84 results about "Disease taxonomy" patented technology

The invention provides a knowledge and data fused medical content image-text generation system and method. Relates to the technical field of digital medical treatment and artificial intelligence. The multi-modal feature extraction module is used for extracting multi-modal features from the multi-modal data; the multi-modal feature fusion module is used for carrying out cross-modal dynamic fusion on the multi-modal features to generate cross-modal high-consistency fusion features; the model training optimization module is used for configuring a soft and hard target loss function and a parameter optimizer; the information analysis and reasoning module is used for carrying out semantic matching on the fusion features, the disease classification and the clinical path based on a dynamic medical knowledge network, and generating disease prediction probability distribution and a semantic reasoning path; and the image-text report generation module is used for generating an image-text report containing a diagnosis conclusion and an interpretation basis according to the prediction probability and the semantic reasoning path. According to the invention, intelligent and precise generation of medical contents can be realized.

The invention discloses a medical image segmentation guide classification system for disease diagnosis, and the system comprises a data importing module which is used for reading and preprocessing disease image data and disease category data, and obtaining an original image, a real semantic segmentation mask image and a real disease category label Label; and the lesion semantic segmentation and disease classification module is used for executing a segmentation task and a classification task on the original image obtained by the data import module by adopting an improved VM-UNet network to obtain a predicted semantic segmentation mask image and a predicted disease category, so that accurate lesion semantic segmentation and accurate disease classification of the medical image are realized. According to the method, the semantic segmentation task and the classification task of disease diagnosis can be optimized at the same time, implicit segmentation guide classification is realized by using the advantage of multi-task learning, and the shape information and texture information of a focus of interest can be enhanced by reusing a prediction semantic segmentation mask image, so that explicit segmentation guide classification is realized, and the classification efficiency is improved. And the method has relatively high calculation efficiency and generalization.

The invention discloses an artificial intelligence auxiliary medical decision-making system, and relates to the technical field of medical assistance. Through the detection suggestion module, preliminary reasoning and observation index judgment can be performed according to patient chief complaint in combination with basic information, and detection suggestions are given in combination with detection cost budget of a patient; through the diagnosis module, preliminary judgment can be made according to detection abnormity, disease classification is judged based on the current diagnosis result, further detection suggestions are given, and finally a doctor makes further diagnosis or treatment suggestions; through the treatment suggestion module, disease causes can be judged according to the diagnosis result, and detection cost budget of a patient is combined, so that the diagnosis and treatment efficiency is improved. Giving out treatment suggestions; the detection suggestion module, the diagnosis module and the treatment suggestion module jointly act to form a medical decision-making system, non-common diseases can be found easily, the problem of cross-department missed diagnosis is solved, the medical budget planning function is achieved, and doctors and patients can provide accurate treatment suggestions easily.

The present invention provides a computer-implemented, machine learning method for generating safe text. A first portion of a trainable prompt is generated using negative influential features and positive influential features of a predicted condition. A second portion of the trainable prompt is trained to steer a pre-trained large language model (PLLM) to generate the safe text using at least the first portion of the trainable prompt. The method has applications including, but not limited to, use cases in medicine (e.g., digital medicine, personalized healthcare, AI-assisted drug or vaccine development, diagnosis or treatment, disease prediction, etc.), and cyber security.

The invention relates to a fish disease detection method based on a deep learning image processing technology. The method comprises the following steps: collecting a fish disease image and carrying out image preprocessing; segmenting the preprocessed image by adopting a layered Mama model so as to extract a fish disease feature region; and performing feature extraction on the segmented disease feature region by using a MobileNet model, and performing disease classification through a full connection layer to obtain probability distribution of disease categories. According to the method, the hierarchical Mama model is adopted for image segmentation, the MobileNet model is adopted for disease classification, the fish disease detection efficiency and accuracy can be effectively improved, meanwhile, the challenges of poor image quality, limited training data and fish disease diversity can be coped with, and high-precision segmentation results and recognition are achieved under the condition of limited training data. According to the invention, accurate disease diagnosis can be provided, breeding operators can be helped to take measures in time, and loss caused by diseases is reduced.

