Diagnostic information acquisition, storage and analysis system for ophthalmic patients

Abstract

The invention provides an ophthalmic patient-oriented diagnosis information acquisition, storage and analysis system. The system comprises a medical information acquisition and interaction module, a medical data storage and management module and an intelligent medical data analysis module, the medical information acquisition and interaction module is used for acquiring and analyzing medical data of an ophthalmology patient; the medical data storage and management module is used for receiving and storing the analyzed medical data; and the intelligent medical data analysis module is used for generating a medical decision auxiliary scheme based on the analyzed medical data. Through centralized management and analysis of detailed medical record information of a patient, a doctor can obtain comprehensive and accurate historical data, so that more accurate diagnosis is made and a personalized treatment scheme is formulated. The system can automatically process and input data, the workload of manual input is greatly reduced, and medical staff can put more time and energy into direct patient nursing. And by means of data mining and the like, visual patient conditions can be better provided for medical personnel.

Description

Technical Field

[0001] The present invention relates to the technical field of data management in ophthalmic hospitals, and specifically to a system for collecting, storing, and analyzing diagnostic information of ophthalmic patients. Background Art

[0002] With the application of artificial intelligence in the medical field, more and more medical diagnosis cases adopt medical diagnosis methods based on artificial intelligence. Relying on image recognition and deep learning technologies, artificial intelligence can preferably solve the problems that may exist in the medical field when manually processing large amounts of imaging data. Among them, as a clinical specialty, the diagnosis modeling of ophthalmic diseases has the eye, the only body surface organ except the skin in the human body, as its research object, and 60-70% of ophthalmic diseases can be diagnosed through image information.

[0003] Based on this, the system for collecting, storing, and analyzing diagnostic information of ophthalmic patients of the present application is proposed to help doctors provide medical decision-making and disease development prediction. Summary of the Invention

[0004] The purpose of the present invention is to provide a system for collecting, storing, and analyzing diagnostic information of ophthalmic patients to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A system for collecting, storing, and analyzing diagnostic information of ophthalmic patients includes: a medical information collection and interaction module, a medical data storage and management module, and an intelligent medical data analysis module;

[0007] The medical information collection and interaction module is used to collect and analyze the medical data of ophthalmic patients;

[0008] The medical data storage and management module is used to receive the analyzed medical data and store it;

[0009] The intelligent medical data analysis module is used to generate a medical decision-making assistance plan based on the analyzed medical data.

[0010] Optionally, the medical information collection and interaction module includes: a service data acquisition sub-module, an external data interaction sub-module, a collection task management sub-module, and a data analysis sub-module;

[0011] The service data acquisition sub-module is used to acquire the data of patients;

[0012] The external data interaction sub-module is used to convert the data format to obtain the target data format that meets the storage requirements of the medical information collection and interaction module;

[0013] The collection task management sub-module is used to set the data collection method;

[0014] The data parsing sub-module is used to organize the collected data according to a predetermined rule.

[0015] Optionally, the medical data storage and management module includes: a data preprocessing sub-module, a data storage sub-module, a data retrieval sub-module, and a data security sub-module;

[0016] The data preprocessing sub-module is used to perform data cleaning on the data;

[0017] The data storage sub-module is used to store the cleaned data;

[0018] The data retrieval sub-module is used to call the reserved data from the data storage sub-module based on a predetermined retrieval formula;

[0019] The data security sub-module is used to encrypt the data stored in the data storage sub-module.

[0020] Optionally, in the step where the data security sub-module is used to encrypt the data stored in the data storage sub-module, the encryption algorithm includes:

[0021] Perform an exclusive OR operation on the key and the data block to obtain the initial data;

[0022] Each byte is replaced with a fixed value to increase non-linearity;

[0023] Perform a shift operation on the rows in the data block to change the position of the data and increase diffusion;

[0024] Perform a linear mixing operation on each column to increase the mixing of the data;

[0025] Perform an exclusive OR operation on the current data and the round key;

[0026] Repeat the above steps to obtain the encrypted ciphertext;

[0027] Use the encrypted ciphertext to encrypt the data stored in the data storage sub-module.

[0028] Optionally, the intelligent medical data analysis module includes: a data feature mining sub-module and a medical decision-making assistance sub-module;

[0029] The data feature mining sub-module is used to perform statistical analysis on the data stored in the medical data storage and management module to show the development trend of the disease;

[0030] The medical decision-making assistance sub-module is equipped with a trained prediction model; the prediction model is used to predict the disease development trend based on the data stored in the medical data storage and management module and form a medical decision for assisting ophthalmologists in treatment.

