45 results about "Rare disease" patented technology

The invention provides a rare disease risk screening model training system and method, a screening system and a medium, and belongs to the technical field of medical information. According to the invention, an innovative three-stage architecture design is adopted, unified identification of all rare disease types is realized, and the blank in the prior art is filled. When only basic clinical information exists, a rare disease high-risk patient can be accurately identified, the diagnosis time is remarkably shortened, and a treatment precedent is won for the patient. The system adopts a small parameter quantity model integration strategy, reduces hardware requirements and maintenance cost, can be widely deployed in basic medical institutions, and promotes balanced distribution of medical resources. Through the technical measures of nested cross validation, algorithm comparison, threshold optimization and the like, the system has high accuracy and reliability, and the credibility of doctors to the system is enhanced.

The invention discloses a multi-intelligent body-assisted growth defect and rare disease intelligent analysis system based on a large language model, and relates to the technical field of artificial intelligence and medical treatment. The objective of the invention is to solve the problem of reasoning deviation of a large language model in medical diagnosis analysis. According to the system, a medical analysis process is decomposed into five steps of inquiry, potential common disease judgment, potential rare disease judgment, disease symptom query and comprehensive analysis, and the five steps are respectively and cooperatively completed by a chief complaint agent, a common disease agent, a rare disease agent, a disease background agent and an analysis agent. All agents are trained in a specific field, and through information interaction and cooperation, full-process processing from patient complaint to standardized medical language conversion, common / rare disease screening, disease background information generation and comprehensive analysis suggestion is achieved. Through intelligent agent division and cooperation, the accuracy and reliability of medical diagnosis analysis are remarkably improved, and the reasoning deviation of a single model is reduced.

The invention discloses a plateau newborn rare disease multi-mode combined screening system and a storage medium, and belongs to the technical field of medical software development. The plateau adaptability newborn rare disease multi-mode combined screening system is constructed, the plateau adaptability of the screening system is enhanced, the cost is remarkably reduced, and the screening system has a good application prospect.

The invention relates to an explainable rare disease auxiliary diagnosis system and method based on multi-modal fusion, and the system comprises an input module which is used for receiving the multi-modal data of a patient user, the input module is connected with a visual encoder and a text encoder, the visual encoder and the text encoder are connected to a modal fusion classifier, and the modal fusion classifier is connected with the input module. The input module, the visual encoder, the text encoder and the modal fusion classifier are respectively connected with the interpretability analysis module, and the visual encoder is used for extracting visual features from multi-modal data; the text encoder is used for extracting text features from the multi-modal data; the modal fusion classifier is used for splicing the visual features and the text features, performing classification diagnosis and outputting a classification recognition result; and the interpretability analysis module is used for generating a thermodynamic diagram containing importance distribution of the multi-modal data to the classification recognition result. Compared with the prior art, the method can provide an accurate rare disease auxiliary diagnosis result, and improves the transparency and credibility of the diagnosis process.

The invention discloses a method and a system for assisting in inputting clinical information of rare diseases and analyzing gene mutation based on phenotypes. The method comprises the following steps: firstly, acquiring clinical information in voice, text and image forms of a patient through a multi-source data acquisition module, converting the clinical information into characters, and performing entity recognition and standardization processing to generate structured medical record data; secondly, extracting clinical phenotypes from the structured data; furthermore, a candidate gene list is obtained according to the gene-disease relationship, comprehensive scoring and sorting are carried out, and a concerned gene list is output. According to the method, efficient structured input and standardization of clinical information are realized, the accuracy and automation level of phenotype-gene matching are remarkably improved, the gene variation interpretation period is effectively shortened, and intelligent support is provided for precise diagnosis of genetic diseases.

