20 results about "Etiology" patented technology

This invention relates to the field of medical intelligent technology, specifically to an artificial intelligence-based patient treatment path planning system, comprising: state inversion, etiology refinement, department matching, path planning, and conflict resolution modules. The state inversion module acquires the patient's initial state vector and, through multiple rounds of reverse traversal of the clinical decision tree, infers all prior pathological states and forms a set; the etiology refinement module removes duplicates from the set and merges them to generate a set of potential etiology nodes; the department matching module matches this set with a departmental capability knowledge graph to calculate the transfer cost; the path planning module invokes a reinforcement learning engine to generate a recommended path, using transfer cost as feedback and minimizing treatment time; and the conflict resolution module rearranges time overlap checks and generates a planning table. This system can comprehensively uncover etiologies, accurately match departments, optimize the treatment process, avoid overlapping examinations, and improve treatment efficiency.

This invention relates to the field of medical data-driven dose prediction technology, and discloses a method, system, device, and storage medium for individualized dose prediction of hypothyroidism based on medical data. The method includes: constructing a standardized feature vector based on the child's weight, age in days, corrected age in months, current L-T4 dose, TSH value, FT4 value, previous TSH value, TSH rate of change, feeding method, month of consultation, etiology of hypothyroidism, comorbidity status, previous dose adjustment magnitude, and age at which TSH first reached target levels; extracting TSH dynamic trajectory features from the child's TSH time-series data from previous follow-ups; obtaining a basic recommended dose using a gradient boosting decision tree model constructed with counterfactual filtering training data; and correcting the basic recommended dose to obtain an individualized recommended dose. This method improves the prediction accuracy of the gradient boosting decision tree model and allows the individualized recommended dose to simultaneously take into account multiple clinical confounding factors.

The invention relates to the technical field of medical services, in particular to a statistical database-based disease cause formation intelligent query analysis system, which comprises a case processing module, a case analysis module and a disease cause query module. The case processing module is used for collecting historical case information and extracting a plurality of classification topics according to the historical case information, each classification topic comprises but is not limited to disease causes and influence factors, a plurality of analysis nodes are divided according to the acquired classification topics and the corresponding disease causes and influence factors, and the analysis nodes are used for analyzing the disease causes and influence factors; determining a corresponding relation of the historical case information among the analysis nodes, and constructing the historical case information into an analysis network according to pathogenesis and influence factors; the case analysis module is used for analyzing the historical case information according to the obtained analysis network, obtaining behavior information corresponding to the patient, setting behavior nodes corresponding to the behavior information, and setting a plurality of behavior tags according to different corresponding behavior nodes of patient behaviors; and the diagnosis efficiency and accuracy are improved.

The present application relates to the technical field of medical information processing, in particular to an abnormal event automatic classification and recording method in a kidney disease follow-up system, comprising collecting kidney function parameters and symptom codes, identifying abnormal sequences, extracting time anchor points and characteristic identifiers, calculating flip frequency and amplitude, constructing a feature vector to evaluate risk and generating event classification records. In the present application, by constructing an abnormal trigger ordered sequence and extracting anchor points to determine composite features, combining symptom flip frequency to construct a persistence intensity coefficient, quantifying disease complexity from the dimensions of time evolution and etiology coupling, using a multi-dimensional feature vector to fuse physiological amplitude and statistical characteristics, obtaining disease risk probability values through weighted calculation, realizing the transition from qualitative classification to quantitative risk grading, solving the false alarm and missed alarm problems caused by single index determination, providing dynamic diagnosis basis containing risk level for clinical, significantly improving the recognition accuracy and intervention timeliness of the hidden deterioration trend in kidney disease follow-up.

