136 results about "Patient characteristics" patented technology

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 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 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.

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 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.

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 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.

This invention discloses a method, apparatus, device, and storage medium for medication risk assessment during pregnancy, relating to the interdisciplinary fields of medical information technology and clinical pharmacy. The method includes: receiving patient information and proposed medication regimen information, whereby the patient information includes gestational age and information on multiple comorbidities; calling the corresponding gestational age adaptive risk weight matrix based on gestational age, weighting the patient features to generate a weighted feature vector, and inputting this vector into a machine learning model to obtain an initial medication risk score; based on a constructed heterogeneous knowledge graph, using entities corresponding to multiple comorbidities and medication regimen information as query starting points, performing multi-hop reasoning to determine the existence and severity of conflicting paths; if a conflicting path is detected, revising the initial medication risk score according to the severity of the conflict to generate a final risk score. This application enables individualized, interpretable, and evidence-based intelligent risk assessment of proposed medication regimens for pregnant women with multiple comorbidities.

The invention relates to the technical field of knowledge management, in particular to a cardiovascular special nursing knowledge management and intelligent auxiliary method and system. The method comprises the following steps: acquiring and storing cardiovascular specialized nursing original knowledge data, executing text analysis and semantic processing, and generating a structured cardiovascular specialized nursing knowledge entry set; performing parameter analysis and weighted matching in combination with nursing demand information or patient characteristic parameters, and screening target nursing knowledge entries; and completing content combination and slot filling based on a preset nursing template, generating individualized cardiovascular specialized nursing guidance information, recording a nursing knowledge calling process and a generation result, and forming nursing behavior record data. According to the method, the accuracy and the individuation degree of calling the cardiovascular special nursing knowledge and the scientificity, the normalization and the intelligent level of nursing decision are improved.

The present application relates to the technical field of postoperative rehabilitation, in particular to a postoperative rehabilitation intervention system for patients with neurological diseases based on evidence-based medical evidence. The steps implemented by the system include: obtaining characteristic data of a patient with a neurological disease to be analyzed and reference patients, a rehabilitation scheme of the reference patients and a postoperative Barthel index, calculating a reference factor of the reference patients corresponding to each rehabilitation scheme under each preoperative state; obtaining a comprehensive Barthel index of each rehabilitation scheme under each preoperative state by using the reference factor and the postoperative Barthel index, determining a correction factor under each preoperative state according to the comprehensive Barthel index, and correcting the comprehensive Barthel index to obtain a target Barthel index; determining a reference rehabilitation scheme by combining the similarity of the characteristic data of the patient with a neurological disease to be analyzed and different reference patients and the target Barthel index. The present application can improve the postoperative rehabilitation intervention effect for patients with neurological diseases.

The invention provides an interpretable postoperative analgesia formula intelligent recommendation method based on a decision tree, and the method comprises the following steps: S1, according to hospital postoperative analgesia case data, determining patient characteristics and analgesia drugs, the patient characteristics comprising continuous variables and discrete variables, the analgesia drugs being the continuous variables, and preprocessing the case data; s2, performing feature construction and feature selection on the patient features, and taking the selected patient features and analgesic drugs as data sources; s3, taking the preprocessed medical record data as a training sample of a decision tree algorithm, and performing analgesia formula model learning to obtain a learned model; and S4, inputting test case data into the learned model for prediction to obtain a final analgesic formula. Through application of the artificial intelligence technology in the field of postoperative analgesia, medical and industrial crossing is promoted, intelligent recommendation of analgesia formula decisions is achieved, and therefore the workload of doctors in the process of formulating analgesia formulas is greatly reduced.

The present invention relates to a computer-implemented method and device for predicting a patient response to an agent, comprising receiving an avatar of the patient and respective data of organoid response for said patient, said avatar comprising biological data and clinical data of said patient, said avatar and said respective data of organoid response defining at least one patient feature and at least one organoid response feature respectively; receiving a first ensemble of one or more trained learning models; calculating the first output vector by providing one or more of said at least one patient feature and one or more of at least one organoid response feature as input to said first ensemble of one or more trained learning models; obtaining at least a PFS and / or an OS for the patient using the first output vector; and obtaining a prediction of the patient response to the agent using said obtained PFS and / or OS.

