1027results about "Pathological references" patented technology

The invention provides a multi-mode prostate cancer biochemical recurrence risk layering prediction system based on artificial intelligence. Based on an Xgboost framework, a postoperative patient pathological panoramic pathological section scanning image is analyzed through end-to-end, multi-scale, multi-center and large-sample analysis, pathological information is utilized to the maximum extent, meanwhile, the prognosis risk of a patient can be evaluated more comprehensively in combination with clinical indexes such as CAPRA-S scores, and the method has obvious advantages compared with a traditional model. The method aims at better fitting the use scene of a hospital, the risk of prostate cancer recurrence of a patient is more efficiently and accurately predicted by fusing pathological section features and clinical features after a radical operation, and the risk of recurrence of the patient within 3 years and longer time after the radical operation can be accurately predicted. And an interpretable module is further combined to assist a doctor to interpret a result, so that precise layering and personalized follow-up visit of the BCR risk of the prostatic cancer patient are realized, the risk of excessive treatment and missed diagnosis is reduced, and the method has a good application prospect.

The invention discloses a special disease queue data capturing method and system based on an intelligent medical knowledge graph, and relates to the technical field of medical information, and the method comprises the following steps: S1, constructing a special disease intelligent medical knowledge graph which comprises a bidirectional mapping relation between standard terms of a single disease category and clinical actual corpora, clinical text data is accumulated in a mode of combining manual annotation and machine learning, and a domain exclusive knowledge base containing symptoms, diagnosis and examination indexes is formed. According to the special disease queue data capturing method and system provided by the invention, by constructing the special disease intelligent medical knowledge graph, bidirectional mapping of single disease specification terms and clinical actual corpora is realized, and the problem of insufficient semantic understanding when non-standardized clinical corpora are processed by a traditional method is effectively solved; the entity information in the unstructured medical data can be accurately extracted by utilizing a natural language processing model and an inference engine.

The invention discloses a medical knowledge constrained multi-modal time series data dynamic evaluation method and a wearable medical system. According to the method, a mixed perception architecture is constructed through an improved Swin-Transform time sequence encoder and a graph neural network, a medical priori mask matrix is embedded to constrain an attention mechanism, and time sequence feature extraction and cross-parameter correlation modeling of multi-modal physiological signals are enhanced; a dynamic quality evaluation model is designed, data integrity, cross-modal consistency and diagnosis effectiveness indexes are fused, an evaluation weight is adjusted in real time in combination with an adaptive weight function, and quantitative association of data quality and clinical diagnosis confidence is achieved; and when the motion artifacts or pathological conflicts are judged to exist, starting an abnormal positioning and correcting mechanism. According to the method, noise such as motion artifacts and interference can be monitored at any time, the attention to different indexes is adjusted in real time according to the risk level, and the pathological recognition accuracy and clinical credibility are improved by fusing multi-mode perception of medical knowledge.

This specification describes systems and methods for performing endoscopy, obtaining medical images for inflammatory bowel disease (IBD) and scoring severity of IBD in patients. The methods and systems are configured for using machine learning to determine measurements of various characteristics related to IBD. The methods and systems may also obtain and incorporate electronic health data of patients along with endoscopic data to use for scoring purposes.

The invention discloses a cervical LSIL progress risk prediction method and system based on multi-modal time sequence fusion, and the method comprises the steps: collecting multi-modal data, and carrying out the standardization processing; extracting dynamic change characteristics in continuous annual TCT liquid-based pictures through a convolutional neural network, and positioning a high-risk cell region; carrying out interval sensing position coding on HPV detection records, constructing an inter-modal causal attention mechanism, and establishing a time sequence causal relationship between HPV infection events and cell abnormal evolution; a discrete time competition risk model is adopted, the progression, regression and maintenance probabilities of different time periods in the future after primary diagnosis are synchronously output, a time-varying covariable LSTM is introduced, and the weight of patient features changing along with time is dynamically updated; and generating a cell evolution thermodynamic diagram, marking a high-risk area space-time evolution path, outputting a time influence curve, and marking a key risk accumulation time window. According to the invention, accurate quantitative evaluation of the cervical low-level lesion progress risk is realized.

