206 results about "Performed Diagnosis" patented technology

The invention relates to an intelligent diagnosis and treatment method and system based on a knowledge graph and a large language model. The method comprises the steps of constructing a basic framework of a medical field knowledge graph based on collected medical field data, constructing multi-level knowledge graph expression, obtaining symptom description of a patient through multiple rounds of dialogues, diagnosing the patient and generating a diagnosis report, rechecking and adjusting the diagnosis report by a professional physician, and finally outputting the diagnosis report. The intelligent diagnosis and treatment system comprises a knowledge graph construction module, an intelligent question and answer system, an entity matching module, an intelligent diagnosis module, a doctor review and diagnosis report adjustment module, a diagnosis report output module and the like. And main diseases are screened, so that the coverage is wider. And three levels of knowledge maps are constructed, so that text matching with finer granularity can be carried out. The non-standardized expression of the patient is firstly converted into standardized medical terms through a large model, and drug allergy and the like are taken into consideration, so that the diagnosis accuracy is improved, and the diagnosis and treatment risk is reduced.

The embodiment of the invention discloses a novel fish acute septicemia detection system based on hyperspectral imaging. The system comprises a data acquisition module, a model detection module and a user interaction module, wherein the data acquisition module acquires hyperspectral image data on the surface of a target fish body; the model detection module carries out diagnosis by taking the hyperspectral image data as input based on a trained deep learning diagnosis model, and the deep learning diagnosis model obtains a pixel space structure and a pixel spectrum sequence of the fish body surface based on the hyperspectral image data; identifying spatial features representing related clinical symptoms based on a pixel spatial structure, and identifying spectral features representing an inositol metabolism condition based on a pixel spectral sequence; and the user interaction module outputs a diagnosis result of the target fish body based on a result output by the model. The characteristic that the aeromonas hydrophila utilizes inositol as a unique carbon source and the map change of typical infection symptoms on the surface of a fish body are combined, and accurate and rapid detection is achieved through hyperspectral imaging and a deep learning algorithm.

The invention provides an Alzheimer disease auxiliary accurate identification method and system based on a multi-omics marker network and an adaptive support vector machine. Accurate identification of Alzheimer's disease and other types of dementia is realized through a marker network based on five modal data and an adaptive support vector machine model. According to the method, multi-source heterogeneous data such as blood, urine, neuroimaging, electrophysiology and clinical evaluation are fused, specific markers are analyzed and screened by adopting a weighted gene co-expression network, and cross-modal feature interactive learning is realized through a self-attention mechanism. An online learning mechanism is introduced to enable the model to adapt to new data distribution, and the contribution degree of each marker to diagnosis is output in combination with an interpretability module. Finally, multi-center data synchronization and model optimization are realized by means of a cloud platform, the accuracy, specificity and early diagnosis capability of AD diagnosis and identification and the adaptability of the model are remarkably improved, the limitation of the prior art is overcome, and doctors are assisted to diagnose and treat the Alzheimer's disease.

The invention provides a hip joint impact syndrome intelligent diagnosis method, system and device based on multi-modal data fusion and a computer readable storage medium. The intelligent diagnosis method for the hip joint impact syndrome based on multi-modal data fusion comprises the following steps: acquiring a hip joint X-ray image and health condition text information of a patient; wherein the health condition text information comprises general information, medical history information, symptom information and physical examination information of the patient; inputting the hip joint X-ray image and the health condition text information into a preset hip joint impact syndrome diagnosis model, and outputting a hip joint impact syndrome diagnosis result; according to the hip joint impact syndrome diagnosis model, text features and image features are extracted and fused for diagnosis. According to the embodiment of the invention, the hip joint impingement syndrome can be diagnosed more accurately.

