702results about "Pathological references" patented technology

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.

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 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 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 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 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.

The invention discloses a brain region correlation analysis system and method for autistic children. The system comprises a brain region division module used for dividing the cerebral cortex into a plurality of brain regions; the training data set construction module is used for constructing a training data set, and each training sample comprises a sensor space function connection matrix and a source space function connection matrix corresponding to the sensor space function connection matrix; the signal preprocessing module is used for acquiring a real electroencephalogram signal and calculating a real value of a corresponding sensor space function connection matrix; the deep learning mapping module is used for learning a mapping relation from the sensor space function connection matrix to the source space function connection matrix and outputting a predicted value of the source space function connection matrix; and the brain region correlation analysis module is used for calculating the correlation between the brain regions. The method can be used for accurately analyzing the correlation between the brain areas of the autism children.

The invention discloses a case resource integration data system based on big data analysis, and belongs to the technical field of medical data. The method comprises the following steps: acquiring hospital case data and corresponding disease type data to construct a resource integration range, acquiring a personal case information set provided by medical consultation of a patient, performing sensitive data extraction on the personal case information set to obtain a dynamic case parameter set, and sending the dynamic case parameter set to a data risk analysis module; the multi-source data acquisition module processes the personal case information set as follows; according to the method, a data integration-risk analysis-clinical intervention closed-loop system is constructed, preorder data standardization integration guarantees analysis reliability, accurate risk analysis provides a direction for intervention, multi-level alarm and pre-plan matching is achieved through linkage of the preorder data standardization integration and the accurate risk analysis, prediction diagnosis reports and intervention suggestions are automatically generated, invalid operations are reduced, the clinical decision-making efficiency is improved, and the system is suitable for large-scale popularization and application. And meanwhile, through dynamic threshold updating and system self-iteration optimization, the adaptability and practicability of the system are continuously enhanced.

The invention discloses a hepatocellular carcinoma postoperative early recurrence prediction method based on multi-modal fusion. The method comprises the following steps: firstly, integrating clinical data of a training set, a preoperative enhanced CT image and a postoperative full-view digital pathological image, and carrying out standardized correction; then, traditional image omics features and deep learning features are extracted from the CT image, cell nucleus morphological features and tumor microenvironment spatial configuration features are extracted from the pathological image, and key feature signatures are screened out through a maximum correlation minimum redundancy algorithm (mRMR) and LASSO regression in combination with clinical features. And then carrying out progressive model construction by adopting an XGBoost algorithm, sequentially establishing a clinical single-mode model, an image single-mode model, a pathological single-mode model and a multi-mode fusion model, and explaining and visualizing the models by utilizing an SHAP value and a Grad-CAM technology. Finally, the performance of the model is evaluated in a multi-dimensional mode through internal cross validation, foresight and external independent validation, risk layering is carried out based on the prediction probability, and individualized postoperative management is guided.

The invention discloses a personalized health management method and system based on an AI electronic medical record, and relates to the technical field of artificial intelligence medical treatment, and the method comprises the steps: analyzing an original electronic medical record to generate a personal health timeline; carrying out feature extraction on the health state evolution sequence, identifying key nodes, and carrying out pathological labeling according to a medical knowledge graph to form a health state evolution sequence with a pathological label; a risk assessment model is constructed, future disease risks are calculated based on the sequence, and a dynamic report is generated; making a personalized health management plan in combination with the living habits and genetic backgrounds of the users; during plan execution, user feedback and monitoring data are collected in real time, and plan content and strength are dynamically adjusted by using a reinforcement learning mechanism. According to the method, the medical interpretability of health state evolution is enhanced through pathological labeling, and dynamic closed-loop optimization of a management plan is realized through reinforcement learning.

A medical system useful in the determination of future disease progression in a subject. More specifically the present invention applies machine learning techniques to aid prediction of disease pathology and clinical outcomes in subjects presenting with symptoms of cognitive decline and to expedite clinical development of novel therapeutics.

This invention discloses a system for predicting the efficacy of inflammatory bowel disease based on multimodal data, relating to the field of medical information processing. The system includes: a multimodal data acquisition module for receiving multimodal data; a text feature extraction module for determining text feature vectors using a text feature extraction model; an endoscopic intestinal mucosal feature recognition module for determining intestinal mucosal features using an endoscopic intestinal mucosal feature recognition model; an image feature recognition module for determining imaging features using an image feature recognition model; a pathological feature extraction module for determining pathological features using a pathological recognition model; and an efficacy prediction module for predicting the efficacy of biologics based on multimodal data features using a Transformer model and fully connected layers. Compared to existing technologies, this invention achieves efficacy prediction of biologics based on the fusion of clinical, pathological, endoscopic, and imaging multimodal features, improving the accuracy and clinical rationality of diagnostic and treatment decisions.

