; through the implementation of the Bayesian network, 17 items were analyzed with conditional probability The overall probability of factor selection for modified kyphoplasty was 80%. The analysis found that the main influencing factors were physiological symptoms, numbness, immobility, and SaO2 in the first minute of bone cement injection were far more important than other factors. The important factors such as pain and etCO2 after bone cement injection, the importance network diagram generated during the analysis process is shown in Figure 2, and the main factors or principal components obtained through the analysis are revealed by the histogram in Figure 3.

Using the Bayesian network sub-module to further analyze the factor weights, we can know the degree of influence of each influencing factor on the dependent phase. It is found that the impact factors of physical examination or physical assessment are far more important than other factors. Important factors include acid, pain , numbness, immobility and weakness, the analysis results also include: the overall probability of predicting each vertebroplasty for each vertebroplasty is 80% based on conditional probability analysis Above, only Yang-deficiency constitution does not reach 70%. The prediction rate of phlegm-dampness constitution was 89.1%, and the main influencing factors were excessive waist circumference and hyperglycemia; the prediction rate of phlegm-dampness constitution was 65.1%, and the main influencing factors were high cholesterol and hyperglycemia. .

Figure 4 shows the network diagram of the single hidden layer constructed by the integrated learning decision-making analysis model of the postoperative situation of the present invention through the implementation of the neural network sub-module for the analysis of the improved kyphoplasty; the network structure of the neural network, Consisting of a single hidden layer can provide sufficient accuracy. For example, the original data of 17 factors listed in Table 1 are placed in the input layer, signal neurons are set in the hidden layer for testing, and the final network output layer Contains only one neuron, as revealed in Figure 4.

Figure 5 is a histogram showing the importance analysis results obtained by the integrated learning decision-making analysis model of the present invention for the improved kyphoplasty through the implementation of the neural network sub-module; for the network parameter setting, the learning rate is better It can be set to 0.2, because lower learning rates usually lead to better learning results, and the training termination condition is when the root mean square error (RMSE) is less than or equal to 0.0001, or at most 1000 times the number of training repetitions, and The network structure with the smallest RMSE test data is selected as the final network structure, and the overall judgment accuracy is 83.5%. The most important factors for choosing different methods of vertebroplasty are the number of breaths, heartbeat changes caused by bone cement infusion, and diastolic blood pressure, etc. factor, as revealed in Figure 5.

In the discriminant analysis, the operation method will be used as the representative of the two clusters, and then the original 17 variables will be used for the discriminant analysis to establish the confusion table shown in Table 3, which shows that the correct distinction rate of the clustering results is 76.4%, and the correct distinction The calculation of the rate is (506+362)/1136=76.4%, which shows that the internal homogeneity of each group is quite consistent. Comparing the symptom characteristics and vital signs of different clusters, kyphoplasty has a high correlation with numbness, immobility, and SaO2 in the first minute, and the difference is significant; Vertebroplasty has a high correlation with pain, immobility, abnormally low DBP, and rapid HR. From the overall important factors in Table 1, kyphoplasty has a higher correct rate of discrimination than vertebroplasty, and the difference is significant (t=-4.62, p <.001).

Fig. 6 is a histogram showing the importance analysis results of traditional vertebroplasty obtained by the integrated learning decision-making analysis model of postoperative status of the present invention through the implementation of the discriminant analysis sub-module; as can be seen from Fig. 6, if you want The important influencing factor of performing vertebroplasty operation was SaO2 in the first minute of bone cement infusion, reaching a statistically significant difference P=0.043.

In the past, the decision on the operation method was mostly determined by the age of the case, preoperative symptoms and personal habits of the doctor. This invention will discuss the selection of operation methods based on personal symptoms and physiological parameters, intraoperative blood oxygen concentration, and postoperative recovery degree. Through three models Integrated analysis found that factors such as pain level and age were not among the important influencing factors, which may be due to the influence of changes in vital signs caused by surgical methods and blood oxygen saturation on postoperative surgery. In comparing the probability of intraoperative bone cement leakage, as shown in Table 1, the incidence of vertebroplasty is 7%, and intraoperative bone cement leakage is positively correlated with blood oxygen saturation concentration, and may also be related to postoperative recovery and hospitalization days. Often related.

