1364results about "Pathological references" patented technology

Systems and methods for improving reimbursement rates from payors for services or procedures provided by a medical care provider are disclosed herein. In some examples, reimbursement can be improved by improving the accuracy of medical coding of services and diagnosis codes through the use of machine learning techniques. In some examples, the system can suggest codes more likely to get reimbursed by a payor through analysis of patient data and / or insurance information. In some examples, the system can determine a likelihood of reimbursement through analysis of patient data and / or insurance information.

The application includes systems and methods for phenotyping. A request is received to identify a target population having a phenotype. Predefined characteristics associated with the phenotype are obtained. Using a retriever model, a set of subjects is identified as potential members of the target population by searching databases. Medical information is obtained by searching a second set of one or more databases and providing corresponding information to an artificial intelligence (AI) component. Natural language instructions are provided to the AI component to provide context to the AI component to determine whether a respective subject in the set of subjects has at least one of the one or more predefined characteristics. A subset of subjects is identified by the AI system to be, or to have a high likelihood of being, a member of the target population.

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 relates to the technical field of medical image processing, and particularly discloses a pathological tissue image-based tumor analysis method, which comprises the steps of data acquisition and preprocessing, network construction, image enhancement, adjacent layer feature fusion, image classification and knowledge distillation. According to the scheme, features are extracted from different levels by adopting an image enhancement and comparative learning method, the capability of extracting global features and capturing local details of the model is improved, and by calculating strong and weak enhancement difference loss, it is ensured that enhancement operation does not excessively change feature representation of the image, and overfitting of the model is prevented; features are extracted and fused from each layer of feature map of the teacher network through an attention mechanism, and the detail capturing ability of the model to a pathological image is enhanced; a knowledge distillation method is used, knowledge of a teacher network is introduced, and a student network not only pays attention to classification of hard tags, but also can capture a relative relationship between classes, so that the classification accuracy is improved, and the calculation resource demand of a model is reduced.

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 provides a lung cancer immunotherapy curative effect prediction method and system based on double CT images, and belongs to the technical field of medical image processing. Firstly, tumor areas of a baseline CT image and a puncture CT image are marked; registering a puncture position in the puncture CT image to a baseline CT image, and dividing the baseline CT image into a plurality of tumor sub-regions; predicting the PD-L1 expression quantity at each tumor sub-region position based on a spatial attention deep learning model, and generating a PD-L1 map; and constructing a double-task prediction model, inputting the clinical information, the baseline CT image and the PD-L1 atlas of the patient into the double-task prediction model, and predicting the curative effect of lung cancer immunotherapy. According to the invention, on the basis of ensuring the effective matching of the image-pathological space of the non-small cell lung cancer, the accurate prediction of the lung cancer immunotherapy curative effect of the non-small cell lung cancer patient can be realized.

Techniques relate to generating and / or verifying content based on protocols and / or user input. The techniques can receive protocol data from an entity, including a procedure, guideline, policy, or standard. User input requesting content generation related to specific parameters can also be received. Based on the protocol data, a data request can be generated and sent to a content generation component. Machine-generated content can be received from the content generation component in response. The machine-generated content can be analyzed based on the protocol data. Output content can be generated based on the machine-generated content and provided to a computing device. In examples, this ensures that generated content aligns with entity-specific protocols and / or user-specified parameters, enabling tailored and / or compliant content delivery for various contexts.

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 tumor recurrence risk assessment and early warning system based on data analysis, and belongs to the technical field of medical information. The invention discloses a tumor recurrence risk assessment and early warning system based on data analysis. The system comprises a multi-data collection module, a big data processing platform, a model architecture system and an individual data processing system. According to the invention, the problems of lack of big data support, limited individualized prediction ability and insufficient real-time performance and continuous monitoring in the prior art are solved; according to the method, large-scale historical case data are integrated, the rich data sets are used for model training, the accuracy and generalization ability of a prediction model can be remarkably improved, multiple data sources such as genomics information, living habits and environmental factors are integrated, personalized risk assessment is provided for each patient, and the risk assessment efficiency is improved. The real-time monitoring interface is connected with various intelligent medical devices to collect the daily physiological parameter change condition of the patient, and continuous health management suggestions are provided for the tumor postoperative patient.

A system and method for analysis of a vessel automatically detects a pathology in a first image of the vessel and attaches a virtual mark to the pathology in the first image. The system may detect the same pathology in a second image of the vessel, based on the virtual mark, and may then provide analysis (e.g., determine an FFR value) of the pathology based on the pathology detected in the first and second images.