[0031] Optionally, the training of the prediction model is a trained machine learning or deep learning model;

[0032] The training method of the prediction model includes:

[0033] Build a deep learning network model;

[0034] Use the disease data in the patient data as the training set and the diagnosis result of the patient as the output set to train the parameters of the deep learning network model and obtain the trained prediction model.

[0035] Optionally, the prediction model is a regression prediction model for predicting the disease progression or clinical parameters;

[0036] Or the prediction model is a time series model for predicting the disease development at a future moment based on historical data.

[0037] Optionally, in the step where the prediction model is a regression prediction model for predicting the disease progression or clinical parameters, the regression prediction model includes:

[0038] Determine the parameter similarity index matrix R between the data of the current patient and the historical data fi

[0039] R fi =[r i1j ,r i2j ,r i3j ,r i4j T

[0040] where r i1j is the Euclidean distance of the j-th medical factor of historical data i to the predicted data f, r i2j is the correlation coefficient of the j-th medical factor of historical data i to the predicted data, r i3j is the Euclidean distance obtained after taking the first-order difference of the j-th medical factor of historical data i and the predicted data with their respective factor values, r i4j is the correlation coefficient obtained after taking the first-order difference of the j-th medical factor of historical data i and the predicted data with their respective medical factor values, i∈[1,n], j∈[1,m], n is the total number of historical data, and m is the total number of medical factors in the historical data.

[0041] Optionally, the r i1j 、ri2j , r i3j and r i4j The formulas for are as follows:

[0042]

[0043] where wd i,j,k = w i,j,k-wi-1,j,k , w i,j,k is the value of the j-th medical factor at the k-th time point of historical data i, is the mean value of the j-th medical factor of historical data i.

[0044] A server, comprising:

[0045] A processor; and

[0046] A memory for storing executable instructions of the processor;

[0047] wherein the processor is configured to load the above-mentioned ophthalmic patient diagnosis information acquisition, storage and analysis system by executing the executable instructions.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows: The present application provides an ophthalmic patient diagnosis information acquisition, storage and analysis system, comprising: a medical information acquisition and interaction module, a medical data storage and management module, and an intelligent medical data analysis module; the medical information acquisition and interaction module is used to acquire and analyze the medical data of ophthalmic patients; the medical data storage and management module is used to receive the analyzed medical data and store it; the intelligent medical data analysis module is used to generate a medical decision-making assistance plan based on the analyzed medical data. First, by centrally managing and analyzing the detailed medical record information of patients, doctors can obtain comprehensive and accurate historical data, so as to make more accurate diagnoses and formulate personalized treatment plans. Second, with the help of advanced optical character recognition (OCR) technology and image recognition functions, the system can automatically process and input data, greatly reducing the workload of manual input, improving work efficiency, and enabling medical staff to devote more time and energy to direct patient care. Finally, through means such as data mining, it can better provide medical staff with an intuitive view of the patient's situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the composition of an ophthalmic patient diagnosis information acquisition, storage and analysis system.

[0050] Figure 2 It is a schematic diagram of the composition of the server in the exemplary embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0051] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0052] In addition, an element in the present invention is referred to as being "fixed to" or "disposed on" another element, and it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration and do not represent the only implementation manner.

[0053] The objective of this application is to construct a system for collecting, storing, and analyzing the diagnostic information of ophthalmic patients. Through the analysis of existing medical devices and medical systems, a patient information collection system is designed to collect information facing the hospital's existing information system, diagnostic electronic files, and diagnostic paper files. By using technologies such as OCR, the collection systems for the above three types of information are completed, and functions such as storage, analysis, and visualization are performed after the collection.

[0054] Specifically, a system for collecting, storing, and analyzing the diagnostic information of ophthalmic patients is provided, including: a medical information collection and interaction module, a medical data storage and management module, and an intelligent medical data analysis module; the medical information collection and interaction module is used to collect and analyze the medical data of ophthalmic patients; the medical data storage and management module is used to receive the analyzed medical data and store it; the intelligent medical data analysis module is used to generate a medical decision-making assistance plan based on the analyzed medical data.

[0055] Specifically: Developing a data management software for ophthalmic hospitals can greatly improve the overall operation efficiency of the hospital and the condition tracking of patients. First, by centrally managing and analyzing the detailed medical record information of patients, doctors can obtain comprehensive and accurate historical data, thereby making more accurate diagnoses and formulating personalized treatment plans. Second, with the help of advanced optical character recognition (OCR) technology and image recognition functions, the system can automatically process and input data, greatly reducing the workload of manual input, improving work efficiency, and enabling medical staff to devote more time and energy to direct patient care. Finally, through means such as data mining, it can better provide medical staff with an intuitive view of the patient's situation.