The invention relates to the field of intelligent medical treatment, in particular to a children disease cause analysis method and device based on interlayer information reuse and a program product. The method includes: acquiring a child case data set and a disease tag; inputting the child case data set and the disease label to a to-be-trained neural network model for training to obtain a child pathogenesis analysis model; the neural network model carries out parameter sharing through interlayer similarity calculation to reduce the total parameter quantity of the model, the model comprises a first attention layer and a second attention layer, the similarity of the first attention layer and the second attention layer is calculated, and when the similarity of the first attention layer and the second attention layer is larger than a preset threshold value, the first attention layer and the second attention layer are selected. If the layer is judged to be a functional similar layer, endowing the parameters of the first attention layer to a second attention layer; otherwise, the second attention layer obtains parameters through vector feature learning. According to the model, the understanding ability and the generation ability of the child disease data can be met, and the time and space overhead needs to be reduced.

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

The invention discloses a method for constructing a social anomaly mouse model, relates to the technical field of animal model construction, and constructs a social anomaly model of a filial generation mouse by performing bisphenol S exposure on a female mouse in a pregnancy and nursing period. According to the mouse model constructed by the invention, in the pregnancy and nursing period, BPS is exposed, and the development of left secondary motor cortex neurons is damaged, so that progeny is induced to have social behavior disorder; the activity of neurons in the left M2 brain region is activated, so that social behavior disorders can be effectively relieved. The invention not only clarifies the neural development mechanism of the environmental endocrine disrupter-induced autism-like social disorder, but also provides a new thought of activating left M2 neurons as a potential intervention means, and can be used for developing drugs for treating the social behavior disorder induced by the environmental endocrine disrupter; the invention provides a new direction for etiological research and treatment strategy exploration of neurodevelopment disorder diseases taking social defects as core symptoms, and is beneficial to promoting deep development of related medical research.

This invention discloses an intelligent triage and etiology early warning system and method for acute abdominal pain, belonging to the field of medical technology. It aims to solve the problem of low triage efficiency in existing technologies for emergency response to public health emergencies when a large influx of patients occurs. The system includes a data acquisition module, an intelligent triage module, an etiology analysis and early warning module, and a terminal interaction module. The data acquisition module collects patient information, including basic information, symptom information, physical signs information, epidemiological history information, and auxiliary examination data. The intelligent triage module, based on patient information and a built-in acute abdominal pain triage model, matches patients' severity levels with the appropriate departments. The etiology analysis and early warning module integrates batch patient information, extracts key features, performs commonality analysis, identifies concentrated etiologies, and issues graded early warnings based on the severity of the etiology. This invention has the advantage of improving triage efficiency.

A device for performing coagulation tests that measure clotting time and clot characteristics in a whole blood sample under different hemostatic conditions. The test results are used to aid in the management of patients with coagulopathy of unknown etiology, helping the physician determine the appropriate clinical action to stop bleeding.

The present application relates to a kind of chronic airway disease diagnosis model based on multi-source feature fusion and machine learning and its construction method.The present application integrates multi-source feature information, accurately captures the pathophysiological characteristics of chronic airway disease, to improve the accuracy of diagnosis.In the data acquisition link, the basic information of the examinee, blood routine data and etiology data are collected, to provide data support for model construction.At the same time, in the process of feature quantity screening and cleaning, clinical features and pathogenic features are processed, and features with poor quality and non-uniform standards are excluded, and statistical tests are used to ensure that the features are significantly associated with the disease label, thereby improving the model quality.In addition, a variety of machine learning models are used for training and optimization, the performance of the model is evaluated by 5-fold cross-validation of the training set, the hyperparameters are optimized by Bayesian optimization method, to determine the optimal parameter combination, and then enhance the accuracy and generalization ability of the model.