The invention provides a multi-modal fusion Bayesian medical auxiliary diagnosis method and system, belongs to the technical field of medical auxiliary diagnosis, and is used for solving the problems of inquiry redundancy, insufficient adaptation of small sample / emergency treatment / rare disease scenes and large disease probability calculation deviation in related technologies. According to the method, a knowledge graph containing disease-symptom exclusion weight and a patient database are constructed, intelligent inquiry is combined with regional correction, core symptoms are screened through real-time evidence, the disease probability is calculated through multi-modal data processing and Bayesian reasoning, and tool collaborative verification and scene adaptation optimization output are carried out. The system comprises a core calculation unit, a multi-modal acquisition unit, a database unit, a detection interaction unit and a result display unit for supporting method execution. The method can improve the inquiry efficiency and diagnosis precision, is suitable for multiple clinical scenes, and provides reliable assistance for doctors.

The invention relates to an information interaction method and device for a rare disease, a storage medium and electronic equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining the query information of a target user for the rare disease; according to the query information, determining an initial query intention of the target user for the rare disease through a preset general intention model; extracting a plurality of actual keywords from the query information, and adjusting the initial query intention according to each actual keyword to obtain a target query intention; and determining associated knowledge data corresponding to the target query intention according to a preset general rare disease knowledge base, generating reply information according to the query information and the associated knowledge data, and sending the reply information to a terminal of the target user. The method has the effect of improving the matching between the rare disease related information queried by the user and the query demand of the user.

The application discloses an information processing method and device for medical image classification, equipment and medium, relates to the technical field of image recognition, and comprises the following steps: dividing a plurality of preset disease categories into a preset head category and a preset tail category; clustering each preset disease category in the preset tail category into a plurality of abnormal disease categories, and determining third training data corresponding to each abnormal disease category based on second training data corresponding to each preset disease category in each abnormal disease category; determining a target data set based on the first training data, the preset disease category corresponding to the first training data, the third training data, and the abnormal disease category corresponding to the third training data; training an initial classification model based on the target data set to obtain a target classification model, and generating a target disease classification result of an initial medical image to be recognized by using the target classification model. The application can efficiently process long-tail distribution data of medical images, and improve the recognition accuracy and reliability of rare diseases.

The invention provides a rare disease follow-up visit system, and the system comprises a queue configuration module which is used for obtaining queue brief introduction information, follow-up visit CRF, queue disease teaching information and queue state information edited by a user in a queue creation interface, creating a target queue, and managing the created queue, each target queue comprises a plurality of patients with the same medical features, a plurality of associated medical centers and patient teaching content, each medical center comprises a doctor of at least one department, and the patient teaching content comprises education information of diseases of the patients. The follow-up visit management of the patient in a complex diagnosis and treatment mode is facilitated, and the follow-up visit quality of the rare patient is improved.

The invention discloses a rare child disease management method and system based on artificial intelligence and electronic equipment, and relates to the technical field of artificial intelligence and health cross, and the method comprises the steps: constructing and training a first preset deep learning model, and obtaining an intelligent question and answer model; and acquiring rare disease associated data of the rare sick child patient in a question and answer mode by using the intelligent question and answer model, and analyzing the rare disease associated data to generate a corresponding personalized health management scheme. According to the method, the intelligent question and answer model is obtained by constructing and training the first preset deep learning model, the problem that in the prior art, a targeted efficient tool is lacked to obtain the associated data of the rare sick children is solved, and scattered data can be effectively integrated. The intelligent question and answer model is used for obtaining data in a question and answer mode and analyzing the data, then the personalized health management scheme is generated, the defects that a traditional method depends on artificial experience and is difficult to meet the requirement for rapidly and accurately formulating the scheme are overcome, and the scientificity and accuracy of the health management scheme are improved.

