126 results about "Disease Screening" patented technology

The invention provides a disease screening system based on a large model. The disease screening system is used for solving the technical problem that an existing disease screening system is intelligently used for single special disease screening. The system comprises a scheduling model and a plurality of AI auxiliary diagnosis models, the scheduling model is connected with the plurality of AI auxiliary diagnosis models, and the scheduling model is connected with a big data disease library. According to the method, a high-level scheduling model is utilized, multiple single AI auxiliary diagnosis models are managed in a centralized mode, automatic calling of a multi-disease AI system is achieved, automatic structured report generation is achieved through a large language model, historical medical history is combined, progress is predicted, treatment suggestions and reference cases are automatically given, an existing clinician film reading workflow is fitted, and the efficiency is improved. The whole process of actual diagnosis decision making of doctors is greatly fitted, and the working efficiency of the doctors can be improved to the maximum extent.

The invention discloses a medical image classification method and system based on multi-scale spatial state modeling, and the method comprises the steps: firstly dividing an input medical image into a plurality of non-overlapping image blocks, and mapping the non-overlapping image blocks to a feature space through a learnable linear projection layer to obtain an initial feature map; then, multiple layers of stacked MS-SMamba blocks are used for carrying out layer-by-layer feature extraction, each MS-SMamba block comprises a main branch, an auxiliary branch, a dynamic gating fusion network, a residual error connection unit and a feedforward network, and long-range dependency relation capture and multi-scale feature fusion are achieved; and finally, processing the last-layer output feature map through a global feature aggregation and classification module, generating a global feature vector, and outputting a classification result. According to the method, the capturing capability of complex pathological features in the medical image is improved, the calculation efficiency and clinical applicability are improved, and the method is suitable for scenes such as disease screening and auxiliary decision making in medical image diagnosis.

The invention discloses a big language model disease screening method and system based on token perception. According to the method, a training sample set containing non-disease and disease samples is constructed, mutual information is utilized to construct a graph network, and multiple rounds of graph propagation are performed to strengthen useful information in tokens. And then, obtaining a final representation through graph aggregation, constructing a final training set, and training a probe to determine an editing direction so as to guide a large language model to generate a text conforming to medical standards. By utilizing the method, the performance of the large language model in screening of mental and psychological diseases can be effectively improved, and a reliable auxiliary tool is provided for clinical diagnosis.

The invention relates to a big-small model collaborative diagnosis system based on federated learning and knowledge transfer. Comprising a data processing layer used for acquiring a to-be-diagnosed multi-modal medical examination data set and performing cross-modal feature processing to obtain cross-modal fusion features; the cross-modal fusion feature comprises a cross-modal alignment feature and text embedding; the large-small model collaborative architecture layer is used for performing disease preliminary screening according to the cross-modal fusion features, and calling a corresponding model for disease diagnosis according to a preset diagnosis strategy to obtain a diagnosis result; the encryption federal layer is used for encrypting bidirectional knowledge transmission between the edge end small model and the cloud end large model by using an encryption mechanism; and the bidirectional dynamic knowledge routing layer is used for bidirectionally optimizing the edge-end small model and the cloud-end large model based on a bidirectional dynamic routing mechanism in combination with the diagnosis result. By adopting the system, the diagnosis performance, the real-time response capability and the privacy protection level can be guaranteed, and meanwhile, the dynamic collaboration among model collaboration, knowledge evolution and data compliance is realized.

The invention provides an electrochemical biosensor for detecting mycoplasma pneumoniae, the core of the electrochemical biosensor comprises a specific capture probe, and the nucleotide sequence of the specific capture probe is a complementary sequence of a 23S-5S rDNA spacer sequence (ITS) of the mycoplasma pneumoniae. In the preparation link, the porous membrane is used as a high-efficiency electron transmission channel, the process is remarkably simplified, and the reaction efficiency is improved. By virtue of a capture probe taking 23S-5S rDNA ITS as a target, the sensor is endowed with excellent specificity and high sensitivity, so that the sensor has excellent performance in complex biological sample detection, can accurately and efficiently recognize a target object and effectively avoids interference of other substances. The electrochemical biosensor is easy and convenient to operate and high in response speed, has wide application prospects in the fields of clinical diagnosis and rapid disease screening, can provide key technical support for timely diagnosis of mycoplasma pneumoniae infection, greatly meets the urgent requirements for rapid and accurate detection in clinical and practical application, and has good application prospects. The development process of a biological detection technology in the mycoplasma pneumoniae detection direction is powerfully promoted.

