CN112037876A - System, device and storage medium for chronic disease course stage analysis - Google Patents
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Abstract
The invention discloses a chronic disease course stage analysis system, which comprises: the acquisition module is used for acquiring the disease course associated information to be analyzed; the disease course related information at least comprises symptom information; the course analysis model is used for obtaining a course stage according to the course correlation information; the disease course analysis model is a machine learning model; and the feedback module is used for feeding back the disease course stage. The invention also discloses a device and a computer readable storage medium, which solve the problems of serious illness, prolonged disease course and vicious circle caused by fear, excessive psychological burden and the like of a patient due to the fact that the patient does not understand the disease course in the prior art.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence, and particularly relates to a chronic disease course stage analysis system, a chronic disease course stage analysis device and a computer storage medium.
Background
At present, some diseases which can be cured but have a longer course in human diseases, such as chronic gastritis, tuberculosis and the like, have more uncomfortable symptoms and are not obviously relieved in a long period of time in the early treatment period, so most patients are nervous, confused and even generate anxiety because the patients do not know the stage of the disease. The disease course is not known, so that fear, psychological burden and the like are caused, and the disease condition is aggravated, the disease course is prolonged, and then vicious circle is caused.
Therefore, the prior art also has the problems that the disease condition is aggravated, the disease course is prolonged and the vicious circle is caused by fear, excessive psychological burden and the like caused by the fact that the patient cannot understand the disease course.
Disclosure of Invention
The invention mainly aims to provide a chronic disease course stage analysis system, a chronic disease course stage analysis device and a computer storage medium, and aims to solve the problems of serious illness, prolonged disease course and vicious circle caused by fear, excessive psychological burden and the like due to the fact that a patient cannot understand the disease course in the prior art.
In order to achieve the above object, the present invention provides a chronic disease course stage analysis system, including:
the acquisition module is used for acquiring the disease course associated information to be analyzed; the disease course associated information at least comprises symptom information;
the course analysis model is used for obtaining a course stage according to the course correlation information; the disease course analysis model is a machine learning model;
and the feedback module is used for feeding back the disease course stage.
In one embodiment, the method further comprises:
and the training module is used for obtaining the disease course analysis model according to the disease course training set.
In an embodiment, the training module is further configured to add the course correlation information acquired by the acquisition module to the course training set.
In one embodiment, the disease course related information further includes at least one of medication information, diet information, living environment information, physical examination data.
In one embodiment, the feedback module is further configured to feed back guidance suggestions corresponding to the current course of disease.
In one embodiment, the guidance advice comprises: at least one of diet advice, exercise advice, medication use advice, and medical advice.
In one embodiment, the disease stage comprises an outbreak period, a remission period, a stabilization period, and a drug withdrawal period.
In one embodiment, the disease course analysis model is processed using a cluster analysis algorithm.
To achieve the above object, the present invention further provides an apparatus including a memory, a processor, and a chronic disease course stage analysis program stored in the memory and executable on the processor, wherein the chronic disease course stage analysis program, when executed by the processor, implements the steps of the chronic disease course stage analysis system as described above.
To achieve the above object, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a chronic disease course stage analysis program, and the chronic disease course stage analysis program, when executed by a processor, implements the steps of the chronic disease course stage analysis system as described above.
According to the chronic disease course stage analysis system, the chronic disease course stage analysis device and the computer storage medium, the disease course relevant information to be analyzed of the patient is obtained at first, and the disease course relevant information at least comprises symptom information; then, the course associated information to be analyzed is searched in a course analysis model obtained through training of a large number of course training sets, wherein the large number of course training sets comprise course associated information of various types of chronic patients, and the course stage where the course associated information to be analyzed is located is searched; and finally, the disease stage result obtained by searching is fed back, so that the patient can clearly know the disease stage of the patient, and the patient can repeatedly inquire and obtain the disease stage of the patient according to the steps, and the patient can conveniently know the disease condition and recovery condition of the patient at any time. Thereby solving the problems of serious illness, prolonged disease course and vicious circle caused by fear, excessive psychological burden and the like of patients due to the inexplicability of the disease course in the prior art.