The invention provides a tumor real-time tracking and benign and malignant detection method based on a mammary gland ultrasonic video, and the method comprises the steps: collecting the mammary gland ultrasonic video in real time, and inputting the mammary gland ultrasonic video into an UltraNet basic detector to generate a detection result of each frame of ultrasonic image; calculating a similarity probability matrix Si, j to reflect the similarity between the ith target and the jth target; performing cascade matching on the target based on the similarity probability matrix Si, j to form a target track pipeline Ti; and carrying out confidence coefficient updating and bounding box position updating on the detection result in each target track pipeline Ti, and finally outputting a breast lesion detection result of each frame of ultrasonic image. The tumor real-time tracking and benign and malignant detection method based on the breast ultrasonic video has the advantages of high calculation efficiency, high precision and strong robustness, can assist a doctor in accurately positioning a focus and performing diagnosis, and is beneficial to early discovery of breast diseases.

The invention relates to a device for risk assessment of nervous system diseases, and a processor of the device is configured to receive eye movement data and electroencephalogram data which are associated in a time sequence, and fuse a double-branch deep neural network in a model through multiple modes to assess the risk of the nervous system diseases, performing feature extraction and attention mechanism-based cross-modal feature fusion on the eye movement data and the electroencephalogram data to obtain cross fusion features; and outputting risk assessment results and risk assessment parameters of at least one type of nervous system diseases through an output layer of the multi-modal fusion assessment model. According to the method and the device, the problem that diagnosis sensitivity and universality are insufficient due to the fact that diagnosis only depends on a single information source in related technologies is solved, disease features can be more comprehensively constructed from two levels of neural activity and behavior response by fusing data of two modes of eye movement and electroencephalogram, parallel diagnosis is further performed for multiple types of diseases, and the diagnosis efficiency is improved. The sensitivity and applicability of neuropsychiatric diagnosis are improved, and particularly, the method has more remarkable accuracy in early stages such as mild cognitive impairment and the like.

The invention discloses an Alzheimer's disease classification method based on multi-modal data to solve the problems that in an Alzheimer's disease auxiliary classification technology, single-modal data is adopted, the clinical AD diagnosis requirement is difficult to meet, and extracted feature information is too single. Comprising the following steps: firstly, performing feature extraction on MRI image data through an improved ResNet, and introducing a coordinate attention mechanism to enable a network to pay attention to more spatial position information in consideration that the network should pay attention to a lesion position of an MRI image; and secondly, according to non-image data characteristics such as scales, biomarkers, genes and the like, a multi-layer perceptron is adopted to extract features of the non-image data, and finally, the image data and the non-image data are subjected to feature fusion to realize Alzheimer's disease classification. According to the method provided by the invention, the Alzheimer's disease can be diagnosed by analyzing from the perspective of various modal data, the diagnosis time of a doctor is shortened, the shortage of medical resources is relieved, and the method has a wide application prospect.

The invention relates to a keratoconus diagnosis model construction method and system, and the method comprises the steps: collecting corneal topographic map data of two eyes, and determining corneal measurement features in the corneal topographic map data; preprocessing to obtain a sample set; dividing a training set and a test set; training the training set; respectively inputting the training set with the convergent loss function into a plurality of single models for training, and fusing the plurality of trained single models to obtain a fusion model; performing hyper-parameter optimization on the fusion model; retraining the fusion model; evaluating performance indexes of the fusion model; and determining a final diagnosis model, obtaining a definite diagnosis symptom, and outputting a diagnosis result. The method has great innovation in the aspects of feature extraction and fusion, and can be used for diagnosing the early screening of the keratoconus; the diagnosis method has high robustness and accuracy, is convenient to use, efficient and convenient, saves time and labor in the diagnosis process, can avoid the misdiagnosis rate, effectively improves the diagnosis and treatment efficiency, and has important clinical value and significance.

The invention discloses an Alzheimer's disease diagnosis classification method based on a multi-modal nerve image, which is used for auxiliary diagnosis of Alzheimer's disease. The method comprises the following steps: firstly, respectively extracting image features of sMRI and PET brain images by using a self-attention vision converter, extracting global information, improving the feature discrimination capability, and enabling a model to capture a remote dependency relationship more easily, and secondly, realizing complementary fusion of the two image features by using an interactive attention fusion network. And finally, using a Stacking ensemble learning framework as a classifier. According to the method provided by the invention, the pathological information of sMRI and PET brain images is combined, the Alzheimer's disease classification accuracy is improved, a doctor can be assisted in diagnosis, and the method has a wide application prospect in the field of computer-aided diagnosis.

