194 results about "Patient characteristics" patented technology

The invention provides a scientific and technological literature intelligent retrieval method and related equipment based on generative artificial intelligence, and the method comprises the steps: carrying out the multi-layer semantic annotation of medical scientific and technological literatures, constructing a symptom-disease dynamic association map, and building a medical scientific and technological literature knowledge base; performing medical context analysis and multi-modal feature extraction on user query, and generating a unified retrieval vector in combination with Boolean operation nested analysis and semantic alignment processing; performing evidence grading retrieval and clinical scene matching based on the unified retrieval vector to obtain a preliminary candidate literature set, and optimizing the preliminary candidate literature set into a target candidate literature set through fine-grained semantic recalculation; and calculating a retrieval prior probability according to the evaluation dimension, performing knowledge weighted fusion on the candidate literature, and generating a medical science and technology literature recommendation report. According to the method, the medical term association relationship is deeply understood through the association map, the result is ensured to be matched with the patient characteristics through evidence grading retrieval and clinical scene matching and screening, and the accuracy of document retrieval is improved.

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 provides a tumor patient clinical test matching system and method based on a large language model and an OCR technology, and is applied to the field of medical data processing. The method comprises the following steps: analyzing clinical data and test information, processing an unstructured text, and generating structured clinical feature data through context association analysis; key data is extracted and subjected to double verification correction, and structured data supplementary information is generated; enhancing the structured clinical feature data and supplementary information based on a multi-modal processing assembly line module, extracting an image quantitative index, analyzing an immunohistochemical result, and generating a comprehensive matching score; through a rule engine and semantic similarity calculation, item-by-item comparison of patient features and entry and exhaust conditions is realized, and a preliminary matching result is generated; edge case misjudgment is corrected through context-aware multi-round reasoning, sorting is adjusted in combination with clinical test priority weights, and an optimized clinical test matching list is generated; and generating a clinical test matching report based on the data.

The embodiment of the invention discloses a spinal metastatic tumor treatment scheme generation system. According to one specific embodiment, the system comprises a data processing server, an information fusion server and a scheme generation server which are in communication connection with one another, and the data processing server is used for preprocessing multi-source patient data to obtain standard multi-source patient data; the information fusion server is used for executing the following steps: performing feature code fusion on standard multi-source patient data to obtain a multi-source patient feature vector; performing feature enhancement on the multi-source patient feature vector to obtain a joint patient characterization vector; generating an initial therapeutic schedule result based on the joint patient characterization vector; and the scheme generation server is used for performing feature decision processing on the initial treatment scheme result to obtain a final treatment report. According to the embodiment, waste of computing resources can be reduced, and system response time can be shortened.

The invention relates to the technical field of anesthesia, and discloses a personalized anesthesia scheme generation method, which comprises the following steps: S1, extracting and coding patient features; s2, modeling a pharmacokinetic model; s3, drug concentration prediction; s4, applying a PD model; s5, performing uncertainty quantification; s6, predicting and outputting; s7, updating and feeding back parameters; s8, verifying the model; in the personalized anesthesia scheme generation process, firstly, effective feature extraction and coding are carried out on multi-modal data of a patient; according to the personalized anesthesia scheme generation method, efficient feature extraction is carried out on multi-modal data (such as electronic health records, genome information and real-time monitored vital signs) through VAE and other deep learning methods, and the differences of patients in the aspects of physique, drug metabolism capability, gene background and the like can be fully described; therefore, the optimal anesthetic medication strategy is customized for each patient.