This invention discloses a syncope medical history collection system, terminal, computer equipment, and storage medium. The medical history collection system includes: an automatic collection module, a recording module, an information filtering module, a structured storage module, an automated etiology discrimination module, a testing and analysis module, and a guidance and suggestion module. This invention, through a syncope medical history auxiliary collection system, combines the needs of clinical diagnosis, treatment selection, and scientific research management. It records, collects, and structures the information related to the admission of syncope patients. The testing and analysis module calls an existing XGBoost training model to automatically discriminate the etiology of syncope in new users. Finally, the predicted etiology results from the testing and analysis module pop up the next set of test kits, guiding the next etiology diagnosis and clinical examination plan, and assisting doctors in investigating the cause of syncope in patients and in clinical diagnosis and treatment.

The application provides a medicine composition for treating primary osteoporosis and a preparation method thereof. The medicine composition is composed of Semen Euryae, Herba Epimedii and the like. The application uses the theory of meridian diseases in traditional Chinese medicine to discuss the etiology and pathogenesis of primary osteoporosis. The medicine composition has the effects of tonifying kidney and filling essence, nourishing yin and supporting yang, strengthening muscles and bones.

The invention relates to the technical field of orthopedic medical treatment, in particular to a system for orthopedic analysis and treatment design, which is used for solving the problems that in the prior art, lesion development cannot be accurately described, state dynamic updating and interpretable causal chain mining cannot be realized, disease progress modes cannot be distinguished and main pathogenic mechanisms cannot be revealed, and the system cannot be applied to the field of medical treatment. And scientific support cannot be provided for personalized treatment. According to the invention, a dynamic graph model is constructed by fusing time sequence evolution of a focus area and structural features through the skeleton pathogenesis reasoning module, lesion development can be accurately described, complex association is expressed through a directed graph of time evolution, mutation and potential causal relationship, and causal learning is performed by combining a graph attention network and causal learning. The state dynamic updating and interpretable causal chain mining are realized, on one hand, the disease progress mode can be distinguished, the future trend of the focus can be predicted, on the other hand, the longest causal chain can be identified to reveal the main pathogenic mechanism, and scientific support is provided for personalized treatment.

People with autism spectrum disorder (ASD) may display unique and recognizable patterns of behavior, communication, social interaction, and cognitive development that differ from what is considered typical. It is critical to acknowledge and understand these distinctions in order to provide appropriate assistance and adjustments. Researchers are continuously conducting studies to elucidate the etiology of ASD and its impact on individuals with ASD. This invention involves a software that provides data collection and analysis regarding children with ASD. This program, which is only available to parents, allows for the monitoring and regulation of children with autism spectrum disorder's behavior, emotions, and range of motion. The use of this software application can help offer insights into the child's developmental progress and needs though gathering data and behavioral analysis.

The invention discloses a traditional Chinese medicine case language generation method and device based on knowledge enhancement. The method comprises the following steps: firstly, constructing a traditional Chinese medicine knowledge graph TCM-KG which comprises entities such as prescriptions, herbal medicines, diseases and symptoms and relationships thereof; then, performing structured information extraction on the input traditional Chinese medicine case, identifying key entities and performing entity linking; then, a mixed retrieval method fusing a structure and semantics is adopted to retrieve related knowledge sub-graphs from the knowledge graph; then, screening the most relevant knowledge sub-graph through comparative learning, and reducing redundancy by adopting a hierarchical representation method; and finally, on the basis of a two-stage generation framework of four elements (etiology and pathogenesis analysis, treatment rule explanation, prescription compatibility analysis and prognosis nursing suggestions), high-quality traditional Chinese medicine case words are generated in combination with the retrieved knowledge. The accuracy and specialty of Chinese medicine case language generation can be effectively improved, and intelligent support is provided for Chinese medicine clinical teaching and diagnosis and treatment.

