13 results about "Disease characteristics" patented technology

The application discloses a multi-source clinical data aided decision method and system for childhood leukemia, and belongs to the field of medical informatization, and comprises the following steps: acquiring a clinical data set for representing disease characteristics, treatment characteristics and physiological characteristics of a target object; performing similarity retrieval in a case database based on the clinical data set to obtain a comparable case set of the target object; and generating medication aided information for the target object based on the clinical data set and the comparable case set. The application realizes fine support for the medication aided decision process of childhood leukemia, improves the accuracy of comparable case screening, enhances the reliability of medication aided information, and reduces decision deviation caused by insufficient data utilization.

The application discloses a kind of myasthenia gravis dynamic diagnosis and treatment path optimization method and system based on multimodal fusion, the method includes: the multimodal data of myasthenia gravis patient is collected, and its feature extraction and enhancement are carried out to obtain multimodal feature;Based on multimodal feature, construct three-dimensional dynamic feature matrix and carry out cross-modal fusion, obtain target fusion feature;Target fusion feature is used to train multi-task depth network model, and the diagnosis and treatment path optimization model is obtained;According to preset interval, data is dynamically collected and input model, and diagnosis and treatment evaluation result is output to realize diagnosis and treatment path dynamic optimization.Due to the application, through multimodal fusion and dynamic optimization mechanism, break the limitation of traditional static path diagnosis and treatment, effectively excavate patient multidimensional disease characteristics, improve diagnosis and treatment evaluation accuracy, realize diagnosis and treatment path dynamic optimization, effectively reduce unreasonable diagnosis and treatment behavior, reduce diagnosis and treatment cost, improve efficiency, provide intelligent support for clinic, improve diagnosis and treatment effect.

The application discloses a kind of based on structured electronic questionnaire etiology screening and auxiliary decision-making method and system, it is related to digital health assessment and clinical decision support technical field, including obtaining the disease characteristics of patient, utilize disease characteristics to enter corresponding etiology diagnosis question group etiology identification, based on feature mapping rule etiology diagnosis question group carries out etiology score calculation, obtains candidate etiology score set, based on etiology threshold etiology score set is judged and the etiology that meets requirement is output, obtains etiology preliminary judgment result;According to etiology preliminary judgment result obtains final auxiliary decision-making;Final auxiliary decision-making is used to assist doctor to make clinical diagnosis and disposal.The application realizes disease history standardization, quantifiable, traceable by structured questionnaire and logic branch, reduces the omission and repeated inquiry situation of patient information acquisition process, improves patient treatment efficiency, reduces the work intensity of doctor.

The present application provides an asphalt pavement apparent disease evolution deduction method, system, device and medium, which belongs to the field of road engineering, transportation infrastructure operation and maintenance and intelligent detection technology, and comprises the following steps: constructing a unified space-time reference system, aligning and normalizing multi-source data in space-time; forming section-level disease quantification characteristics based on the recognition results of the inspection images and the structural indexes; event coding the maintenance measures of the road surface and pre-processing the section-level disease quantification characteristics corresponding to the time of the maintenance measures; constructing the correlation, time lag effect and spatial consistency between the disease types, disease characteristics and structural indexes to generate the collaborative evolution characteristics of the coupling relationship between diseases; constructing a deep time series prediction model to obtain the disease development trend at a specified time scale in the future, and forming the section-level disease evolution prediction result. The present application can depict the disease development law at different road sections and different time scales, and predict the future disease expansion.

The invention discloses a children leukemia-oriented multi-source clinical data aided decision-making method and system, and belongs to the field of medical informatization, and the method comprises the steps: obtaining a clinical data set used for representing disease features, treatment features and physiological features of a target object; performing similarity retrieval in a case database based on the clinical data set to obtain a comparable case set of the target object; and based on the clinical data set and the comparable case set, generating medication auxiliary information of the target object. According to the method and the device, the refined support of the children leukemia medication auxiliary decision-making process is realized, the accuracy of comparable case screening is improved, the reliability of medication auxiliary information is enhanced, and the decision-making deviation caused by insufficient data utilization is reduced.

The application provides a rheumatism and immunity disease feature classification method and system based on SHAP values, and the method comprises the following steps: based on rheumatism and immunity disease information, constructing a rheumatism and immunity disease original feature data set and a rheumatism and immunity disease type data set; based on the constructed data set, determining a classifier to be used, evaluating the importance of features, selecting features, and forming a rheumatism and immunity disease feature classification method based on SHAP values. By using the disease feature classification method, the disease feature classification result and important features are given to doctors; and a suitable deep learning model is used to classify the rheumatism and immunity disease features. Compared with the existing rheumatism and immunity disease feature classification method, the rheumatism and immunity disease feature classification method can determine the most critical features of each disease in the face of a large number of symptoms, provide important data for doctors to better classify and make decisions on diseases, improve the diagnosis efficiency, and quickly classify similar symptoms and reduce the misdiagnosis rate.