The invention requests to protect a dual-core decision-making examination reservation system based on a rule engine and deep reinforcement learning. The dual-core decision-making examination reservation system comprises a data infrastructure layer, an intelligent decision-making layer and a decision-making output layer, wherein the data infrastructure layer is used for forming reservation data according to data of equipment and patients; the intelligent decision-making layer is used for constraining patient appointment according to the appointment data; and the decision output layer provides a scheduling scheme according to the reservation data and the constraint conditions. The dynamic state space constructs a higher-dimensional state vector space, the reservation data comprises a patient feature domain, an equipment state domain and an environment dynamic domain, and comprehensive dynamic data support is provided for decision making in combination with personalized features of the patient and real-time environment information. The intelligent decision-making layer has a hybrid decision-making mechanism and comprises a rule decision-making module and an optimization module, the rule decision-making module verifies hard rules of the reservation data, the optimization module performs weighted integration on non-hard rules of the reservation data, and finally the decision-making layer outputs a scheduling scheme.

According to the time sequence risk control method and system based on the multi-level mixed experts, heterogeneous data in the medical field is fused and mapped to the unified representation space through multi-modal representation learning, and therefore data information is utilized more comprehensively. The system adopts a multi-level hybrid expert (MoE) architecture, tasks can be accurately routed to a more professional expert group according to patient characteristics through a hierarchical gating network, and the accuracy and resource utilization efficiency of the model are greatly improved. Meanwhile, through bidirectional interpretability and a causal inference mechanism, not only can the reason of a risk prediction result be explained, but also anti-fact analysis and specific intervention measure guidance can be provided, so that the model is converted from a pure prediction tool to an intelligent partner for auxiliary decision making.

The invention relates to the technical field of medical image diagnosis, and discloses a tooth and fracture line recognition treatment method based on the combination of an AI technology and a CBCT image, and the method comprises the steps: obtaining and preprocessing the CBCT image, and carrying out the multi-scale analysis and recognition of a tooth structure, a microcrack and a fracture line through a first AI model. And the second AI model combines the identification result and the patient characteristics, and generates a personalized treatment scheme through multi-objective optimization. Clinical feedback is used for continuously iteratively optimizing double models, and the diagnosis and treatment precision and effect are improved. The system comprises an image data acquisition unit, an image data preprocessing unit, a tooth and fracture line identification unit, a personalized treatment scheme generation unit and a feedback and optimization unit. Through AI and CBCT image fusion, accurate identification of teeth and fracture lines is realized, a personalized treatment scheme is recommended in combination with individual features of a patient and a multi-objective optimization algorithm, rapid response is realized, a closed-loop feedback mechanism continuous optimization model is established, and diagnosis and treatment precision, efficiency and individualization level are remarkably improved.

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 discloses a semi-supervised prognosis prediction system based on irregular sampling medical data pre-training, and the system comprises a clinical electronic medical record data collection and preprocessing module which automatically collects original clinical electronic medical record EHR data from a medical database; and the pre-training module is used for receiving the patient feature vector sequence output by the clinical electronic medical record data acquisition and preprocessing module and carrying out feature representation learning on the preprocessed clinical time sequence data by utilizing a combined multi-task self-supervised learning mechanism. And the classifier fine tuning and pseudo-label iterative optimization module is used for training the pre-trained model through fine tuning of samples with labels and carrying out iterative optimization through guidance of pseudo-labels to obtain a prediction result. And the application display module is used for displaying and outputting a post-hospital-admission vital sign sequence and a prediction result. According to the method, irregular sampling and missing data are effectively processed, information loss is avoided, and the prediction capability of the model and the performance of the model under the conditions of data imbalance and label scarcity are improved.

The invention discloses a rapid progression type nasopharyngeal carcinoma risk prediction method based on an artificial neural network, and relates to the field of medical informatics crossing. The invention provides a rapid progressive nasopharyngeal carcinoma risk prediction method based on an artificial neural network, and aims to solve the problem that a rapid progressive nasopharyngeal carcinoma patient is difficult to recognize in time by depending on TNM staging and experience judgment in the prior art. According to the method, historical case data collection, missing value filling and standardization preprocessing, core feature determination through feature screening, class imbalance correction, feature coding and feature matrix construction are sequentially carried out, an artificial neural network model is trained and optimized under a cross validation framework, and performance and threshold values are determined on a validation set. During clinical application, patient features are input, and the model outputs a rapid progress risk probability and a risk level. Compared with a conventional staging or linear model, the method can improve the prediction accuracy, and achieves the early recognition and individualized treatment of a high-risk patient.

