CN111223567A - Suspected disease risk range calculation method based on regional medical image - Google Patents
Suspected disease risk range calculation method based on regional medical image Download PDFInfo
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- CN111223567A CN111223567A CN202010027575.8A CN202010027575A CN111223567A CN 111223567 A CN111223567 A CN 111223567A CN 202010027575 A CN202010027575 A CN 202010027575A CN 111223567 A CN111223567 A CN 111223567A
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H30/00—ICT specially adapted for the handling or processing of medical images
- G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
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Abstract
The invention provides a method for calculating a suspected disease risk range based on a regional medical image, which relates to the technical field of medicine, and the method for calculating the suspected disease risk range based on the regional medical image comprises the steps of measuring through data mining, regression analysis and combination of range marking, a disease risk control system and the like, controlling and calculating the suspected disease range in an early warning region, combining a mass data extraction algorithm technology and a regression analysis technology in a computer system, quantitatively analyzing the correlation degree of disease outbreak risk by combining the frequency of a specific disease in a regression model, and simultaneously providing a distribution map of the risk range by combining patient position information. Statistically analyzing the data and predicting disease, preventing and controlling the risk of disease outbreaks in the area.
Description
Technical Field
The invention relates to the technical field of medicine, in particular to a method for calculating a suspected disease risk range based on regional medical images.
Background
The disease risk index is a tool for evaluating disease incidence, the authenticity and reliability of the disease risk index and the beneficial effect of implementing personalized prognosis on risk factors need to be further verified by prospective research in regions, the types of diseases evaluated by the disease risk index also need to be continuously expanded in practice, the calculation of the disease risk index has a plurality of difficult factors: information isolated island, long statistical period, wide statistical range, low authenticity, multiple disease types, complex regional environment and poor network environment.
The problems to be solved by using a mathematical model to predict diseases are that each prediction method has own application conditions and advantages and disadvantages, a prediction method is sometimes difficult to achieve expected effects, the mathematical model is used as a prediction tool to scientifically estimate and calculate the future to obtain only theoretical values, any model is simplified and abstracted for an actual system, limitation and incompleteness are inevitable, samples are limited, any phenomenon is inevitable to exist or change of some unpredictable quantity occurs in the continuous development and evolution process, and the original fitting model is not tried out or the performance of analysis and prediction is reduced.
The method is used for assisting regional health functional departments (Weijian Commission) and the like to establish a method for calculating the risk based on regional image suspected disease range, providing basis for preventing and controlling the image suspected disease, carrying out suspected disease analysis on a source diagnosis report of a PACS information management system, displaying the statistical result of PACS data, and improving regional suspected disease prevention and treatment service capacity by showing the trend of increasing (decreasing) year by year and higher disease incidence in winter according to current situations, and has great significance for paying attention to regions (counties) with serious epidemic situations and large fluctuation amplitude; the management and health education of common suspected diseases of rural residents should be promoted to reduce the spread and control the popularity of the diseases.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a method for calculating the suspected disease risk range based on regional medical images, which solves the prior technical problem.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: a suspected disease risk range calculation method based on regional medical images is characterized in that a suspected disease range control calculation method in an early warning region is measured through data mining, regression analysis and combination of range markers, a disease risk control system and the like, a mass data extraction algorithm technology and a regression analysis technology are combined in a computer system, the correlation degree of disease outbreak risks is quantitatively analyzed through combination of the frequency of specific diseases appearing in a regression model, and meanwhile, a distribution map of a risk range is given through combination of patient position information.
Preferably, the information is obtained through medical images marked by the radiology department, the diagnostic report uses an image recognition technology and a data mining technology, the quantitative relation between the suspected diseases and the influencing factors is found through the statistical processing and analysis of a large amount of data through a regression analysis method, a regression model is established, and the risk value of the suspected disease outbreak in a certain area is predicted.
Preferably, the regional images are stored in a real-time transmission cloud terminal, remotely diagnosed, subjected to a big data technology, and subjected to statistics and analysis of regional suspected disease range control risks by using a modern mathematical model.
Advantageous effects
The invention provides a method for calculating a suspected disease risk range based on regional medical images.
The method has the following beneficial effects:
1. the method is simple, rapid and convenient, can visually display the influence degree of suspected diseases in a certain detection area, can break the spatial barriers of administrative regions and the medical information islands, collects regional medical data, counts and analyzes data and predicts diseases, and prevents and controls the disease outbreak risk in the region.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
(1) cross-domain real-time image data collection
Images are transmitted by means of digital image acquisition, video acquisition, film scanning and the like through a DICOM3.0 message protocol, unstructured data (NOSQL) such as image data and the like are managed by using a distributed file system, structured data (SQL) such as patient information is stored and managed by using an HIS (medical imaging system), and relevant medical image diagnosis data in a regional medical union are collected widely and in real time.
