43 results about "Medical evaluation" patented technology

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 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).

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.

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 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.

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 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 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 application discloses a kind of intelligent effect evaluation methods for curcumin composite fiber wound dressings, belong to medical evaluation technical field, specifically include: by constructing the dynamic wound simulation environment of multiple parameter coupling;Complete cycle is run and the fiber topography, environmental pH, curcumin concentration and viable bacteria number data are synchronously collected by placing the dressing sample in the environment;According to the change of environment and flora data, the continuous characteristic stage is divided;The geometric characteristic parameter of fiber topography in each stage is extracted, and the structure-release correlation parameter is obtained by time-domain alignment and correlation analysis of corresponding curcumin concentration data;Based on the parameter and environmental pH data, the drug release-antibacterial synergistic coefficient of each stage is calculated by staging model;Finally, according to the change relationship of synergistic coefficient between continuous characteristic stages, generate dynamic evaluation atlas.The application realizes the precise quantification and staging evaluation of dressing intelligent response behavior in the simulated real wound dynamic complex environment.

The invention discloses a nerve-mediated syncope risk prediction method and system, and relates to the technical field of medical evaluation.The method comprises the steps that firstly, units are divided according to patient identity types and syncope scenes in a two-dimensional mode, and then a scene-syncope association model is constructed according to historical syncope records; through multi-dimensional data acquisition, matching of inducement features corresponding to user identity types, locking of associated suspicious syncope scenes, prediction of the risk of user syncope, when the risk exists, intervention measures are provided for the user, the intervention condition is monitored, and corresponding feedback is carried out at the same time, the method can identify multiple syncope scenes and multiple identity types, and the safety of the user is improved. The collaborative risk is quantified, the accuracy of risk level judgment is guaranteed, the prediction precision is greatly improved, early-stage and high-accuracy risk prediction is achieved, scene-identity-intervention association rules are established, the individuation and pertinence of intervention measures are improved, the intervention effect is guaranteed, and therefore the safety of a user is improved.

The invention discloses a getting-up walking experiment testing method and device based on binocular vision, and relates to the technical field of medical evaluation.The getting-up walking experiment testing device comprises a testing mechanism, a walking mechanism is arranged at the top of the testing mechanism, and shielding mechanisms A are arranged on the two sides of the walking mechanism; a shielding mechanism B is jointly arranged on any shielding mechanism A and the testing mechanism, a lifting mechanism used for adjusting the posture of the walking mechanism is arranged in the testing mechanism, and an unfolding mechanism used for adjusting the posture of the shielding mechanism A is arranged on the lifting mechanism. According to the method, related equipment does not need to be bound outside an old person during actual testing, operation steps are simplified, meanwhile, the conditions that the old person feels uncomfortable due to equipment binding and the testing precision is affected can be avoided, in addition, collection of flat ground walking data and uphill and downhill walking data can be completed, and the testing result is more comprehensive.

The invention relates to the technical field of medical evaluation, in particular to a painless endoscopic patient leave-room evaluation system based on multi-modal biological feature recognition, comprising: a multi-source data acquisition module configured to be capable of acquiring multi-source data of a patient entering a target area; the data fusion processing module is configured to be capable of acquiring the multi-source data from the multi-source data acquisition module and extracting a plurality of key features after preprocessing the multi-source data to form a key feature set; the decision module is configured to acquire the key feature set from the data fusion processing module and generate an evaluation report and an off-room decision by using a decision model according to the key feature set; and the traceability module is configured to construct a traceability framework and perform traceability on the evaluation report and the off-room decision based on the traceability framework.

The invention provides equipment, a method and a system for evaluating prognosis of cervical adenocarcinoma and squamous carcinoma and application of the equipment, the method and the system, and belongs to the technical field of medical evaluation. According to the invention, two biomarkers, namely adenylate kinase 4 and cavernous malformation 2 protein, for evaluating prognosis of cervical adenocarcinoma and squamous carcinoma 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 prognosis of cervical adenocarcinoma and squamous carcinoma are constructed, and the evaluation equipment, the computer execution method, the computer system and the like are used for accurately judging prognosis conditions of cervical cancer patients.