The invention discloses a disease risk assessment method and screening device based on a multi-group student physical collaborative digital network, and relates to the field of intelligent medical detection. In order to solve the defect that multi-omics-level system collaborative analysis and robust risk assessment are difficult to realize in the prior art, the technical scheme provided by the invention is as follows: acquiring a plasma sample, acquiring a spectral signal by adopting an attenuated total reflection Fourier transform infrared spectrum, and establishing a plasma spectrum digital information space; the method comprises the following steps: constructing a biological collaborative digital network containing four nodes of protein, lipid, saccharides and nucleic acid based on pathophysiology priori knowledge, and defining node strength, edge weight and network collaborative efficiency; a health baseline configuration file is established by using a health sample, a standardized deviation score of a to-be-tested sample is calculated, a comprehensive risk score is obtained, a disease screening result is output in combination with a machine learning model, and digital evaluation of multi-omics collaborative characteristics is realized. The method is suitable for non-invasive rapid screening and risk assessment work of neurodegenerative diseases and mental diseases.

The invention relates to novel expiration detection equipment based on the quantitative analysis method, and the novel expiration detection equipment is applied to the fields of human health assessment, disease screening, clinical diagnosis and the like. The expiration diagnosis is an emerging in-vitro diagnosis method, the clinical application range of the expiration diagnosis is gradually expanded, real-time and on-line detection of human expiration is achieved, a quantitative method for markers in expiration is established, reliable endogenous marker experimental data is obtained, a human expiration marker data model is constructed, and the real-time detection of the human expiration is achieved. Powerful support can be provided for exhaled air physiology research, clinical application of exhaled air diagnosis and environmental exposure research. And quantitative analysis is carried out on healthy people and sick people in different areas, and hit rules of different people are set according to weight values, so that the accuracy of quantitative analysis is improved, and the requirement on the computing power of data analysis is reduced.

The invention discloses a municipal road surface intelligent inspection method and system based on an unmanned aerial vehicle array, and the method comprises the steps: constructing a municipal digital twin base, fusing multi-source data, and carrying out the preliminary screening of diseases through a convolutional neural network; a quantum key and elliptic curve encryption mixed mode is adopted to perform data security enhanced transmission; obtaining the position of a relay node of the unmanned aerial vehicle based on an edge computing node, and constructing a microwave link through multi-module cooperation; measuring link quality, and dynamically planning an unmanned aerial vehicle inspection array; a three-level intelligent diagnosis system is constructed based on multi-algorithm coupling, and disease diagnosis is carried out; evaluating the inspection result, and updating the inspection scheme. According to the invention, rapid routing inspection is carried out through the unmanned aerial vehicle array, data transmission safety is guaranteed by adopting an encryption technology, transmission reliability is improved by dynamic link aggregation, a routing inspection scheme is dynamically adjusted, a routing inspection path and the unmanned aerial vehicle array are automatically optimized, a virtuous circle is formed, and intelligent development of municipal road routing inspection is promoted.

The invention provides an intelligent evaluation method, system and equipment for screening multiple diseases of bone joints based on deep learning and a computer readable storage medium. The method comprises the following steps: acquiring a bone joint X-ray image and text information of a patient; wherein the bone joint X-ray image comprises a hip joint X-ray image or a knee joint X-ray image; the text information comprises bone joint questionnaire information and historical case information, and the bone joint questionnaire information comprises identity information and bone joint function evaluation information of the patient; inputting the bone joint X-ray image and the text information into a preset deep learning network model, and outputting a bone joint multi-disease disease screening intelligent evaluation result; wherein the deep learning network model is used for respectively extracting image features and text features; and carrying out feature fusion on the image features and the text features. According to the embodiment of the invention, screening and intelligent evaluation of multiple diseases of bone joints can be carried out more accurately.