Figure 7 is a histogram showing the importance analysis results obtained by each sub-module of the integrated learning decision-making analysis model for the postoperative situation of the present invention for traditional vertebroplasty; as shown in Figure 7 and Table 4, all models are in performed well in predicting the resolution of different modified kyphoplasty procedures, individual The area of the ROC curve for the Bessie module was calculated to range from 0.51 to 0.78. Under the ideal threshold, the sensitivity, specificity and accuracy of each sub-module are greater than 70%. Finally, when the analysis of each sub-module is completed, the integrated learning decision-making analysis model of the postoperative situation of the present invention will give different weight factors to the analysis results of each sub-module, and then sum and average the analysis results of each sub-module , to obtain a set of more reliable analysis results, assist medical staff to refer to, and perform postoperative evaluation and decision-making.

In order to implement the method, model and system of the present invention in medical institutions, the method and model of the present invention can be further packaged into web page services by applying PaaS and SaaS technologies through information and communication (ICT) technology, or through dynamic Update the data set library to gradually modify the model, and finally provide the best decision-making assistance model to the physician.

Various types of user equipment, including notebook computer 101, desktop computer 103, smart phone 105 and tablet device 107, can be provided to the user to execute the front-end application program of the smart nursing platform, preferably an Android or iOS application program, the front-end application program can execute the user interface with shortcut operation program components to generate and provide a shortcut operation interface for the user to operate, and the front-end application program transmits the received body assessment input to the remote server 109 The integrated learning and decision-making analysis model of the post-operative situation of the present invention installed and executed on the computer, the post-operative situation integrated learning and decision-making analysis model is used for calculation, and the analysis results of each sub-module are returned to the front-end application program, and immediately fed back to the user through the user interface. User read.

After the shortcut operation program component is executed, it can be displayed on the display unit of the user device If but not limited to a display or a touch screen to generate a shortcut operation interface, the shortcut operation interface is preferably such as but not limited to: a series of graphical user interfaces (GUI), shortcut menus based on one-click operations ( quick menu), quick operation web page, quick list, shortcut keys, graphical quick operation interface, floating convenient option menu window, drop-down option convenient menu, or shortcut option menu, to provide users with touch screen Unit selection and operation.

Fig. 8 is a flow chart showing the operation steps of the method for assisting decision-making for postoperative condition assessment after spinal surgery of the present invention; the operation process of the method for assisting decision-making for postoperative condition assessment for spinal surgery 200 of the present invention generally includes the following steps as disclosed in Fig. 8: Access the medical record information of the assessee, and extract the assessment information from the medical record information, and obtain the assessment information about the assessee including background health information and operation record information (step 201); or provide an operation interface to display A plurality of assessment fields are provided for the user to input the assessment information of the subject to obtain the assessment information about the subject including background health information and operation record information (step 202); the obtained assessment information is provided to the remote The integrated learning and decision-making analysis model of postoperative conditions contained in the terminal server (step 203); by executing the integrated learning and decision-making analysis model of postoperative conditions, the probability of occurrence of multiple postoperative conditions of the subject is estimated based on the evaluation information (step 204 ); and provide the probability of occurrence of these postoperative conditions to the user to read through the display interface (step 205).

The above embodiments of the present invention can be arbitrarily combined or replaced with each other, thereby deriving more implementation forms, but none of them depart from the scope of protection intended by the present invention. More embodiments of the present invention are further provided as follows:

Embodiment 1: A decision-making assistance method for assessing postoperative conditions of spinal surgery, which includes: obtaining assessment information about the subject including background health information and surgical record information; providing the assessment information to the postoperative condition included in the remote server Integrated learning decision analysis model; Estimate the occurrence probability of multiple postoperative conditions of the assessee based on the evaluation information by executing the integrated learning decision analysis model for postoperative conditions; and provide the occurrence probabilities of the postoperative conditions to the user through a display interface.