The invention discloses a Chinese patent medicine prescription intelligent assistance method and system, and the method comprises the steps: determining a possible traditional Chinese medicine diagnosis range through disease information / patient information, carrying out the further analysis, obtaining the disease feature information / patient feature information which needs to be obtained for determining the traditional Chinese medicine diagnosis, and determining the traditional Chinese medicine diagnosis according to the related information. The Chinese patent medicine prescription intelligent auxiliary system comprises a demand acquisition module, a feature information analysis module, an information acquisition module and a traditional Chinese medicine diagnosis analysis module. According to the Chinese patent medicine prescription intelligent assistance method and system, doctors and patients can be helped to accurately select Chinese patent medicines, and damage caused by misuse of the medicines is prevented.

The invention discloses a sepsis clotting disease subtype characteristic and typing system, which comprises a basic data acquisition module used for acquiring SIC patient characteristics; a potential category analysis module configured to identify potential groups of the SIC patients based on the SIC patient features; the K-means clustering module is configured to cluster the characteristics of the SIC patients and obtain a clustering result of the SIC patients; and the typing result output module is configured to output subtype features and typing results of the SIC patients based on the potential groups of the SIC patients and the clustering results of the SIC patients. The subtype classification of the SIC patient can be rapidly and accurately analyzed, and a direction is provided for subsequent treatment.

The invention discloses a traditional Chinese medicine prescription intelligent recommendation method based on a multi-source heterogeneous graph, a medium and equipment, and the method comprises the steps: firstly extracting a patient symptom feature vector through a natural language processing technology, and constructing a patient comprehensive feature in combination with a physical feature vector; generating a prescription feature vector based on prescription composition, efficacy classification and historical diagnosis and treatment data; a heterogeneous graph knowledge graph containing nodes of symptoms, physiques and prescriptions is constructed, node features are iteratively aggregated by using a graph convolutional network, and deep correlation modeling among symptoms, physiques and prescriptions is realized. And finally, a personalized recommendation result is output by calculating a matching score of the patient characteristics and the prescription nodes. According to the method, modern clinical data and the theory of traditional Chinese medicine are creatively combined, the accuracy, individuation and clinical applicability of prescription recommendation are remarkably improved through dynamic map construction and deep learning technologies, and an effective solution is provided for intelligent diagnosis and treatment of traditional Chinese medicine.

The invention discloses a tumor patient portrait construction method and system based on multi-source heterogeneous data. The method comprises the following steps: collecting heterogeneous data related to a tumor patient; preprocessing the heterogeneous data, including format standardization, missing value completion, time sequence alignment and privacy desensitization processing, to obtain a data set which can be used for unified modeling; respectively extracting disease features, psychological features, behavior features, social features and regional features, and generating corresponding feature vectors for various features; mapping the feature vectors to a unified feature space by using a heterogeneous graph neural network based on an attention mechanism, dynamically calculating contribution degrees of different features to patient portraits, and establishing a multi-dimensional feature association graph of the tumor patients; and generating a structured tumor patient portrait including a disease dimension, a psychological dimension, a behavior dimension, a social dimension and a regional dimension. According to the method, high-precision, multi-dimensional and explainable patient feature description is realized, and a data basis is provided for precise service.