The application provides a rare disease occurrence probability prediction method and device and an electrocardiogram analysis system. The method comprises the following steps: acquiring a plurality of to-be-evaluated electrocardiogram data and corresponding to-be-evaluated medical record text data; inputting the to-be-evaluated electrocardiogram data into an electrocardiogram pre-training model to obtain a target electrocardiogram feature vector; inputting the medical record text data corresponding to the to-be-evaluated electrocardiogram data into a medical text pre-training model to obtain a target text feature vector; inputting the to-be-evaluated electrocardiogram data into a preset risk detection model to obtain an electrocardiogram rare disease occurrence probability coefficient; presetting a rare disease feature dictionary; determining the occurrence probability of each rare disease according to the similarity between the target electrocardiogram feature vector and the reference text feature vector of each rare disease, the similarity between the target text feature vector and the reference text feature vector of each rare disease, and the electrocardiogram rare disease occurrence probability coefficient. The occurrence probability of the rare disease can be predicted without training a large number of electrocardiogram samples of the rare disease.

A learning device includes a memory and processing circuitry configured to register at least inspection data and medical care data of a patient who has developed a rare disease from a plurality of medical institutions perform predetermined preprocessing on inspection data and medical care data of a patient estimate an onset probability of an estimation target patient for each of a plurality of rare diseases based on the inspection data and medical care data of the estimation target patient after the preprocessing by using an estimation model that estimates an onset probability for each of the plurality of rare diseases and use at least the inspection data and the medical care data of the patient who has developed a rare disease after the preprocessing as learning data, and cause the estimation model to learn a relationship between the inspection data and the medical care data and an onset probability.

This application discloses a retrieval model training method, a retrieval-enhanced generation-based Bert-Hogg-Dubbs syndrome (BHD) identification method and system, relating to the field of rare disease identification. The training method includes acquiring positive and negative sample pairs; inputting the positive and negative sample pairs into an initial retrieval model; calculating the loss function of the initial retrieval model; and dynamically adjusting the angle margin of the loss function in real time according to a metric variance adaptive mechanism. The initial retrieval model is then optimized based on the loss function calculation results. This application forcibly expands the angle interval between BHD and non-BHD by using the angle margin of the loss function, and uses a metric variance adaptive mechanism to dynamically adjust the angle margin based on the statistical variance of the cosine similarity among all positive sample pairs in the current training batch. This solves the problem of weak image differences and blurred category decision boundaries in DCLDs caused by the highly similar imaging features of various rare diseases, thus improving the recognition accuracy of large models for query information.

Hypomelanosis of Ito is a clinical term for patients with mosaic syndromes characterized by skin hypopigmentation and developmental disorders. The genetic causes of these rare diseases remain largely unclear. Here, we report that GNA13 is a new gene that causes Hypomelanosis of Ito. We identified an identical mutation in this gene in four unrelated patients exhibiting pigmentary mosaicism. In depth functional investigations revealed that this is an activatory mutation that alters the cytoskeleton and morphology of melanocytes via a hyperactivation of the RHOA / ROCK signalling pathway. Our results also indicate that this pathology does not necessarily originate from a decreased production of melanin, but can originate from a defect in melanosome transfer to keratinocytes due to cell shape alterations. Thus, our findings suggest for the first time a mechanism by which the clinical symptoms of patients with Hypomelanosis of Ito appear, and pave the path for new therapeutic approaches. Altogether, the present invention relates to a method for treating a patient suffering from hypomelanosis of Ito by administering a ROCK inhibitor and / or RHOA inhibitor.

The present disclosure provides a vertebral rare disease vertebral body morphology analysis processing method, system and device, the method comprising: acquiring a plurality of sample vertebral body images under a target scene; performing segmentation processing on each sample vertebral body image to obtain a plurality of sample segmentation results of vertebral bodies; acquiring a plurality of dimension sample morphology representation parameters corresponding to each vertebral body; performing clustering processing based on different sample morphology representation parameters to obtain different clustering ranges to acquire target classification association information for distinguishing different vertebral body morphologies of vertebral rare diseases. In the present disclosure, a multi-segmentation model fusion segmentation processing is adopted for any one unlabeled sample vertebral body image to obtain the optimal segmentation area of any one vertebral body; clustering processing is performed based on the sample morphology representation parameters of each vertebral body to quickly and accurately obtain relevant information for distinguishing different vertebral body morphologies of vertebral rare diseases, thereby ensuring the accuracy and efficiency of the analysis and processing of vertebral body morphology.