The invention relates to the technical field of medical evaluation. Provided is a coronary artery function evaluation device, comprising: a data acquisition module for synchronously acquiring multi-modal physiological data of a target coronary artery; the data acquisition module comprises a radiography image acquisition unit, a pressure waveform acquisition unit, an electrocardiogram acquisition unit and a blood flow velocity acquisition unit; the data processing module is in communication connection with the data acquisition module, is internally provided with a hemodynamic parameter calculation circuit and is used for performing filtering, fusion and parameter operation on the multi-modal physiological data and outputting an instantaneous blood flow state parameter, an instantaneous blood flow resistance parameter, a blood flow dynamic simulation parameter and a reserve function derivative parameter; and the evaluation module is in communication connection with the data processing module and is used for outputting quantitative evaluation data of the coronary artery function based on a preset hardware evaluation model. According to the device, the coronary artery blood flow and function can be comprehensively and quantitatively evaluated accurately, and the operation complexity and the cost burden are reduced while high accuracy is kept.

A system for automating patient condition analysis and prescription generation is disclosed, including an application server in operable communication with the user network to host an application program for providing healthcare services and enabling an interaction between a healthcare provider and a patient. A conversation engine analyzes and evaluates patient symptoms and patient medical information to identify a non-qualified condition, a qualified condition that allows for a prescription, and a high risk condition that necessitates a face-to-face medical evaluation. A comparator is in communication with the conversation engine to compare the patient symptoms and patient medical information to a clinical dataset to enable the determination of a medication from a medication dataset. A prescription generation module associates the medication with the patient to generate an e-prescription which is transmitted to a pharmacy.

The application belongs to the technical field of medical evaluation and detection, and particularly relates to a method for constructing an animal model of diabetes combined with kidney lipid injury. The method comprises the following steps: taking experimental animals, feeding the animals with high-fat feed for 3-5 weeks, feeding the animals with ordinary feed for 4-6 days in succession, injecting streptozotocin into the abdominal cavity of the animals, the amount of streptozotocin injected into each animal per day being 35-45 mg / Kg, and then feeding the animals with high-fat feed for 11-13 weeks, so that a disease animal of diabetes combined with kidney lipid injury is obtained. The disease animal model of diabetes combined with kidney lipid injury has high repeatability, prominent kidney lipid injury, and simple operation, and has important value and significance in basic research.

The invention relates to the technical field of medical evaluation, in particular to a recording device for teenager mental health evaluation, which comprises a shell, the top of the shell is rotationally matched with a selection rotating disc, and the bottom of the selection rotating disc is coaxially and fixedly connected with a main shaft; the bottom end of the spindle penetrates through the interior of the shell, the spindle is coaxially and fixedly connected with a main gear, and the main gear is circumferentially meshed with a plurality of driven gears; the bottoms of the driven gears are coaxially and fixedly connected with selection cams, the inner top wall of the shell is fixedly connected with a plurality of microswitches, and the selection cams make contact with the microswitches. Wherein the selection rotary disc is transmitted to different selection cams through a main gear and a driven gear, so that the different selection cams rotate to trigger a microswitch, and evaluation contents of different dimensions are loaded; the shell is further provided with a setting assembly used for setting the age and a recording assembly used for recording the evaluation content. Through mechanical transmission and visual operation design, the convenience is remarkably improved, and the preference of teenagers for entity interaction is met.

The application relates to the technical field of artificial intelligence and the field of digital medicine, and discloses a medical data processing method, device and equipment based on artificial intelligence and a medium, which comprises the following steps: preprocessing collected initial medical data to obtain a medical sample set; training a multi-modal basic model by using the medical sample set based on a self-supervised learning method to obtain an initial medical analysis model; fine-tuning the initial medical analysis model by using a multi-task learning strategy to obtain a first medical analysis model; optimizing the first medical analysis model by using a multi-objective optimization strategy to obtain a second medical analysis model; improving the second medical analysis model by using a feedback learning method to obtain a target medical analysis model; and evaluating target medical data based on the target medical analysis model to obtain a medical evaluation result. The medical analysis model can be stored in a blockchain. The application improves the evaluation efficiency of medical data and ensures the accuracy of the medical evaluation result.