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FIG. 1 is a schematic diagram of an apparatus according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of the chronic disease stage analysis system of the present invention.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention discloses a chronic disease course stage analysis system, which comprises: the acquisition module is used for acquiring the disease course associated information to be analyzed; the disease course related information at least comprises symptom information; the course analysis model is used for obtaining a course stage according to the course correlation information; the disease course analysis model is a machine learning model; and the feedback module is used for feeding back the disease course stage. Because the course associated information to be analyzed of the patient is firstly obtained, the course associated information at least comprises symptom information; then, carrying out course stage search on the analyzed course associated information in a course analysis model obtained by training a large number of course training sets, wherein the large number of course training sets comprise course associated information of various types of chronic patients, and searching to obtain a course stage where the course associated information to be analyzed is located; and finally, the disease stage result obtained by searching is fed back, so that the patient can clearly know the disease stage of the patient, and the patient can repeatedly inquire and obtain the disease stage of the patient according to the steps, and the patient can conveniently know the disease condition and recovery condition of the patient at any time. Thereby solving the problems of serious illness, prolonged disease course and vicious circle caused by fear, excessive psychological burden and the like of patients due to the inexplicability of the disease course in the prior art.
As an implementation manner, fig. 1 may be shown, where fig. 1 is a schematic structural diagram according to an embodiment of the present invention.
It will be appreciated that memory 1200 in embodiments of the invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile Memory may be a Read Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data Rate Synchronous Dynamic random access memory (ddr Data Rate SDRAM, ddr SDRAM), Enhanced Synchronous SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 1200 of the systems and methods described in connection with the embodiments of the invention is intended to comprise, without being limited to, these and any other suitable types of memory.
For a software implementation, the techniques described in this disclosure may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described in this disclosure. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
The invention provides a chronic disease course stage analysis system.
Example 1
Referring to fig. 2, fig. 2 is a block diagram of the components of the chronic disease stage analysis system of the present invention. The system comprises an acquisition module 10, a disease course analysis model 20 and a feedback module 30.
In this embodiment, the chronic disease stage analysis system further includes a training module 40.
It should be noted that chronic diseases refer to diseases with a long disease course, such as chronic gastritis, tuberculosis, diabetes, cardiovascular diseases, respiratory diseases, etc., which cause great troubles to patients, because the disease course of the chronic diseases is long and the recovery time is correspondingly long, patients are not suitable for more symptoms and have no obvious links in a long time in the initial stage of treatment, and most patients are nervous and confused because the patients are not clear at what stage of the disease, even anxiety is generated, the disease course is prolonged, and the vicious circle is further caused.
In this embodiment, the obtaining module 10 is configured to obtain disease course related information to be analyzed, where the disease course related information at least includes symptom information.
A module refers to a section of program or subprogram required for completing a certain function in program design; or an independent program unit that can be handled by a compiler, assembler, or the like; or to a portion of a larger software system. The disease course related information refers to various information which has influence on the disease course stage of the chronic disease, the disease course related information at least comprises symptom information, and the symptoms refer to abnormal feelings and states of the organism which are shown due to the occurrence of diseases, such as cough, night sweat, afternoon fever and the like which are symptoms of the pulmonary tuberculosis of the person; such as dull pain in the upper abdomen, anorexia, postprandial fullness, acid regurgitation, nausea, etc., are symptoms of chronic gastritis in humans.
The chronic disease course stage analysis system can be established on the basis of web technology, and the equipment terminal with the internet webpage browsing function can be used without installing any auxiliary environment. The chronic disease course stage analysis system provided by the application can be applied to an intelligent terminal as an application program, the intelligent terminal comprises but is not limited to intelligent terminals such as a smart phone, a tablet computer, a computer and wearable portable intelligent equipment, the application program of the chronic disease course stage analysis system provides a chronic disease course stage query platform, and disease course related information can be input by a patient or other people according to symptoms of the patient; the acquisition module acquires the disease course associated information to be analyzed, which is provided by the patient and at least comprises symptom information, namely the current symptom expression of the patient.