[0056] In a specific embodiment, the medical information collection and interaction module includes: a service data acquisition sub-module, an external data interaction sub-module, a collection task management sub-module, and a data parsing sub-module;

[0057] The service data acquisition sub-module is used to acquire patient data; it supports the input of service data in various types and formats, including but not limited to patient personal information (such as age, gender, etc.), medical history records, ophthalmological examination results (such as visual acuity tests, intraocular pressure measurements, etc.).

[0058] The external data interaction sub-module is used to convert the data format to obtain the target data format that meets the storage requirements of the medical information collection and interaction module; it supports the import and parsing of data in various formats such as EXCEL and CSV, and supports the integration and export of data in various forms such as charts and reports.

[0059] The collection task management sub-module is used to set the data collection method; it supports the task-based management of data collection functions, enables the repeatable execution of data collection tasks, and supports personalized functions such as scheduled collection and custom information collection content.

[0060] The data parsing sub-module is used to organize the collected data according to predetermined rules; it formulates a data directory and sorts out the data pedigree based on data tags and categories, and classifies and manages data assets to facilitate subsequent storage and analysis.

[0061] In a specific embodiment, the medical data storage and management module includes: a data preprocessing sub-module, a data storage sub-module, a data retrieval sub-module, and a data security sub-module;

[0062] The data preprocessing sub-module is used to perform data cleaning on the data; it supports and implements data cleaning and preprocessing functions such as filling in missing data values, detecting abnormal data, and removing redundant data;

[0063] The data storage sub-module is used to store the cleaned data; it establishes a secure and reliable data storage mechanism to ensure that all patient information can be properly saved; it supports the regular automatic backup of data and has the ability to recover from disasters to ensure that normal operation can be quickly restored even in case of accidents;

[0064] The data retrieval sub-module is used to call the reserved data from the data storage sub-module based on a predetermined retrieval formula; it needs to provide an efficient retrieval function, allowing users to perform precise searches according to different conditions (such as patient ID, examination date, etc.);

[0065] The data security sub-module is used to encrypt the data stored in the data storage sub-module; comply with the requirements of relevant laws and regulations for personal privacy protection, and adopt technical means such as encryption to ensure data security; perform permission control on the data to facilitate data collaboration and prevent illegal data access and data leakage.

[0066] In a specific implementation, in the step where the data security sub-module is used to encrypt the data stored in the data storage sub-module, the encryption algorithm includes:

[0067] Perform an exclusive OR operation on the key and the data block to obtain the initial data;

[0068] Each byte is replaced with a fixed value to increase non-linearity;

[0069] Perform a shift operation on the rows in the data block to change the position of the data and increase diffusion;

[0070] Perform a linear mixing operation on each column to increase the mixing of the data;

[0071] Perform an exclusive OR operation on the current data and the round key;

[0072] Repeat the above steps to obtain the encrypted ciphertext;

[0073] Use the encrypted ciphertext to encrypt the data stored in the data storage sub-module.

[0074] In this embodiment, a decryption algorithm is also provided: AES decryption is the reverse process of the encryption process. The same key and round key are used, but the operation order is reversed. For example, the operations during decryption include "Inverse SubBytes", "Inverse ShiftRows", and "Inverse MixColumns". Since AES is symmetric encryption and the same key is used for decryption and encryption, only these reverse operations need to be performed.

[0075] In a specific implementation, the intelligent medical data analysis module includes: a data feature mining sub-module and a medical decision-making assistance sub-module;

[0076] The data feature mining sub-module is used to perform statistical analysis on the data stored in the medical data storage and management module to show the development trend of diseases; integrate and perform feature analysis on the data through statistical methods, and generate intuitive charts and reports to visually show the development trend of diseases;

[0077] The medical decision-making assistance sub-module is equipped with a trained prediction model; the prediction model is based on the data stored in the medical data storage and management module to predict the disease development trend and form medical decisions for assisting ophthalmologists in treatment; the system historical medical data is modeled through machine learning algorithms to predict the disease development trend and assist ophthalmologists in completing medical decisions.

[0078] In a specific implementation manner, the training of the prediction model is a machine learning or deep learning model that has been trained;

[0079] The training method of the prediction model includes:

[0080] Build a deep learning network model;

[0081] Use the disease data in the patient data as the training set and the patient's diagnosis result as the output set to train the parameters of the deep learning network model and obtain the trained prediction model.