The invention provides an auxiliary doctor seeing decision method and system based on multiple agents and a storage medium, and the method comprises the steps: carrying out the structural information collection of a target patient according to a pre-diagnosis agent, and obtaining the structural data of the patient; inputting the structured data of the patient into a diagnostic reasoning agent for diagnostic reasoning to obtain a diagnostic predicted disease; inputting the diagnosed and predicted disease and the current medical resource load information of the doctor-seeing hospital into a resource scheduling agent for diagnosis and treatment analysis to obtain a diagnosis and treatment recommendation scheme; inputting the diagnosis and treatment recommendation scheme into a medical insurance strategy agent for medical insurance analysis to obtain a medical insurance recommendation scheme; and combining the patient structured data, the diagnosis and prediction disease, the diagnosis and treatment recommendation scheme and the medical insurance recommendation scheme to obtain auxiliary treatment information. According to the embodiment of the invention, based on the auxiliary doctor-seeing information, the auxiliary doctor-seeing decision corresponding to the disease diagnosis, the diagnosis and treatment scheme and the medical insurance scheme can be effectively output, and the diversity of the auxiliary doctor-seeing decision function is guaranteed.

The invention discloses an AI auxiliary inquiry method, and relates to the technical field of intelligent inquiry, personalized diagnosis is realized through combination of a dynamic weighting model and individual features of a patient, electronic health records and medical history data of the patient do not only serve as static references any more, but directly influence the weight of each symptom in diagnosis, and the diagnosis accuracy is improved. The importance of symptoms can be dynamically adjusted according to different patient groups, high-risk symptoms of patients can be more accurately recognized through individualized dynamic weighting, reasonable diagnosis inference is made according to individual characteristics of the high-risk symptoms, the possibility of misdiagnosis or missed diagnosis is greatly reduced, meanwhile, a symptom relation map is introduced, and the diagnosis accuracy is improved. According to the method, the mutual dependency relationship among different symptoms can be visualized, the symptom relationship map presents the correlation degree among the symptoms in the form of nodes and edges, and a doctor can quickly know the overall symptom expression and potential cause of disease of a patient through the map, so that diagnosis is more efficient and accurate.

The invention discloses an internet-based online gynecological disease intelligent consultation system, and relates to the field of data processing, and the system comprises the steps: generating a public and private key pair of a patient through employing an RSA asymmetric encryption method; a BERT model based on deep learning is adopted to process the decrypted inquiry information, processing is carried out on local equipment, and structured patient condition description is obtained; the encrypted patient condition description is uploaded to a server, a system private key is used for decryption on the server, diagnosis is carried out by using a reasoning method based on a knowledge graph according to the patient condition description, and a disease candidate list is obtained; transmitting the encrypted candidate list to local equipment of the patient, decrypting by using a private key of the patient, and reasoning by adopting a CART decision tree according to the disease candidate list to obtain a final diagnosis result; aiming at the low precision of online gynecological disease consultation diagnosis in the prior art, asymmetric encryption, knowledge graph reasoning, a CART decision tree and the like are adopted, so that the diagnosis precision is improved while the privacy of a patient is protected.

The invention discloses a patient diagnosis package information extraction method, which comprises the following steps: S1, acquiring a case through a diagnosis information output circuit, extracting tumor diagnosis and pathological diagnosis according to the dimension of the case, performing corresponding classification, and generating a patient diagnosis package in a matching manner according to a classification result; s2, the patient diagnosis package is filled and revised; s3, after filling and revision of the S2 are executed on each diagnosis package, a disease diagnosis code and a pathological diagnosis code are extracted, diagnosis package code conversion is carried out, and then patient diagnosis package extraction information is obtained; and S4, displaying the patient diagnosis package extraction information through the diagnosis information record display device. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