The present application relates to a kind of medical entity identification and knowledge base alignment method for rare disease scene, belong to medical data processing technical field, solve the weak rare disease recognition ability in existing medical entity identification, serious mis-matching problem, single matching strategy, knowledge base alignment difficulty and lack of explainability problem.It includes obtaining medical text to be processed, the semantic analysis of the medical text, identify drug entity and disease entity, and based on the identified entity, obtain the candidate entity name list;The candidate entity name list is input into the pre-constructed medical standard terminology knowledge graph, and structured verification is carried out using a four-stage cascade matching strategy, including: for the current candidate entity, sequentially matching from the first strategy, if the current level strategy successfully matches the entity name in the knowledge base, then output the matching result and terminate subsequent matching, otherwise enter the next level strategy.Medical entity identification and knowledge base alignment are realized.

The invention discloses application of a DNAH9 composite heterozygous mutation site in identification of a rare disease primary ciliary dyskinesia 40 type companion inversion, and belongs to the technical field of biology. According to the invention, DNAH9 composite heterozygous mutation sites (including c.11845G and c.308del) are used as biomarkers for identifying the rare disease primary ciliary dyskinesia type 40 accompanying inversion, a detection reagent and a kit are developed, and rapid screening and diagnosis of primary ciliary dyskinesia type 40 accompanying inversion gene mutation can be assisted.

Comprehensive and high-quality disease dictionaries are invaluable resources for tasks such as building ontologies, automated relation extraction, text summarization, question answering etc. Such curated resources are useful to clinicians, researchers, and various Biomedical Natural Language Processing tasks. However, these are manually curated and are labor and time intensive, and additionally suffer from lower recall and coverage is also less. Present disclosure provides systems and methods for augmenting rare disease dictionaries, wherein the system retrieves (new) rare diseases terms from medical literature that are related to the given dictionary terms (seed terms) and recommends new terms (or NPs) in a ranked order. This method is useful for rare diseases dictionary augmentation as a significant fraction of the top recommendations are new synonym candidates for dictionary augmentation. The method uses syntactic and semantic similarity measures in combination with efficient nearest neighbor search for efficient retrieval.

The invention discloses a method for evaluating and recommending a treatment scheme of a rare patient. The method comprises the following steps: acquiring data including structured data, semi-structured data and unstructured data; carrying out unified indexing on the integrated multi-modal data; setting a unified time grid to divide the multi-modal data, and mapping each observation data to the latest time according to timestamp information; carrying out feature extraction on the multi-modal data, unifying the modal features, splicing, and carrying out dimensionality reduction on the unified modal features through a linear projection layer to obtain a multi-modal fusion sequence; taking the multi-modal fusion sequence as input, and performing risk prediction on an output vector corresponding to a time step in the time sequence state matrix of the complete sequence through a prediction function; performing forward propagation on the same input by using a Monte Carlo method to obtain a plurality of groups of prediction results; the completeness of patient state features and the risk prediction precision are improved, and the problems that traditional reinforcement learning is prone to over-fitting and unstable in training are solved; and the purposes of transparency and verifiability of the result are achieved.

The invention provides a rare immune fixation electrophoretogram identification system based on a two-stage classification normal form, which is not limited to classification of common disease types, but adds identification of rare disease types on the premise of ensuring common disease type identification precision, so that the identification accuracy of the common disease types is improved. The vacancy of an existing immunofixation electrophoretogram recognition method based on deep learning in the aspect of rare disease type recognition is made up, and universality and practicability are greatly enhanced; meanwhile, a double-stage classification normal form method is adopted, so that the class imbalance ratio of the data set is effectively improved, and the influence caused by class imbalance of the data set is reduced; finally, a loss function LMF specially used for solving data class imbalance in the medical field is adopted, the recognition precision of a few classes is improved on the premise that the recognition precision of most classes is guaranteed, and then the overall recognition precision is improved.