The invention provides an intelligent assessment method, system and device for screening multiple diseases of lumbar vertebra based on deep learning and a computer readable storage medium. The lumbar vertebra multi-disease screening intelligent evaluation method based on deep learning comprises the following steps: acquiring a lumbar vertebra normal position X-ray image and a lumbar vertebra lateral position X-ray image; inputting the lumbar vertebra normal position X-ray image and the lumbar vertebra lateral position X-ray image into a preset lumbar vertebra multi-disease disease diagnosis model, and outputting a lumbar vertebra multi-disease disease diagnosis result; wherein the diagnosis results of the multiple diseases of the lumbar vertebra comprise lumbar spondylolisthesis, degenerative lumbar spondylolisthesis, lumbar degeneration and lumbar vertebra fracture; the lumbar degeneration comprises intervertebral space change and osteophyte; the lumbar fracture comprises compression fracture, burst fracture and comminuted fracture. According to the embodiment of the invention, the accuracy of diagnosis of multiple diseases of the lumbar vertebra can be improved.

The invention provides an arrhythmia special disease screening system, and belongs to the technical field of medical information. The screening system comprises a plurality of data sources, a data acquisition module and a screening module, the plurality of data sources are used for providing different types of medical data related to arrhythmia; the data acquisition module is used for acquiring various medical data of a target object from the plurality of data sources by adopting a middle library mode, and sending the various medical data to the screening module; the screening module is used for processing various medical data of the target object by adopting a machine learning model to obtain a screening result of the target object, and the screening result comprises whether the target object suffers from arrhythmia or not and the type of the arrhythmia under the condition that the target object suffers from the arrhythmia. According to the technical scheme, the screening efficiency and accuracy of arrhythmia can be improved.

The present invention discloses the application of TMPRSS9 gene in the diagnosis and treatment of venous thromboembolism, and belongs to the field of medical diagnosis and treatment technology. The present invention verifies the association between missense mutations of TMPRSS9 gene and thrombophilia in human population, in vivo and in vitro, and draws the following conclusions: nonsense mutations and missense mutations at the sites of c.334C>T, c.692C>T, c.917G>A, and c.1709C>T of TMPRSS9 gene are representative in the effect of TMPRSS9 gene mutation on the occurrence of venous thrombosis. The discovery of the above-mentioned pathogenic gene mutations can prevent and treat thrombophilia at an early stage, which is of great significance for the diagnosis, treatment and management of thrombophilia. At the same time, the inclusion of the new TMPRSS9 gene enriches the genetic variation spectrum of venous thromboembolic diseases, which is of guiding significance for the screening of diseases.

The invention provides a data analysis system and method for intraperitoneal hyperthermic perfusion chemotherapy, and relates to the field of data intelligent analysis. Basic information and illness state characteristics of a patient are subjected to embedded coding by using a data processing technology based on deep learning; splicing the embedded patient basic information embedded features and the illness state embedded features, and meanwhile, carrying out semantic embedded coding on various adaptation disease screening rules; therefore, whether the intraperitoneal hyperthermic perfusion chemotherapy is suitable for being used as a first-line treatment strategy or an auxiliary treatment means in an operation or not is intelligently analyzed according to the selection range ratio query response representation between the patient information-illness state cascade coding feature and the screening rule semantic embedding feature of each indication. Therefore, more accurate and timely treatment strategy suggestions can be provided, and doctors can be helped to make more scientific and reasonable decisions.