The invention discloses a semantic enhancement auxiliary inquiry method and system based on a medical knowledge graph. According to the method, natural language processing, knowledge graph construction and reasoning and other means are fused, and by means of hierarchical entanglement state tracking, dynamic prompt template design, undirected isomeric graph construction and other methods, patient information is accurately obtained through multiple rounds of dialogues, the semantic understanding and reasoning ability is enhanced, an optimization scheme is provided for intelligent inquiry, and the intelligent inquiry efficiency is improved. And disease classification and treatment schemes can be determined in an assisted manner.

Systems and methods are disclosed herein for classifying one or more disease conditions. In some embodiments, an application stores a common extraction model, the common extraction model trained using training examples for a plurality of diseases. The application stores a plurality of disease classifiers, each disease classifier configured to output whether or not its respective disease is present, each disease classifier trained using training examples for its respective disease. The application receives a selection of a disease and selects a disease classifier from the plurality of disease classifiers corresponding to the disease. The application inputs an image into the common extraction model and receives, as output from the common extraction model, a set of biomarkers extracted from the image. The application inputs the set of biomarkers into the selected disease classifier, the selected disease classifier configured to output whether or not the disease is present in the image.

A plasma digital marker mining and disease classification device comprises a plasma sample spectrum acquisition module used for determining a digital marker sorting cluster in a plurality of plasma samples; the plasma candidate digital marker selection module is used for iterating plasma spectrum samples in the initial disease classification device based on the digital marker sorting cluster to obtain a plasma candidate digital marker cluster; the pathogenic protein digital marker cluster acquisition module is used for acquiring pathogenic protein digital marker clusters corresponding to spectral absorption peaks of various related pathogenic proteins; the plasma digital marker mining module is used for determining an intersection of the plasma candidate digital marker cluster and the pathogenic protein digital marker cluster as a plasma digital marker; and the disease classification device construction module is used for training a disease classification device according to the plasma digital markers to obtain a trained disease classification device. By adopting the technical scheme, a disease classification device capable of accurately distinguishing MCI patients can be constructed.

The invention provides a rare disease consultation data processing system and method, and relates to the technical field of medical treatment, the processing system comprises a rare disease analysis terminal and a plurality of medical department clients, and the rare disease analysis terminal is used for receiving a rare disease MDT collaborative analysis request sent by any target medical department client; extracting a semantic feature vector corresponding to the disease prediction result of the subject expert, and extracting a picture feature vector according to the medical image picture of the target patient; inputting the semantic feature vector and the picture feature vector into a pre-trained disease classifier to obtain a comprehensive disease type prediction result output by the disease classifier; enabling a medical department client corresponding to the comprehensive disease type prediction result to determine the disease probability of the comprehensive disease type prediction result based on all medical detection data and medical images; and determining a final disease type prediction result of the target patient according to the disease probability and a comprehensive disease type prediction result output by the disease classifier.

The invention discloses a disease classification method and system based on multilevel prompt learning, and belongs to the technical field. For the problem that a traditional meta-learning method has key defects in a small sample skin disease classification scene, complementary semantic clues are provided by using multi-level image features and text information, and the accuracy of small sample disease classification is improved. Meanwhile, through a learning prompt mode, base class knowledge and new class fine tuning are fused, the generalization ability of the model is enhanced, and an effective solution is provided for clinical diagnosis. Compared with a traditional method, the disease classification method based on multi-level prompt learning provided by the invention can better utilize limited data and improve the robustness of a model, can realize fusion of cross-modal information and enhance the interpretability of a diagnosis result, provides an effective solution for solving the problem of small samples, and has a good application prospect. The method has a remarkable application prospect in the fields of skin disease diagnosis and the like.

The invention discloses a multi-omics data integration and classification method, system and device based on hierarchical attention, and is applied to the field of precise medical big data analysis. The method comprises the following steps: firstly, generating feature embedding and feature importance scores through a plurality of parallel feature-level attention modules; then, embedding and inputting all the characteristics of the omics into a unified omics-level attention module, and generating omics embedding and omics importance scores; and finally, a classification prediction task is executed based on omics embedding, and a classification result is output for disease classification. The invention completely abandons a traditional dependency graph convolutional network and an integration normal form of variants of the dependency graph convolutional network, and provides a universal hierarchical attention integration architecture. The framework supports classification tasks of any complex diseases, is not limited by omics data types and combination modes, not only is remarkably superior to a traditional integration normal form in classification performance, but also shows a unique negative generalization distance, and proves that the framework has excellent generalization ability. Meanwhile, features and omics importance scores automatically output by the model provide a powerful analysis tool for biomarker discovery and precise diagnosis and treatment of complex diseases.