The invention relates to a rare disease identification method and system based on multi-scale vision-language prompt multi-instance learning, and the method faces a full-view digital pathological image (WSI), and introduces the prior knowledge of the pathological field under the weak supervision condition of only using a slice-level label, so as to identify the rare disease. Constructing a multi-scale vision-language collaborative prompt mechanism: generating descriptive text prompts corresponding to image blocks of different scales by utilizing a large language model, designing a prototype-guided image block decoder to aggregate massive image block features, and meanwhile, realizing cross-modal alignment in combination with a context-guided text decoder; the method simulates the diagnosis process of a pathologist, achieves high-precision classification under small samples, remarkably improves the recognition accuracy of rare diseases, has good cross-center generalization ability, supports interpretable evidence output, does not need large-scale data labeling or de novo pre-training, and facilitates clinical rapid deployment.

The invention discloses an incremental tuning and knowledge injection method and system for few samples / rare diseases, and relates to the field of continuous learning of a large language model (LLM) and medical artificial intelligence. In order to solve the technical problem that catastrophic forgetting is easily generated when an existing model is subjected to incremental tuning in small-sample and high-value fields (such as rare diseases), the invention provides an optimization strategy based on PEFT (Parameter Efficient Fine Transfer) and knowledge integration loss. The method comprises the following steps: initializing a PEFT module and adding the PEFT module to a pre-training LLM; taking few-sample data labeled by experts as new task data; constructing a dual loss function containing new task loss and knowledge distillation loss (measuring the difference between the output of the new model and the output of the basic model); only by optimizing the parameters of the PEFT module, the basic universality is protected from being damaged to the maximum extent while it is guaranteed that the model quickly masters rare disease knowledge. According to the invention, the efficiency and the stability of the medical LLM in a continuous updating scene are remarkably improved.

The invention provides an internal threat identification and early warning method based on behavior analysis, and the method comprises the steps: analyzing the matching degree of the record of a query object unassociated patient and the retrieval range exceeding the responsibility authority for a classified behavior feature subset, and carrying out the comparison through a pre-established field responsibility authority library, if the query time is concentrated in a non-working period or the retrieval time presents a periodic rule, judging that the behavior deviates from an expected range, and determining a preliminary abnormal behavior mark; and for the deep behavior analysis result, analyzing the similarity between the rare disease category related to the query content and the specific medical history focused by the retrieval content, and if the retrieval range exceeds the responsibility authority and the query behavior lacks the business context, determining the classification label of the illegal behavior to obtain the illegal behavior judgment conclusion.

The invention discloses a disease auxiliary diagnosis system based on an interactive symptom union set. The disease auxiliary diagnosis system comprises a user interaction layer, a core processing layer and a data storage layer, the user interaction layer is used for realizing interaction between a patient and the system, receiving complaint information input by the patient and displaying symptom collection guide information, a preliminary diagnosis result and matched doctor information to the patient; meanwhile, personalized demand information submitted by the patient is received. According to the method, the pre-trained BERT-BiLSTM-CRF medical entity recognition model is adopted, so that the unstructured text of the complaint of the patient can be accurately analyzed, and the medical entity and the modification relationship thereof are extracted; meanwhile, symptom information is supplemented through interactive guidance, a complete symptom union set is constructed, the problem that in the prior art, the generalization ability of fuzzy or rare disease description is poor is effectively solved, and accurate data support is provided for follow-up disease diagnosis.

