73 results about "Medical evaluation" patented technology

The invention provides an electronic clinical medical assessment method and system for an ophthalmology diagnosis and treatment scheme, and relates to the technical field of medical information, and the method comprises the steps: 1, carrying out the multi-scale edge detection calculation of an input ophthalmology image-text mixed medical record image, and obtaining a medical record image; the method comprises the following steps: extracting a boundary contour of a text labeling area and a hand-drawn lesion schematic diagram through adaptive threshold segmentation and morphological closed operation, and establishing a coordinate mapping table containing text block circumscribed rectangular coordinates, geometric positions of key anatomical mark points of the schematic diagram and symbol spacing characteristics; 2, constructing a two-channel attention gating network based on the coordinate mapping table, fusing the semantic features of the text region and the morphological features of the schematic diagram through a dynamic weight distribution strategy, and generating a multi-modal fusion feature matrix with a spatial alignment relationship; according to the method, through multi-modal feature fusion, triangular verification region confidence regulation and control and three-dimensional topology modeling, analysis and structured output of ophthalmology image-text medical records are realized, and the spatial alignment of diagnosis and treatment information is improved.

The invention discloses a human pancreas islet function digital evaluation method and system based on an AI algorithm, and relates to the technical field of artificial intelligence medical evaluation, and the method comprises the steps: collecting and preprocessing multi-modal physiological data; extracting multi-source features to generate a low-dimensional input vector; performing dynamic weighted fusion based on a space-time attention mechanism; constructing a neural differential equation model in which a beta cell two-phase secretion mechanism is introduced, and outputting pancreas islet function evaluation indexes; and a dynamic evaluation report is generated through a visualization and early warning module. According to the method, the modeling accuracy of the pancreas islet function under the interference of complex behaviors can be improved, the individualized quantitative evaluation of function decline trend prediction and sensitivity scoring is realized, the clinical staging reference value and the model interpretation ability are enhanced, and the technical problems of low fusion precision, poor dynamic nature and lack of physiological constraints in the existing method are solved.

The invention relates to the technical field of medical evaluation, and provides an evaluation system and method for potential complications of the department of cardiology, and the method comprises the following steps: S1, collecting information data of a patient, and carrying out the data verification and data preprocessing; s2, based on the obtained information, classifying types of complications which may occur on the patient; and S3, based on a complication type classification result, risk factors are extracted, and a risk factor network is constructed. By classifying the complication types, constructing the risk factor network and comprehensively considering multiple factors to calculate the risk index, compared with a traditional assessment mode, the risk of potential complication of a patient can be assessed more comprehensively and accurately, misdiagnosis and missed diagnosis caused by incomplete assessment are reduced, and the risk assessment efficiency is improved based on the risk score, the risk index and the risk type. The drug dosage or the measure intensity is adjusted through the patient specificity index, the prevention measures are dynamically adjusted in combination with the treatment time, and the pertinence and effectiveness of the prevention scheme are improved.

The invention relates to the technical field of medical evaluation, and discloses an Alzheimer's disease early cognition evaluation system and method based on combination of traditional Chinese medicine and western medicine. The system comprises a tongue image acquisition module, a pulse condition fluctuation signal acquisition module, a western medicine information acquisition module, a data preprocessing module, a feature extraction module, a data fusion module, a comprehensive data set establishment module and an evaluation decision module, wherein the tongue image acquisition module acquires tongue images, pulse condition fluctuation signals, inquiry text data and inquiry sound information; and the western medicine information acquisition module performs multi-modal fusion on feature data of traditional Chinese medicine and western medicine to construct a comprehensive data set. According to the system, multi-modal data is collected by integrating a traditional Chinese medicine four-diagnosis module and a western medicine detection module, the multi-modal data is processed and fused through algorithms including attention mechanism image processing, signal analysis and the like, and then an evaluation model is trained through integrated learning, transfer learning and the like. The method comprises the steps of equipment deployment, data acquisition and processing, model training, evaluation application and the like, multi-dimensional accurate evaluation is realized by combining optimization of a generative adversarial network, fuzzy logic and the like, and technical support is provided for early diagnosis.

