40 results about "Disease taxonomy" patented technology

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 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 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.

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.

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 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 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 the technical field of patient information management, in particular to a patient information query system based on cloud computing, which comprises a data preprocessing module, a feature mapping module, a trust evaluation module, a permission auditing module and a feedback iteration module. According to the method, the consistency of basic data is improved by integrating multi-source data and de-duplication standardization based on a distributed architecture, accurate layered mapping of physiological indexes and disease types is realized in combination with a disease classification standard, a reliable analysis reference is provided for cross-mechanism cooperation, and symptom fitting degree analysis is accelerated by adopting a parallel computing architecture; high-correlation cases are screened to improve clinical diagnosis efficiency and precision, asymmetric encryption is combined with a dynamic trust scoring mechanism, the risk of abnormal data leakage is blocked, remote diagnosis and treatment scene abnormal requests are identified through streaming data processing, the problem of lagging of traditional manual auditing is solved, chronic disease management data are continuously collected through a standardized feedback interface, and the accuracy of remote diagnosis and treatment is improved. And the model adaptive capability is enhanced, and the data sharing efficiency and security requirements are balanced.

The present invention relates to the technical field of medical informatization, and in particular to a neurosurgical disease diagnosis and prognosis prediction system and method based on machine learning. The system includes six modules: a data collection module, a data processing module, a disease diagnosis module, a prognosis prediction module, a model evaluation module, and a result output module. The system integrates clinical data, image data and survival data. After preprocessing, screening and feature extraction, support vector machine and random forest algorithms are used to perform disease classification and prognosis prediction. By comparing the results of the algorithms, a diagnosis and a prognosis prediction are obtained. The model evaluation module dynamically adjusts the weights of the algorithms to optimize the prediction accuracy. Finally, the system integrates diagnosis, prediction and evaluation results to generate a visual diagnostic report. This innovative design enables multi-source data fusion, multi-model collaboration and dynamic optimization, significantly improves the diagnostic accuracy and prediction reliability, provides strong support for clinical decision-making, and is expected to play an important role in improving the level of diagnosis and treatment, improving patient prognosis, and optimizing the allocation of medical resources.

A grade of a specific microbial disease in a biological sample of a subject is determined. In one example, masked microbial reference genomes are used to determine the amount of free DNA molecules corresponding to a particular microbial species associated with a particular microbial disease. The masking may remove an area shared with another. In another example technique, end motifs are used for free DNA fragments from a subject and from a particular microbial species. A correlation between a subject and an amount of a set of terminal sequence motifs of a particular microbial species may be determined. The two groups of quantities of positive subjects are substantially more correlated than negative subjects.

The application discloses a disease classification ICD automatic coding method and device based on contrast learning, and belongs to the field of natural language processing and artificial intelligence. The technical problem to be solved by the application is how to use natural language processing technology to match corresponding diagnosis coding, i.e., disease classification coding, for patient electronic medical record texts, so as to reduce the workload of medical personnel, improve the automatic coding matching accuracy, and improve the matching speed. The technical scheme adopted is as follows: ① a disease classification ICD automatic coding method based on contrast learning, which comprises the following steps: S1, constructing an ICD automatic coding model training data set; S2, constructing an ICD automatic coding model; and S3, training the ICD automatic coding model. ② an ICD automatic coding device based on contrast learning, which comprises an ICD automatic coding model training data set construction unit, an ICD automatic coding model construction unit and an ICD automatic coding model training unit.

The invention discloses a common animal disease early warning method and system based on breeding environment detection data, and relates to the technical field of animal disease early warning, and the method comprises the steps: interacting a target breeding environment, and obtaining the breeding environment detection data and neighborhood environment data; determining breeding object information of the target breeding environment, calling a pre-constructed disease classifier to perform qualitative classification analysis, and outputting a result as a potential disease set; according to the historical disease data, determining a risk accumulation rate set of M environmental indexes in the breeding environment detection data for a potential disease set; according to the neighborhood environment data and the breeding environment detection data, generating predicted environment sequence data of the target breeding environment, and performing risk accumulation calculation on the potential disease set to obtain an accumulated disease risk of each potential disease; and generating an animal disease early warning report based on the plurality of accumulated disease risks, and executing an early warning response. The technical problem of inaccurate animal disease early warning in the prior art is solved.

The invention provides a distance perception Transform-based disease classification method and system, and relates to the technical field of image processing, and the method comprises the steps: obtaining a data set which comprises a plurality of case images and corresponding real disease classification labels; pre-processing the case image, and inputting the pre-processed case image into a constructed distance perception-based Transform disease classification model to obtain a corresponding predicted disease classification label; constructing a total loss function according to the real disease classification label and the predicted disease classification label, and training the distance perception Transform-based disease classification model; and inputting a to-be-classified case image into the trained distance perception-based Transform disease classification model to obtain a corresponding disease classification result. According to the method, distance perception fusion of case images is realized by constructing and training a distance perception-based Transform disease classification model, and the spatial continuity of a human body structure is emphasized in the fusion process, so that accurate classification of diseases is realized.