The present application relates to the technical field of urinary calculus risk assessment, and particularly relates to a kind of upper urinary tract calculus postoperative recurrence risk assessment system based on multi-modal data.The present application trains neural network according to the training stone state in patient body and previous examination vector, determines the expected comprehensive load index of verification patient and the expected local load index under each mode using trained neural network, and obtains the prediction contribution weight of each mode;Using prediction contribution weight to adjust the expected local load index of all modes of the patient to be tested during follow-up, obtain the initial recurrence risk degree, and combine the life habits similarity and disease characteristics similarity of the patient to be tested and historical recurrence patients, obtain the calculus recurrence risk degree;Based on calculus recurrence risk degree, the recurrence risk of upper urinary tract calculus of the patient to be tested after operation is evaluated.The present application combines the static basic risk and dynamic disease risk of patient, reduces the influence of multi-modal data loss during follow-up on recurrence risk assessment.

The application discloses a railway bridge performance evaluation method, comprising the following steps: S1, photographing a bridge surface to obtain a bridge apparent image; S2, combining discrete bridge apparent images into a bridge overall image; S3, obtaining disease occurrence positions, disease characteristics and disease degree data through image recognition on the bridge overall image; S4, respectively calculating bridge static evaluation indexes and bridge dynamic evaluation indexes according to the disease occurrence positions, the disease characteristics and the disease degree data; S5, correcting the bridge static evaluation indexes through the bridge dynamic evaluation indexes to obtain a corrected bridge fusion evaluation result; and S6, grading the corrected bridge fusion evaluation result. The application fuses inspection, detection and structural response monitoring indexes for evaluation, and solves the limitation problem of a traditional bridge safety detection monitoring evaluation system.

The invention provides a thyroid-related eye disease mouse model evaluation method. The thyroid-related eye disease mouse model evaluation method comprises the following steps: S1, appearance phenotype evaluation; s2, thyroid function index evaluation; s3, carrying out iconography evaluation; s4, performing immunological phenotype evaluation; and S5, carrying out histopathologic evaluation. Compared with a traditional research method only paying attention to single eye phenotype or single histological change, the thyroid-related eye disease mouse model evaluation method provided by the invention comprehensively reflects disease characteristics of a TAO mouse model from five dimensions of appearance phenotype, thyroid function, immune phenotype, histopathology and iconography; multi-dimensional comprehensive evaluation of the TAO mouse model is realized, and a scientific, comprehensive and standardized evaluation method is established.

The invention discloses a model training method and a hospital medical information processing method and device, and relates to the technical field of intelligent medical treatment. A specific embodiment of the invention comprises the following steps: receiving user information and illness state information of a user; using a target semantic processing model to extract fusion disease features of the user from the user information and the illness state information; using the target full-connection neural network model and the fused disease features to output multiple pieces of recommended examination item information of the user; sending each piece of recommended inspection item information to the user; and recommending medical information to the user based on the recommended examination items and the medical knowledge base. According to the embodiment of the invention, the examination items and the medical treatment information are recommended for the user through the artificial intelligence model, the medical treatment process duration of the user is shortened, the medical treatment efficiency of the user is improved, and the medical treatment experience of the user is improved.

The invention relates to the field of medical information processing, in particular to a stroke home rehabilitation guidance method and system based on multi-dimensional classification. The method comprises the following steps: acquiring basic conditions of a stroke patient, and classifying disease characteristics, neurological function defect parts, severity and complications to form a classification result; executing patient group mapping based on a classification result, and establishing corresponding key monitoring related indexes; change collection and change analysis are carried out according to key monitoring related indexes, and stage rehabilitation scheme suggestions are generated; and carrying out abnormal judgment and online guidance according to the stage rehabilitation scheme suggestions, forming adjustment suggestions, and writing back the adjustment suggestions. According to the invention, the classification result is used as an organization basis throughout the whole process, so that a consistent closed-loop processing link is formed by monitoring, analysis, guidance and write-back, and the continuity, traceability and collaborative stability of the scheme generation and adjustment process in the stroke home rehabilitation guidance process are improved.

The application discloses a kind of crop disease identification method, system, device and storage medium, wherein method includes: obtaining crop disease dataset, data set is preprocessed and data augmentation, obtain the data set after preprocessing;Disease identification model is constructed, the disease identification model is trained using the obtained data set, and the model after training is used for crop disease identification;Wherein, the disease identification model uses CSPResNet50 as main network, and the residual block in main network is replaced with 3x3 convolution in pyramid convolution;In the residual block of main network, attention mechanism module is added.The application increases the receptive field of convolution kernel by adopting pyramid convolution module, improves the extraction ability of network model to multi-scale information.In addition, by using attention mechanism module, the model pays more attention to important disease characteristics.The application can be widely applied to the field of crop disease image recognition technology.

The invention provides an asphalt pavement apparent disease evolution deduction method, system, equipment and medium, and belongs to the technical field of road engineering, traffic infrastructure operation and maintenance and intelligent detection, and the method comprises the steps: constructing a unified space-time reference system, and carrying out the space-time alignment and normalization of multi-source data; based on an identification result of the inspection image, combining the structural indexes to form section-level disease quantitative features; carrying out event coding on the maintenance measures of the pavement and carrying out feature preprocessing on the section-level disease quantitative features at the moments corresponding to the maintenance measures; constructing correlation among disease types, disease characteristics and structural indexes, and generating co-evolution characteristics of a coupling relationship among the diseases according to a time-lag effect and space consistency; and the constructed depth time sequence prediction model obtains a disease development trend of a specified time scale in the future, and forms a section-level disease evolution prediction result. According to the method, the disease development rule can be described in different road sections and different time scales, and future disease extension is predicted.