Embodiment 2: The method for assisting decision-making in assessment of postoperative spinal surgery conditions as described in Embodiment 1, further comprising one of the following: accessing the medical record information of the subject to be evaluated, and extracting the evaluation information from the medical record information; The shortcut operation interface displays a plurality of assessment fields for the user to input the assessment information of the assessee; the shortcut operation interface is displayed to the user through the web browser installed on the user's equipment, and received through the web browser The evaluation information input by the user through operating the shortcut operation interface; displaying the shortcut operation interface to the user through a touch screen unit configured on a user device and providing the user with a click operation to operate the shortcut operation interface, and use the touch screen unit to receive the click operation, so as to receive the evaluation information input by the user through the shortcut operation interface; to the user through a front-end application program installed on the user equipment displaying the shortcut operation interface, and receiving the evaluation information input by the user through the shortcut operation interface through the front-end application program; and executing the shortcut operation program element on the user device to generate the user device Quick operation interface.

Embodiment 3: The method for assisting decision-making in assessment of postoperative spinal surgery conditions as described in Embodiment 2, wherein the user equipment is selected from one of mobile devices, smart phones, tablet devices, desktop computers and notebook computers.

Embodiment 4: The decision-making assistance method for assessment of postoperative spinal surgery conditions as described in Embodiment 1, further comprising one of the following steps: providing a plurality of assessment information data sets of patients who have undergone spinal surgery, and the assessment information data sets include plural Background health information dataset and multiple surgeries Record information data sets; implement data pre-processing procedures to process the evaluation information data sets into data types that can be read by the integrated learning decision analysis model for postoperative conditions; divide the pre-processed evaluation information data sets into training set and test set; the training set is input into the integrated learning decision analysis model of postoperative status, and the integrated learning decision analysis model of postoperative status is based on the training set to estimate the given background health information data sets and the Under the conditions of the surgical record information data set, the occurrence probability of the postoperative situation, and accordingly train the postoperative situation integrated learning decision analysis model; and input the test set into the postoperative situation integrated learning decision analysis model to verify And adjust the integrated learning decision analysis model of the postoperative situation, so as to establish the integrated learning decision analysis model of the postoperative situation.

Embodiment 5: As described in embodiment 4, the postoperative status evaluation decision-making aid method for spinal surgery, wherein the postoperative status at least includes the number of days in hospital, complications, risk of complications, degree of postoperative recovery, postoperative recovery speed and One of its combinations, the background health information and the background health information data set at least include age, sex, height, weight, symptoms and one of their combinations, the symptoms include at least soreness, pain, numbness, weakness, inability to walk and One of its combinations, the operation record information and the operation record information data set at least include operation name, anesthesia record, vital signs, operation time, bone cement infusion to completion time, blood oxygen saturation concentration, end-tidal carbon dioxide concentration, One of days from surgery to discharge, complications, degree of postoperative recovery, postoperative recovery speed, and a combination thereof.

Embodiment 6: The decision-making assistance method for assessment of postoperative spinal surgery status as described in Example 1, wherein the integrated learning decision analysis model for postoperative status selectively integrates the classification results of multiple classifiers, and these classifiers are selected from Bayesian Networks, Bayesian Classifiers, Linear Discriminant Analysis, Neural Networks, Deep Neural Networks, Recurrent Neural Networks, Long Short-Term Memory Models, Multilayer Perception Models, weak learning classifiers, strong learning classifiers, strong voting classifiers, weak voting classifiers, support vector machines, decision trees, unsupervised learning classifiers, supervised learning classifiers, semi-supervised learning classifiers and their Combine one of them.