The invention discloses a disease classification prediction method and device based on prototype transfer learning, and a readable medium, and relates to the field of data processing, and the method comprises the steps: obtaining H-NMR metabolic spectrum data of serum samples of a person suffering from a first disease and a person suffering from a second disease, and carrying out the preprocessing, corresponding metabolome features are obtained, and a source domain data set and a target domain data set are constructed; constructing a disease classification prediction model, wherein the disease classification prediction model comprises a feature encoder and a classifier; performing prototype transfer learning-based training on the disease classification prediction model by using the source domain data set and the target domain data set to obtain a trained disease classification prediction model; and acquiring H-NMR metabolic spectrum data of a serum sample of a to-be-predicted person suffering from the second disease, performing preprocessing to obtain corresponding metabolome characteristics, and inputting the metabolome characteristics into the trained disease classification prediction model to obtain a disease classification result of the to-be-predicted person. According to the method, the classification accuracy and the model generalization performance can be effectively improved.

A medical record intelligent agent system for intelligent auxiliary diagnosis and treatment of hepatobiliary and pancreatic diseases by using medical record information, the present application relates to a medical record intelligent agent system and belongs to the field of medical information processing. The present application aims to solve the problems that the existing technology system does not sufficiently mine medical record information, does not make good use of various types of data, and the overall reasoning process is invisible, which makes the system poor in interpretability. The system comprises: a data processing module, a numerical type hepatobiliary and pancreatic disease classification module, a natural language type hepatobiliary and pancreatic disease classification module, an image examination data type hepatobiliary and pancreatic disease classification module, an integration module, and an evidence judgment module; the numerical type hepatobiliary and pancreatic disease classification module is used for obtaining numerical type hepatobiliary and pancreatic disease classification results; the natural language type hepatobiliary and pancreatic disease classification module is used for obtaining natural language type hepatobiliary and pancreatic disease classification results; and the image examination data type hepatobiliary and pancreatic disease classification module is used for obtaining image examination type hepatobiliary and pancreatic disease classification results.

Systems and methods are disclosed herein for training a manifold foundational model to autonomously diagnose a new disease classification. In some embodiments, an application receives training data for a plurality of diseases, the training data for each disease including a training examples, each example having an image of a patient, a set of biomarkers indicative of a disease condition depicted in the image, and a label indicating whether the patient has the disease condition. The application trains a common extraction model using the training data for the plurality of diseases, where the common extraction model is configured to take images as input and to output biomarkers, and trains a plurality of disease classifiers, each disease classifier configured to take the output of the common extraction model as input and to output whether a respective disease for which the disease classifier is trained to detect is present in the images.

The present invention provides a method for data labeling of a sample data set, comprising the following steps: S1, obtaining a sample data set, wherein each sample in the sample data set includes one or more classification labels respectively labeled by multiple annotators; S2, merging the label types of samples containing multiple classification labels to merge related classification label pairs and using one label in the label pair as the merged label; wherein the related classification label pair refers to a pair of different labels annotated to the same sample by different annotators; S3, re-labeling the samples in the sample data set based on the merged classification label. Compared with the existing technology, the method of the present invention can realize preprocessing of data with a certain degree of subjectivity to objectify the subjective evaluation using other relevant indicators to obtain universal labels to achieve data labeling, and then train the relevant classification model.

Disclosed is a computer-implemented method, comprising receiving an element of input image data representative of an image. The method further comprises an estimating step comprising estimating for the element of input image data by means of a machine learning model a set of facial feature(s) and at least one of (i) a set of disease classification confidences and (ii) a set of disease similarity estimation values, each of the disease classification confidences and / or each of the disease similarity estimation values corresponding to a respective disease from a list of diseases. Further, the method comprises outputting a result of the estimating step and a pre-training step. The pre-training step comprises pre-training the machine learning model with a face recognition data set comprising a plurality of training image data elements representative of a face photo. The method further comprises a fine-tuning step. The model further comprises a feature vector part computing a feature vector based on the input image data, at least one fully connected facial-feature estimation layer, and a disease estimation component. The method comprises the at least one fully connected facial-feature estimation layer estimating a set of classification confidences for a list of facial features based on the feature vector. Estimating the set of facial feature(s) comprises selecting the facial features from the list of facial features based on the estimated classification confidence. The method also comprises the disease estimation component estimating at least one of the set of disease classification confidences and the set of disease similarity estimation values. The fine-tuning step further comprises obtaining the at least one fully connected facial-feature estimation layer and the at least one fully connected disease estimation layer based on training data for fine-tuning. Also disclosed is a system comprising a data-processing system. The system is configured for carrying out the method. Further, a computer program product comprising instructions which, when the program is executed by a data-processing system, cause the data-processing system to carry out the method is disclosed. Also, a use of the method or the system to diagnose a genetic disease is disclosed.

