Integrated management system for ankylosing spondylitis

Abstract

The invention relates to the technical field of data processing, in particular to an integrated management system for ankylosing spondylitis. Carrying out fusion processing on monitoring state values generated in a plurality of continuous historical monitoring periods corresponding to the current monitoring moment, traversing a numerical value sequence formed by a plurality of monitoring state values, and constructing a state value distribution hash table; generating a sub decision value corresponding to each monitoring state value according to the state value distribution hash table; and generating an abnormal judgment value corresponding to the current monitoring moment, thereby avoiding the problem that the obtained evaluation index has an error due to the error of the related information obtained at the single monitoring time, and overcoming the defect that in the prior art, although various related symptom information of the AS patient at the current time period can be collected by setting a questionnaire, the evaluation index has an error. However, due to the influence of subjective feelings of users and other emergencies, related information deviation is generated, so that the obtained evaluation indexes have large errors, and accurate early warning cannot be carried out.

Description

Background Art

[0002] AS (Ankylosing Spondylitis) is a highly disabling lifelong disease that can cause disability due to the formation of spinal osteophytes and hip joint destruction. Previous data show that up to 40% of AS patients eventually have complete spinal fusion, or even extremely distorted body shapes. Since AS is more common in young men, it has a greater impact on young men who engage in high-intensity exercise, such as soldiers or athletes. Usually, the spinal pain and stiffness caused by AS will directly affect training and daily life, increase the risk of training injuries, and lead to longer treatment cycles and higher rates of medical retirement, which is one of the important factors causing attrition.

[0003] In the prior art, various relevant symptom information of AS patients in the current period can be collected by setting up questionnaires, such as the degree of pain in the relevant parts, the degree of fatigue, and the duration of morning stiffness, and the evaluation index of the severity of AS can be obtained after processing the information of each dimension through the corresponding algorithm, so as to give the patient corresponding risk reminders. However, since the information collection will be affected by the user's subjective feelings and other emergencies, it is easy for the relevant information currently collected to deviate, which will lead to large errors in the evaluation index and the inability to provide accurate warnings. Summary of the invention

[0004] In view of the above technical problems, the technical solution adopted by the present invention is:

[0005] According to one aspect of the present invention, there is provided an ankylosing spondylitis comprehensive management system, the system comprising: a determination module;

[0006] The determination module is used to fuse the monitoring state values ​​generated by multiple continuous historical monitoring periods corresponding to the current monitoring moment to generate an abnormal determination value corresponding to the current monitoring moment; the monitoring state values ​​include first-type state values ​​respectively used to indicate that the target event is in a normal state; and second-type state values ​​and third-type state values ​​indicating that the target event is abnormal, wherein the degree of abnormality of the target event corresponding to the second-type state value is greater than the degree of abnormality of the target event corresponding to the third-type state value;

[0007] Fusion processing includes:

[0008] Traverse the numerical sequence composed of multiple monitoring state values ​​to construct a state value distribution hash table; the key of the state value distribution hash table is the first type state value or the second type state value or the third type state value, each type state value corresponds to a state value position sequence, wherein the state value position sequence Key corresponding to the i-th type state value i =(wz i1. wz i 2. …, wz i m., …, wz i f(i)), wz i m is the position serial number of the m-th i-th type status value in the numerical sequence; f(i) is the total number of the i-th type status values in the numerical sequence; m = 1, 2, …, f(i); i = 1 or 2 or 3;

[0009] Generate a sub-determination value corresponding to each monitoring status value according to the status value distribution hash table;

[0010] Among them, the sub-determination value P corresponding to the i-th monitoring status value i Satisfies the following conditions:

[0011]

[0012] Among them, S i Is the number of times the i-th monitoring status value appears in the numerical sequence, S sum Is the total number of monitoring status values in the numerical sequence, μ w Is the mean of the position distribution of the i-th monitoring status value in the numerical sequence, Is the density of the position distribution of the i-th monitoring status value in the numerical sequence, P i 1 Is the abnormal score value corresponding to the i-th monitoring status value;

[0013] Generate an abnormal determination value P corresponding to the current monitoring moment according to the sub-determination values P1, P2, and P3 corresponding to the first type status value, the second type status value, and the third type status value respectively; P satisfies the following conditions: P = P3 + P2 - P1.

