2164results about "Laboratory analysis data" patented technology

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

A federated distributed computational system enables secure oncological therapy optimization through multi-expert integration and advanced uncertainty quantification. The system implements a multi-expert integration framework that coordinates domain-specific knowledge through token-space communication for precision oncological treatment, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration. Through a distributed graph architecture, the system enables advanced fluorescence imaging with wavelength-specific targeting, multi-level uncertainty estimation combining epistemic and aleatoric approaches, and multi-scale tensor-based integration with adaptive dimensionality control. The system implements light cone search and planning for adaptive treatment strategy optimization, enabling medical institutions and research organizations to collaborate on complex oncological therapy projects while maintaining strict data privacy controls.

The invention relates to the technical field of medical data processing systems, in particular to an artificial intelligence-driven medical diagnosis and treatment data processing method and system. The method comprises the steps that a multi-modal medical data acquisition module acquires and processes multi-source heterogeneous medical data of a patient, and a standardized data set is generated; a medical feature depth extraction module performs multi-dimensional feature extraction on the data set, and constructs a dynamic evolution feature matrix; a multi-dimensional health state space construction module constructs a patient health state multi-dimensional space according to the matrix and determines a key medical early warning index set; the real-time medical data fusion module maps real-time data to the space to generate real-time health risk factors; and the medical risk prediction and decision-making module establishes a personalized model, outputs a disease occurrence probability and generates personalized treatment suggestions. The system solves the problems that medical data processing is difficult, diagnosis analysis is not comprehensive, and a treatment scheme lacks personality, and diagnosis accuracy and treatment pertinence are improved.

The invention provides a multi-modal large language model for generating a hepatocellular carcinoma key pathological diagnosis report, a framework main body is a visual coding module, and a multi-modal feature alignment module, a multi-head low-rank attention mechanism, an enhanced medical MoE mechanism and a structured output decoding layer are also introduced. The visual coding module is constructed on the basis of a Swin Transform architecture, visual pre-training is completed on hepatocellular carcinoma MRI data, and after a task specific classification head is stripped, a trunk feature extraction network is reserved to serve as an image modal representation encoder. The multi-modal feature alignment module guides the model to learn a cross-modal semantic mapping relation between a hepatocellular carcinoma MRI image and a key pathological diagnosis report language, image modal input is a visual feature sequence, and text modal output is a structured description text; and the structured output decoding layer generates six types of liver cancer focus attributes. According to the method, the pre-operative multi-parameter and multi-stage enhanced MRI image is utilized, and the open-source large model is finely adjusted to generate a matched liver cancer postoperative pathology report.

The invention provides an intelligent medical information system based on multi-system integration, a data security management method and device, electronic equipment and a storage medium, and is applied to the technical field of medical data processing. According to the method, three types of information including core security elements, multi-system heterogeneous adaptation (including HIS / LIS / EMR interface specifications and the like) and full-process auditing (including operation logs and the like) are obtained. Normalizing the core security elements by using technologies such as AI multi-mode privacy identification and the like to generate a standardized security baseline file; heterogeneous adaptation information is processed through a lightweight API connector and the like, and a multi-system security access scheme is formed. Associating the baseline file with the access scheme, and generating a full-life-cycle protection strategy in a zero-trust architecture; and processing the audit information to generate a dynamic security audit report. And finally, based on a security-adaptation linkage decision engine and compliance verification, outputting a cross-system data security sharing scheme and a privacy disclosure emergency response instruction, and realizing a medical data security management closed loop.

A platform for characterizing biomolecules, biological tissue sections, viruses, or other particles of interest in a non-invasive, high-throughput, cost-effective, and label-free manner. The platform includes a merged set of imaging modalities including and capable of interferometric microscopy and wide-field or confocal microscopy (such as, for example, a wide-field super-resolution surface enhanced Raman spectroscopy, SERS), although some aspects may be employed with data sets only received from wide-field or confocal microscopy. Related devices, systems, methods, techniques, and variations to the same are also provided.