The invention relates to the technical field of clinical diagnosis, and discloses a neurology clinical diagnosis device which comprises a hammer handle and a main hammer body at one end of the hammer handle and further comprises a moving-out through opening formed in the bottom of the main hammer body and a hammer head replacement part arranged in the main hammer body. And the pain diagnosis component is arranged at the top of the main hammer body. According to the clinical diagnosis device for the neurology department, four sets of rubber hammering heads of different models are arranged on a replacement disc, when diagnosis operation is carried out, a rotating disc can be rotated to rotate the corresponding rubber hammering heads according to diagnosis requirements, so that a doctor can adjust the stimulation intensity according to the specific conditions of a patient, and the diagnosis efficiency is improved. Therefore, the reflex activity of the patient can be evaluated more accurately, a doctor can know the nervous system condition of the patient more comprehensively, and a more reliable basis is provided for diagnosis.

The invention belongs to the technical field of diagnosis training, and particularly relates to a traditional Chinese and western medicine clinical diagnosis training system, which constructs diversified virtual gynecological patients by integrating gynecological case data from different hospitals, creates detailed virtual diagnosis space for each virtual patient, and on the basis of the virtual diagnosis space, provides a clinical diagnosis system for the traditional Chinese and western medicine. A gradient pressure limiting scene is set for each virtual gynecological patient, so that a student selects virtual gynecological patients in different gradient pressure limiting scenes from the constructed virtual gynecological patients for diagnosis training, diagnosis training can be closer to an actual clinical situation, more opportunities for diagnosis training under extreme conditions are provided for the student, and the diagnosis training efficiency is improved. And meanwhile, in the process of performing diagnosis training on the selected virtual gynecological patient, simulation evaluation of the training scene is performed by collecting interaction data between the student and the virtual patient, so that interaction feedback defects existing in the virtual training scene can be found in time, and the student is prevented from generating a sense of disengagement.

The invention provides a crop leaf disease image recognition method, which comprises the following steps: collecting crop leaves to capture a scab area image, obtaining original pixel-level data and a boundary contour, and obtaining an initial data set containing a scab form; angle matching and direction comparison are carried out on the extracted contour coordinates and the main extension direction angle and a preset vein trend template, and the coincidence degree of the extension direction and the vein trend is evaluated; according to the coincidence degree of the expansion direction and the leaf vein trend, identifying the difference between the expansion rate of the disease spot along the leaf vein direction and the expansion rate vertical to the leaf vein direction, and analyzing to obtain a directivity judgment standard after dynamic adjustment; performing diagnosis specificity strong characterization identification classification processing on the morphological characteristics of the non-uniform disease spots according to the dynamically adjusted directivity judgment standard, and determining the tightness degree of judgment between the disease spot morphology and the pathogen species; and integrating the main expansion direction and the vein trend distribution data according to pathogen types, and generating a disease type discrimination report.

The invention relates to a medical record report generation method and device, equipment, a storage medium and a program product, which are applied to diagnostic equipment. The method comprises the steps of firstly obtaining question and answer data of a user in a current inquiry process, then inputting the question and answer data into a preset diagnosis model for diagnosis and recognition to obtain a diagnosis result, and then generating a medical record report according to the question and answer data and the diagnosis result; the diagnosis result comprises diagnosis information and suggestion information, and the preset diagnosis model is obtained based on sample question and answer data and initial diagnosis model training. According to the method, the preset diagnosis model is trained in advance to diagnose the diagnosis result of the user according to the question and answer data, and then the corresponding medical record report is generated based on the diagnosis result and the question and answer data, so that the process reduces the burden of manually inputting the medical record by a doctor and improves the working efficiency.