The invention relates to a local anesthesia dosage control system based on CFD (Computational Fluid Dynamics) and PC-MRI (Personal Computer-Magnetic Resonance Imaging) coupling and a working method thereof, and the system comprises an information collection unit which is used for collecting physical examination and medical evaluation information of a patient; the geometric unit is used for acquiring a T2 weighted MRI image sequence; the calculation model unit is used for determining the range of a lumbar, thoracic and vertebral segment calculation domain; a measurement unit for phase encoding using PC-MRI; the cerebrospinal fluid dynamic unit is used for acquiring an initial flow field of cerebrospinal fluid flow of the spinal canal of the patient; the dosage selection unit is used for selecting the injection dosage of the local anesthetic; the intrathecal administration calculation unit is used for establishing coupling analysis to obtain a local anesthetic concentration value of a blocking plane; and the dose evaluation unit is used for quantitatively evaluating the concentration value of the local anesthetic. The system is beneficial for improving the accuracy and safety of local anesthesia medication.

The invention relates to the technical field of dynamic evaluation of spinal morphology, in particular to an intelligent evaluation method and system for children's spinal morphology based on big data analysis. Comprising the following steps: S1, acquiring 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; and taking a complete time period from the time point when the child spine form receives medical evaluation for the first time to the time point when the system confirms that the child spine function and form reach the steady state as an evaluation and diagnosis period, and distributing the static data and the dynamic data to the evaluation and diagnosis period to obtain a data-stage distribution result. According to the method, an intelligent evaluation system is constructed, the development of the spine of the child is accurately tracked, data acquisition and stage distribution are dynamically optimized through error analysis and similarity comparison, and individualized, high-efficiency and self-adaptive closed-loop diagnosis management is realized.

The invention discloses an elderly patient discharge standard evaluation system and method based on a standard reaching theory, and relates to the technical field of medical evaluation, the system comprises a data acquisition module, a preprocessing module, a hierarchical mapping evaluation module, a family function evaluation module and a comprehensive standard reaching judgment module; according to the invention, through the hierarchical mapping evaluation module, the basic standard reaching layer, the optimized standard reaching layer and the personalized standard reaching layer are divided based on the treatment process parameters, the refined evaluation of each link of the treatment process is realized, the standard reaching rate of each level can be quantified, and the overall standard reaching score can be calculated through a hierarchical mapping evaluation score algorithm; the standard reaching condition of the treatment process is visually reflected, and more importantly, when the standard reaching rate of any level is lower than a threshold value, the defects in the treatment link can be accurately positioned by marking short plate parameters, scientific support is provided for providing targeted improvement suggestions for medical staff, and therefore the treatment effect is improved, and it is ensured that a patient is discharged from a hospital safely.

In the disclosed systems and methods for categorizing medical data, a computer system obtains, in electronic form, a plurality of medical records. Each medical record includes corresponding medical data from a respective medical evaluation and corresponding metadata comprising a plurality of attributes about the respective medical evaluation. Each respective attribute comprises a corresponding string of text. The computer system determines, for each respective pair of medical records consisting of a first medical record and a second medical record, a corresponding pairwise similarity between, for each respective attribute in a set of attributes, the corresponding string of text for the first medical record and the corresponding string of text for the second medical record. The computer system identifies a first subset of the plurality of medical records. Each respective medical record in the first subset is connected to each other through pairwise similarities that each satisfies a similarity threshold.

Described is a method, comprising: receiving, a template electronic report configured to display information in a graphical format associated with one or more preferences of a physician; receiving, patient information associated with a medical assessment conducted on a patient of the physician; generating a summary electronic report by populating the template electronic report at least in part by processing the patient information and the one or more preferences of the physician with a trained machine learning model; wherein the summary electronic report is configured to display a summary of the medical assessment in the graphical format associated with the one or more preferences of the physician; wherein the summary of the medical assessment comprises a subset of information comprised within the complete electronic record of the medical assessment; and wherein the subset of information comprises at least one image.