[0082] In a specific implementation manner, the prediction model is a regression prediction model for predicting the disease progression or clinical parameters;

[0083] Or the prediction model is a time series model for predicting the disease development at a future moment based on historical data.

[0084] In a specific implementation manner, in the step where the prediction model is a regression prediction model for predicting the disease progression or clinical parameters, the regression prediction model includes:

[0085] Determine the parameter similarity index matrix R between the data of the current patient and the historical data fi

[0086] R fi =[r i1j ,r i2j ,r i3j ,r i4j T

[0087] where r i1j is the Euclidean distance of the j-th medical factor of historical data i to the predicted data f, r i2j is the correlation coefficient of the j-th medical factor of historical data i to the predicted data, r i3j is the Euclidean distance obtained after taking the first-order differences of the j-th medical factors of historical data i and the predicted data respectively with their respective factor values, r i4jis the correlation coefficient obtained by taking the first-order difference between the historical data i and the j-th medical factor of the predicted data, respectively, and their respective medical factor values. Here, i ∈ [1, n], j ∈ [1, m], n is the total number of historical data, and m is the total number of medical factors in the historical data.

[0088] In a specific embodiment, the r i1j , r i2j , r i3j and r i4j formulas are respectively:

[0089]

[0090]

[0091] Wherein, wd i,j,k = w i,j,k-wi-1,j,k , w i,j,k is the value of the j-th medical factor at the k-th time point of the historical data i, is the mean value of the j-th medical factor of the historical data i.

[0092] This application provides a server. The following describes a server 200 according to this embodiment of the present invention with reference to Figure 2 . Figure 2 The server 200 shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0093] As Figure 2 shown, the server 200 is presented in the form of a general-purpose computing device. The components of the server 200 may include, but are not limited to: the above-mentioned at least one processing unit 210, the above-mentioned at least one storage unit 220, and a bus 230 connecting different system components including the storage unit 220 and the processing unit 210.

[0094] Among them, the storage unit stores program codes, and the program codes can be executed by the processing unit 210, so that the processing unit 210 executes the systems according to various exemplary embodiments of the present invention described in the above "Exemplary System" part of this specification. For example, the processing unit 210 can execute the medical information collection and interaction module to collect and analyze the medical data of ophthalmic patients; the medical data storage and management module is used to receive the analyzed medical data and store it; the intelligent medical data analysis module is used to generate a medical decision-making assistance plan based on the analyzed medical data.

[0095] The storage unit 220 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit RAM2201 and / or a cache storage unit 2202, and may further include a read-only storage unit ROM2203.

[0096] The storage unit 220 may also include a program / utilities 2204 having a set of at least one program module 2205. Such program modules 2205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0097] The bus 230 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 processing unit, or a local bus using any of a variety of bus structures.

[0098] The server 200 may also communicate with one or more external devices 100 such as a keyboard, a pointing device, a Bluetooth device, etc., and may also communicate with one or more devices that enable a user to interact with the server 400, and / or communicate with any device that enables the server 200 to communicate with one or more other computing devices such as a router, a modem, etc. Such communication may be carried out through an input / output I / O interface 250. Moreover, the server 200 may also communicate with one or more networks such as a local area network LAN, a wide area network WAN, and / or a public network, such as the Internet, through a network adapter 260. As shown in the figure, the network adapter 260 communicates with other modules of the server 200 through the bus 230. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the server 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0099] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software, or can be implemented by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product. The software product 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, and includes 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 embodiments of the present disclosure.

[0100] In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium, on which a program product capable of implementing the above methods in this specification is stored. In some possible implementation manners, various aspects of the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of this specification.

[0101] In addition, the above drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present invention, rather than for limiting purposes. It is easy to understand that the processes shown in the above drawings do not indicate or limit the chronological order of these processes. Additionally, it is also easy to understand that these processes can be executed synchronously or asynchronously in, for example, multiple modules.

[0102] Those skilled in the art will readily think of other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

[0103] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only defined by the appended claims.

[0104] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to embrace all changes falling within the meaning and scope of the equivalent elements of the claims in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

[0105] In addition, it should be understood that although this specification is described according to embodiments, not every embodiment only contains an independent technical solution. This narrative manner of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments understandable by those skilled in the art.

Claims

1. An ophthalmology patient-oriented diagnosis information acquisition, storage and analysis system, characterized in that, Including: Medical information collection and interaction module, medical data storage and management module, intelligent medical data analysis module; The medical information collection and interaction module is used to collect and analyze the medical data of ophthalmic patients; The medical data storage and management module is used to receive and store the analyzed medical data; The intelligent medical data analysis module is used to generate a medical decision-making assistance plan based on the analyzed medical data.