In this embodiment, the disease course analysis model 20 is configured to obtain a disease course stage according to the disease course related information; the disease course analysis model is a machine learning model.
Machine learning is a multi-field cross discipline, and relates to a plurality of disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like. The special research on how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the computer. It is the core of artificial intelligence and the fundamental way to make computer have intelligence.
And then performing machine learning on the collected large disease course training sets to form a disease course analysis model. There are a number of processing algorithms in machine learning, and in the present application, the course of disease analysis model is processed using a cluster analysis algorithm. Clustering analysis, also known as cluster analysis, is a statistical analysis method for studying (sample or index) classification problems, and is also an important algorithm for data mining. Clustering (Cluster) analysis is composed of several patterns (patterns), which are typically vectors of a metric (measure) or a point in a multidimensional space. Cluster analysis is based on similarity, with more similarity between patterns in one cluster than between patterns not in the same cluster. Algorithms for cluster analysis can be classified into a Partitioning method (Partitioning Methods), a Hierarchical method (Hierarchical Methods), a density-Based method (density-Based Methods), a grid-Based method (grid-Based Methods), and a Model-Based method (Model-Based Methods).
In this embodiment, two algorithms are preferably adopted for processing, the first algorithm is a kernel K-means clustering algorithm (unsupervised algorithm Kearnal K-means); the second is a DBSCAN algorithm based on density clustering.
The kernel K-means clustering algorithm combines a kernel learning method in a support vector machine with clustering in data mining, and utilizes a Mercer kernel to map samples of an input space to a high-dimensional feature space and then perform clustering in the feature space. The clustering method is greatly improved in performance compared with a classical clustering algorithm, and useful features can be well distinguished, extracted and amplified through nonlinear mapping, so that more accurate clustering is realized; meanwhile, the convergence rate of the algorithm is high. And under the condition that the classical clustering algorithm is invalid, the kernel clustering algorithm can still obtain correct clustering.
For data with irregular cluster shapes, partitioning-based methods like k-means (cluster analysis: k-means algorithm) are no longer applicable, since the partitioning methods (including hierarchical clustering algorithms) are used to find "spherical clusters". To solve the problem of clustering of any cluster shape, a clustering method different from division clustering or hierarchical clustering, namely density-based clustering, is adopted. For Density-Based Clustering, the most common algorithm is DBSCAN (Density-Based Spatial Clustering of Application with Noise) as referred to herein, which translates to "Density-Based Spatial Clustering with Noise Application". The core idea is to find the points with higher density, and then connect the close high density points into one piece step by step, so as to generate various clusters. The algorithm is implemented by drawing a circle (called neighborhood eps-neighbor bourhood) with eps as a radius by taking each data point as a center of the circle, and counting how many points are in the circle, wherein the number is the density value of the point. Then we can choose a density threshold value MinPts, such as the point with the circle center point whose number of points in the circle is less than MinPts is the low density point, and the point with the circle center point whose number is greater than or equal to MinPts is the high density point (called Core point). If there is a high density of points within the circle of another high density of points, we connect the two points so that we can connect multiple points in series. Then, if there is a point of low density also within the circle of points of high density, it is also connected to the nearest point of high density, called the boundary point. Thus all points that can be joined together are in a cluster, while low density points that are not within the circle of any high density points are outliers.
Carrying out disease course stage search on the acquired disease course related information to be analyzed and the trained disease course training set in the disease course analysis model to obtain a disease course stage result of the patient; it also includes how many patients show the symptoms in more than a few percent of patients at this stage, for example, patients with chronic gastritis have symptoms of vague pain in the upper abdomen in 95% of the outbreak period.
In one embodiment, the feedback module 30 is configured to feedback the stage of the disease process.