The invention discloses a method for screening a biomarker for early non-invasive Alzheimer's disease screening based on machine learning. The method comprises the following steps: acquiring blood-derived cell free RNA (Ribonucleic Acid) sequencing (cfRNA-seq) and brain-derived single cell transcriptome sequencing (scRNA-seq) data of an Alzheimer's disease (AD) patient and an age-matched contrast; and based on the standardized data, screening out genes presenting the same expression mode in the two types of data as the biomarker for non-invasive Alzheimer's disease screening. According to the method, 34 characteristic genes which are jointly and differentially expressed in cfRNA and scRNA data sets are found, and the AD diagnosis classifier is successfully constructed by using the 34 characteristic genes. A diagnosis classifier can accurately predict AD patients and effectively distinguish AD early-stage patients, and meanwhile, the risk of an individual suffering from AD is evaluated. Results show that the key marker genes can be applied to early AD non-invasive screening and prevent disease progression; meanwhile, AD patients with different disease progresses can be distinguished, and support is provided for personalized treatment schemes of the AD patients.

The invention provides a space disease physiotherapy system fusing multi-source heterogeneous biological information, and relates to the technical field of intelligent spaceflight physiotherapy. The system comprises a broadcast module, a first acquisition module, a second acquisition module, a category determination module and a physiotherapy scheme module. The broadcasting module is used for generating and broadcasting space disease diagnosis problems according to a preset symptom description list, the first acquisition module and the second acquisition module are used for acquiring answer voice information and physiological index data, the category determination module is used for determining category information of space diseases suffered by astronauts, and the physiotherapy scheme module is used for determining physiotherapy schemes. According to the method and the device, accurate space disease screening prediction can be carried out on the astronaut according to the collected answer voice and the physiological indexes without guidance of professional physicians, dependence on the professional physicians is reduced, the accuracy and the efficiency of screening prediction of the space disease suffered by the astronaut are improved, effective support is provided for further determination of a physical therapy scheme, and the method and the device are suitable for popularization and application. And the physical therapy effect of space diseases is improved.

The present application provides a method for recommending examination items based on a Bayesian network and an auxiliary decision-making system. The method includes: obtaining a first conditional probability distribution of the examined items for the target disease, a second conditional probability distribution of the examined items for the to-be-examined items, and a third conditional probability distribution of the examined items for the to-be-examined items based on the target disease according to the disease occurrence probability grading corresponding to the examined items and the disease occurrence probability grading corresponding to the to-be-examined items; calculating a fourth conditional probability distribution of the to-be-examined items for the target disease based on the examined items according to the first conditional probability distribution, the second conditional probability distribution, and the third conditional probability distribution; and selecting the first priority recommended examination items from the to-be-examined items according to the fourth conditional probability distribution and the confidence level corresponding to the to-be-examined items. By adopting the above method, the present application solves the problem that the existing disease screening methods fail to fully utilize the known patient diagnosis data, resulting in inaccurate recommendation results.

A rapid diagnostic device for detecting multiple biomarkers in bodily fluid samples, particularly useful for disease screening including breast cancer. The device comprises a housing containing at least one test strip with a sample receiving zone, conjugate pad, and multiple test zones. The test strip utilizes specific materials including Cytiva GF / DVA and Ahlstrom 1667 sample pads, an Ahlstrom 6613 conjugate pad with carboxylated 150 nm gold nanoshell conjugates, and a Sartorius CN140 membrane with precisely positioned test and control zones. The device simultaneously detects multiple biomarkers, such as hCG and CA-15-3, using specific antibodies. Features include a built-in safety lancet, integrated buffer system, and compatibility with various sample types. Results are available within ten minutes, with options for quantitative analysis via a dedicated reader or smartphone application. This multi-biomarker approach potentially improves screening accuracy compared to single-marker tests, offering versatility for detecting various medical conditions.