The invention discloses an aspect emotion triple extraction method for a large language model annotation data set, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out the initial triple labeling of a standardized text data set, calculating a cognitive bias index based on a labeling reference set, and generating a data set with a bias label; a dynamic attenuation suppression method is adopted to generate a bias mask matrix, exponential mask enhancement is performed on samples exceeding a preset bias threshold value, and bias mask enhancement representation is obtained; inputting the bias mask enhanced representation into a differentiable grammar parser, calculating a grammar dependency matrix by using an attention mechanism, optimizing a topological structure of the grammar dependency matrix in combination with structural entropy loss, and generating a boundary judgment matrix; and extracting candidate triple embedding based on a boundary judgment matrix, calculating a contradiction coefficient by using a quantum emotion entangled state, and generating a correction triple set. According to the method, the accuracy and availability of emotion recognition results in the fields of medical evaluation and the like are enhanced.

The invention discloses a rehabilitation medical assessment management system based on dynamic multi-scale adaptation and automatic calculation, and particularly relates to the technical field of medical assessment management. The assessment management system comprises a patient information acquisition module, a dynamic scale adaptation module, an automatic data acquisition and calculation module, a data analysis and report generation module, a rehabilitation management module and a database storage center, and the patient information acquisition module is used for acquiring patient information and storing the acquired information in the database storage center. According to the rehabilitation medical assessment management system based on dynamic multi-scale adaptation and automatic calculation, through dynamic multi-scale adaptation, the most suitable assessment scale combination can be selected according to individual differences and rehabilitation stages of patients, the rehabilitation conditions of the patients are comprehensively and accurately reflected, and the accuracy of assessment results is improved.

A Predictive Diagnostic Information Capability-Technology (PreDICT™) system (100) enables users including expert and nonexpert users to provide information regarding a condition of a subject and receive timely and accurate information regarding risk stratification, treatment options and other medical evaluation information. The illustrated system (100) generally includes a user device (102) for use by a user assisting a subject (104), a processing platform (108), and a network (106) for connecting the user device (102) to the processing platform (108). The system (100) may also involve an emergency response network (130) that includes public-safety answering points (PSAPs) (132). The processing platform (108) processes the sensor information and other information from the user device (102), determines risk stratification information as well as medical diagnosis and treatment option information based on machine learning technology, and provides output information to the user device to assist the user in treating the subject (104).

This invention is a method, device, or system to guide a person concerning how to operate a conventional phone to record more-accurate images of a pathology or injury (e.g. such as a wound or skin lesion) on a body for remote medical evaluation purposes. In an example, machine learning and / or artificial intelligence can be used to guide the person concerning how to move a phone along a virtual arcuate path or shape in space over, across, or around the pathology or injury. In an example, this guidance can be auditory, visual, and / or tactile.

The invention provides equipment, a method and a system for evaluating prognosis of cervical cancer, squamous carcinoma and adenocarcinoma and application of the equipment, the method and the system, and belongs to the technical field of medical evaluation. Two biomarkers, namely adenylate kinase 4 and cavernous malformation 2 protein, for evaluating prognosis of cervical squamous cell carcinoma and adenocarcinoma are obtained through multivariate statistical analysis, difference screening, COX regression analysis and other biosignal analysis methods. Based on the biomarker, evaluation equipment, a computer execution method, a computer system and the like for evaluating the prognosis of cervical squamous cell carcinoma and adenocarcinoma are constructed, and the evaluation equipment, the computer execution method, the computer system and the like are used for accurately judging the prognosis condition of a cervical cancer patient.

The invention belongs to the field of auxiliary data processing, and particularly relates to a guiding type auxiliary evaluation information processing method and system.The method includes the steps that a matching decision parameter set of an initial target and a standard knowledge base is constructed, and user portraits and decision parameters are optimized step by step in combination with a dynamic step-by-step guiding question and answer strategy; dynamically adjusting the question and answer guide frequency by adopting a probability sequence iteration updating mechanism; a matching target confidence coefficient sequence is calculated based on a decision judgment model, multi-round feedback optimization is achieved through threshold value comparison, when the confidence coefficient reaches the standard, an optimal strategy is output, and when the confidence coefficient does not reach the standard, step-by-step enhancement is executed circularly till a preset round threshold value is reached, and finally accurate decision making of medical evaluation is achieved. A dynamic discrimination process driven by confidence is adopted; the accuracy and reliability of an evaluation result in a complex target scene are remarkably improved, and meanwhile, an abnormal round control mechanism is provided to guarantee the integrity of a processing flow.