The invention provides a nuclear magnetic image-based endometrial cancer classification and diagnosis system, and the working principle is that the advantages of artificial intelligence in image recognition and visual interpretation are utilized, a deep learning theory is adopted, a picture sample generated by clinical nuclear magnetic resonance examination is taken as a data basis, and image data are sliced, labeled and transformed to obtain an endometrial cancer classification and diagnosis result. A region of interest is selected from the data, and stage and classification diagnosis of the endometrial cancer is realized through a deep learning neural network model; therefore, the method does not need to perform an operation on the patient, can perform stage diagnosis on the endometrial cancer of the patient in a non-invasive manner, has the advantages of being non-invasive, accurate and the like, and can assist a doctor to perform professional and accurate diagnosis on the endometrial cancer in a classified manner; meanwhile, compared with common doctors, the method has higher diagnosis accuracy and higher diagnosis speed, can assist primary doctors in providing diagnosis opinions, and has important significance on treatment of endometrial cancer, improvement of the survival rate of patients and reduction of public health burden.

The invention discloses a depression auxiliary identification method and device based on man-machine fusion, and the method comprises the steps: obtaining an EEG signal, carrying out the data preprocessing of the EEG signal, and carrying out the LZ complexity analysis; then, analyzing a signal by using a CNN-LSTM confidence analysis model, performing model evaluation judgment and result verification by using improved k-fold cross validation, and feeding back the confidence obtained by feedback, the original electroencephalogram and the LZ complexity to an expert for diagnosis and decision making; and finally, determining a TMS stimulation target by combining deep learning and a neural navigation system, determining stimulation parameters by using IAF, performing TMS stimulation, retaining stimulation positions and parameters, and displaying the stimulation positions and parameters to experts for further decision making and judgment. The LZ complexity can be used as an auxiliary algorithm to distinguish normal people from depression patients. The method focuses on the LZ complexity, and the LZ complexity can also be used as the input of CNN-LSTM, so that front and back connection and integration are realized.

A platform is provided for generating 3D models of a tumor segmented from a series of 2D medical images and for identifying from these 3D models, radiomic features that may be used for diagnostic, prognostic, and treatment response assessment of the tumor. The radiomic features may be shape-based features, intensity-based features, textural features, and filter-based features. The radiomic features are compared to remove sufficiently redundant features, thereby producing a reduced set of radiomic features, which is then compared to separate genomic data and / or outcome data to identify clinically and biologically significant radiomic features for diagnostic, prognostic, and treatment response assessment, other applications.

A method of automatically diagnosing pneumonia in a patient includes obtaining values of two or more diagnostic parameters of the patient from a caregiver of the patient using an input / output interface device. The method includes applying, using a processor connected to an input / output interface, two or more diagnostic parameters to an electronic memory storing a plurality of pre-compiled pneumonia diagnostic models to identify an optimal diagnostic model for performing the diagnosis. The values of the two or more diagnostic markers are applied to the identified optimal diagnostic model to generate a diagnostic output. An input / output interface device is operated according to the diagnostic output to indicate to the caregiver the presence or absence of pneumonia in the patient. The caregiver may use the diagnostics to provide appropriate care for the patient. A pneumonia diagnostic model is from investigations of pneumonia-positive and non-pneumonia population of subjects.

Methods for the diagnosis, prognosis, patient stratification and prediction of pharmacological response in patients afflicted by psychiatric disorders based on electroretinography (ERG) parameters are described.

The present invention provides a diagnostic data analysis method and system, the method comprising: obtaining a medical knowledge subgraph corresponding to target diagnostic history data, and performing vector conversion on the medical knowledge subgraph to obtain a disease code embedding vector; based on a relational attention mechanism, a node attention mechanism, and a message passing mechanism, performing feature extraction according to the disease code embedding vector, and aggregating the extracted vector features to obtain a feature vector representation; performing diagnostic prediction on the feature vector representation to obtain diagnostic prediction data. The present invention combines a medical knowledge graph with a hierarchical attention relationship network constructed based on a relational attention mechanism, a node attention mechanism, and a message passing mechanism, and performs diagnostic prediction based on diagnostic history data, thereby obtaining more accurate diagnostic prediction result data.