[0014] Furthermore, the fusion processing further includes:

[0015] If P > Y1, generate an abnormal reminder message; Y1 is the abnormal threshold.

[0016] Furthermore, the fusion processing further includes:

[0017] If P < Y1, do not generate an abnormal reminder message; Y1 is the abnormal threshold.

[0018] Furthermore, it further includes: an information acquisition module;

[0019] The information acquisition module is used to acquire a characteristic observation sequence corresponding to each monitoring period, and the characteristic observation sequence includes attribute values of at least one observable dimension corresponding to the target event.

[0020] Furthermore, it further includes a target hidden Markov model, and the target hidden Markov model is respectively communicatively connected to the information acquisition module and the determination module;

[0021] The target hidden Markov model is used to generate a monitoring state value corresponding to each historical monitoring period according to a characteristic observation sequence of multiple continuous historical monitoring periods corresponding to the current monitoring moment.

[0022] Furthermore, the target hidden Markov model is obtained as follows:

[0023] Collect historical information of target events in multiple continuous historical monitoring periods; the historical information includes characteristic observation sequences and monitoring status values ​​corresponding to the target events;

[0024] The initial hidden Markov model is trained using the historical information of the target event to generate a target hidden Markov model.

[0025] Furthermore, it also includes a model updating module, which is used to

[0026] Obtaining user feedback information on the accuracy of each abnormal prediction information generated according to the abnormal determination value; the feedback information includes whether the prediction information is accurate, the prediction information is too high, and the prediction information is too low;

[0027] when When the user is updated, the historical information of the target event in multiple continuous historical monitoring periods generated by the current user in this update cycle is used to generate the user feature update training set; G1 is the number of accurate feedback information obtained from the last update to the current period; G is the total number of feedback information obtained from the last update to the current period; Y2 is the model update threshold;

[0028] The monitoring state value Q in the user feature update training set satisfies the following conditions: Q = Q1 × K;

[0029] Wherein, Q1 is the original abnormal prediction information corresponding to Q; K is the adjustment coefficient of the user's feedback information on Q1, when the feedback information is that the prediction information is accurate, K = 1, when the feedback information is that the prediction information is too high, K = 0.8, when the feedback information is that the prediction information is too low, K = 1.2;

[0030] The target hidden Markov model is updated and trained using the user feature updated training set.

[0031] Further, observable dimensions include the degree of pain and fatigue in the neck, back or hips.

[0032] The present invention has at least the following beneficial effects:

[0033] In the present invention, multiple state degrees corresponding to the target event in a certain time period are formed by obtaining multiple monitoring state values ​​obtained in a continuous time. At the same time, by calculating the distribution position, distribution density and quantity proportion of the monitoring state values ​​of three different abnormal states, the proportion of various types of abnormal states in the entire numerical sequence and the approximate distribution position can be calculated, thereby reflecting the change trend of the target event in the monitoring time period. And by considering the calculation of the state values ​​of different multiple monitoring time periods to generate the final abnormal judgment value, the influence of the current state value error on the final abnormal judgment value error can be reduced when only the current state value is directly used as the abnormal judgment value. And then it can be avoided as much as possible that the relevant information obtained in a single monitoring time has errors, and then the evaluation index of the AS severity obtained has large errors, so as to improve the accuracy of the early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 A structural block diagram of an ankylosing spondylitis comprehensive management system provided by an embodiment of the present invention;

[0036] Figure 2 A schematic diagram of a fusion process provided by an embodiment of the present application is shown;

[0037] Figure 3 The following is a diagram showing the implementation environment architecture of an ankylosing spondylitis comprehensive management system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0039] As a possible embodiment of the present invention, Figure 1 As shown, a comprehensive management system for ankylosing spondylitis is provided, which includes: an information acquisition module, a target hidden Markov model and a determination module, and the target hidden Markov model is communicated with the information acquisition module and the determination module respectively.