The invention discloses a clinical medicine practical skill multi-dimensional evaluation and analysis system, and relates to the technical field of clinical medicine education and skill evaluation, and the system comprises a multi-source data collection module which obtains operation and other multi-dimensional data; the data standardization preprocessing module unifies formats, corrects exceptions, encrypts and desensitizes; the data intelligent quantitative calculation module converts a quantitative result by using an algorithm; the multi-dimensional dynamic evaluation module constructs a three-dimensional model, sets three stages of periods, and combines AI and manual output scores; the personalized feedback module generates a report and a scheme and supports multi-scene data linkage; according to the method, multi-source data is integrated, the limitation of single data of traditional evaluation is broken through, the difficult-to-quantify ability is converted into objective indexes through an intelligent algorithm, a three-dimensional evaluation model and a three-level evaluation period are constructed, skill development is dynamically tracked, AI and manual rechecking are fused, efficiency and accuracy are balanced, and a personalized scheme and a multi-scene report are generated. And clinical medicine evaluation upgrading is promoted.

The invention relates to the technical field of medical evaluation, in particular to a basic drug use tendency evaluation method and device, a medium and a product. The method comprises the following steps: acquiring a disease type, a patient specific attribute and medication information of a target patient; according to a standard evidence-based intensity database, determining intermediate evidence-based intensity of each drug type in the medication information in disease types, patient specific attributes and medication schemes; obtaining attribute information of a target hospital seeing a doctor by the target patient, and adjusting the intermediate evidence-based intensity based on the attribute information to obtain the actual evidence-based intensity of the target patient; determining the average standard evidence-based intensity of each base drug type corresponding to the disease type; and calculating a difference value between the actual evidence-based intensity and the average standard evidence-based intensity as a base drug use tendency degree, and generating a drug use rationality evaluation result for the target patient according to the base drug use tendency degree. According to the invention, multi-dimensional evaluation can be carried out on the drug use rationality of the patient, and the accuracy of the evaluation result is improved.

An electrode placement device allows placement and fixation of electrocardiogram (EKG) electrodes. Proper adherence of electrodes to predetermined positions on the epidermal surface is facilitated by a plurality of elongated placement members extending from a base that align with the electrodes. Each electrode is biased against the patient sensing region, typically the chest of an EKG patient, by prongs flanking the electrode at the distal end of each of the placement members. Flanking prongs engage an outer perimeter of a flexible, skin placed electrode, while a gap between the prongs allows for a signal wire to the electrically conductive center. Problems with weak adhesion from sweat, dirt or poor adhesive are avoided by a modest biasing force imposed equally on all electrodes from manual pressure applied to the base.

Systems and methods for deriving composite scores are illustrated. One embodiment includes a method for optimizing a clinical trial configuration. The method derives item scores for each of a plurality of subjects where each: is based subject data corresponding to a randomized control trial; and answers items from a medical evaluation. The method identifies a parameter to optimize vectors of item weights, wherein: the vectors of item weights are derived using a mean-variance analysis; and each includes a non-negative number. The method determines, for each of the plurality of subjects, an initial composite score, from: one of the at least one vector of item weights; and the plurality of item scores. A resulting collection of composite scores includes the initial composite score determined for each of the plurality of subjects. The method applies the resulting collection of composite scores to implementing a clinical trial.

The invention belongs to the technical field of medical evaluation, and relates to a method for evaluating damage of air pollution to human skin health based on a PPO-LSTM fusion algorithm. The method comprises the following steps: acquiring multi-source data; preprocessing data, and constructing a multi-dimensional time sequence feature data set; establishing a PPO-LSTM fusion model, performing feature extraction and reinforcement learning optimization on the multi-dimensional time sequence features, and outputting skin health core indexes; performing prediction and quantitative evaluation on the functional states of the cuticle, the epidermal layer and the corium layer; generating a personalized nursing decision scheme for skin health maintenance; outputting corresponding early warning information and coping suggestions by judging the risk levels of the skin barrier function and the dermal metabolism function; and updating parameters of the skin health injury evaluation model. According to the method, multi-dimensional data are integrated in the same model at the same time, the evaluation dimension is more comprehensive, the accuracy of skin health injury evaluation is improved, and a complete closed loop from detection evaluation to nursing scheme generation and risk prompt is realized through decision making and early warning.