For example, the chronic disease course stage analysis system is applied to the intelligent terminal as an application program, and the feedback module feeds back a disease course stage result obtained by searching the disease course stage in the disease course analysis model to the display page of the intelligent terminal.
In the technical scheme provided by the embodiment, since the course associated information to be analyzed of the patient is obtained at first, the course associated information at least includes symptom information; then, the course associated information to be analyzed is searched in a course analysis model obtained through training of a large number of course training sets, wherein the large number of course training sets comprise course associated information of various types of chronic patients, and the course stage where the course associated information to be analyzed is located is searched; and finally, the disease stage result obtained by searching is fed back, so that the patient can clearly know the disease stage of the patient, and the patient can repeatedly inquire and obtain the disease stage of the patient according to the steps, and the patient can conveniently know the disease condition and recovery condition of the patient at any time. Thereby solving the problems of serious illness, prolonged disease course and vicious circle caused by fear, excessive psychological burden and the like of patients due to the inexplicability of the disease course in the prior art.
Example 2
In the above embodiment of the chronic disease course stage analysis system, the training set module 40 is further configured to add the course related information acquired by the acquiring module 10 to the course training set.
In an embodiment, the training set module adds the course correlation information acquired by the acquisition module into the course training set, so that when the chronic course stage analysis system is provided for a patient to inquire the course stage, more course correlation information of the chronic disease is collected, the course correlation information is added into the course training set to enable the course analysis model to be optimized in an iterative manner through machine learning, the accuracy of inquiry of the chronic course stage is improved, and the problems of disease aggravation, course lengthening and vicious circle caused by fear, excessive psychological burden and the like due to unsolvable course of the patient are further solved.
Example 3
In embodiment 1, the obtaining module 10 may obtain the disease course related information to be analyzed, which at least includes symptom information. In this embodiment, the disease course related information to be analyzed further includes at least one of the following information: medication information, diet information, living environment information, physical examination data. The information is explained above, and therefore is not described in detail.
In this embodiment, the medication information, the diet information, the living environment information, and the physical examination data are the detailed information of the patient himself or herself who inquires about the chronic disease course. The accuracy of query in the chronic disease course stage is improved by acquiring the information, and the problems of disease aggravation, disease course lengthening and vicious circle caused by fear, excessive psychological burden and the like due to the fact that the patient cannot understand the disease course are further solved.
Example 4
In this embodiment, the feedback module 30 is further configured to feed back a guidance suggestion corresponding to the current disease course.
The guidance suggestion includes: at least one of diet advice, exercise advice, medication use advice, and medical advice. In the above embodiment, a large amount of information related to the course of various types of chronic diseases is trained in the training module to obtain the course training set, so that the optimal way that the patients are in different chronic disease course stages can be obtained, and certainly, more data given by specialized doctors need to be obtained. For example, a patient in the outbreak period of chronic gastritis has symptoms such as epigastric dull pain, anorexia, postprandial fullness, acid regurgitation, nausea and the like, and if the relevant information of the disease course to be analyzed is matched with the symptoms, the patient is identified as being in the outbreak period of chronic gastritis, and then corresponding guidance suggestions, diet suggestions, are given according to the suggestions of professional doctors and comprehensive evaluation of a large number of patients in the outbreak period of chronic gastritis: avoiding the consumption of pickled products, smoked products and cold, irritating, hard, too sour and too sweet foods which are kept for a long time; the times of drinking strong tea, strong coffee and the like are reduced; avoid the excessive consumption of alcoholic beverages. And (4) motion suggestion: suitable exercise can be performed by walking slowly, etc. If the symptoms in the acquired disease course related information to be analyzed are reduced, for example, the symptoms in the remission stage of chronic gastritis are manifested as anorexia, postprandial fullness and acid regurgitation (here, only the expression of different disease course stages is expressed more vividly, and more professional opinions need to be given by a professional doctor), the medicine taking of the patient in the disease course analysis model in the disease course stage is probably reduced, and then the medicine taking reduction suggestion is given. And if the patient with repeated and serious symptoms in the disease course related information to be analyzed is obtained, prompting the patient to seek medical treatment in time.