A computer-implemented method of determining one or more sets of features to predict the presence of a particular phenotypic characteristic comprises: (a) receiving patient data comprising, for each of a plurality of patients: a feature profile comprising a respective feature status for each of a plurality of features for that patient; and an indication of whether that patient expresses the particular phenotypic characteristic; (b) using a genetic algorithm to generate a plurality of generations of individuals, wherein each individual comprises a subset of the predetermined plurality of features, each generation of individuals generated based, at least in part, on a plurality of fitness scores, each fitness score corresponding to a respective individual in the previous generation, and parameterizing a predictive accuracy of the set of features, each fitness score being calculated based at least in part on the patient data; (c) repeating step (b) until it has been performed N times; (d) from the plurality of individuals generated in steps (b) and (c), selecting a subset of the individuals based on their fitness scores; (e) clustering the selected subset of individuals to generate a plurality of clusters of individuals, based on the similarity of their respective subsets of features; (f) from each cluster, identifying a respective characteristic feature set based on the frequency with which features appear in individuals in that cluster.

The invention provides a cardiovascular and cerebrovascular disease classification model construction method, which comprises the following steps: receiving dynamic signal data of a cardiovascular and cerebrovascular disease patient, and constructing a dynamic signal matrix; global pathological features of patients with cardiovascular and cerebrovascular diseases are collected to serve as static feature vectors, and time dimensions of the static feature vectors and the dynamic signal feature matrix are unified to construct a fusion feature matrix; constructing a common disease association network; when patient group grouping is carried out, similarity mapping from individuals to groups is carried out through similarity calculation of features of each time slice and a group feature center to generate patient group features, and the patient group features are aligned with a patient feature center matrix; and constructing a deep classifier for cardiovascular and cerebrovascular disease classification, and finally outputting a classification result by the classifier. The method has significant breakthroughs in the aspects of dynamic feature modeling, disease relevance modeling and personalized adaptation capability, and an efficient and accurate technical means is provided for cardiovascular and cerebrovascular disease classification.

Systems and methods for remote verification of medication administration are disclosed herein. In some aspects, a system receives a request for verifying administration for a medication including an image. The system may identify regions of interest that include medication identifying features from the image. The system may compare a feature representation of each medication identifying feature to reference feature representations managed by authorized operator devices. The system may identify a matching medication and trigger an image acquisition session to obtain a time-series of images, at least one of the images comprising a patient in proximity to the medication. The system may extract patient features and a posture of the medication in relation to the patient and input the extracted data and entity-issued administration instructions into a model to obtain a set of patient-specific administration instructions.

The invention discloses a sepsis blood perfusion dynamic monitoring method combining blood oxygen and blood flow parameters, and belongs to the technical field of medical monitoring. The method specifically comprises the following steps: S1, constructing a sepsis patient grading evaluation diagnosis and treatment model, and obtaining sepsis patient information, index data, sepsis severity at that time, a treatment scheme and post-treatment index data to train the model; s2, information and index data of current sepsis patients are collected, blood flow and blood oxygen are measured, the current sepsis patients are matched through the sepsis patient grading diagnosis and treatment model, the severity degree of sepsis of the current patients is judged, and the information, the index data and the severity degree of sepsis of the current patients are displayed. By acquiring basic information, physiological indexes, severity, treatment schemes and post-treatment data of sepsis patients, a database covering a whole diagnosis and treatment process is constructed, so that the model can learn association between different patient features and diagnosis and treatment results, and comprehensiveness of evaluation and prediction is improved.

The invention belongs to the technical field of data fusion, and discloses a medical data fusion method and system based on a large model and heterogeneous hypergraph learning. According to the method, simple superficial fusion strategies such as splicing and addition are abandoned, and a fusion framework based on heterogeneous hypergraph contrast learning is innovatively adopted. The framework can naturally model multi-modal data into a heterogeneous hypergraph so as to explicitly capture complex high-order topological relations and cross-modal interactions between patients and features. And in combination with a contrast learning strategy of a mask auto-encoder, the robustness of the model under data missing and noise interference is further enhanced, and deep fusion in a real sense is realized.