The present application relates to the technical field of dynamic evaluation of spinal morphology, and particularly relates to a child spinal morphology intelligent evaluation method and system based on big data analysis. The method comprises the following steps: S1: obtaining static data and dynamic data, dividing the static data into a high-priority evaluation data set and a basic reference data set, and dividing the dynamic data into a key change data set and an auxiliary monitoring data set; taking the complete time period between the time point when the child spinal morphology first receives medical evaluation and the time point when the system confirms that the child spinal function and morphology reach a steady state as an evaluation and diagnosis cycle, and distributing the static data and the dynamic data to the evaluation and diagnosis cycle to obtain a data-stage distribution result. The present application constructs an intelligent evaluation system, accurately tracks child spinal development, dynamically optimizes data collection and stage distribution through error analysis and similarity comparison, and realizes individualized, efficient and self-adaptive closed-loop diagnosis management.

The invention discloses a tumble risk assessment method and system based on individualized information, and relates to the technical field of medical assessment and artificial intelligence, and the method comprises the following steps: a data acquisition step, a support period detection step, an individualized feature fusion step, a gait period segmentation step, a feature extraction step, and a time sequence coding step. A risk assessment step; firstly, multi-channel plantar pressure time sequence data of a subject and individualized information of the subject are obtained, and then through five-layer progressive processing, the problems of supporting period self-adaptive detection, gait cycle precise segmentation, multi-dimensional feature extraction, time sequence dynamic modeling and self-adaptive model training are solved respectively. And finally, accurate individual fall risk assessment is realized. Thresholds are automatically adapted for subjects with different physiological features, the gait deterioration trend caused by fatigue is captured through periodic-level double-flow time sequence modeling, and self-adaptive personalized feature learning is achieved through cascaded personalized information layer-by-layer fusion.

The application discloses a medical quality quantitative evaluation method and system based on a multi-dimensional symbiotic value network. The method obtains inpatient indication evaluation results, medical connotation quality evaluation results, doctor diagnosis and treatment level evaluation results and their converged evaluation results; generates three-party fusion value evaluation results based on patient result value indexes, medical insurance purchase value indexes and doctor professional value indexes; generates cross-dimension collaborative driving indexes and four-column collaborative quality indexes based on the differences between multi-dimensional evaluation results; and introduces patient five-dimensional values to generate five-dimensional symbiotic evaluation results. The application can further convert basic medical evaluation results into three-party value fusion, cross-dimension consistency diagnosis and patient real value symbiotic evaluation results, and improve the quantification degree, state positioning ability and interpretability of inpatient diagnosis and treatment whole-process evaluation.

The invention discloses a method and system for monitoring carbon monoxide in real time during extracorporeal circulation, and relates to the technical field of medical monitoring, and the method comprises the following specific steps: light path construction and gas sampling: presetting distributed monitoring nodes in an extracorporeal circulation pipeline, integrating a corresponding laser light source and a photoelectric detector by each probe, sample gas of each node of a body is synchronously collected through the micro-fluidic chip and the micro-flow gas pump array; according to the invention, distributed monitoring nodes are constructed, multi-node synchronous detection is realized by adopting a tunable diode laser absorption spectrum technology, and multi-position synchronous gas sampling is realized by combining a micro-fluidic chip and a micro-flow gas pump array, so that the problems that real-time and non-invasive monitoring cannot be realized and space-time resolution capability is lacked in the prior art are solved; the spatial distribution difference of the carbon monoxide concentration of different parts of the extracorporeal circulation pipeline can be obtained in real time, the dynamic law of the carbon monoxide concentration along with time change is analyzed, and more comprehensive and accurate data support is provided for medical evaluation.