The invention relates to the technical field of medical big data analysis and intelligent monitoring, in particular to a medical time series data anomaly detection system which comprises a multi-modal data fusion module used for obtaining a physiological sensor data stream of a target object, performing multi-source heterogeneous synchronization and tensor coding on the physiological sensor data stream, and obtaining a multi-modal data fusion result; constructing a multi-modal physiological time sequence tensor; and the phase-space reconstruction module is used for performing high-dimensional dynamic mapping on the multi-modal physiological time sequence tensor. The one-dimensional time sequence signals are mapped to the high-dimensional Euclidean space through the phase-space reconstruction module, the dynamic manifold structure of the physiological system is restored, abnormity is recognized by detecting the morphological variation of attractor tracks in the high-dimensional space, and even if the physiological parameters do not reach the alarm threshold value in numerical value, the abnormity is recognized. As long as an internal nonlinear dynamic structure is changed, the system can carry out sensitive capture, so that the problem that a traditional system misses detection of early-stage hidden pathological features is effectively solved.

The invention relates to the technical field of data processing, and discloses an old people health state assessment data processing system based on multi-modal data, and the system comprises a medical data integration module which obtains electronic medical record data and a medical examination report through an FHIR interface, and extracts a structured health index; the cross-modal causal fusion processing module is used for fusing the monitoring data and the medical text through an image, text and image interlayer architecture; the health state evolution modeling module is used for mapping the health feature vectors into physiological function, cognitive level and athletic ability three-dimensional state indexes; an evaluation report backtracking module; and a decision output module. Through an image, text and image interlayer architecture, deep semantic fusion of multi-modal features is realized under the constraint of medical pathology rules, feature weight adaptive distribution is dynamically guided based on a medical causal atlas, a high-dimensional fusion vector retaining key pathology information is generated, the semantic integration ability of health data is remarkably improved, and the health data fusion efficiency is improved. And the reliability of discrimination and decision making is improved.

Presented herein are systems and methods that provide for improved computer aided display and analysis of nuclear medicine images. In particular, in certain embodiments, the systems and methods described herein provide improvements to several image processing steps used for automated analysis of bone scan images for assessing cancer status of a patient. For example, improved approaches for image segmentation, hotspot detection, automated classification of hotspots as representing metastases, and computation of risk indices such as bone scan index (BSI) values are provided.

The invention provides a WebSocket-based pathological section streaming transmission and real-time preview method and device and a readable storage medium thereof. The method comprises a customized mixed data frame structure, an integrated block index, a priority mark and a hash check value; high-resolution block priority transmission is ensured based on dynamic priority scheduling of user viewport coordinates and focus areas; combining pyramid layering and blocking with LSTM prediction to load a model; the block transmission state is stored by using the block chain, and only missing blocks are retransmitted during reconnection; a QUIC + WebSocket mixed transmission mode is adopted, QUIC processes low-priority blocks and supports over 1000 paths of concurrence, and WebSocket guarantees that real-time instruction interaction delay is smaller than 50 ms. The technical bottleneck of large file transmission and real-time interaction is broken through, an efficient and reliable solution is provided for scenes such as remote pathological diagnosis and multi-doctor cooperation, and the medical image transmission efficiency and the user experience are remarkably improved.

Provided herein are systems and methods for improving healthspan. For example, provided herein are systems and methods that generate actionable interventions, based on expert curated artificial intelligence (AI) systems, to increase healthspan.

The invention relates to the technical field of artificial intelligence and biomedicine, and provides a multi-agent diagnosis planning device and method based on a consultation thinking process. The multi-agent diagnosis planning device comprises a diagnosis planning agent, a pathological section analysis agent, an in-hospital information aggregation agent, an information search agent, a knowledge base search agent, an evaluation agent, an arbitration agent and a risk assessment agent. Each agent realizes data interaction through a dynamic priority message bus, and the evaluation agent and the arbitration agent form a progressive verification closed loop: after the evaluation agent outputs a question evidence chain, the arbitration agent triggers diagnosis correction only when question items are greater than 3 items, and otherwise, final diagnosis is output based on a preset rule. According to the invention, the thinking process of multidisciplinary expert collaboration in clinical consultation is simulated by constructing a multi-agent collaborative diagnosis framework, and the whole process intelligence from medical data acquisition and analysis to diagnosis decision is realized.