The invention discloses an automatic coding method for realizing international disease classification, and the method comprises the steps: obtaining electronic medical record data from a medical database, and carrying out the preliminary cleaning of the data; extracting an electronic medical record text, associating the electronic medical record text with an ICD code diagnosed by a doctor, and constructing a training data set; a multivariate knowledge base is constructed, wherein the multivariate knowledge base comprises a UMLS synonym base, an unstructured knowledge base and an auxiliary knowledge base generated through LLM; constructing an ICD coding model which comprises a context coding module, an auxiliary knowledge screening module, a multiple attention interaction module and a fusion prediction module; a binary cross entropy loss function is adopted to optimize an ICD coding model, and automatic coding of international disease classification is achieved. According to the method, multi-level semantic interaction between the text and the code can be effectively captured, and the distinguishing capability and the coding precision of different levels of semantic information of the complex text are improved; the key information can be extracted more accurately, and the coding quality under complex cases and fuzzy diagnosis scenes is improved.

Provided herein are multi-modal methods and systems of diagnosing one or more disease, as described elsewhere herein.

The invention relates to the technical field of medical informatization, in particular to a neurosurgical disease diagnosis and prognosis prediction system and method based on machine learning, and the system comprises a data collection module, a processing module, a disease diagnosis module, a prognosis prediction module, a model evaluation module and a result output module. The system fuses clinical, image and survival data, and after preprocessing, screening and feature extraction, disease classification and prognosis prediction are performed by using a support vector machine and a random forest algorithm. Diagnosis and prognosis prediction are obtained by comparing algorithm results, and the model evaluation module dynamically adjusts the algorithm weight and optimizes the prediction accuracy. And finally, the system integrates diagnosis, prediction and evaluation results to generate a visual diagnosis report. According to the innovative design, multi-source data fusion, multi-model collaboration and dynamic optimization are achieved, the diagnosis accuracy and prediction reliability are remarkably improved, powerful support is provided for clinical decision making, and the method is expected to play an important role in improving the diagnosis and treatment level, improving patient prognosis and optimizing medical resource allocation.

In some implementations, the system may include one or more processors. In addition, the system may include a non-transitory computer-readable storage medium containing instructions that, when executed by the one or more processors, are configured to cause the system to: store a plurality of data embeddings characterizing and corresponding to an external knowledge base; receive first input data, the first input data characterizing a first patient; receive one or more medical images associated with the first patient and a corresponding prompt requesting a classification of disease characteristics present within the one or more medical images; identify first portions of the knowledge base based on the first input data; and generate, based on at least the first portions of the knowledge base, the one or more medical images, and the corresponding prompt, the classification of disease characteristics and one or more suggested treatment modalities based on the classification of disease characteristics.

The invention provides a leaf disease identification method and a readable storage medium, and the method comprises the steps: taking a leaf image of a to-be-detected crop as an input, and employing a feature extraction network to extract shallow scab features and deep scab features from the leaf image; receiving the shallow scab features and the deep scab features as input, and fusing the shallow scab features and the deep scab features by using a multi-scale fusion network to generate target fusion features; and receiving the target fusion feature, and obtaining a disease classification result by using a classification network. According to the method, more accurate classification of the leaf disease images is realized.

The invention relates to the technical field of natural language processing, and particularly discloses a disease classification method and device and a medium, and the method comprises the following steps: carrying out the data preprocessing and data enhancement of a medical text data set, and obtaining medical text data; analyzing the medical text data, and constructing a heterogeneous graph comprising patient nodes, symptom nodes and disease nodes; performing hierarchical attention training on the heterogeneous graph in combination with a relationship among patient nodes, symptom nodes and disease nodes to obtain a disease classification model; and inputting the patient node data in the heterogeneous graph into a disease classification model, outputting a disease probability, and completing disease classification. According to the method, sparse prevention is achieved through data enhancement, then relation modeling is achieved by constructing the heterogeneous graph of the medical text, and therefore data fragmentation is prevented, finally, dynamic weight screening is conducted through hierarchical attention training, effective information spreading is guaranteed, and robustness and interpretability of a medical text classification result are guaranteed.