The invention relates to the technical field of disease screening, and discloses a big data-based lymphedema disease screening system which overcomes the defects of singleness, subjectivity and low efficiency of a traditional screening means by integrating multi-source data, optimizing a processing flow, accurately calculating a screening index and automatically generating an analysis result. According to the method, the accuracy, efficiency and operability of screening are remarkably improved, doctors are helped to quickly identify high-risk patients, meanwhile, the dependence of the patients on expensive detection means is reduced, optimal configuration of medical resources is achieved, comprehensive and reliable technical support is provided for early screening and intervention of lymphedema, and through multi-dimensional collection of patient information, the screening accuracy, efficiency and operability are improved. High-quality input is provided for intelligent calculation and analysis, operation of subsequent modules is more efficient and accurate, high adaptability of data in the screening process is guaranteed through the targeted design, and the problems of misdiagnosis and missed diagnosis caused by data incompleteness or deviation are solved.

The invention discloses a health state monitoring management system for a traditional Chinese medicine AI digital health manager, and belongs to the technical field of health management. Comprising an information receiving module; a health index calculation module; a personal case library construction module; a primary disease screening module; a secondary disease screening module; and a disease diagnosis module. According to the invention, by constructing a personal case library, adopting primary and secondary grading screening mechanisms and combining with an analytic hierarchy process to dynamically set feature weights and standardize data, accurate personalized health state monitoring, efficient disease screening and diagnosis and scientific quantitative evaluation effects conforming to traditional Chinese medicine syndrome differentiation logic are achieved; the problems of insufficient utilization of personalized health data, low disease screening efficiency and unscientific health index quantitative evaluation in the prior art are solved.

The application discloses a kind of children's rare lung disease multi-modal intelligent screening method, system and equipment, belong to medical artificial intelligence technical field.The method includes: obtaining the medical image data, clinical text data and corresponding visit time information generated by the children at multiple time points, and the multi-modal data is standardized;Based on the multi-modal time series data after processing, construct cross-modal time series logic diagram, the cross-modal time series logic diagram is used to represent the association between different modal data and its evolution characteristics with time change;The cross-modal time series logic diagram is input into the children's rare lung disease screening model for analysis, and the risk prediction result of suffering from rare lung disease is output.The screening model includes multi-modal feature extraction module, cross-modal feature fusion module and global comprehensive prediction module, by the joint modeling of image feature, text feature and time series information, realize the intelligent screening of children's rare lung disease.The application can overcome the problem of multi-modal information fragmentation and insufficient use of time series association in the prior art, and is suitable for the auxiliary screening and risk assessment scene of children's rare lung disease.

1. Name of the product in this design: Display screen panel with a graphical user interface for disease screening. 2. Purpose of this design: To display a graphical user interface. 3. The key design features of this product are its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. The display screen panel uses a standard design, omitting other views. 6. Purpose of the graphical user interface: Used for screening diseases after a physical examination. Human-computer interaction and state changes in the graphical user interface: The upper part of the main view displays a 3D human body model, and the "Respiratory System" button is lit. The lower part displays a floating window introducing the symptoms corresponding to the respiratory system. Swiping left or right on the 3D human body model in the main view allows you to rotate and view the model. For example, swiping left on the 3D human body model leads to state change diagram 1. Clicking the "Circulatory System" button in the main view leads to state change diagram 2. The lower part of state change diagram 2 displays a floating window introducing the symptoms corresponding to the circulatory system. Swiping up on the floating window in state change diagram 2 leads to state change diagram 3, where the floating window becomes longer. Swiping down on the longer floating window in state change diagram 3 returns you to state change diagram 2. In the interface change state diagram 3, the gray blocks represent replaceable content screens. 7. Other situations requiring explanation: This display panel can be used with desktop computers, laptops, tablets, televisions, and mobile phones.

The invention provides a multi-modal fusion-based congenital disease early screening method and device, and the method comprises the steps: collecting a target case ultrasonic image and a case report, building a cue word template, and guiding a large language model to output a structured text; extracting visual semantic description based on a Q-former architecture, obtaining image features and enhanced text features, fusing the image features and the enhanced text features to obtain joint features, inputting the joint features to a pre-trained classifier to obtain a disease prediction result, and establishing a total loss function optimization classifier; and for a plurality of ultrasonic images of a single case, outputting a final disease prediction result by adopting a majority voting strategy so as to realize early screening of congenital diseases. According to the method, image features are subjected to semantic supplementation by introducing migration knowledge and text information of a large language model, a deep interaction mechanism of an image modal and a text modal is constructed, the problem of alignment of different-source modal data is solved, effective fusion of cross-modal deep features is realized, and thus the accuracy and reliability of disease screening are remarkably improved.