The invention provides a drug relocation method fusing automatic meta-path selection, and relates to the technical field of drug relocation, and the method comprises the steps: taking a drug entity and a disease entity as input through view embedding learned by a first meta-path, and carrying out meta-path feature aggregation based on a self-attention mechanism; calculating the cosine similarity between each view embedding and other view embedding, sorting the importance scores of the views, and taking the first k views to update the disease tensor and the drug tensor; based on a Transform mutual attention multi-view fusion module, multi-view embedding of drugs and diseases is fused; the feature information of the protein is dynamically transmitted to medicine and disease nodes through a heterogeneous graph neural network; embedding of drugs and diseases is achieved, and prediction output of association scores is conducted through an MLP layer. According to the technical scheme, the problem that in the prior art, medicine and disease association cannot be deeply excavated is solved.

The invention relates to a heart failure home patient-oriented multivariable trend anomaly detection method, which comprises the following steps of: constructing an initial contour of a multivariable health trend for a patient, and introducing a trend inertia vector: updating trend inertia to form an individual trend trajectory relationship; detecting an inertia breaking point in the trend trajectory diagram; analyzing whether the variables within 12-36 hours before and after the focusing breaking point analysis generate collaborative disturbance or not; performing reprocessing through a disturbance amplification operator to form a potential early warning factor; constructing a multivariable intervention graph by using the disturbance cooperation matrix; monitoring the offset direction and strength of the causal propagation chain on each path; if the plurality of paths are subjected to direction deviation in the same period at the same time, judging that the deviation is pathological trend deviation; converting the causal offset path into a single trend risk factor, spreading a plurality of weak signals in a variable graph, evaluating systematic influence, forming a fuzzy risk scoring curved surface relationship, and outputting an individual trend risk level; the sensitivity and continuity of trend identification are improved, individual differences are adapted, and the false alarm rate is reduced.

The invention discloses a dynamic identification method for unusual cells before young tumors based on multi-omics data, and relates to the technical field of cell unusual identification. A dynamic correlation intensity matrix and a cumulative effect contribution matrix are constructed, a differentiation screening strategy is implemented according to individual response characteristics, and the unusual cells before young tumors are identified. And the abnormal dynamic high-fidelity identification of the young tumor pre-cells is realized. And aiming at individuals of different response types, an instant path, a long-term path or a double-path fusion strategy is respectively adopted, key behavior data is accurately screened, and the input quality is improved. According to the method, redundant interference is effectively eliminated, the simulation capability of the model on key processes such as immunosuppression and DNA damage accumulation is enhanced, the biological rationality and prediction precision of a cell state evolution sequence are remarkably improved, and the problems of model response lag, low calculation efficiency and output distortion caused by data noise in the prior art are solved; and a reliable technical support is provided for early warning and individualized intervention of precancerous lesions.

The invention relates to the field of computer-aided drug design (CADD) and molecular informatics, in particular to a multi-modal representation learning method based on cyclopeptide molecules, which is used for predicting cell membrane permeability of cyclopeptide. The method mainly comprises the following steps: (1) data collection: integrating cyclic peptide permeability data from a ChEMBL database, a CycPeptMPDB database and a CyclicPepeda database and patent literatures; (2) multi-modal learning: for different modal data, a deep learning model is adopted to extract feature representations of the data; the method comprises the following steps of: encoding an SMILES sequence by using ChemBERTa (ChemBERTa); using Vision Transform to extract molecular image features, and learning a molecular image structure and 3D coordinate information based on GNN; (3) multi-modal feature fusion: adopting a self-adaptive extensible fusion mechanism, integrating SMILES feature information into image, graph and 3D coordinate features through a cross-modal feature fusion mechanism, and splicing all modal features to obtain multi-modal molecular representation; and (4) permeability prediction: sending the multi-modal molecular representation into a full connection layer for regression prediction so as to evaluate the permeability of the cyclopeptide.