The invention discloses a multi-modal data fused pituitary adenoma invasion behavior characteristic modeling method and system. The method comprises the following steps: acquiring multi-modal medical data of pituitary adenoma; preprocessing the multi-modal medical data to obtain standardized multi-modal data; extracting a multi-modal feature set from the standardized multi-modal data; fusing the multi-modal feature set based on a multi-modal fusion model to obtain a fused feature vector; and constructing a pituitary adenoma invasion behavior feature model based on the fusion feature vector. Compared with the prior art, the method has the following advantages and effects that the feature model capable of comprehensively and accurately predicting the pituitary adenoma invasion behavior is constructed by fusing the multi-modal medical data, the prediction accuracy and robustness are remarkably improved, and a more reliable objective basis is provided for clinical diagnosis and treatment decisions.

The invention discloses a morphological feature-based cell segmentation and cell nucleus identification method for a turned unstained bone tissue pathological image. The method comprises the following steps of: 1, eliminating tool marks by adopting a tool mark elimination method combining local frequency domain analysis and directional suppression; 2, performing cell segmentation by using a K-means method, and performing morphological expansion and topological analysis on a segmented single cell image to identify a cell nucleus in the single cell image; step 3, calculating morphological characteristic indexes of each region; the method comprises the following steps: establishing a multi-dimensional Gaussian mixture model according to existing bone cell labeled sample information, performing outlier detection according to statistical data analysis, and removing results which do not conform to cell morphology; classifying different regions, and removing non-cell regions; by calculating morphological characteristic indexes of each region, different regions are distinguished according to the indexes, and cells are preliminarily screened. According to the method, high-precision cell segmentation and cell nucleus identification can be carried out on the cut undyed bone tissue pathological image.

The invention discloses a glioma radiotherapy postoperative risk assessment method based on a magnetic resonance image, particularly relates to the field of glioma radiotherapy patient health risk assessment, and is used for solving the problem that an existing assessment mode depends on manual interpretation and is difficult to predict bad clinical outcomes in advance. The method comprises the following steps: performing clinical data gridding reconstruction on a corticoid use cycle of a patient and tumor molecular typing, and combining morphological characteristics of an edema region in a magnetic resonance image to generate time-aligned clinical comprehensive characteristic vectors; mining a frequent association item set between the comprehensive feature vector and the pathological process to construct a mapping relation model; establishing a probability graph reasoning model of the bad outcome based on the pathological process vector sequence and the probability weight; and integrating the two types of models to form a causal reasoning network, and inputting a target patient feature vector to calculate an accumulated risk value of reaching a bad outcome. According to the method, an interpretable individual risk assessment result can be output, and a basis is provided for postoperative follow-up visit and intervention.

The invention relates to the technical field of bioinformatics, particularly discloses a metabolite target interaction prediction method and system for myocardial injury, and aims to solve the problems that complex heterogeneous data processing is insufficient, and dynamic changes of a metabolic network are difficult to capture and fine time sequence prediction is difficult in the prior art. According to the method, multi-source heterogeneous data is integrated, a myocardial injury risk factor and metabolite target knowledge base is constructed, and machine learning and deep learning technologies are utilized to realize risk factor weighting and static and sequential dynamic risk assessment. By fusing the static risk level and the dynamic adjustment factor, the system can predict the metabolite target interaction related to the individual high-risk state and generate personalized prevention and intervention strategy suggestions according to the metabolite target interaction. The system comprises a plurality of functional modules including data acquisition and preprocessing, knowledge base construction, feature engineering, risk modeling, time sequence optimization, target prediction and the like, so that early, dynamic and accurate prediction of myocardial injury risks and recognition of personalized intervention targets are realized.

The invention discloses a newborn health monitoring system based on multi-modal data fusion, and belongs to the technical field of medical monitoring. According to the system, aiming at the problem of misjudgment caused by complex background interference and newborn development difference, a background perception feature decoupling enhancement network is constructed, a dual-channel architecture is matched with an adversarial loss function, foreground skin and background features are forcibly separated, and weak pathological features are enhanced in combination with a color space attention mechanism. Meanwhile, a development stage self-adaptive classification network is utilized, the gestational age serves as prior information to generate development codes, and dynamic modulation and weighted evaluation are conducted on fusion features through a premature infant and full-term infant double-branch classifier. According to the invention, the influence of environmental noise is effectively eliminated, and differentiated accurate health state recognition and risk early warning are realized.