This invention provides a semantically guided hybrid expert-based multi-disease classification method and system, relating to the fields of artificial intelligence and medical information technology. The method includes: forming robust disease classification and expert assignment strategies by performing semantic embedding and clustering analysis on data samples; constructing a multi-disease classification network model based on disease categories and the number of experts; the multi-disease classification network model uses a hybrid gating mechanism to generate weight vectors that fuse the classification probabilities output by multiple experts, and is jointly trained using a dynamic classification loss including dynamic grouping to full classification and a gating loss including assignment conflict penalties. This method effectively decomposes complex classification tasks into manageable sub-tasks, handles different disease types or feature patterns through specialized mechanisms, simplifies the learning of complex decision boundaries, and improves the accuracy and robustness of multi-category disease classification.

A few-shot learning method for ICD disease classification, using an automatic ICD coding method based on deep learning, learning the standard name of each code in the international disease classification using a gating mechanism and regularization method, the standard name of each code in the international disease classification as an additional knowledge, and through a gating mechanism and regularization method, the standard name of the ICD code is integrated into the model, which can enrich the features learned by the model, so that the model can learn a small amount of samples to achieve high classification performance.

The invention discloses a disease classification method fusing multi-modal brain function connection space-time and causal features, which comprises the following steps: acquiring brain image data of a subject to construct a data set for training; constructing a deep learning network model which comprises a time sequence coding module, a cross attention fusion module, a modal fusion module and a graph convolution prediction module; and training the deep learning network model by using the data set to obtain a prediction model for disease classification. The invention also provides a disease classification system. According to the method provided by the invention, the time sequence features, the relevance features and the causality features are efficiently fused, and finally accurate disease classification is realized.

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

The present disclosure provides systems and methods for machine learning classification and assessment of disease based on gene expression data. In an aspect, a method for determining a disease state of a subject may comprise: (a) assaying a biological sample obtained or derived from the subject to produce a data set comprising gene expression measurements of the biological sample at each of a plurality of disease-associated genomic loci; (b) computer processing the data set to determine the disease state of the subject; and (c) electronically outputting a report indicative of the disease state of the subject. In some embodiments, the plurality of disease-associated genomic loci comprises single nucleotide polymorphisms (SNPs). In some embodiments, the disease comprises a lupus condition. In some embodiments, the disease comprises cardiovascular disease (CVD).

The application discloses a common animal disease early warning method and system based on breeding environment detection data, and relates to the technical field of animal disease early warning. The method comprises the following steps: interacting with a target breeding environment, obtaining breeding environment detection data and neighborhood environment data; determining breeding object information of the target breeding environment, calling a pre-constructed disease classifier for qualitative classification analysis, and outputting a result as a potential disease set; determining a risk accumulation rate set of M environment indexes in the breeding environment detection data for the potential disease set according to historical disease data; generating predicted environment sequence data of the target breeding environment according to the neighborhood environment data and the breeding environment detection data, performing risk accumulation calculation on the potential disease set, and obtaining an accumulated disease risk of each potential disease; generating an animal disease early warning report based on the multiple accumulated disease risks, and performing early warning response. The application solves the technical problem of inaccurate animal disease early warning in the prior art.

The present disclosure provides systems and methods for machine learning classification and assessment of disease based on gene expression data. In an aspect, a method for determining a disease state of a subject may comprise: (a) assaying a biological sample obtained or derived from the subject to produce a data set comprising gene expression measurements of the biological sample at each of a plurality of disease-associated genomic loci; (b) computer processing the data set to determine the disease state of the subject; and (c) electronically outputting a report indicative of the disease state of the subject. In some embodiments, the plurality of disease-associated genomic loci comprises single nucleotide polymorphisms (SNPs). In some embodiments, the disease comprises a lupus condition. In some embodiments, the disease comprises cardiovascular disease (CVD).

The application discloses a disease classification model training method and device and a disease classification system, and applies to the technical field of data processing. The method comprises the following steps: obtaining target disease gene data and a transcriptome data set associated with a target disease; performing classification processing on the transcriptome data set to obtain a discovery set, a training set and a test set; obtaining a plurality of target gene pair features associated with the target disease according to the discovery set and the target disease gene data; processing the training set and the test set according to the plurality of target gene pair features to obtain a processed training set and a processed test set; and performing model training and testing by using a machine learning method according to the processed training set and the processed test set to obtain a disease classification model. In the classification system, the transcriptome data of a to-be-tested sample associated with the target disease is classified according to the disease classification model, and the category of the to-be-tested sample is obtained. The application improves the training precision and the training efficiency of the disease classification model.