Embodiment 7: A postoperative condition assessment decision support system for spinal surgery, which includes: a remote server, which is installed with a computer program product including an integrated learning decision analysis model for postoperative conditions; and a user device, which is connected to the remote terminal server communication link, and provide a shortcut operation interface that includes multiple evaluation fields for the user to perform input operations, so that the user can input multiple evaluation information of the assessee, and the user device will input the evaluation information transmitting to the integrated learning decision analysis model for postoperative conditions, estimating the occurrence probability of multiple postoperative conditions of the subject based on the assessment information by executing the integrated learning decision analysis model for postoperative conditions, and calculating the probability of occurrence of multiple postoperative conditions of the subject The probability of occurrence is provided for the user to read through the shortcut operation interface.

Embodiment 8: The postoperative condition assessment and decision support system for spinal surgery as described in Embodiment 7, wherein the user equipment further includes one of the following: a web browser configured to display the shortcut operation interface to the user, and receive the evaluation information input by the user through the operation of the shortcut operation interface through the web browser; touch screen unit, which is used to display the shortcut operation interface to the user and provide the user with a click operation to operate the shortcut operation interface, and use the touch screen unit to receive the click operation, so as to receive the evaluation information input by the user through the shortcut operation interface; and a front-end application program configured to display the shortcut to the user The operation interface is used, and the evaluation information input by the user through the shortcut operation interface is received through the front-end application program.

Embodiment 9: As described in embodiment 7, decision aid for assessment of postoperative condition of spinal surgery system, wherein the computer program product includes the back-end management platform installed and operated on the remote server, the front-end application program executed on the user device, the shortcut operation interface displayed on the user device and its Combine one of them.

Embodiment 10: As described in Embodiment 7, the postoperative condition evaluation and decision-making support system for spinal surgery, wherein the shortcut operation interface is selected from a graphical user interface, a shortcut menu, a shortcut operation webpage, a shortcut list, a shortcut button, and a graphical user interface. One of the shortcut operation interface, the floating shortcut option menu window, the drop-down option shortcut menu, and the shortcut option menu.

The various embodiments of the present invention can be combined or replaced arbitrarily with each other, thereby deriving more implementation forms, but none of them depart from the intended protection scope of the present invention, and the definition of the protection scope of the present invention is fully defined by the patent scope of the present invention application The recorder shall prevail.

200: Postoperative condition assessment and decision-making assistance method for spinal surgery of the present invention

201-205: Implementation steps

Claims (10)