The invention discloses an abdominal aortic aneurysm progress prediction method and system based on cross-modal knowledge distillation. The method comprises the following steps: collecting blood of a target object; the method comprises the following steps: detecting the concentration of blood protein fingerprint markers of blood of a target object, wherein the blood protein fingerprint markers comprise matrix metalloproteinase-12, calcitonin-related polypeptide-alpha, uromodulin, nerve injury induction protein-1, glycosylated hemoglobin A1c and fibroblast growth factor-9; carrying out abdominal aortic aneurysm risk calculation by calling Student-net of cross-modal knowledge distillation based on the blood protein fingerprint marker concentration; wherein the input of the Teaser-net of the cross-modal knowledge distillation comprises a CTA image feature and a pathological section feature, and a category probability vector output by the Student-net is aligned with a category probability vector output by the Teaser-net; and outputting the risk level of the abdominal aortic aneurysm and the probability of occurrence / progress / rupture of the abdominal aortic aneurysm in the preset year. And the development of the aortic aneurysm disease is accurately predicted.

A federated distributed computational system enables secure drug discovery and resistance tracking through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates molecular dynamics simulations with machine learning models for drug discovery analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for molecular dynamics simulation and resistance pattern detection. Through a distributed graph architecture, the system enables real-world clinical data integration, resistance evolution tracking, and multi-scale tensor-based analysis with adaptive dimensionality control. The system implements real-time drug response prediction through multi-modal data analysis, enabling pharmaceutical companies and research institutions to collaborate on complex drug discovery projects while maintaining strict data privacy controls.

The invention discloses a method and system for predicting juvenile depression based on intestinal flora, and relates to the technical field of bioinformatics and artificial intelligence, and the method comprises the steps: firstly, obtaining an original sequence of a microbiome, carrying out the preprocessing of the original sequence of the microbiome, and obtaining a feature matrix; and screening core flora characteristics with stable trans-folding by adopting characteristic importance evaluation and interpretability analysis based on a gradient boosting decision tree. A mixed weighted graph is constructed based on Spearman correlation and a proximity relationship, and an absolute value of a correlation coefficient is taken as an edge weight and an edge density is adjusted through a threshold adaptive strategy. And finally, through an improved graph attention neural network, based on edge weight attention, layer normalization and random inactivation, enhancing robustness, and adopting adaptive optimization to complete parameter learning. And determining a dynamic classification threshold according to the AUC of the target patient, and outputting a sample discrimination result and confidence. According to the method, the accuracy, stability and biological interpretability of juvenile depression recognition are remarkably improved.

The invention provides a full-autonomous cell operation system based on body intelligence, and relates to the field of cell operation, the system comprises a system hardware architecture and a system software architecture; the system hardware architecture comprises a micromanipulation execution system, a precision positioning system, a multi-mode sensing system and an intelligent calculation and control center. The micromanipulation execution system is used for cell environment interaction, the precision positioning system is used for sample bearing and environment control, the multi-modal sensing system is used for providing key information, and the intelligent calculation and control center is used for operation decision making; the system software architecture comprises a sensing layer, a planning layer, an execution layer and an interaction layer. According to the application, cell recognition, tool selection, path planning, operation execution and result feedback can be automatically completed without manual intervention; through tool autonomous switching and strategy dynamic adjustment, various operations such as cell clamping, injection, controllable deformation and micro-assembly can be completed, and the problems that traditional cell operation depends on manual work, efficiency is low, and consistency is poor are solved.

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 for performing a clinical prediction are provided. In one example, the method comprises: receiving first molecular data of a patient, the first molecular data including at least gene expressions of the patient; receiving first biopsy image data of the patient; processing, using a machine learning model, the first molecular data and the first biopsy image data to perform a clinical prediction of the patient's response to a treatment, wherein the machine learning model is generated or updated based on second molecular data including at least gene expressions and second biopsy image data of a plurality of patients; and generating an output of the clinical prediction.

The invention discloses a breast cancer lymph node metastasis prediction system based on a gene spectrum, and relates to the technical field of breast cancer prediction systems. Comprising a data acquisition module which collects gene spectrum data of a breast cancer patient and collects detailed clinical information of the patient; the preprocessing module is used for carrying out data cleaning and data normalization processing on the collected data; and the feature extraction module is used for extracting principal component features by applying principal component analysis on the basis of the gene expression data. According to the method, gene expression data are considered, various gene spectrum data such as gene mutation and copy number variation and detailed clinical information are integrated, the biological characteristics of the breast cancer can be reflected more comprehensively, and the prediction accuracy is improved.