The application relates to the technical field of medical treatment and discloses a liver chronic disease screening method, a device and a storage medium.The method comprises the following steps: a liver detection system collects liver stiffness data and liver fat attenuation parameters of a target object; a mobile terminal system receives the liver stiffness data and the liver fat attenuation parameters, combines the liver stiffness data and the liver fat attenuation parameters with basic information of a preset target object, generates acquisition characteristic data, carries out label marking matrix processing on pretreatment integrated data of the cloud service system, generates a label marking matrix, carries out fitting residual processing on the label marking matrix, obtains extended decision tree data, carries out discrete sampling processing on the extended decision tree data, obtains a sparse vector, carries out classification calculation processing on the sparse vector, obtains a classification result, and generates a disease screening report based on the classification result.In the embodiment of the application, through system architecture and cloud service system data processing, the early warning timeliness and accuracy of liver chronic disease screening are improved.

The application provides a FFA image generation system, method, medium and device based on a dual-domain constraint Mamba diffusion model, which integrates dual spatial and frequency domain constraints for cross-modal generation of FFA images from CFP images. Through a learnable wavelet feature extractor, the key frequency components of blood vessels are effectively extracted from the fundus CFP image, enhancing the structured guidance of the vascular edge representation in the generated FFA image. In the spatial domain, through a dual-pyramid feature extractor, the local details and global semantics of the fundus lesion area are effectively fused, improving the consistency of the lesion representation in the generated FFA image. The dual-domain conditional Mamba module serves as the denoising core, effectively coordinating spatial and frequency information to improve the clarity and accuracy of the image. The application is verified on the Hajeb, MPOS and clinical data sets, with PSNR reaching 30.11 and 29.40, effectively generating high-quality FFA images that retain vascular details, providing a new method for retinal disease screening.

The application discloses a kind of X-ray film lung disease screening systems of fusing multi-level features, including lung parenchyma segmentation module, lung disease preliminary screening module, multi-level feature construction module, lung disease screening module;Lung parenchyma segmentation module is used to obtain lung parenchyma part from complete chest X-ray film segmentation;Lung disease preliminary screening module is used to construct based on feature consistency variation auto-encoding generation adversarial network and train it, and obtains preliminary screening abnormal score using trained generation adversarial network;Multi-level feature construction module is used to extract features of different semantic levels, including the extraction of bottom visual features, middle layer depth features and high layer experience features;Lung disease screening module is used to construct lung disease screening model using multi-level features and the abnormal score obtained by lung disease preliminary screening module, and obtains the final lung disease classification result.The present application can effectively solve the problem that current computer-aided lung disease screening field research is difficult to be applied to practical clinical scene.

The utility model discloses a sealing structure of a disease screening test tube, which comprises a test tube body and a cover covering the test tube body, a first sealing structure is arranged on the cover and the test tube body, and a second sealing structure is arranged on the test tube body. The first sealing structure comprises a first sealing ring which is formed on the outer side of the middle of the cover and abuts against the inner wall of the test tube body after the cover is closed, and a second sealing ring which is formed on the outer side of the lower end of the cover and abuts against the inner wall of the test tube body after the cover is closed. The first sealing ring and the second sealing ring are both in interference fit with the inner wall of the test tube body. According to the sealing structure of the disease screening test tube, reagent filling and sealing are facilitated, the sealing performance is good after the cover is closed, peculiar smell of a sample and the reagent cannot be leaked, deodorization is effectively achieved, and use by people is facilitated.