(2) Regional medical and suspected disease feature models
The method comprises the steps of fully utilizing massive unstructured image diagnosis data, conducting fast analysis and image diagnosis data mining, extracting image data from an image diagnosis database, extracting image characteristics, newly adding the characteristics into a characteristic database, conducting matching query on image characteristic information by utilizing a search engine, conducting algorithm analysis on the image diagnosis data, and finding out rules of themes, characteristics, relations and the like of the data.
(3) Visualization of GIS five-dimensional space coordinate model
The data visualization and interactive presentation are friendly, and the data visualization and interactive presentation are carried out according to dynamic data such as longitude, latitude, altitude, temperature, humidity, climate and the like, and also according to population density age distribution, local historical data and the like.
(4) Risk model feature algorithm
Based on a regression model, reasonably segmenting the screened disease feature data by using a segmentation algorithm, extracting the subject features implicit in the text by using a three-layer Bayesian subject model, and performing semantic analysis by using a multi-granularity convolutional neural network algorithm by using a Tensorflow deep learning platform so as to accurately dig out the key information in the text.
(5) Big data service platform development and big data application technology
The method comprises the steps of deep learning of the AI of a computer, establishing a simple linear thinking to complex neural network model, establishing morphological structure apparent data to image texture deep analysis, aiming at massive diagnosis reports, pathological data, medicine lists and treatment schemes, establishing a disease database based on case characteristics by means of a big data platform, extracting useful information from clustering data and converting the useful information into an understandable object, and combining a characteristic value database of related diseases to apply to an actual risk control system.
The operation steps are as follows:
(1) collecting image diagnosis data, integrating and cleaning a data source, and cleaning Sqoop data by using a Hadoop tool;
(2) image diagnosis data analysis and mining technology research and development, AI deep learning, keyword meaning and professional data are combined;
(3) developing a regional gridding map, wherein a web is 3.0, and designing a plurality of chart high-brightness models;
(4) developing risk model characteristics, and applying a variant prediction algorithm to big data;
(5) developing a big data service platform, facing user type agile development, facing local health and fitness committee and other responsive development;
the invention defines a core formula:
defining a multidimensional multi-element linear regression equation:
where i is a space unit, j is a time unit, k is a finite number of eigenvalues, x is an image big data eigenvalue,
for brevity, this is:
F(X)＝WXT
wherein
X＝(xijoxij1… xijk)
The linear regression loss function adopts a standard deviation formula,
by using a computer, using a gradient descent method,
obtaining a group of W so that the value of S (W) is minimum;
defining a first version of wind control formula as an algorithm core formula
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (3)
1. A suspected disease risk range calculation method based on regional medical images measures and controls and calculates the range of suspected diseases in an early warning region through data mining, regression analysis combined with range markers, a disease risk control system and the like, and is characterized in that: the method comprises the steps of combining a mass data extraction algorithm technology and a regression analysis technology in a computer system, combining the frequency of a specific disease appearing in a regression model, quantitatively analyzing the correlation degree of the disease outbreak risk, and simultaneously combining the position information of a patient to give a distribution map of a risk range.
2. The method of claim 1, wherein the method comprises: the information is marked by medical images of radiology departments, the diagnostic report uses an image recognition technology and a data mining technology, the quantitative relation between suspected diseases and influencing factors is found out by observing the statistical treatment and analysis of a large amount of data through a regression analysis method, a regression model is established, and the outbreak risk value of the suspected diseases in a certain area is predicted.
3. The method of claim 1, wherein the method comprises: the regional images are stored in a real-time transmission cloud terminal, remote diagnosis is carried out, a big data technology is adopted, a modern mathematical model is used, and regional suspected disease range control risks are counted and analyzed.
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| CN111798988A (en) * | 2020-07-07 | 2020-10-20 | 医渡云（北京）技术有限公司 | Risk area prediction method and device, electronic equipment and computer readable medium |
| CN112786200A (en) * | 2021-01-18 | 2021-05-11 | 吾征智能技术(北京)有限公司 | Intelligent diet evaluation system based on meal data |
| CN113782189A (en) * | 2021-09-16 | 2021-12-10 | 平安国际智慧城市科技股份有限公司 | Intelligent auxiliary diagnosis and treatment method and device based on regional disease map |
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| CN113782189A (en) * | 2021-09-16 | 2021-12-10 | 平安国际智慧城市科技股份有限公司 | Intelligent auxiliary diagnosis and treatment method and device based on regional disease map |
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