[0040] The information acquisition module is used to obtain the characteristic observation sequence corresponding to each monitoring period, and the characteristic observation sequence includes the attribute value of at least one observable dimension corresponding to the target event. In this embodiment, the target event can be the patient's current AS severity. The observable dimensions may include multiple symptom manifestations used to reflect the severity of AS, such as the degree of pain in the neck, back or buttocks, the degree of fatigue and the duration of morning stiffness. The specific dimension settings can be adaptively adjusted according to different populations. In the prior art, there are applications that collect the above information in the form of questionnaires, and the acquisition of characteristic observation sequences can be achieved by calling corresponding other APIs. The characteristic observation sequence in this embodiment can generally be a fixed-length vector, such as (A1, A2, A3, A4, A5, A6), in which each element represents the evaluation value of the corresponding symptom.

[0041] The target hidden Markov model is used to generate a monitoring state value corresponding to each historical monitoring period according to the characteristic observation sequence of multiple continuous historical monitoring periods corresponding to the current monitoring moment. The monitoring state value includes a first type of state value used to indicate that the target event is in a normal state; and a second type of state value and a third type of state value when the target event is abnormal, and the degree of abnormality of the target event corresponding to the second type of state value is greater than the degree of abnormality of the target event corresponding to the third type of state value.

[0042] Specifically, in this embodiment, the monitoring state value is a stiffness index used to evaluate the severity of AS, and the value range of the value is [0,10]. The larger the value, the more severe the AS.

[0043] In this embodiment, the monitoring status values ​​can be divided into three types according to the value of the stiffness index. The specific classification criteria are as follows:

[0044] First, if the value range of the stiffness index is [0, 2.1], the monitoring state value is a first type state value; the first type state value in this embodiment may indicate that the patient is in a healthy state or the condition has been alleviated or the disease activity is low.

[0045] Secondly, if the value range of the stiffness index is (2.1, 3.5], the monitoring state value is a second type state value; the second type state value in this embodiment may indicate that the patient is in an unhealthy state and has a high disease activity.

[0046] Third, if the value range of the stiffness index is (3.5, 10], the monitoring state value is a third type state value. The third type state value in this embodiment may indicate that the patient is in a more serious unhealthy state and has an extremely high disease activity. In this embodiment, the monitoring state value output by the target hidden Markov model may be the corresponding stiffness index, and then when a numerical sequence consisting of a plurality of monitoring state values ​​is subsequently formed, each monitoring state value may be converted into a first type state value, a second type state value, or a third type state value according to the above-mentioned classification criteria.

[0047] The target hidden Markov model in this embodiment is a model that predicts the hidden state sequence (ie, tonic index sequence) corresponding to each feature observation sequence through the current multiple feature observation sequences. Specifically, the Viterbi algorithm can be used to find the most likely hidden state sequence of multiple feature observation sequences.

[0048] Since the evaluation index of AS severity required in the present invention cannot be directly observed, but is indirectly reflected through a series of observable symptoms, physiological indicators, etc. HMM (Hidden Markov Model) models these health states that cannot be directly observed by introducing hidden states, so that the model can capture the changes in the patient's health state.

[0049] Specifically, the target hidden Markov model in this embodiment can be obtained according to the following method:

[0050] Collect historical information of target events in multiple continuous historical monitoring periods. The historical information includes the characteristic observation sequence and monitoring state value corresponding to the target event.

[0051] Specifically, the historical information can be constructed according to the following Table 1:

[0052] Table 1

[0053]

[0054] The initial hidden Markov model is trained using the historical information of the target event to generate a target hidden Markov model.