The application discloses an application of a prostate hyperplasia related marker in preparation of a product for detecting prostate hyperplasia, and the prostate hyperplasia related marker is at least one selected from MINP1, ACTBL and APOC4 proteins. In the technical scheme, MINP1, ACTBL and APOC4 are combined as a detection marker for prostate hyperplasia (BPH), so that the complementarity of the three in different stages of diseases can be fully exerted, and the comprehensive performance of the detection product is obviously improved. MINP1 and ACTBL participate in disease progression by regulating prostate cell proliferation and apoptosis, and APOC4 can present significant expression changes in blood cells of early BPH patients, so that BPH disease screening can be accurately performed. When the three are applied in cooperation, early screening of BPH can be realized, and the activity and severity of the lesion can be evaluated, so that a detection system covering the whole process of disease occurrence and development is formed, the limitation of a single marker in the diagnosis dimension is effectively made up, and the comprehensiveness and accuracy of BPH detection are greatly improved.

The invention discloses a urinary incontinence prediction device and method based on machine learning, and relates to the technical field of urinary incontinence prediction. The method comprises the steps that a database is built on the basis of the device, and the database comprises electronic data of urinary incontinence disease screening; preprocessing the data in the database; selecting features related to urinary incontinence from the preprocessed data; dividing data into a training set and a test set, and introducing a cost function to optimize the initial XGBoost prediction model to obtain an optimized function; and performing prediction by using the optimized XGBoost function so as to solve prediction data of related urinary incontinence prediction. The device comprises a bladder pressure monitor body, a display screen is fixedly arranged on the front face of the bladder pressure monitor body, a pressure measuring connector is arranged at the top of the bladder pressure monitor body, a catheter is arranged on one side of the bladder pressure monitor body, and a cleaning device is arranged on the front face of the bladder pressure monitor body. And a first motor is arranged in the cleaning device. By arranging the first motor, the lead screw, the nut seat and the cleaning rod, the first motor drives the lead screw to rotate, the rotating lead screw drives the nut seat and the cleaning rod to vertically move, the vertically moving cleaning rod can clean the surface of the display screen, attached dust is cleaned down, and the display screen is kept clean; and dust is prevented from hindering a worker to read bladder pressure monitoring data displayed on the display screen.

The application discloses a magnetic capsule gastroscopy vehicle and a magnetic body thereof. The magnetic capsule gastroscopy vehicle comprises a vehicle body, the vehicle body comprises a vehicle compartment, the inside of the vehicle compartment is divided into a front cavity and a rear cavity arranged in front and back through an isolation door, the front cavity is a medical staff control area for medical staff activities, and the rear cavity is a patient disinfection and detection area for patient activities; the isolation door is a sliding glass isolation door; a front door for entering the front cavity is arranged at the front end of the vehicle compartment, a rear door for entering the rear cavity is arranged at the rear end of the vehicle compartment, and a general rear door for sealing the rear end of the rear cavity is further arranged at the rear side of the vehicle compartment; a check bed is detachably arranged in the rear cavity, and a magnetic body movement mechanism is arranged beside the check bed; a console is arranged in the front cavity, and the console is connected with the magnetic body movement mechanism in control. The application is convenient for flexible and mobile gastrointestinal disease screening, realizes contactless examination, expands the working range and the beneficiary population, and can provide medical aid in special environments such as infectious diseases and wars.

The present invention discloses a method for constructing a disease recognition model of medical data based on multi-task self-supervised learning, which belongs to the field of laboratory medicine and disease recognition technology, and includes: S1, extracting medical data of a target population to form an original sample data set; S2, preprocessing the original sample data set; S3, constructing a missing value filling model and a sample data homology test model using the preprocessed missing value filling data set and the sample data homology analysis data set; S4, using a multi-task self-supervised learning model, taking missing value filling and sample data homology prediction as auxiliary tasks, taking known disease screening as a downstream task, and realizing disease risk assessment of the original sample data through transfer learning. The present invention adopts the above-mentioned method for constructing a disease recognition model of medical data based on multi-task self-supervised learning, which can extract more comprehensive and accurate features to achieve the purpose of improving the generalization performance of downstream tasks.