The application relates to the technical field of natural language processing, and particularly discloses a disease classification method and device and a medium, the method comprising the following steps: performing data preprocessing and data enhancement on a medical text data set to obtain medical text data; analyzing the medical text data to construct a heterogeneous graph comprising patient nodes, symptom nodes and disease nodes; performing hierarchical attention training on the heterogeneous graph in combination with the relationships among the patient nodes, the symptom nodes and the disease nodes to obtain a disease classification model; inputting patient node data in the heterogeneous graph into the disease classification model to output disease probability and complete disease classification. The method prevents data sparseness through data enhancement, realizes relationship modeling by constructing a heterogeneous graph of medical text, thereby preventing data fragmentation, and finally adopts hierarchical attention training to dynamically screen weights, ensures effective information transmission, and ensures the robustness and interpretability of the medical text classification result.

The present invention provides a computer-implemented method for creating an interpretable machine learning classification model, the method being implemented on one or more processors connected to a memory. According to an embodiment, the method includes the steps of defining the model by setting up a chained component classification (CBC) architecture (300) including several multiple chained component classification (CBC) blocks (310), training the model end-to-end with available input data (402) including forward propagating samples (401) of the input data (402) through the chained CBC architecture (300), and interpreting the output of each CBC block (310) of the chained CBC architecture (300) except for the last CBC block (310) as a likelihood vector of the concept detected in the respective CBC block (310). The present invention can be used for several anticipated medical / healthcare use cases, such as transparent disease classification by analyzing markers in blood tests and knowledge discovery for disease classification.

Disclosed in the present invention is a usage method for a dietary recipe recommendation and selection system. A recipe requirement and menu recommendation module intelligently classifies all menus. After entering the module, a user selects options according to gender, age, likes, dislikes, diseases in disease classifications, and postoperative diets, then inputs his / her name, and clicks to search for a diet suitable for the user. The method is also suitable for self-organized multi-person groups. Each diet is unique and special. Users can choose to purchase takeout or prepare diets recommended in recipes themselves. Whether the diets are takeout or homemade, the diets are uniformly displayed. The diet of each user is independently displayed and corresponds to the name of the user. By means of the present invention, a corresponding dietary recipe recommendation can be provided according to the physical condition of a user, and popular science knowledge is provided in a targeted manner, thereby improving the physical quality of the user and assisting in rapid recovery of the user; and a short-term recipe and a long-term recipe can be provided according to the requirements of the user, thus making it convenient for the user to adjust the recipe as required in a timely manner while allowing the user to enjoy "once for all" convenience.

Systems and methods are disclosed herein for classifying one or more disease conditions. In some embodiments, an application stores a common extraction model, the common extraction model trained using training examples for a plurality of diseases. The application stores a plurality of disease classifiers, each disease classifier configured to output whether or not its respective disease is present, each disease classifier trained using training examples for its respective disease. The application receives a selection of a disease and selects a disease classifier from the plurality of disease classifiers corresponding to the disease. The application inputs an image into the common extraction model and receives, as output from the common extraction model, a set of biomarkers extracted from the image. The application inputs the set of biomarkers into the selected disease classifier, the selected disease classifier configured to output whether or not the disease is present in the image.

In one embodiment, a method is disclosed that includes accessing radiographic images of an animal, wherein one or more first radiographic images of the radiographic images each depict the animal from one or more views and one or more second radiographic images of the radiographic images each depict one or more body parts of the animal; identifying a disease classification for the animal based on analysis of the radiographic images with a machine learning model; generating a diagnostic report for the animal based on the machine learning model, the diagnostic report including the disease classification and a natural language text radiology report; and sending instructions to a user device to present the diagnostic report.

The application discloses an anterior segment disease identification method, system, device and storage medium. The anterior segment disease identification method comprises the following steps: acquiring an image to be identified; inputting the image to be identified into a trained semi-supervised segmentation model to obtain a first image segmentation result output by the trained semi-supervised segmentation model; extracting a feature vector of the first image segmentation result; inputting the feature vector into a trained support vector machine model to obtain a disease classification result output by the trained support vector machine model; selecting a corresponding high-precision disease diagnosis model from a high-precision disease diagnosis model set based on the disease classification result, and taking the high-precision disease diagnosis model as a high-precision disease diagnosis model to be used; determining a second image segmentation result from the first image segmentation result based on the disease classification result; inputting the second image segmentation result and the image to be identified into the high-precision disease diagnosis model to be used to obtain an anterior segment disease identification result of the image to be identified, and the efficiency and accuracy of diagnosis are improved.