The invention relates to a method for constructing a cross-modal time sequence diagram model of a complex common disease network, and belongs to the technical field of common disease networks, and the method comprises the following steps: S1, dividing the diagnosis and treatment data of a patient into text data, image data and time sequence data classification; s2, processing text information by adopting a word embedding model, processing image information by adopting a convolutional neural network, and processing time sequence data by adopting a time sequence modeling module; then, a cross-modal multi-head attention CM-MHA module is adopted to carry out cross-modal fusion on the three features, and a feature matrix Ffuse with a time sequence is formed; s3, according to the current diagnosis and treatment information of the patient, establishing a complex common disease network by adopting an advantage ratio RR method; and S4, embedding the feature matrix Ffuse with the time sequence into the complex common disease network by adopting a graph embedding technology, and according to a time sequence data updating rule, forming a cross-modal time sequence common disease network.

The invention relates to an adjusting system applied to continuous kidney replacement treatment of acute kidney injury patients. Comprising the following steps: capturing the blood flow velocity and vascular endothelial permeability of glomerular capillaries in real time by using a micro sensor or a nano-scale optical imaging technology; the method comprises the following steps: pre-judging a risk time point of insufficient tissue perfusion through dynamic modeling of a capillary structure and a functional relationship; combining the microcirculation data with conventional biochemical indexes of the patient to create an early intervention model; when it is monitored that microcirculation perfusion is greatly reduced or kidney oxygen supply is insufficient, continuous renal replacement therapy CRRT parameter fine adjustment is triggered in advance; the method comprises the following steps: collecting plasma toxins of different patients at clinical and molecular levels, and constructing a common toxin map of the acute kidney injury AKI; whether the specific toxin concentration exceeds the standard or not is judged by combining a real-time sensor, and the removal priority is output in real time; a membrane material with an adjustable aperture or a special coating is adopted, so that inflammatory mediators and bacterial endotoxin molecules are adsorbed or intercepted, and the permeability of micromolecular electrolyte is kept.

The invention discloses a mobile rescue intelligent management method based on RFID automatic identification and AI artificial intelligence interaction, and belongs to the field of mobile rescue intelligent management. According to the invention, the problem of low efficiency of medicine and material inventory management of the rescue carriage in an inpatient area due to an existing manual inventory mode is solved, batch accurate identification can be carried out by adopting an RFID remote identification technology, batch inventory is realized to replace one-by-one checking, the inventory efficiency is effectively improved, and the inventory management cost is reduced. Manual electrocardiogram data checking is replaced by image visual recognition of electrocardiogram data, secondary recording is not needed, rescue data can be automatically recorded, rescue efficiency is improved, medical advice recognition is assisted by AI voice instead of manual recording of medical advice, the workload of nurses is reduced, the workload of secondary recording is reduced, and rescue efficiency is improved. And through a rescue scene full-process digital AI closed loop, digital tracing can be completed in the rescue process, and the problems that traditional handwritten records and data are not real-time and inaccurate are solved.

The invention discloses a multi-modal cancer survival prediction method based on a potential differentiation variational auto-encoder, and the method comprises the following steps: carrying out the tissue region segmentation of an input full-view digital slice, extracting the pathological features, and carrying out the grouping extraction of the grouping features of input genome data according to the function category; generating compressed pathological feature potential distribution through an information bottleneck theory and an attention mechanism; potential distribution of genome data is learned through global posteriori, specific potential variables are generated through a functional differentiation network, and missing genome features are reconstructed; integrating pathology and genome posteriori based on an expert product technology, and introducing alignment loss to constrain consistency of posteriori distribution; and screening survival related features through a co-attention mechanism, and outputting a survival probability and risk layering result. By adopting the multi-modal cancer survival prediction method based on the potential differentiation variational auto-encoder, the problem of calculation redundancy is solved, multi-modal joint distribution estimation under missing data is realized, and the clinical applicability is improved.

The invention relates to a pathological section intelligent auxiliary differential diagnosis system based on multi-modal fusion, in particular to the field of clinical pathology, semantic unification of multi-modal data is achieved through meta-task construction and a cross-modal alignment technology, and transferable diagnostic knowledge is extracted by utilizing a meta-learning framework; the method combines a generative model and knowledge constraints to generate high-quality synthetic data, and finally fuses real and synthetic samples through a self-adaptive diagnosis mechanism, thereby remarkably improving the differential diagnosis capability of rare lesions, effectively solving the problem of model generalization in a training data scarcity scene, and improving the accuracy of model identification. And efficient and reliable intelligent auxiliary decision support is provided for clinical pathological diagnosis.