[0055] After collecting a large amount of historical information, the model parameters of the HMM can be estimated based on the historical information using the Baum-Welch algorithm (also known as the forward-backward algorithm, which is an unsupervised learning algorithm) or the Viterbi training algorithm (supervised learning algorithm), that is, the parameters of the initial state distribution π, the state transfer matrix A and the emission matrix B are determined so that the model can better fit the observed data.

[0056] In addition, in order to enable HMM to generate prediction results more accurately according to the characteristics of different users, the system of this embodiment also includes a model updating module, which is used to obtain feedback information from users on the accuracy of abnormal prediction information generated each time according to the abnormal judgment value; the feedback information includes accurate prediction information, high prediction information and low prediction information.

[0057] when When the user is updated, the historical information of the target event in multiple continuous historical monitoring periods generated by the current user in this update cycle is used to generate the user feature update training set; G1 is the number of accurate feedback information obtained from the last update to the current period; G is the total number of feedback information obtained from the last update to the current period; Y2 is the model update threshold; in order to obtain more training samples, the value of G should be greater than 100.

[0058] The monitoring state value Q in the user feature update training set satisfies the following conditions: Q = Q1 × K;

[0059] Among them, Q1 is the original abnormal prediction information corresponding to Q; K is the adjustment coefficient of the user's feedback information on Q1. When the feedback information is that the prediction information is accurate, K=1; when the feedback information is that the prediction information is too high, K=0.8; when the feedback information is that the prediction information is too low, K=1.2.

[0060] The target hidden Markov model is updated and trained using the user feature updated training set.

[0061] Usually, different users have different physical conditions. In order to make the prediction results of HMM closer to the user's actual situation, the user's own data can be used to fine-tune the HMM. At the same time, the prediction value of the model can be fine-tuned by obtaining the user's feedback information, so that the hidden state value in the user feature update training set is closer to the user's own performance, thereby improving the prediction accuracy of the model after the training is updated again.

[0062] The determination module is used to perform fusion processing on the monitoring state values ​​generated by multiple continuous historical monitoring time periods corresponding to the current monitoring moment, so as to generate an abnormal determination value corresponding to the current monitoring moment.

[0063] like Figure 2 As shown, the fusion process includes:

[0064] S100: Traverse the numerical sequence composed of multiple monitoring state values ​​to construct a state value distribution hash table. The key of the state value distribution hash table is the first type state value or the second type state value or the third type state value. Each type of state value corresponds to a state value position sequence, where the state value position sequence Key corresponding to the i-th type state value isi =(wz i 1. wz i 2. ..., wz i m, ..., wz i f(i)), wz i m is the position number of the mth i-th type status value in the numerical sequence; f(i) is the total number of the i-th type status value in the numerical sequence; m=1, 2, ..., f(i); i=1 or 2 or 3; that is, the type of the monitoring status value in this embodiment is the first type status value or the second type status value or the third type status value.

[0065] S200: generating a sub-determination value corresponding to each monitoring state value according to the state value distribution hash table;

[0066] Among them, the sub-determination value P corresponding to the i-th monitoring state value i The following conditions must be met:

[0067]

[0068] Among them, S i is the number of times the i-th monitoring state value appears in the numerical sequence, S sum is the total number of monitored state values ​​in the numerical sequence, μ w is the mean value of the position distribution of the i-th monitoring state value in the numerical sequence, is the density of the position distribution of the i-th monitoring state value in the numerical sequence, P i 1 is the abnormal score corresponding to the i-th monitoring state value.