This disclosure relates to a method, device, equipment, and medium for screening patients with chest pain. The method acquires multivariate clinical data from patients with chest pain, including quantitative data on chest pain symptom elements, physical signs, laboratory test data, and imaging examination data. This multivariate clinical data is then input into a pre-trained risk assessment model, which is trained using machine learning methods based on a clinical dataset of chest pain patients. The risk assessment model outputs predictions for the etiology classification and risk stratification of chest pain patients. This method utilizes a pre-trained model to replace some manual reasoning, enabling rapid, batch intelligent analysis, improving the efficiency of emergency triage and the initial accuracy of diagnosis. Especially when facing cases with atypical symptoms or complex data, it provides strong assistance to inexperienced physicians, effectively overcoming the shortcomings of traditional scoring models, such as narrow coverage, reliance on experience, and slow processing speed.

The invention discloses application of 2-hydroxy-4-(2-hydroxy-3, 5-dichlorobenzyl) aminobenzoic acid 2-borneol ester in treatment of mental diseases, belongs to the field of pharmacy, and provides a novel medicine for treating mental diseases. Excitation / inhibition unbalance of a nerve circuit can be adjusted through mechanisms such as nNOS-PSD95 uncoupling and alpha2GABAA receptor agonistic action, and an obvious inhibition effect on immune inflammation which has an important position in occurrence and development of mental diseases is achieved. In consideration of complex causes of mental diseases and difficulty in good treatment effect of single-target mechanism medicines, the novel medicine provided by the invention further inhibits central inflammation on the basis of regulating excitation / inhibition imbalance of a central nervous circuit, comprehensively aims at various etiological mechanisms of the mental diseases, is remarkably different from an existing treatment medicine mechanism, and has a good application prospect. The traditional Chinese medicine has the advantages of remarkable treatment effect, quick response, small side effect, no response to the existing medicine and the like.

The invention discloses a septicopyemia early warning method, device and system and a storage medium. The septicopyemia early warning method comprises the following steps: acquiring patient observation data of a target patient under continuous time steps; acquiring a hidden state vector of the target patient under the current time step and a corresponding individualized baseline reference interval; generating an anti-fact observation result in each anti-fact disease cause scene; calculating a disease cause attribution vector and an infection specific residual error of the target patient under the current time step; calculating a septicopyemia event window risk value of the target patient in a preset time window; and outputting risk prompt information of the target patient in the current time step. By adopting the technical scheme of the invention, the problem that the existing early warning method is difficult to distinguish the causes of diseases among patients with multiple basic diseases is solved.

The invention discloses a birth defect full-cycle intelligent management system and method based on a multi-modal large model, and relates to the technical field of artificial intelligence, medical informatics and public health crossing, and the system comprises a multi-modal data acquisition and preprocessing module, a medical knowledge graph construction module and a multi-modal large model analysis module. The multi-modal data acquisition and preprocessing module acquires defect multi-modal data from multiple sources and preprocesses the defect multi-modal data; the medical knowledge graph construction module constructs a medical knowledge graph based on etiology, genetics, diagnosis and treatment guidelines and scientific achievements; and the multi-modal large model analysis module is used for generating risk level information and prevention and control guidance suggestions by combining the multi-modal large model with the knowledge graph processing data. According to the invention, data integration, knowledge updating and accurate identification are realized through multi-module cooperation, a complete technical path from data acquisition to intervention suggestion is established, and an intelligent solution is provided for birth defect full-period prevention and control.

The invention belongs to the technical field of medical artificial intelligence, and provides a medical risk factor causal relationship extraction method based on double-constraint guidance. The method comprises the following steps: firstly, preprocessing clinical medical data containing patient disease categories and physiological index risk factors, and then constructing an MI constraint matrix for quantifying variable dependence and an M constraint matrix for describing an association relationship by using mutual information and an MGM method respectively; initializing a multi-linear / non-linear causal structure learning device by using the M constraint matrix, and obtaining a plurality of causal intensity matrixes in combination with double constraint correction; and generating a consensus causal intensity matrix through a aggregation operator, and finally obtaining a clear causal structure between the disease and the risk factor after ring removal processing and threshold interception. The method is high in precision and generalization ability, can effectively identify the core causal relationship, and provides reliable technical support for clinical cause inference and accurate intervention.