The invention relates to the technical field of medical severe illness monitoring and management systems, in particular to an intensive illness medical patient monitoring and management system based on multi-modal data fusion, and the system comprises a physiological feature extraction module which collects a first feature set and processes the first feature set into a second feature set containing an electroencephalogram entropy value and cerebral perfusion pressure; the pathological state decoupling module is used for inversely calculating an observation vector, calculating a pharmacological stress vector in combination with a pharmacological baseline and a drug dosage, obtaining a pathological vector after stripping, and generating a pathological index; the evolution trend prediction module is used for predicting risk factors according to the pathological vector time sequence data; the closed-loop intervention decision-making module is used for combining the pathological indexes and the risk factors, calculating the medicine dosage adjustment amount according to a second mapping relation, and updating the infusion rate to achieve closed-loop regulation and control, and according to multi-modal data fusion and module cooperation, pharmacological stress is stripped, and the pathological indexes are generated so as to accurately judge the illness state; the risk can be actively predicted, and the drug dosage is automatically adjusted in combination with pathological indexes, so that the management efficiency and safety of critical patients are improved.

The present invention relates to a computer-implemented multi-module classifier method and system for providing a pathogenicity classification score of a variant of a protein of interest. The classifier comprises a sequence module based on a protein language model (PLM); a structure module based on a graph neural network (GNN); a property module; and a unified head module based on a machine learning model. The invention further relates to methods for preparing, training, and implementing the multi-module classifier system.

Methods and systems disclosed herein can improve inference of gene expression using cell-free DNA fragments. In an aspect, the present disclosure provides a computer-implemented method for inferring gene expression, the method comprising: obtaining a biological sample from a subject; extracting cell-free deoxyribonucleic acid (cfDNA) from the biological sample, wherein the cfDNA comprises a plurality of cfDNA fragments; performing a sequencing assay on the plurality of cfDNA fragments to generate a plurality of cfDNA sequencing fragments; computer processing the plurality of cfDNA sequencing fragments; and calculating, based at least in part on the computer processing, a gene expression score for a gene in a plurality of genes, wherein the gene expression score indicates a probability of expression or non-expression of the gene in the plurality of genes.

Systems and methods for computer modeling in medicine. A sort of period table of medical models is described for personalized diagnostics, prognostics and therapeutics, including at least 80 major categories of medical models. Generative artificial intelligence and geometric deep learning techniques, and algorithms including 2D and 3D graph machine learning and GenAI algorithms, are described, tailored and applied to diagnostic disease description, prognostic prediction and therapeutic development and management, including generation of novel synthetic drugs. The AI and machine learning techniques and algorithms are applied to understand each individual's genetic, RNA and protein anomalies that represent the source of many unique patient diseases. AI-enabled software agents assist physicians and researchers in building patient medical models. Several personalized medicine applications of individualized medical modeling include cardiovascular disease, cancer, neurological disorders, immune system disorders and genetic diseases.

The invention provides a mammary gland pathological cell image detection method and system based on an improved YOLOv7 model, and the method comprises the steps: S1, collecting mammary gland pathological section images, and generating a data set; s2, carrying out frame labeling on cells obtained in the data set; s3, segmenting the marked data set into a training set and a verification set; step S4, establishing a YOLOv7 network, and respectively adding an ASFF module and a CBAM module in three branches of the YOLOv7 network to obtain an improved YOLOv7 network; and S5, inputting the training set into the improved YOLOv7 network for training, and verifying to obtain the high-precision breast pathological section cell detection model. The improved model can effectively avoid a large semantic gap between non-adjacent levels of mammary gland cell characteristics, greatly enhance the detection capability of multi-scale cell targets such as tiny early cancer cells and large normal mammary gland tissue cells, and improve the recognition precision of early mammary gland lesion cells.