The invention provides an immunodetection method of a multi-index immune checkpoint detection kit, which belongs to the technical field of immunodetection and comprises three main stages of sample preparation, immune reaction and data analysis. The method comprises the following steps: firstly, carrying out centrifugal separation to obtain a plasma sample, and preparing a fluorescent microsphere coupled capture antibody and biotin labeled detection antibody compound; then carrying out immune reaction, forming a sandwich type immune complex through specific binding, and adding streptavidin marked by phycoerythrin for signal amplification. Detecting a fluorescence signal by adopting a flow cytometer to obtain an original data matrix; a contribution feature vector, a utility feature vector and an expression feature matrix are extracted through a feature decomposition algorithm, and a deep learning model of a total-score-total structure is established for data analysis. Finally, a detection report containing a quantitative result, an importance score and reliability evaluation is generated, and the technical problem that in the prior art, the analysis accuracy of a multi-index immune checkpoint detection result is insufficient is solved.

The invention discloses a spatio-temporal trajectory fusion-based closed-loop monitoring method and system for drug-resistant bacteria in a hospital. The method comprises the following steps: collecting and processing strain drug sensitive test results and patient clinical index data in a hospital information system, and screening out key features through an improved genetic algorithm; wherein the clinical index data of the patient comprises original spatio-temporal trajectory data and nursing operation records; analyzing spatio-temporal trajectory data of the patient according to the key features, combining with drug sensitivity spectrum consistency, and constructing a propagation network model by using a graph convolutional network to identify infection source nodes and corresponding topological influence; and generating an early warning signal based on the topological influence of the infection source node, and continuously optimizing prevention and control measures by dynamically adjusting model parameters and a real-time feedback mechanism. By implementing the method provided by the invention, accurate analysis and real-time prevention and control of a drug-resistant bacterium transmission link can be realized.

System and methods for alerting a healthcare provider to prescribed treatments having reduced or no effectiveness due to genetic composition is provided. A database containing treatments known to have reduced or no efficacy in persons having particular genetic markers is queried to determine whether any treatments prescribed by, or likely to be prescribed by, a healthcare provider to the patient are known to have reduced or no efficacy in persons having the same certain genetic markers as the patient. An alert indicating such information is displayed at a healthcare provider system.

Methods for multiple instance learning of tissue sample images are described. The methods may comprise, for example, receiving a whole slide image from a needle core biopsy sample from a subject; identifying a tissue region in the whole slide image; selecting a set of image patches from the identified tissue region; resampling the set of image patches at a plurality of image scales to generate a plurality of resampled image patches; generating image representations for the plurality of resampled image patches; extracting feature vectors based on the image representations; providing the feature vectors as input to a trained machine learning model configured to predict a gene alteration state; and outputting the predicted gene alteration state for the needle core biopsy sample for the subject.

The invention provides a captive panda oestrus starting prediction method and system based on machine learning, and relates to the technical field of panda breeding, and the method comprises the steps: obtaining original data; isomerous data stream synchronization is carried out according to the original data, and a synchronized spatio-temporal data matrix is obtained; performing nonlinear interaction relation modeling according to the synchronized spatio-temporal data matrix to construct a spatio-temporal dependency relation graph; performing environment constraint feature dimension reduction according to the space-time dependency graph to obtain a low-dimensional state representation vector; performing oestrus state transition probability calculation according to the low-dimensional state representation vector to generate a probability evolution trajectory; and according to the probability evolution trajectory, turning point identification is carried out, and an estrus starting moment prediction value is output. Through collaborative inflection point detection of progesterone gradient characteristics and an ingestion mutation mode in the probability evolution trajectory, an estrus starting moment prediction value is accurately output, and finally the biological interpretability of estrus critical state evolution of captive pandas is improved.

The invention relates to an old inpatient intestinal preparation failure risk prediction method and system, and the method comprises the steps: collecting the multi-dimensional electronic medical data of a patient from a hospital information system (HIS), a laboratory information system (LIS) and a pharmacy information system (PIS), carrying out the cleaning, missing value filling and standardization preprocessing of the original data, so as to construct a high-quality data set, and carrying out the prediction of the intestinal preparation failure risk of the old inpatient. Generating three core risk characteristics based on clinical knowledge: an age-weighted complication index, a specific drug use mark and a key physiological index anomaly mark; inputting the features into a preset rule engine, performing weighted calculation according to a fixed weight coefficient to obtain a comprehensive risk score, finally dividing patients into low, medium and high risk levels according to a risk threshold value determined by historical data, and outputting a result and storing the result into a database for clinical retrieval and early warning. The method can effectively evaluate the intestinal preparation failure risk of the elderly patient, and provides an objective basis for clinical intervention.