The invention relates to the technical field of electronic medical record quality evaluation through knowledge engineering and artificial intelligence, and discloses an electronic medical record quality evaluation method and system. The evaluation method comprises the following steps: S1, constructing a western medicine diagnosis and treatment standard metadata ontology according to a DC metadata standard, an industry guidance specification, international disease classification and expert knowledge, and supplementing ontology knowledge to form a knowledge organization strategy; s2, aiming at the western medicine diagnosis and treatment standard data set, organizing through the knowledge organization strategy, and constructing a western medicine diagnosis and treatment standard knowledge base; s3, performing quality evaluation on the electronic medical record according to the western medicine diagnosis and treatment standard knowledge base; the quality evaluation comprises form normalization evaluation, content semantic correctness evaluation and logic consistency evaluation. Fine-grained, intelligent and scientific evaluation can be carried out on the electronic medical records, and hospitals are assisted in improving the medical supervision level.

The present invention relates to methods for the detection, diagnosis or prognosis of a liver disease and related methods, compositions and kits. The method relates to the administration of a unique combination of exogenous VOCs which demonstrate an improved accuracy of disease classification compared to using a single exogenous VOC, as the administered exogenous VOCs each display distinct time resolution.

The invention discloses a disease classification method and system based on knowledge enhanced image text alignment, and belongs to the technical field of artificial intelligence and digital medical treatment. In order to solve the problems that in an existing method, only medical entity information is considered to promote alignment between a medical image and a text report, and graph structure information of a knowledge graph is ignored, firstly, data of the medical image and the text report are preprocessed; connecting the processed medical image and report data with a medical knowledge graph UMLS to construct an external knowledge graph, constructing a disease classification network model, and training the disease classification network model by using the processed medical image and report data and the external knowledge graph; and finally, performing disease classification on the medical image of the new patient by using the trained disease classification network model. The diagnosis efficiency is improved by outputting the category information of the medical image of the diagnosed patient.

The application discloses a kind of multi-disease prediction systems based on binocular fundus image, including fundus feature extraction module, similarity-difference feature fusion module, clinical feature extraction module, multi-modal feature fusion module and multi-disease classification prediction module.System uses unified backbone network to extract left and right fundus image features, by similarity-difference feature fusion module, the features of two-way are added to each element to obtain preliminary fusion features, and the absolute value is obtained by subtracting each element to obtain different features, after normalization and 1 complement operation generates dissimilarity weight and similarity weight, separate out dissimilarity information and similarity information after weighting fusion according to hyperparameter α Fusion fundus feature is obtained;Multi-dimensional clinical variable features are extracted by two fully connected layers, after fundus features and clinical features are spliced, input multi-disease classification prediction module, and output disease prevalence probability.The application makes full use of the correlation and complementarity of binocular fundus image, and realizes high-precision, low-cost multi-disease screening combined with clinical indicators.

The invention relates to an epidemic situation management device and an epidemic situation management method. In an example embodiment, an epidemic management system includes a memory device that stores instructions defining a graphical database for disease epidemic tracking. The instructions specify the creation of a host node for a given host and the connection of a seizure node to the host node through a "case" link. The seizure nodes are associated with seizure parameters, the seizure parameters relating to a disease classification of the host. In addition, the instruction specifies that the epidemic node is connected to the attack node via a'partial 'link to indicate that the host has become part of the disease epidemic, the epidemic node is connected to the definition node via a'defined as' link, and the definition node specifies a disease parameter of a disease related to the epidemic node.

Techniques for classifying one or more cancers of a subject are described. An example method includes identifying data indicative of cell-free DNA (cfDNA) from a sample derived from a subject. A fragment image is generated based on the data. The fragment image indicates a plurality of fragments among the cfDNA corresponding to a region of a genome. Input data, including the fragment image, is input into a model configured to generate a prediction of whether the subject has at least one cancer. A report is generated based on the prediction.

The invention discloses an X-ray chest radiograph report generation method fusing spatial information and joint loss optimization. The method comprises the following steps: constructing an image feature extractor, constructing a spatial information matrix learning layer, constructing a disease classifier, constructing a convolution block attention module, and constructing a codec. The text report loss and the disease classification loss are taken as the joint loss, and the generation of the report is guided by using the disease classification result, so that the accuracy of generating the report is improved; learning rich spatial information existing in the feature map by adding a spatial information matrix; an accurate feature map space attention relation is learned by adding a convolution block attention module.