The invention provides a multi-modal kidney pathological picture processing method and device, electronic equipment and a medium. The method comprises the following steps: acquiring an immunofluorescence picture included in a to-be-processed multi-modal kidney pathological picture and a PAS staining pathological picture corresponding to the immunofluorescence picture; inputting the immunofluorescence pictures into a pre-trained first network model to perform nephropathy type classification to obtain nephropathy types; judging whether the nephropathy type output by a pre-trained first network model belongs to a preset nephropathy type or not; inputting a PAS staining pathological picture corresponding to the immunofluorescence picture into a pre-trained second network model for pathological structure identification when the immunofluorescence picture belongs to the preset nephropathy type, and outputting a label for the identified pathological structure; and adding an Oxford typing label to the to-be-processed multi-modal kidney pathological picture based on a label and a pathological structure output by a pre-trained second network model. According to the scheme, the nephropathy type can be automatically distinguished, the Oxford typing label is added, the processing speed is high, and the accuracy rate is high.

The invention discloses a heart sound and electrocardio acquisition and analysis system, which relates to the technical field of biomedical engineering, and comprises a time-frequency analysis module for acquiring a heart sound and electrocardio original data set through an electrocardio and heart sound lead suction ball and an electrocardio and heart sound simulation complete machine and transmitting the data set to an upper computer to execute wavelet decomposition and short-time Fourier transform, the system comprises a heart sound time-frequency spectrum matrix and an electrocardio time-frequency energy matrix output module, a chaotic feature extraction module, the heart sound time-frequency spectrum matrix and the electrocardio time-frequency energy matrix are subjected to phase-space reconstruction, a high-dimensional dynamic track is formed, an improved wolf algorithm is applied to conduct dynamic index calculation on the high-dimensional dynamic track, and a dynamic parameter set is obtained. According to the method, through the improved wolf algorithm and the constructed heart sound space propagation model, the multi-modal fusion capability and analysis discrimination between the heart sound and the electrocardiosignal are improved, and the intelligent level of heart sound and electrocardiosignal collection and analysis is also improved.

The invention discloses a retina thickness prediction method and system based on a multi-modal image, and the method and system achieve the effective estimation of the retina thickness under a low-cost condition through feature alignment and fusion modeling, and improve the basic screening and follow-up visit capability. According to the invention, through fusion of the multi-mode retina image data, the structure and function information of the optic nerve can be more comprehensively obtained, and the prediction accuracy of the thickness of the retina nerve fiber layer (RNFL) is improved. The OCT high-resolution hierarchical structure and the wide-view texture features of the eye fundus image are combined, so that anatomy and pathological states of optic nerves can be truly restored. The method can be used as an auxiliary method for early screening and early warning of optic neurodegenerative diseases such as glaucoma, and provides support for low-cost and high-efficiency primary screening and clinical auxiliary decision making.

The invention relates to the field of clinical diagnostics, and discloses an elderly NSC emergency risk layering method and device and a medium, and the method comprises the steps: S1, obtaining multi-dimensional evaluation data of an elderly NSC patient; s2, adopting a preset risk mapping rule to convert the risk values into single risk values of a unified scale; s3, dividing the single risk value into a plurality of risk sub-models, and calculating a dimension risk score of each risk sub-model; s4, based on a preset fusion strategy, integrating the risk sub-model and the single risk value interaction effect, and calculating to obtain a total risk score of the patient; and S5, determining the risk level of the patient according to the total risk score, and outputting a clinical diagnosis and treatment suggestion. According to the method, multiple biomarkers and key clinical parameters are integrated, a multi-dimensional combined risk assessment model is constructed, and the potential pathological state of a patient can be reflected more comprehensively.

The invention relates to the technical field of medical image diagnosis, and discloses a cardiovascular disease diagnosis model construction method based on image processing. The method comprises the steps that cardiac medical image data of a target patient is collected, a standardized data set is generated through preprocessing, and a morphological and hemodynamic feature set is extracted; establishing a heart state evolution characteristic spectrum according to a characteristic dynamic evolution rule, dividing a pathological state space, and calculating the characteristic distribution density of a historically diagnosed case; acquiring real-time image data of a patient to be diagnosed, and constructing a real-time diagnosis feature vector; mapping the vector to a pathological state space, and calculating a space matching degree to generate a pathological association index; and combining the association index and the two types of feature sets to construct a heart pathology probability prediction model, outputting a pathology probability prediction value and generating a hierarchical diagnosis suggestion. According to the method, through multi-dimensional feature analysis and space matching analysis, precise and graded diagnosis of the cardiovascular diseases is realized, and an efficient and feasible technical path is provided for diagnosis of the cardiovascular diseases.