The present invention discloses a method for extracting aspect sentiment triples from a large language model annotated dataset, relating to the field of natural language processing technology. The method comprises the following steps: performing initial triple annotation on a standardized text dataset, calculating a cognitive bias index based on an annotation reference set, and generating a bias-labeled dataset; employing a dynamic attenuation suppression method to generate a bias mask matrix, performing exponential mask enhancement on samples exceeding a preset bias threshold, and obtaining a bias mask enhanced representation; inputting the bias mask enhanced representation into a differentiable grammar parser, calculating a grammatical dependency matrix using an attention mechanism, and optimizing the topology of the grammatical dependency matrix using structural entropy loss to generate a boundary decision matrix; extracting candidate triple embeddings based on the boundary decision matrix, calculating the contradiction coefficient using quantum emotional entangled states, and generating a corrected triple set. The present invention enhances the accuracy and usability of sentiment recognition results in fields such as medical evaluation.

The application provides a medical image segmentation method and a computer readable storage medium, and the medical image segmentation method comprises the following steps: obtaining multiple medical images, classifying the medical images into training images, verification images and test images, and performing change processing on the training images; performing centroid extraction on the training images; calculating the polar coordinates of the pixel points of the training images in a polar coordinate system under a rectangular coordinate system with the image centroid (c x ,c y ) as the center; performing segmentation on the changed images based on an improved UNet network; calculating the rectangular coordinates of the pixel points of the segmented images in a rectangular coordinate system under a polar coordinate system with the image centroid (c x ,c y ) as the center to obtain a result image in the rectangular coordinate system. After the above technical scheme is adopted, the efficiency of automatic medical evaluation can be greatly improved, and the pressure of manually segmenting medical images can be relieved.

1. Name of the design product: Graphical user interface for basic patient information of an electronic device. 2. Use of the design product: An electronic device. 3. Design key points of the design product: Lies in the graphical user interface. 4. Picture or photo that best shows the design key points: Front view. 5. The electronic device has a conventional design, and the rear view, left view, right view, top view and bottom view are omitted. 6. Use of the graphical user interface: This design shows the basic patient information interface of a medical evaluation and training system. Fill in the basic patient information in the middle area of the front view, and click the "Save" button in the lower area of the front view to enter the interface change state diagram.

In at least one embodiment, a hemoglobin measuring device may include a plurality of sensors, a display configured to present information, and a computing device in communication with the sensors and display and having a processor and a memory storing instructions executable by the processor. The instructions may include instructions to receive, from the sensors, measurements of one or more forms of glycated hemoglobin in a blood samples, determine a total fetal hemoglobin value in the blood sample by performing a peak area summation of all forms of fetal hemoglobin, subtract the total fetal hemoglobin value from 100 to derive a total adult hemoglobin value, determine a glycated fetal hemoglobin value, compare the glycated fetal hemoglobin value to a reference range to obtain a comparison, determine a baseline glucose control value based on the comparison, and output the baseline glucose control value and the comparison to a display for medical evaluation.

The present invention relates to the technical field of healthcare systems, and particularly to a medical evaluation system and method for infectious and endemic disease patients based on feature analysis. The present invention performs disease condition matching analysis based on the stable operation data in the disease condition data of patients during examinations and the equipment operation data of medical examinations, performs infectious anomaly analysis based on the infectious characteristic data of infectious diseases and the virus disinfection level data in the equipment operation data of medical examinations, and performs medical equipment inspection evaluation based on the results of the disease condition matching analysis and the infectious anomaly analysis. This application comprehensively performs medical equipment inspection matching analysis based on the results of the disease condition matching analysis and the infectious anomaly analysis, and can reasonably arrange the use of medical equipment according to the disease conditions and infection risks of patients. For patients with severe diseases and high infectivity, they are preferentially arranged for examinations on equipment with good virus disinfection conditions to avoid cross-infection.

The invention discloses an intelligent effect evaluation method for a curcumin composite fiber wound dressing, and belongs to the technical field of medical evaluation, and the method specifically comprises the following steps: constructing a multi-parameter coupled dynamic wound simulation environment; placing the dressing sample in an environment to run for a complete period, and synchronously collecting data of fiber morphology, environment pH value, curcumin concentration and viable count; dividing continuous characteristic stages according to environment and flora data changes; extracting geometrical characteristic parameters of the fiber morphology at each stage, and performing time domain alignment and correlation analysis on the geometrical characteristic parameters and corresponding curcumin concentration data to obtain structure-release correlation parameters; on the basis of the parameters and environmental pH value data, calculating a drug release-antibacterial synergistic coefficient of each stage through a staged model; and finally, generating a dynamic evaluation map according to the change relation of the collaborative coefficient between the continuous characteristic stages. According to the invention, accurate quantification and staged evaluation of dressing intelligent response behaviors under the condition of simulating a real wound dynamic complex environment are realized.