A method for assisting decision-making in assessing conditions after spinal surgery, comprising: obtaining an assessment information about a subject including a background health information and a surgical history information; providing the evaluation information to a postoperative condition integrated learning decision analysis model contained in a remote server; estimating the probability of occurrence of multiple postoperative conditions of the subject based on the assessment information by executing the postoperative condition ensemble learning decision analysis model; and The probability of occurrence of these postoperative conditions is provided to a user for reading through a display interface. The post-surgery condition assessment and decision-making assistance method described in claim 1 further includes one of the following: accessing one of the subject's medical records and retrieving the assessment information from the medical records; Displaying a plurality of assessment fields for the user to input the assessment information of the assessee by providing an operation interface; displaying the operation interface to the user through a web browser installed on the user's equipment, and receiving the evaluation information input by the user through operating the operation interface through the web browser; Display the operation interface to the user through a touch screen unit configured on a user device and provide the user with the operation interface, and use the touch screen unit to receive operations from the user to receive the use or the evaluation information input through the operation interface; and display to the user through a front-end application installed on the user's device the an operation interface, and receive the evaluation information input by the user through the operation interface through the front-end application program. The method for assisting decision-making in assessing post-spine surgery conditions as described in claim 2, wherein the user equipment is selected from one of a mobile device, a smart phone, a tablet device, a desktop computer, and a notebook computer . The method for assisting decision-making in assessing conditions after spinal surgery as described in claim 1 further includes one of the following steps: Provide one of the assessment information datasets of multiple patients who have undergone spinal surgery, the assessment information datasets include multiple background health information datasets and multiple surgical record information datasets; Implementing a data pre-processing procedure to process the evaluation information data set into a data type that can be read by the integrated learning decision-making analysis model of the postoperative situation; dividing the pre-processed evaluation information data set into a training set and a test set; Inputting the training set into the postoperative status integrated learning decision analysis model, through the postoperative status integrated learning decision analysis model based on the training set, it is estimated that given the background health information data sets and the surgical record information data sets Under the conditions of the condition, the probability of occurrence of the postoperative condition, and accordingly train the integrated learning decision analysis model of the postoperative condition; and The test set is input into the integrated learning decision analysis model of postoperative status to verify and adjust the integrated learning decision analysis model of postoperative status, so as to establish the integrated learning decision analysis model of postoperative status. The post-surgery status evaluation decision-making support method as described in claim 4, wherein the post-operative status at least includes a length of hospitalization, a complication, a complication risk degree, and a post-operative recovery process degree, postoperative recovery speed and a combination thereof, the background health information and the background health information data set include at least one of age, gender, height, weight, symptom and a combination thereof, the symptom Contain at least one of soreness, pain, numbness, weakness, immobility and their combination. The operation record information and the operation record information data set include at least one operation name, one anesthesia record, one vital sign, one Operation time, bone cement infusion to completion time, blood oxygen saturation concentration, end-tidal carbon dioxide concentration, postoperative to discharge days, complications, postoperative recovery degree, postoperative recovery speed and their combinations one of them. As described in claim 1, the decision-making assistance method for assessing postoperative status of spinal surgery, wherein the integrated learning decision analysis model for postoperative status selectively integrates the classification results of multiple classifiers, and these classifiers are selected from a Bayesian network A Bayesian classifier, a linear discriminant analysis, a class of neural networks, a deep neural network, a recurrent neural network, a long short-term memory model, a multi-layer perceptron model, a weak learning classifier, a strong Learning classifier, a strong voting classifier, a weak voting classifier, a support vector machine, a decision tree, an unsupervised learning classifier, a supervised learning classifier, a semi-supervised learning classifier and combinations thereof one. A postoperative condition assessment decision support system for spinal surgery, comprising: a remote server installed with a computer program product including an ensemble learning decision analysis model for postoperative conditions; and A user device, which communicates with the remote server, and provides and displays a shortcut operation interface including multiple assessment fields for a user to perform an input operation, so that the user can input multiple assessment information of an assessee , Wherein the user equipment transmits the inputted evaluation information to the postoperative condition integration Learning a decision analysis model, estimating the probability of occurrence of multiple postoperative conditions of the subject based on the evaluation information by implementing the postoperative condition integration learning decision analysis model, and using the shortcut operation interface to estimate the occurrence probability of these postoperative conditions Provided for this user to read. The post-surgery status assessment and decision-making support system according to claim 7, wherein the user equipment further includes one of the following: a web browser configured to display the shortcut operation interface to the user, and receive the evaluation information input by the user through the operation of the shortcut operation interface through the web browser; A touch screen unit, which displays the shortcut operation interface to the user and allows the user to operate the shortcut operation interface through a click operation, and receives the click operation through the touch screen unit to receive the user or the evaluation information input through the quick operation interface; and A front-end application program is configured to display the shortcut operation interface to the user, and receive the assessment information input by the user through the shortcut operation interface through the front-end application program. The postoperative condition assessment and decision support system for spinal surgery as described in Claim 7, wherein the computer program product includes a back-end management platform installed on the remote server and the front-end application executed on the user equipment One of the program, the shortcut operation interface displayed on the user equipment and a combination thereof. As described in claim 7, the postoperative condition evaluation and decision-making aid method for spinal surgery, wherein the shortcut operation interface is selected from a graphical user interface, a shortcut menu, a shortcut operation web page, and a quick operation interface One of a shortcut list, a shortcut key, a graphical shortcut operation interface, a floating convenient option menu window, a drop-down option shortcut menu, and a shortcut option menu.

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