The invention provides a distributed rehabilitation data analysis method based on federal learning, and the method comprises the steps: obtaining an encrypted rehabilitation medical data abstract, and obtaining an anonymized patient feature data set suitable for multi-party cooperation; performing learning model training on the anonymized patient feature data set in the local environment to obtain a shared global model parameter set; simulating distribution characteristics of rare disease cases based on parameters, performing adversarial data generation processing on the global model parameter set to obtain a patient information supplementary data set, and fusing real patient rehabilitation data to obtain a comprehensive rare disease case characteristic representation set; according to the comprehensive rare disease case feature representation set, a patient data distribution equilibrium index is calculated, a final distributed patient rehabilitation medical data analysis result is obtained, the problem of data insufficiency under privacy protection is effectively solved, the comprehensiveness and accuracy of rare disease case feature representation are improved, and the patient rehabilitation medical data analysis efficiency is improved. And comprehensive medical data analysis of multi-mechanism safety cooperation is realized.

The invention discloses an immunosuppressor administration recommendation system based on deep reinforcement learning, and relates to the technical field of medical medication management, and the system comprises a model construction module, an information acquisition module and a scheme recommendation module. The model construction module comprises a strategy sub-module, an environment sub-module, a reward sub-module and a training sub-module; the strategy sub-module constructs a drug administration recommendation model, and the drug administration recommendation model is used for receiving patient characteristics and then outputting a drug administration recommendation scheme; the environment sub-module predicts the blood concentration of the patient after medication according to the patient characteristics and the medication recommendation scheme; the reward sub-module evaluates the blood concentration of the patient after medication to obtain a reward and punishment value; the training sub-module trains a drug administration recommendation model based on the reward and punishment values, and sends the drug administration recommendation model to the scheme recommendation module when a training termination condition is met; and the scheme recommendation module inputs the target characteristics of the target patient sent by the information acquisition module into a drug administration recommendation model to obtain a target drug administration scheme so as to reduce the decision-making difficulty of the immunosuppressor drug administration scheme.

Systems and methods are disclosed for using patient-specific anatomical models and physiological parameters to predict viability of a target tissue or vessel to guide diagnosis or treatment of cardiovascular disease. One method includes: receiving a patient-specific vessel model and a patient-specific tissue model of a patient anatomy; receiving one or more patient-specific physiological parameters (e.g. blood flow, anatomical characteristics, etc.) for one or more physiological states; estimating a viability characteristic of the patient-specific tissue or vessel model (e.g., via a trained machine learning algorithm), using the patient-specific physiological parameters; and outputting the viability characteristic to an electronic storage medium or display.

The invention discloses a bidirectional reasoning diagnosis method based on a large model, which can be applied to a clinical intelligent diagnosis scene. Firstly, key abnormal features in hospital admission records of patients are concerned through abnormal feature extraction; secondly, similar medical records are retrieved as diagnosis experience learning; thirdly, forward reasoning is deduced from the features of the patient, and possible diseases are deduced; in the reverse reasoning, a cause tracing method is adopted, and related characteristics are traced back from a diagnosis result. And through combination of deduction and induction, the reasoning ability of the large model is enhanced. And finally, carrying out correction and confidence evaluation on the diagnosis result to realize iterative reflection optimization and ensure the reliability and interpretability of the diagnosis process. An automatic diagnosis tool based on a large model is provided for the medical field, the diagnosis accuracy and efficiency can be improved, and an innovative solution is provided for clinical intelligent diagnosis.

The invention provides a large-scale medical inquiry system based on multidimensional reinforcement learning (MDRL) and Markov decision process (MDP) optimization, and aims to solve the problem of uncertainty in medical inquiry and realize personalized diagnosis and treatment. The system is composed of a feature information distribution module and a dynamic optimization module. The feature information distribution module uses natural language processing (NLP) and machine learning to extract patient features from an inquiry text and construct an information distribution map, thereby improving the accuracy of state judgment under an uncertain condition. The dynamic optimization module dynamically evaluates the illness state through a medical knowledge graph and an MDP model, generates symptom query and optimization inquiry prompts, and helps doctors to make accurate decisions. The system combines a reinforcement learning mechanism, continuously optimizes a decision process, has modular characteristics, supports flexible upgrade, adapts to a complex medical environment, and provides an efficient health solution.