The invention provides an aortic valve calcification degree evaluation method based on autonomous learning, and relates to the technical field of medical evaluation, and the method comprises the steps: collecting medical image data containing an aortic valve; segmenting and identifying an aortic valve region in the medical image data based on a feature extraction algorithm of an adaptive pixel distance; performing quantitative analysis on the calcification degree of the segmented aortic valve region by using a pre-trained deep learning model; and outputting a calcification degree evaluation result of the aortic valve. Through the scheme of the invention, the accurate and efficient automatic evaluation of the aortic valve calcification degree can be realized.

The application discloses a fall risk assessment method and system based on individual information, and relates to the technical field of medical assessment and artificial intelligence, and comprises the following steps: a data acquisition step, a support period detection step, an individual feature fusion step, a gait cycle segmentation step, a feature extraction step, a time sequence coding step, and a risk assessment step. First, multi-channel foot pressure time sequence data of a subject and individual information of the subject are acquired, then through five-layer progressive processing, problems of support period adaptive detection, gait cycle accurate segmentation, multi-dimensional feature extraction, time sequence dynamic modeling and adaptive model training are solved respectively, and finally precise individual fall risk assessment is realized. The application automatically adapts to the threshold of subjects with different physiological characteristics, captures the gait deterioration trend caused by fatigue through cycle-level double-flow time sequence modeling, and realizes adaptive personalized feature learning through layer-by-layer fusion of cascaded individual information.

The invention relates to the technical field of artificial intelligence, in particular to a fracture healing period callus growth dynamic evaluation method based on multi-modal data. The method comprises the following steps: firstly, obtaining a callus evaluation index by combining a medical evaluation index under a callus growth condition in a fracture healing period and subjective evaluation of a doctor on a patient; analyzing the dynamic change of each type of modal data and the callus evaluation index, and determining the value of each type of modal data for callus growth evaluation and the relative importance of each type of modal data for callus growth evaluation; according to the value of each type of modal data for callus growth evaluation and the relative importance of each type of modal data for callus growth evaluation, determining the contribution weight of each type of modal data; and carrying out weighted feature fusion on the contribution weight of each mode at each time and the corresponding feature vector, inputting an obtained fusion feature sequence into the time sequence model, and outputting a callus growth evaluation result. According to the method, the weight of each modal is adaptively adjusted in different healing stages, and the evaluation quality of callus growth in the fracture healing stage is improved.

A system and method for clinical disorder assessment are disclosed. The method and the medical assessment system using the method include: obtaining sensor data indicative of movement of a user; generating a movement dataset by reducing dimensions of the sensor data; generating a plurality of submovement datasets based on the movement dataset; extracting a movement feature from a first subset of the plurality of submovement datasets; analyzing the movement feature from the first subset of the plurality of submovement datasets to a reference to determine a potential clinical disorder of the user; and generating a report that includes an indication and severity of the potential clinical disorder of the user. Other aspects, embodiments, and features are also claimed and described.

The invention belongs to the technical field of rehabilitation medical evaluation, and particularly relates to a spasm evaluation method and device based on double-threshold decision. A rehabilitation doctor drives a patient to bend and stretch an upper limb elbow joint in a cycle at a proper speed, and electromyographic signals x (t) of biceps brachii or triceps brachii of the patient are collected through an electromyographic signal collection module; the joint angle theta (t) of the elbow joint of the patient is collected through an angle measurement sensor; an impedance force measuring module (1) installed on the forearm of the patient is used for collecting impedance force F (t) when the elbow joint stretches or bends; according to the method, multi-dimensional information in the passive flexion and extension process of a subject is utilized, the characteristic that nerve reflex is earlier than biomechanical reflex is utilized, and the time difference of spasm reactions of the nerve reflex and the biomechanical reflex is controlled to be between-30ms and 200ms, so that false positive judgment is avoided, the spasm occurrence time and the joint angle value during spasm occurrence can be accurately and timely judged, and the joint angle value can be accurately and accurately judged. A doctor only needs to drive a patient to do stretching / buckling movement once, and then spasm evaluation can be completed.