The invention relates to the technical field of artificial intelligence, in particular to a machine learning-based double-J tube indwelling membranous fungus infection risk medication decision support method, and aims to solve the problems of inaccurate double-J tube indwelling membranous fungus infection risk assessment and medication decision experience. According to the method, the machine learning model for predicting infection risks, pathogenic bacteria and drug curative effects is constructed by collecting multi-dimensional data and performing feature engineering, and personalized medication suggestions and visual decision support are provided in combination with a knowledge base and a rule engine. By adopting the scheme, accurate risk prediction can be realized, drug curative effect evaluation can be optimized, drug use reasonability and safety can be ensured, and clinical decision-making efficiency can be improved.

The invention discloses a net ultrafiltration rate intelligent regulation and control device and method for CRRT treatment, and relates to the technical field of medical equipment control, and the device comprises a sensor module, a data processing module and a regulation and control module; the sensor module is used for acquiring CRRT treatment associated data of a preset patient; the data processing module is used for predicting a blood volume tolerance curve of a preset patient by utilizing an artificial intelligence technology according to historical CRRT treatment data and CRRT treatment associated data of the preset patient, and generating a net ultrafiltration rate time sequence scheme according to the predicted blood volume tolerance curve and a preset treatment target of the preset patient; and the regulation and control module is used for automatically regulating the rotating speed of a blood pump and the rotating speed of a displacement liquid pump which are used for performing CRRT treatment on the preset patient according to the net ultrafiltration rate time sequence scheme, and regulating the dehydration rate of the preset patient. According to the invention, a more accurate intelligent regulation and control mode can be provided for CRRT treatment.

A computer-implemented method for treating a patient suffering from Type I Diabetes (T1D). The method can include quantifying physical activity (PA) of the patient; calculating an accumulated PA periodically based on the quantified PA, the accumulated PA indicating an aggregate of the PA; and generating an activity informed insulin bolus by adjusting a prevalent functional insulin therapy bolus with a previous activity component, wherein the previous activity component is based on the accumulated daily PA, an activity profile, and an activity factor of the patient. The method can include determining an additional glucose uptake within a time period, the additional glucose uptake being caused by a PA; translating the additional glucose uptake into a number of insulin units with a same BG lowering impact; and generating an activity informed insulin bolus by adjusting a prevalent functional insulin therapy bolus with the insulin units.

Systems and methods are provided for monitoring health. An exemplary method includes: collecting a first data regarding a patient during an in-office visit; providing a remote monitoring service for remotely monitoring the patient's health; remotely collecting, using the remote monitoring service, a second data of the patient; providing a probabilistic network for assigning metric-based information to the plurality of data using a plurality of conditional probabilities; processing, using the probabilistic network, the first data and the second data using the probabilistic network; generating, using the processed plurality of data, one or more machine learning models for producing a knowledge base trained to recognize pattern types in the data; generating, using the knowledge base, one or more artificial intelligent features for recommending treatment options based on the data regarding the patient; and providing, using the one or more artificial intelligent features, one or more treatment recommendations for improving the patient's health.

The invention discloses a medical knowledge graph construction method and device for multi-source heterogeneous data fusion and incremental updating, and relates to the technical field of medical knowledge graphs. The method comprises the steps that multi-source medical data such as outpatient records, hospitalization medical records, inspection reports and image data of a patient in the whole life cycle are collected; performing time sequence alignment by adopting a space-time alignment algorithm to obtain standardized medical data, calculating a data fusion weight and constructing an incidence matrix; performing singular value decomposition on the incidence matrix to extract a feature vector, and obtaining a dynamic update coefficient through standardization, cosine similarity calculation, convolutional neural network processing and exponential weighted moving average; adjusting the reference map according to the dynamic updating coefficient increment to generate an updated medical knowledge map, and outputting diagnosis and treatment decision support information; and multi-dimensional medical data space monitoring abnormity can be constructed, a joint optimization model is established to generate a personalized diagnosis and treatment scheme, and edge deployment is realized through a knowledge distillation compression map.