[0069] In this embodiment, by calculating the position distribution mean of each monitoring state value in the numerical sequence, the approximate position distribution of the monitoring state value in the numerical sequence can be determined, that is, whether the time when the monitoring state occurs is close to the current time. In this embodiment, the higher the rank of the monitoring state value in the numerical sequence, the closer it is to the current time. Therefore, the larger the position distribution mean, the closer it is to the current monitoring time. The closer it is to the current monitoring time, the closer this state is to the current real state and should be given more weight. At the same time, by calculating the standard deviation σ of the position distribution w , to determine the density of the distribution of the monitoring status value. Generally, the higher the density (that is, the lower the standard deviation), the more densely the health state will appear around the mean of the position distribution. Therefore, the ratio of the position distribution mean and the density of the position distribution can reflect the credibility of the position distribution mean corresponding to each state, that is, the size of the position distribution mean can be further adjusted by the density of the position distribution, making the calculation result more accurate.

[0070] S300: Generate an abnormal determination value P corresponding to the current monitoring moment according to the sub-determination values P1, P2, and P3 corresponding to the first-type status value, the second-type status value, and the third-type status value respectively. P satisfies the following condition: P = P3 + P2 - P1.

[0071] Furthermore, the fusion processing further includes:

[0072] S400: If P > Y1, generate an abnormal reminder message. If P < Y1, do not generate an abnormal reminder message. Y1 is an abnormal threshold. The specific value of Y1 can be determined according to the actual scenario.

[0073] In the present invention, by obtaining multiple monitoring status values obtained in a continuous period of time, multiple status degrees corresponding to the target event within a certain time period are formed. At the same time, by analyzing the distribution position, distribution density, and quantity ratio of the monitoring status values of three different abnormal states, the proportion and approximate distribution position of various types of abnormal states in the entire numerical sequence can be calculated, thereby reflecting the change trend of the target event during the monitoring period. And by considering calculating the status values of different multiple monitoring periods to generate the final abnormal determination value, the influence amplitude of the error of the currently generated status value on the error of the final abnormal determination value can be reduced when the currently generated status value is directly used as the abnormal determination value. Furthermore, it is possible to avoid, as much as possible, the problem that the evaluation index of the AS severity has a large error due to the error of the relevant information obtained at a single monitoring time, so as to improve the accuracy of early warning.

[0074] As another possible embodiment of the present invention, the technical solution in the above embodiment can also be applied to the detection of the remaining service life of relevant hardware devices in a computer, such as in the monitoring scenario of the remaining service life of a hard disk. Specifically, the remaining service life of the hard disk (i.e., the duration until the next failure) is a value that cannot be directly observed. However, the values of multiple characteristic dimensions indicating the current operating state of the hard disk can be directly observed and obtained, such as the hard disk operating temperature, the raw read error rate, the start / stop count, the command timeout count, etc. Similarly, in actual data collection, errors may occur in some or all of the characteristic data collected due to sensor failures. In the prior art, the method of predicting the remaining service life of the hard disk based on the currently collected characteristics will result in false alarms.

[0075] Based on this, the methods of S100 to S400 used by the determination module in the present invention can be used in the monitoring scenario of the remaining service life of the hard disk to reduce the probability of false alarms.

[0076] Specifically, in this embodiment, the monitoring status value is the operation status index (equivalent to the stiffness index in the above embodiment), and the operation status index is the reciprocal of the actual number of days corresponding to the remaining service life of the hard disk. In this embodiment, the value range of the actual number of days corresponding to the remaining service life of the hard disk is [1,30]. The monitoring status value can be divided into three types by the operation status index. The specific classification criteria are as follows:

[0077] First, if the value range of the operation status index is (0, 1 / 30], the monitoring status value is a first type status value; the first type status value in this embodiment may indicate that the hard disk is in a healthy state and will not fail in the near future.

[0078] Secondly, if the value range of the operating status index is (1 / 30, 1 / 10], the monitoring status value is a second type status value; the second type status value in this embodiment may indicate that the hard disk is in an unhealthy state and is prone to failure in the near future.

[0079] Third, if the value range of the stiffness index is (1 / 10, 1], the monitoring state value is a third type state value. The third type state value in this embodiment may indicate that the hard disk is in a state that is extremely vulnerable to damage.