The invention relates to a method and a device (200) for grouping a group of patients into at least one subgroup of patients on the basis of medical data relating to the patients of the group and of a predetermined predictive task, the predetermined predictive task being associated with at least one disease which affects at least one of the patients of the group. The method and the device make it possible to obtain a model specific to the at least one disease by means of a specialisation method; to create a vector representation of the medical data; and to group patients of the group into at least one subgroup of patients, so as to obtain, for each patient of the group, information on state of health and / or a health result, relating to the at least one disease, based on the grouping of each patient with respect to the at least one subgroup of patients.

The present application provides detection and prognosis of infectious diseases. In particular, the present application provides fragment length profiles of nucleic acid libraries, methods of generating fragment length profiles of nucleic acid libraries, and methods of using the fragment length profiles for diagnosis and / or prognosis. The present application further provides methods, compositions, and kits for determining the stage or site of infection in a subject.

In accordance with at least one aspect of this disclosure, a method for preparing a patient for a diagnostic procedure and / or for receiving a therapy using a disease targeted drug is provided. The method can include the steps of a) identifying one or more soluble targets detectable in plasma of the patient; and b) extracorporeally removing the soluble targets from the plasma of the patient. Extracorporeally removing the soluble targets from the plasma of the patient can include using plasmapheresis or immunoadsorption to prepare a patient for a procedure using disease targeted treatment or diagnostics. Additionally, or alternatively, dose modulation can be used to proportionally increase the concertation of the diagnostic / treatment agent relative to the concentration of the soluble target in the blood, improving the outcome treatment and diagnostic procedures over conventional methods.

Provided are methods for determining the risk of, diagnosing, preventing, or treating Crohn's Disease (CD), ulcerative colitis (UC) and / or inflammatory bowel disease (IBD) in an individual. Some embodiments provide kits and computer program products for determining the risk of or diagnosing Crohn's Disease (CD), ulcerative colitis (UC) and / or inflammatory bowel disease (IBD) in an individual. Other example embodiments are described herein. In certain embodiments, the disclosed methods and kits are accurate, cost-effective and non-invasive.

The invention discloses an intelligent diagnosis method and device, a storage medium and a program product, and relates to the technical field of automobile diagnos.The method comprises the steps that vehicle fault information is recognized through a pre-constructed diagnosis model; generating diagnosis schemes according to the vehicle fault information, and performing popularity ranking of the diagnosis schemes; polling the popularity rank of the diagnosis scheme, and judging whether the currently displayed diagnosis scheme conforms to expectation or not; if the currently displayed diagnosis scheme accords with the expectation, confirming the currently displayed diagnosis scheme as a final diagnosis scheme; otherwise, acquiring matching parameters of the diagnosis scheme by guiding real-time input information so as to update the popularity rank of the diagnosis scheme until the diagnosis scheme is confirmed to serve as a final diagnosis scheme. According to the method, the vehicle fault is identified through the heat ranking of the diagnosis model and the polling diagnosis scheme, and the diagnosis scheme can be quickly found without additionally inputting information; and if the final diagnosis scheme does not exist, realizing model iteration and scheme popularity ranking dynamic updating by using real-time input information of a user.

The invention discloses an Alzheimer's disease classification method based on sMRI and PET brain images. The Alzheimer's disease classification method is used for auxiliary diagnosis of the Alzheimer's disease. The method comprises the following steps that firstly, a feature extraction network is used for extracting image features of sMRI and PET brain images, and the feature extraction network enhances the spatial position information capturing capacity and the redundant information inhibiting capacity of the network by embedding a coordinate attention mechanism and space-channel reconstruction convolution; secondly, realizing complementary fusion of two image features by using a parallel interaction network based on proxy cross attention; and finally, classifying by using a full connection layer as a classifier. According to the method provided by the invention, the pathological information of sMRI and PET brain images is combined, the Alzheimer's disease classification accuracy is improved, a doctor can be assisted in diagnosis, and the method has a wide application prospect in the field of computer-aided diagnosis.

The invention provides a method for pooling samples prior to a diagnostic screen for a non-infectious disease. The method includes pooling only a portion of the obtained samples and storing the other portion for later use. Pooling samples represents a more time and cost-effective way to perform diagnostic screening.