The invention provides methods, systems, and computer readable medium for detecting ploidy of chromosome segments or entire chromosomes, for detecting single nucleotide variants and for detecting both ploidy of chromosome segments and single nucleotide variants. In some aspects, the invention provides methods, systems, and computer readable medium for detecting cancer or a chromosomal abnormality in a gestating fetus.

The invention relates to the technical field of medical treatment, and particularly discloses a compound prognosis model for cancer prognosis evaluation, a prognosis evaluation method and a calculator. The compound prognosis model is constructed based on inflammation burden related prognosis indexes ALNCR and TNM by stage combination; the creation method comprises the following steps: step 1, collecting albumin concentration, lymphocyte count, neutrophil count and C-reactive protein concentration data of a cancer patient; 2, calculating an ALNCR index according to a formula; 3, according to a clinical classic TNM staging system, the patients are divided into a TNM staging stage I, a TNM staging stage II, a TNM staging stage III or a TNM staging stage IV; 4, the ALNCR index obtained in the step 2 and the TNM staging result obtained in the step 3 are associated and integrated, and the individualized prognosis score of the ALNCR.TNM composite prognosis model is obtained through calculation. The method is suitable for clinical rapid application; high-risk patients can be accurately distinguished, a quantitative basis is provided for individualized treatment and follow-up visit strategy formulation, and excessive medical treatment or insufficient treatment is reduced.

The invention provides an electronic medical record data patient information desensitization and inspection data standardization method, and relates to the technical field of medical big data processing and privacy protection. The method comprises the following steps: collecting structured and unstructured electronic medical record data from a hospital information system; carrying out automatic identification and desensitization processing on the sensitive information of the patient; performing time alignment, missing value filling and normalization processing on laboratory inspection data; key features are extracted, a prediction model is constructed, and the adaptive capacity of the model in different medical policy environments is improved in combination with policy stage division and feature importance analysis. And the interpretability and prediction performance of the model are enhanced through multi-model fusion modeling and an SHAP interpretation mechanism. According to the method, efficient and compliant data desensitization is realized, the consistency of test data and modeling quality are improved, dynamic response policy changes are supported, and the method has good practicability and popularization value.

A device may include a data acquisition layer comprising a patient information collector and a drug information collector, each operable to ingest patient-specific clinical data and drug-specific data, respectively. A device may include an analysis engine comprising a multi-criteria ranking engine configured to compute composite suitability score for candidate medications by weighting one or more of medication efficacy, medication safety, medication resistance, medication cost, or patient-specific coverage factors. A device may include a presentation layer comprising a summary dashboard configured to display a ranked medication recommendation set produced by the analysis engine.

A Health Value Analytics (HVA) system includes an electronic medical record (EMR) / HVA access device in the form of comprising machine instructions for facilitating a physician's workflow. A processor at a physician's work terminal executes the machine instructions to connect the terminal with a HVA server and / or an EMR server. The processor sends a medical information request for a patient and information related to characteristics and parameters of the terminal, to one or both the HVA server and the EMR server. The processor receives, in response, an EMR page generated and adapted by the EMR server for display on the terminal. The EMR page displays an electronic medical record of the patient, and one or more HVA data objects generated by the HVA server, after the EMR server adapts the EMR page based on mode information that includes characteristics of the terminal.

The invention discloses a multi-modal data processing method and device based on an attention mechanism, equipment and a medium. Living habits of a user and living and medical characteristics of genes are combined on the basis of image data and electronic medical records. The method is advantaged in that the problem of difficult fusion of heterogeneous multi-source data is solved through uniform characterization of features of four-modal data, adaptive adjustment of attention weight of each modal is carried out through the entity association relationship in the entity relationship knowledge graph, and fusion precision of full-modal fusion features is improved. Through the anti-illusion prediction model, disease type and probability prediction is carried out for individual differences (user tags and full-modal fusion features) of target users, and the accuracy of disease prediction results is improved. The multi-modal data processing method can be applied to user body evaluation in the financial field and disease type prediction in the medical field so as to improve the risk prediction accuracy in the financial field and the disease type prediction accuracy in the medical field.