In the technical scheme provided by the embodiment, a feedback module feeds back a guidance suggestion corresponding to the current course of disease; wherein the guidance suggestion includes: at least one of a diet recommendation, an exercise recommendation, a medication use recommendation, and a medical visit recommendation; provides guidance suggestions of different chronic disease process stages for patients, and further solves the problems of disease aggravation, disease process lengthening and vicious circle caused by fear, excessive psychological burden and the like due to the fact that the patients cannot understand the disease process.
The present invention also provides an apparatus comprising a memory, a processor, and a chronic disease process phase analysis program stored in the memory and executable on the processor, the chronic disease process phase analysis program when executed by the processor implementing the steps of the chronic disease process phase analysis system as described above.
The present invention also provides a computer-readable storage medium characterized in that the computer-readable storage medium stores a chronic disease course stage analysis program which, when executed by a processor, implements the steps of the chronic disease course stage analysis system as described above.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A chronic disease process stage analysis system, comprising:
the acquisition module is used for acquiring the disease course associated information to be analyzed; the disease course associated information at least comprises symptom information;
the course analysis model is used for obtaining a course stage according to the course correlation information; the disease course analysis model is a machine learning model;
and the feedback module is used for feeding back the disease course stage.
2. The chronic disease stage analysis system of claim 1, further comprising:
and the training module is used for obtaining the disease course analysis model according to the disease course training set.
3. The chronic disease stage analysis system of claim 2 wherein the training module is further configured to add the disease process-related information obtained by the obtaining module to the disease process training set.
4. The chronic disease stage analysis system of claim 1 wherein the disease stage associated information further comprises at least one of medication information, diet information, environment of life information, physical examination data.
5. The chronic disease stage analysis system of claim 4 wherein the feedback module is further configured to feedback guidance suggestions currently corresponding to the disease stage.
6. The chronic disease stage analysis system of claim 1 wherein the instructional advice comprises: at least one of diet advice, exercise advice, medication use advice, and medical advice.
7. The system for analyzing chronic disease stage of claim 1, wherein the disease stage comprises an outbreak period, a remission period, a stabilization period, and a drug withdrawal period.
8. The chronic disease stage analysis system of claim 1 wherein the disease analysis model is processed using a cluster analysis algorithm.
9. An apparatus comprising a memory, a processor, and a chronic disease process phase analysis program stored in the memory and executable on the processor, the chronic disease process phase analysis program when executed by the processor implementing the steps of the chronic disease process phase analysis system of any one of claims 1-8.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a chronic disease course stage analysis program which, when executed by a processor, implements the steps of the chronic disease course stage analysis system according to any one of claims 1 to 8.
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| CN113140326A (en) * | 2020-12-31 | 2021-07-20 | 上海明品医学数据科技有限公司 | New crown pneumonia detection device, intervention device and detection intervention system |
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| US20190385751A1 (en) * | 2018-06-15 | 2019-12-19 | Zhu Yifan | Disease condition information research method and system, and storage medium |
| CN110164520A (en) * | 2019-05-24 | 2019-08-23 | 南京邮电大学 | The associated chronic diseases management device of space-time big data |
| CN110176283A (en) * | 2019-05-24 | 2019-08-27 | 南京邮电大学 | The associated chronic diseases management method of space-time big data, storage medium and terminal |
| CN111161813A (en) * | 2019-11-28 | 2020-05-15 | 泰康保险集团股份有限公司 | Method, device and equipment for processing chronic disease information and storage medium |
| CN111063437A (en) * | 2019-12-12 | 2020-04-24 | 中科海微(北京)科技有限公司 | Personalized chronic disease analysis system |
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| CN113140326A (en) * | 2020-12-31 | 2021-07-20 | 上海明品医学数据科技有限公司 | New crown pneumonia detection device, intervention device and detection intervention system |
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