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 provides an esophageal cancer marker screening method based on enzyme transfer method methylation sequencing, which comprises the following steps: S1, collecting esophageal early cancer and para-carcinoma tissue samples, and carrying out enzyme transfer method whole genome methylation sequencing; s2, integrating methylation data of esophageal cancer and para-carcinoma tissues in databases of TCGA, GEO, MethMarker DB and the like; s3, screening differential methylation sites, preferably sites which are hypermethylated in esophageal cancer but hypomethylated in samples such as leukocytes, so as to reduce plasma background interference; s4, obtaining a candidate marker combination based on an integrated methylation feature screening model of LASSO regression and a random forest algorithm; s5, constructing a classifier model based on the candidate marker combination, carrying out training and reasoning test on the model, and evaluating the prediction performance of the model; and S6, obtaining the esophageal cancer marker combination. According to the method, enzyme transfer sequencing and machine learning are combined, the marker prediction accuracy is remarkably improved, and a new reference is provided for clinical research of esophageal cancer.

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.

Knowledge graph guide and entity controlled techniques for generating synthetic doctor-patient conversations. In one particular aspect, a method is provided that includes obtaining an original dataset containing textual dialogue associated with a plurality of individual doctor-patient conversations for training a machine learning model, constructing input data by using named entity recognition to capture and categorize named medical entities present in the dialogue, generating prepared input data by arranging the input data in an annotated turn-by-turn conversation format using an input data preparation algorithm having various control parameters, training the machine learning model using the prepared input data, utilizing a knowledge graph to identify a plurality of symptoms mapped to a randomly selected disease, and causing the trained machine learning model to generate a synthetic doctor-patient conversation by inputting the plurality of symptoms to the machine learning model as a first control parameter of a conversation generation control algorithm.

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 belongs to the technical field of medical pathological images, and particularly relates to a multi-instance multi-task learning pan cancer biomarker prediction method and system, and the method comprises the steps: obtaining a full-view digital pathological image and pan cancer biomarker expression data of a solid tumor patient; performing preprocessing and feature extraction based on the full-view digital pathological image to obtain a pathological feature data set; based on the pathological feature data set, inputting the pathological feature data set into a twin multi-example full convolutional network model for training to obtain pathological low-dimensional features, and calculating an attention score corresponding to each pathological feature in combination with a linear attention mechanism; and in combination with each pathologic low-dimensional feature and the corresponding attention score, calculating to obtain a pathologic depth feature, and inputting the pathologic depth feature into a multi-task classification prediction model for classification prediction to obtain a prediction result of each biomarker in the pan cancer.

The invention relates to the technical field of biomedicine, and discloses a rectal cancer postoperative recurrence risk prediction method based on a prediction model, and the method comprises the steps: obtaining a patient tumor in the in-vivo image data of a rectal cancer patient; extracting imaging features of the tumor of the patient, and identifying recurrence risk features of the rectal cancer patient from the imaging features; analyzing clinical pathological characteristics of the rectal cancer patient according to the preoperative evaluation data and the postoperative pathological report; key recurrence influence factors of the rectal cancer patients are identified, recurrence events and follow-up visit time of the rectal cancer patients are identified, risk scoring is carried out on the rectal cancer patients, and a risk scoring result is obtained; based on the risk scoring result, the recurrence risk stratification of the rectal cancer patient is defined, postoperative recurrence risk analysis is conducted on the rectal cancer patient in combination with the recurrence risk stratification, the recurrence risk characteristics and the risk scoring result, and a risk analysis result is obtained. According to the invention, the comprehensiveness and accuracy of the rectal cancer recurrence risk analysis result can be improved.

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.

A computer system, computer program product and method for determining a probability of attaining a PK-PD target associated with efficacy for a patient that includes a processor(s) determining that a trigger event related to a patient, wherein the patient record comprises an order for a current drug regimen, has occurred in an electronic health record (EHR) system communicatively coupled to the one or more processors. The processor obtains descriptive information relating to a patient. The processor selects a pharmacokinetic model. The processor applies the pharmacokinetic model and utilizes the information relating to the patient to determine, for each of the one or more drug therapies, a probability of attaining a PK-PD target associated with efficacy for the patient with the infection. The processor generates rankings for the drug therapies and determines if the current drug regimen comprises a probability above a threshold. The processor generates a new order.

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 child growth and development monitoring and evaluation method based on medical big data, and relates to the technical field of medical health and child growth and development monitoring. The method comprises the following steps: step 1, respectively reading height data, weight data, corresponding acquisition time points and diagnosis records of children from databases of at least two medical institutions; according to the child growth and development monitoring and evaluation method based on the medical big data, the fine piecewise linear reference growth interval is established, the difference coefficient is quantified, and multiple threshold detection is performed, so that data redundancy and misjudgment are effectively reduced, and early recognition and timely intervention of potential abnormalities are realized by using a dynamic monitoring interval and a hierarchical intervention mechanism. Through regular review and adaptive adjustment, and sustainable optimization of a reference model and a threshold value, the defects of incomplete data, dull monitoring frequency and difficulty in timely capture of abnormity in a traditional method are overcome, and child growth and development monitoring has accuracy, flexibility and expandability.