This invention relates to the field of medical artificial intelligence technology, and in particular to an AI-assisted diagnostic decision-making system for rare bladder diseases. The system includes modules for data acquisition, feature extraction and quantification, an AI rule-based reasoning engine, error warning, and treatment decision output. Its core lies in using natural language processing technology to transform unstructured clinical text into feature tags. The AI ​​engine then executes a multi-level identification process of "excluding infection – rapid screening of medical history – precise matching," outputting a list of candidate diseases sorted by probability. Simultaneously, the system can automatically warn of clinical errors such as antibiotic abuse and overtreatment, and generate a comprehensive report including primary care treatment plans and referral criteria. This invention combines a rule engine with intelligent reasoning, providing primary care physicians with a standardized and easy-to-use auxiliary diagnostic tool, which helps improve the efficiency and standardization of rare disease diagnosis and has high clinical translational value.

The present application relates to the technical field of disease prediction, and discloses a phenotype analysis method for disease prediction, which comprises the following steps: constructing a database, determining the phenotype similarity of rare diseases and common diseases, referring to the difference of gene data of rare diseases and common diseases, constructing a "rare disease-common disease" common database, processing patient data to find the best combination of phenotype characteristics, calculating the disease matching score, calculating the cross-entropy loss of the phenotype characteristic matching model of disease prediction, taking the weighted sum of the classification loss function in the "rare disease-common disease" common database and the cross-entropy loss of the phenotype characteristic matching model as the total loss function of the disease prediction model, outputting the prediction result, extracting effective phenotype characteristics based on a graph convolution network for data comparison of rare diseases and common diseases, and extracting disease matching difference from the commonality of rare diseases and common diseases to predict diseases, which can solve the problem of easy confusion between diseases and to a certain extent, avoid misdiagnosis and missed diagnosis of rare diseases.

This application discloses a device and storage medium for calculating incidence rates based on medical record data. The device includes: a data acquisition module for acquiring medical record data from multiple patients; a data filtering module for filtering the medical record data from multiple patients to obtain medical record data from a target patient suffering from a rare disease; a data prediction module for analyzing the number of second inpatient cases provided by a second category of medical institutions based on the number of first outpatient cases and first inpatient cases provided by a first category of medical institutions to obtain a prediction result of the number of second outpatient cases suffering from rare diseases in the second category of medical institutions; and a first data determination module for determining the incidence rate of the rare disease based on the medical record data of the target patient and the prediction result. Thus, even in the case of missing medical data, a relatively reliable incidence rate estimation scheme can still be achieved, thereby improving the accuracy of the final obtained incidence rate of rare diseases.

The present invention discloses a medical intelligent dialogue method based on deep learning, which relates to the field of intelligent medical technology. The present invention constructs a rare disease knowledge graph. Even when the training data is insufficient, the implicit connection between the disease and the symptoms can be deduced through the correlation relationship in the graph, which greatly enhances the ability to handle complex cases such as rare diseases. Through the graph reasoning mechanism, a more accurate list of rare diseases can be provided, and relevant diagnostic solutions can be reasonably and preferentially displayed to users to reduce the risk of misjudgment. Through the multimodal Transformer model, multi-source data such as medical records, medical images and laboratory test results can be effectively integrated to generate a unified comprehensive feature representation. The deep integration has the ability to comprehensively analyze the patient's health status from different angles, thereby improving the accuracy of diagnosis. Based on the preliminary diagnosis results, the order of inquiries and the depth of questions can be adjusted according to the specific conditions of different patients, providing a more accurate and personalized inquiry experience.

The present invention relates to the field of Internet technologies, and particularly to a method, device, electronic device, and medium for predicting the development trend of rare diseases. The method includes: dividing all physical examination data according to the data characteristics of all physical examination data within the current time period to obtain physical examination data within multiple target time periods; respectively inputting the physical examination data within each target time period into a disease index determination model corresponding to the rare disease type to obtain the disease index corresponding to each target time period; generating a target trend line graph with the target time period as the abscissa and the corresponding disease index as the ordinate; calculating the similarity between the target trend line graph and each control trend line graph; and determining the development trend of the rare disease patient for the rare disease type according to the similarity. Through the present application, the development trend of rare diseases can be accurately predicted.