Provided is a method for screening myocardial therapeutic targets for ischemic heart failure by using single-cell sequencing, which method comprises the following steps: S1, sample preparation; S2, construction of a single-cell expression matrix; S3, cell quality control; S4, cell type annotation; S5, cell communication analysis; and S6, co-expression network analysis. The provided method for screening myocardial therapeutic targets for ischemic heart failure by using single-cell sequencing comprises performing single-cell sequencing on hearts of healthy mice and IHF mice, screening for cell types with significant differences in cardiac transcriptional profiles of the healthy mice and IHF mice, then exploring interaction characteristics of various types of cells in malignant fibrotic IHF hearts, revealing potential regulatory modules and pathways related to malignant myocardial fibrosis in single-cell expression data of IHF hearts, and performing screening to obtain Pdgfb and Tnfsf12 genes which can be used as therapeutic targets for treating myocardial fibrosis in ischemic heart failure.

The invention provides a pathological section human-like reading track generation method based on reinforcement learning. The method comprises the following steps: constructing a training data set; the training data set comprises a plurality of WSIs and corresponding doctor film reading track data; an RL frame is built, and parameters of the built RL frame are initialized; wSI local image features and a WSI current film reading state are taken as a state S, position movement in eight directions and a preset fixed step length is taken as an action A, and a pathological expectation value output by a PEAN model is taken as a reward R; and training a PEAN model agent based on the deep reinforcement learning Q network and a sequence of the state S, the action A, the reward R and the next state S stored in the experience playback pool to realize iterative optimization of the deep reinforcement learning Q network so as to finally generate a human-like film reading track of which the coincidence degree with the doctor film reading track is greater than or equal to a preset coincidence degree. According to the method, the macroscopic and microcosmic film reading logic of a doctor is reproduced, and the WSI diagnosis efficiency is greatly improved.

The invention relates to the technical field of medical data analysis, in particular to a urinary system tumor big data analysis system which comprises a target kernel generation module, a kernel matrix calculation module, a parameter optimization module and a risk division module. According to the method, a target kernel matrix reflecting clinical prognosis differences is constructed and serves as an optimization reference, a radiomics and genomics feature kernel matrix is generated through hardware acceleration parallel computing, kernel function width parameters are dynamically iteratively updated based on an alignment degree numerical value so as to ensure that multi-modal feature distribution is highly matched with a prognosis label, and the accuracy of the multi-modal feature distribution is improved. A multi-dimensional feature space containing rich pathological information is constructed by combining a weighted fusion mechanism after centralization processing, so that a support vector machine is trained to determine a high-robustness decision boundary, and precise division of tumor risk levels is realized while high-dimensional data calculation delay is greatly reduced; and the reliability and timeliness of auxiliary diagnosis and treatment results in a complex pathological environment are effectively improved.

The invention relates to the technical field of medical image diagnosis, and discloses a radiation-pathological diagnosis evaluation method based on weak supervision cross-modal deep fusion. The method comprises the following steps: receiving case-level radiation image data and pathological section data, combining with a weak supervision consistency label, realizing cross-modal semantic alignment through a double-branch feature extraction network, and generating aligned radiation feature vectors and pathological feature vectors; based on the aligned feature vector, a cross-modal attention fusion mechanism is adopted to complete deep fusion, and a fusion feature vector is obtained; a consistency evaluation task is executed based on a multi-task learning framework, and a consistency classification result, an inconsistency attribution result and a risk area positioning result are output; and based on the evaluation result, generating a visual diagnosis report through an interpretability analysis model. According to the method, cross-modal data can be effectively fused under a weak supervision condition, the accuracy and interpretability of diagnosis consistency evaluation are improved, and clinical data annotation requirements are met.

An example computer-implemented method for determining patient-specific tumor dynamics is described herein. The method includes receiving an image of a tissue sample that includes a distribution of tumor cells and non-tumor cells; applying a spatial correlation function to the image; and obtaining a power spectral density of the spatial correlation function of the image. The method also includes fitting the power spectral density of the spatial correlation function of the image to the power spectrum of a reaction-diffusion model; and inferring a patient-specific tumor-dynamic coefficient based on the reaction-diffusion model of cancer.