The invention relates to the technical field of medical treatment, and discloses a lung cancer personalized treatment scheme recommendation method and system and a storage medium. The method comprises the following steps: acquiring clinical and molecular indexes of a patient, and constructing a simplified feature set; distributing weights for treatment targets according to the simplified feature set, constructing and optimizing a scheme evaluation matrix, and generating a preliminary scheme sorting list; through threshold screening and patient feature matching degree verification, a verified scheme set is obtained; the schemes are classified based on gene mutation and driver gene features, and classified optimization scheme subsets are obtained; constructing an interaction model to analyze interaction influence of toxic and side effects and life quality on curative effects, and dynamically adjusting scheme scores; and if the score is lower than a threshold value, triggering iterative optimization, obtaining a scheme list after iteration, and further determining an optimal treatment scheme according to treatment collaboration and target balance. According to the method, intelligent and closed-loop optimization from multi-source data to personalized treatment decision is realized, and the personalization and accuracy of a treatment scheme are improved.

Systems and techniques to determine programming of an implantable electrical neurostimulation device, through chronic pain treatment modeling that evaluates pain experience states and transitions for a patient. In an example, a system to determine programming of a neurostimulation device performs operations to: determine possible pathways to traverse pain experience states of a chronic pain condition, as the possible pathways provide respective paths among the states from a starting state to one or more intermediate states to a goal state; determine transition costs between the states used in the possible pathways, with respective pain experience states being associated with different pain management characteristics of neurostimulation therapy; identify a path of the possible pathways to reach the goal state, based on the transition costs and patient characteristics; and select programming parameters for the neurostimulation device, to provide neurostimulation therapy based on the identified path to achieve the goal state.

A method for selection of priority-wise artificially intelligent mechanisms per one or more characteristics, said method comprising: receiving (DIM) images as data items (DI); identifying (IDR) an artificially intelligent system (AI1, AI2, AI3, . . . , AIn) used for determination of efficacy, each of the data items (DI), being processed by one or more identified artificially intelligent systems; parsing (SP, PRP), and outputting, scan characteristics and patient characteristics, from the data items (DI), output of said parsing (SP, PRP) being first output (scan characteristics) (O1) and second output (patient characteristics) (O2); analysing (AE) to receive a first output and / or a second output and to receive feedback signal from a feedback model (FM1, FM2, FM3); and serving, as an output (OM), upon analysing (AE), a selection (S) of a priority-wise-ranked artificially intelligent system, said selected system being per parsed scan characteristic (O1) and / or per parsed patient characteristic (O2).

The invention discloses a radiodermatitis dermatoscope imageomics prediction method and system and an application program, the system comprises a hardware module and a software module, the hardware module comprises a dermatoscope image acquisition device, and the dermatoscope image acquisition device is used for acquiring a high-resolution dermatoscope image of a radiotherapy treatment area of a patient; the software module is integrated in an application program of a mobile terminal and comprises a radiomics feature extraction module used for extracting quantitative features from dermatoscope images; the parameter calculation module is used for calculating radiomics parameters associated with the skin microstructure change; and the risk prediction module is used for constructing an interpretable machine learning risk prediction model and inputting the patient feature data and the radiomics parameters into the risk prediction model to generate a personalized radiodermatitis risk score for the patient. According to the invention, accurate prediction of acute radiodermatitis is realized.

The invention belongs to the field of prediction, and particularly relates to a drainage tube blockage risk prediction method and system, specifically, historical flow data and patient physiological parameters in a preset time window are obtained; extracting a flow macroscopic fluctuation degree, screening a matched patient subgroup from the reference database according to the similarity between the patient information and the flow fluctuation degree, constructing a multidimensional normal distribution model in a normal state, and calculating a mahalanobis distance between a current patient feature and the center of the model to obtain a baseline deviation degree; performing time-frequency analysis on the weighted flow sequence to obtain an energy spectrum, calculating physiological association weights of different frequency bands by combining with a blood coagulation function index, and further extracting energy distribution of a key frequency band in the weighted energy spectrum as a flow fluctuation feature; and inputting the baseline deviation degree and the flow fluctuation characteristics into a pre-training model, and outputting a drainage tube blockage risk value.