[0080] In addition, in order to make the value range of the running state index in this embodiment closer to the value range of the stiffness index in the above embodiment to facilitate subsequent calculations, the value range of the running state index can also be normalized so that its value range becomes (0, 10].

[0081] The feature dimensions in the corresponding feature observation sequence in this embodiment may include hard disk operating temperature, raw read error rate, start / stop count, command timeout count, etc., which can reflect the feature values ​​of the hard disk operating status.

[0082] Therefore, after being processed by the corresponding methods from S100 to S400, the continuous operation health of the hard disk within a certain period of time can be formed by obtaining multiple monitoring status values ​​obtained in a continuous period of time. At the same time, by calculating the distribution position, distribution density and number proportion of the monitoring status values ​​of three different abnormal states, the proportion of various types of abnormal states in the entire numerical sequence and the approximate distribution position can be calculated, thereby reflecting the changing trend of the hard disk health during the monitoring period. In this way, the problem of large errors in the evaluation index of the hard disk health obtained due to errors in the relevant observation information obtained in a single monitoring time can be avoided, so as to improve the accuracy of the early warning.

[0083] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0084] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0085] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0086] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0087] The electronic device according to this embodiment of the present invention is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0088] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one storage device mentioned above, and a bus connecting different system components (including storage devices and processors).

[0089] The storage stores program codes, which can be executed by the processor, so that the processor executes the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0090] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).

[0091] The storage may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0092] The bus may represent one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0093] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device (e.g., routers, modems, etc.) that enables the electronic device to communicate with one or more other computing devices. Such communication may be performed through an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter. The network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0094] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0095] refer to Figure 3 , Figure 3 A schematic diagram of the structure of a computer system of an electronic device or server suitable for implementing an embodiment of the present application is shown.

[0096] like Figure 3As shown, the computer system includes a central processing unit (CPU), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) or the program loaded from the storage part into the random access memory (RAM). In the RAM, various programs and data required for the operating instructions of the system are also stored. The CPU, ROM and RAM are connected to each other through a bus. The input / output (I / O) interface is also connected to the bus.

[0097] The following components are connected to the I / O interface; an input section including a keyboard, a mouse, etc.; an output section including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section including a hard disk, etc.; and a communication section including a network interface card such as a LAN card, a modem, etc. The communication section performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface as needed. Removable media, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., are installed on the drive as needed so that a computer program read therefrom is installed into the storage section as needed.

[0098] In particular, according to an embodiment of the present application, the above reference flow chart Figure 2 The described process can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, and the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above-mentioned functions defined in the system of the present application are executed.

[0099] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0100] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0101] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0102] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0103] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0104] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0105] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0106] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A comprehensive management system for ankylosing spondylitis, characterized in that: The system includes: a determination module; The determination module is used to perform fusion processing on the monitoring status values generated in multiple consecutive historical monitoring periods corresponding to the current monitoring moment to generate an anomaly determination value corresponding to the current monitoring moment; the monitoring status values include a first type of status value respectively used to represent that the target event is in a normal state, and a second type of status value and a third type of status value for which the target event has an anomaly, and the degree of anomaly of the target event corresponding to the second type of status value is greater than the degree of anomaly of the target event corresponding to the third type of status value; The fusion processing includes: Traverse the numerical sequence composed of multiple monitoring state values ​​to construct a state value distribution hash table; the key of the state value distribution hash table is the first type state value or the second type state value or the third type state value, each type state value corresponds to a state value position sequence, wherein the state value position sequence Key corresponding to the i-th type state value i =(wz i 1. wz i 2. ..., wz i m, ..., wz i f(i)), wz i m is the position number of the mth i-th type state value in the numerical sequence; f(i) is the total number of i-th type state values ​​in the numerical sequence; m=1, 2, ..., f(i); i=1 or 2 or 3; Generating a sub-determination value corresponding to each monitoring status value according to the status value distribution hash table; Generating an anomaly determination value corresponding to the current monitoring moment according to the sub-determination values P1, P2, and P3 corresponding to the first type of status value, the second type of status value, and the third type of status value respectively.