2. The ophthalmology patient-oriented diagnosis information acquisition, storage and analysis system according to claim 1, characterized in that The medical information collection and interaction module includes: business data acquisition sub-module, external data interaction sub-module, acquisition task management sub-module, data analysis sub-module; The business data acquisition sub-module is used to acquire patient data; The external data interaction sub-module is used to convert the data format to obtain the target data format that meets the storage requirements of the medical information collection and interaction module; The acquisition task management sub-module is used to set the data acquisition method; The data analysis sub-module is used to organize the collected data according to a predetermined rule.

3. The ophthalmology patient diagnosis information acquisition, storage and analysis system according to claim 1, wherein, The medical data storage and management module includes: data preprocessing sub-module, data storage sub-module, data retrieval sub-module, data security sub-module; The data preprocessing sub-module is used to perform data cleaning on the data; The data storage sub-module is used to store the cleaned data; The data retrieval sub-module is used to call the reserved data from the data storage sub-module based on a predetermined retrieval formula; The data security sub-module is used to encrypt the data stored in the data storage sub-module.

4. The ophthalmology patient diagnosis information acquisition, storage and analysis system according to claim 3, characterized in that In the step where the data security sub-module is used to encrypt the data stored in the data storage sub-module, the encryption algorithm includes: Performing an exclusive OR operation on the key and the data block to obtain the initial data; Replacing each byte with a fixed value to increase non-linearity; Performing a shift operation on the rows in the data block to change the position of the data and increase diffusion; Performing a linear mixing operation on each column to increase the mixing of the data; Performing an exclusive OR operation on the current data and the round key; Repeating the above steps to obtain the encrypted ciphertext; Using the encrypted ciphertext to encrypt the data stored in the data storage sub-module.

5. The ophthalmology patient-oriented diagnosis information acquisition, storage and analysis system according to claim 1, characterized in that, The intelligent medical data analysis module includes: data feature mining sub-module, medical decision-making assistance sub-module; The data feature mining sub-module is used to perform statistical analysis on the data stored in the medical data storage and management module to show the development trend of the disease; The medical decision-making assistance sub-module is equipped with a trained prediction model; the prediction model is based on the data stored in the medical data storage and management module to predict the development trend of the disease and form a medical decision for assisting ophthalmologists in treatment.

6. The ophthalmology patient-oriented diagnosis information acquisition, storage and analysis system according to claim 5, wherein The training of the prediction model is a machine learning or deep learning model that has been trained; The training method of the prediction model includes: Building a deep learning network model; Using the disease data in the patient data as the training set and the patient's diagnosis result as the output set to train the parameters of the deep learning network model to obtain the trained prediction model.

7. The system for collecting, storing and analyzing ophthalmic patient diagnosis information according to claim 6, characterized in that, The prediction model is a regression prediction model for predicting the progression of the disease or clinical parameters; Or the prediction model is a time series model for predicting the development of a disease at a future time based on historical data.

8. The ophthalmology patient diagnosis information acquisition, storage and analysis system according to claim 7, characterized in that, In the step, the prediction model is a regression prediction model for predicting the progression of a disease or clinical parameters. The regression prediction model includes: Determine the parameter similarity index matrix R between the data of the current patient and the historical data fi R fi ＝[r i1j ,r i2j ,r i3j ,r i4j ] T where r i1j is the Euclidean distance of the historical data i to the j-th medical factor of the predicted data f, r i2j is the correlation coefficient of the historical data i to the j-th medical factor of the predicted data, r i3j is the Euclidean distance obtained after taking the first-order differences of the historical data i and the j-th medical factor of the predicted data with their respective factor values, r i4j is the correlation coefficient obtained after taking the first-order differences of the historical data i and the j-th medical factor of the predicted data with their respective medical factor values, i ∈ [1, n], j ∈ [1, m], n is the total number of historical data, and m is the total number of medical factors in the historical data.

9. The ophthalmology patient-oriented diagnosis information acquisition, storage and analysis system according to claim 8, characterized in that Said r i1j , r i2j , r i3j and r i4j The formulas are respectively: where wd i,j,k = w i,j,k-wi-1,j,k , w i,j,k is the value of the j-th medical factor at the k-th time point of historical data i, is the mean value of the j-th medical factor of historical data i.

10. A server, characterized in that, It includes: A processor; And A memory for storing executable instructions of the processor; Wherein, the processor is configured to carry the ophthalmology patient-oriented diagnosis information acquisition, storage and analysis system according to claims 1-9 by executing the executable instructions.

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