The invention discloses a multi-factor risk assessment method for ovarian hyperstimulation, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: based on a multi-source baseline data packet, calculating the trend of follicle volume along with time through ovarian reaction simulation to obtain a follicle reaction curve, calculating the probability of vascular injury through puncture path bleeding simulation, and calculating the risk of ovarian hyperstimulation according to the probability of vascular injury. Obtaining a bleeding probability, and fusing the follicle reaction curve and the bleeding probability to form a patient characterization risk vector; associating the intervention suggestions in the structured strategy list with the ovarian overstimulation risk score and the bleeding risk score to generate a risk change description, and forming a risk assessment report according to the risk change description; according to the method, the patient characterization risk vector fusing the follicle dynamic response and the puncture bleeding risk is constructed, so that the safety and individuation level of assisted reproduction treatment are effectively enhanced.

The invention discloses a medical prescription auditing and medication safety management and control method, device and equipment and a storage medium, and relates to the technical field of medical informatization and health big data, the method comprises the following steps: obtaining drug information, patient allergy history and multi-dimensional health data in a target prescription, and constructing a prescription data set; in combination with a medicine knowledge base and a taboo database, intelligent verification is carried out on the medication rationality of the prescription by utilizing a Duros rule engine and a graph neural network model, and a verification result and verification detail data are output; if the verification result is unreasonable, marking the prescription as a risk prescription, generating a medication risk assessment report and a prescription modification suggestion based on verification detail data, and pushing the report and the suggestion to a corresponding doctor terminal; and if the verification result is reasonable, generating personalized medication guidance through prescription-patient feature matching, and sending the personalized medication guidance to the doctor terminal, the patient terminal and the pharmacy terminal. According to the application, intelligent prescription checking and medication safety guarantee can be realized through risk assessment and personalized adaptation.

Methods and systems for determining an expected disease treatment outcome upon treating a subject, methods and devices for selecting a treatment option for the subject, and methods of treating a subject for a disease, are described herein. The method can include receiving a plurality of subject characteristics for the subject; accessing a tree-based model corresponding to a treatment option for the disease, wherein the tree-based model is generated based on a plurality of prior patient characteristics and an associated treatment outcome for the corresponding treatment option; and determining from the plurality of subject characteristics and the tree-based model, an expected treatment outcome for the subject if the subject were treated with the corresponding treatment option.

Disclosed herein is a system for administering a substance to a patient, the system comprising a reservoir for storing the substance, an administration apparatus configured to administer the substance to the patient, a pump for directing the substance from the reservoir to the delivery mechanism, a controller configured to operate the pump, a communication unit configured to communicate with at least one server, at least one processor configured to receive data associated with patient characteristics, calculate a substance administration dose according to the data, determine a preferred substance administration process, and operate the pump to administer the substance to the patient.

The invention relates to the technical field of intelligent management, and particularly discloses a high-value consumable management and control system based on an intelligent operating room, and the system comprises a mark tracking module which is used for obtaining consumable related data of high-value consumables based on a preset identification tag, carrying out the consumable integration, and obtaining a real-time consumable database of the intelligent operating room; the label setting module is used for analyzing the consumable types of the real-time consumable database so as to determine consumable use labels of the high-value consumables of each type; the personalized management and control module is used for determining a high-value consumable set of each operation in combination with patient characteristics and doctor preferences, adjusting consumable related data in real time based on the operation progress of the patient, and updating the consumable related data; the analysis and decision module is used for obtaining a consumable trend curve based on the consumable related data, performing consumable demand prediction and performing consumable inventory turnover; the method and the device are used for improving the use efficiency of high-value consumables of an intelligent operating room, improving the refined consumable management and control efficiency and optimizing cost control.