2. The ankylosing spondylitis comprehensive management system according to claim 1, characterized in that: The fusion processing further includes: The sub-determination value P corresponding to the i-th monitoring state value i The following conditions must be met: Among them, S i is the number of times the i-th monitoring state value appears in the numerical sequence, S sum is the total number of monitored state values ​​in the numerical sequence, μ w is the mean value of the position distribution of the i-th monitoring state value in the numerical sequence, is the density of the position distribution of the i-th monitoring state value in the numerical sequence, P i 1 is the abnormal score corresponding to the i-th monitoring state value.

3. The ankylosing spondylitis comprehensive management system according to claim 2, characterized in that: The fusion processing further includes: If P>Y1, an anomaly reminder message is generated; Y1 is an anomaly threshold; If P<Y1, no anomaly reminder message is generated; Y1 is an anomaly threshold.

4. The ankylosing spondylitis comprehensive management system according to claim 1, characterized in that: It further includes: An information acquisition module; The information acquisition module is used to acquire a characteristic observation sequence corresponding to each monitoring period, and the characteristic observation sequence includes attribute values of at least one observable dimension corresponding to the target event.

5. The ankylosing spondylitis comprehensive management system according to claim 4, characterized in that: It further includes a target hidden Markov model, and the target hidden Markov model is respectively communicatively connected to the information acquisition module and the determination module; The target hidden Markov model is used to generate a monitoring status value corresponding to each historical monitoring period according to the characteristic observation sequences of multiple consecutive historical monitoring periods corresponding to the current monitoring moment.

6. The ankylosing spondylitis comprehensive management system according to claim 5, characterized in that: The monitoring status value is the spasticity index.

7. The comprehensive management system for ankylosing spondylitis according to claim 6, wherein If the value range of the spasticity index is [0, 1.3], the monitoring status value is the first type of status value; If the value range of the spasticity index is (1.3, 2.1], the monitoring status value is the second type of status value; If the value range of the spasticity index is (2.1, 10], the monitoring status value is the third type of status value.

8. The ankylosing spondylitis comprehensive management system according to claim 5, characterized in that: The target hidden Markov model is obtained according to the following method: Collecting the historical information of the target event in multiple consecutive historical monitoring periods; the historical information includes the characteristic observation sequence and the monitoring status value corresponding to the target event; Training the initial hidden Markov model with the historical information of the target event to generate the target hidden Markov model.

9. The ankylosing spondylitis comprehensive management system according to claim 8, characterized in that: It further includes a model update module, and the model update module is used to Obtaining feedback information on the accuracy of the anomaly prediction information generated each time according to the anomaly determination value; the feedback information includes that the prediction information is accurate, the prediction information is on the high side, and the prediction information is on the low side; when When the user is updated, the historical information of the target event in multiple continuous historical monitoring periods generated by the current user in this update cycle is used to generate the user feature update training set; G1 is the number of accurate feedback information obtained from the last update to the current period; G is the total number of feedback information obtained from the last update to the current period; Y2 is the model update threshold; The monitoring status value Q in the user feature update training set satisfies the following condition: Q = Q1×K; Wherein, Q1 is the original anomaly prediction information corresponding to Q; K is the adjustment coefficient of the feedback information of the user on Q1. When the feedback information is that the prediction information is accurate, K = 1. When the feedback information is that the prediction information is on the high side, K = 0.

8. When the feedback information is that the prediction information is on the low side, K = 1.2; The target hidden Markov model is updated and trained using the user feature update training set.

10. The ankylosing spondylitis comprehensive management system according to claim 4, characterized in that: Observable dimensions include pain levels in the neck, back, or hips, and fatigue levels.

Images