46 results about "Reference intervals" patented technology

The invention provides a patient pathological data analysis and evaluation method for clinical nursing, and relates to the technical field of nursing informatization. Comprising the steps of collecting biochemical and pathological data of a patient and standardizing units and reference values, analyzing the dynamic trend of detection values and classifying change directions and amplitudes, matching pathological labels and establishing association with nursing items, comparing differences between nursing records and the pathological data and establishing a transverse index relationship, and judging whether the nursing task difference exceeds a threshold value or not and adjusting a nursing path generation scheme. By integrating multi-source pathological data and standardizing units and reference intervals, data consistency and comparability are ensured, the index fluctuation trend is dynamically tracked, the change direction and amplitude are quantitatively analyzed, disease course evolution is accurately captured, direct mapping between pathological categories and nursing items is established, logic binding is clear, intervention precision is improved, and the method is suitable for clinical application. Differences between nursing records and pathological data are transversely compared, nursing dynamic adaptation is optimized, a path scheme is rapidly adjusted through threshold judgment, and the lagging or misjudgment risk is reduced.

The invention provides a patient vital sign anomaly detection method based on an artificial intelligence technology, and relates to the technical field of data processing, and the method comprises the steps: collecting original sign data of a patient; calculating multi-dimensional characteristic parameters; establishing an individual baseline model, and determining a comprehensive reference interval in the model; the vital sign features monitored in real time are constructed into multi-dimensional feature vectors, the multi-dimensional feature vectors are input into the individual baseline model, and the deviation degree of the real-time multi-dimensional feature vectors in the comprehensive reference interval is calculated; performing a clustering analysis to identify an anomalous aggregation region; performing trend analysis, calculating change direction consistency and continuous change amplitude of the multi-dimensional feature vector, and generating a trend analysis result; calculating an accumulated change index in a continuous time window according to a trend analysis result to obtain a dynamic confidence score; when the dynamic confidence score continuously exceeds an adaptive threshold value, determining that a vital sign abnormal event exists; the autonomy and accuracy of the method for detecting the vital sign abnormity of the patient are improved.

The invention discloses a smart old-age service platform based on Internet of Things big data, and belongs to the technical field of Internet of Things old-age care, and the platform specifically comprises a voice instruction collection module which is used for collecting the interaction voice data of an old person and smart home equipment in real time; the voice feature extraction module is used for carrying out feature separation processing on the collected original voice data and extracting quantized instruction indexes; the baseline model construction module is used for establishing a voice health baseline model based on user characteristics and generating a personalized voice index reference interval according to the historical voice data of the old; the voice instruction comparison module is used for dynamically comparing the real-time instruction index with the baseline model, generating a voice health deviation degree curve, and triggering early warning when curve fluctuation exceeds a preset morphological characteristic; the interaction mode adjusting module is used for adjusting the interaction mode of the smart home equipment according to the voice health deviation degree; the health of the elderly is judged through the voice index change, and the smart home service quality is improved.

The invention relates to the technical field of data analysis, and particularly discloses a Down's syndrome screening system based on multiple key indicators, which comprises the following steps: analyzing historical sample data including pregnant woman age, body weight, race background, pregnancy history record, serum PAPP-A concentration, free beta-hCG concentration, neck transparent layer thickness and the like; mining a chromosome abnormality risk distribution rule; standardizing detection data by adopting a principal component analysis method, grouping pregnant woman features by utilizing a clustering algorithm, and determining a feature offset coefficient; establishing a personalized index reference interval by combining factors such as fetal structure abnormality, chromosome abnormality and family genetic disease history; generating personalized risk probability distribution by fusing individual features and ultrasonic measurement data; according to the method, the risk value is calculated and the dynamically adjusted risk prediction curve is generated according to the real-time detection data and the personalized reference interval, so that an accurate Down's syndrome risk assessment basis is provided for clinical decisions.

The invention relates to the technical field of health monitoring, in particular to a thyroid dysfunction detection system based on big data, which comprises a hormone data management module, a trend inflection point identification module, a section structure judgment module, a label dynamic updating module and a periodic evolution evaluation module. According to the method, continuous modeling is carried out on hormone concentration dynamic characteristics through multi-time-point data time sequence arrangement in hormone detection records, local extreme points of hormone values are recognized, key fluctuation inflection point positions are defined, an abnormal fluctuation range is recognized, and a reference interval ratio judgment mechanism is introduced to improve the abnormal recognition precision; the dynamic tracking of the function state change process is realized by recording the label evolution trajectory through the continuous state label comparison and updating process, the identification capability of potential function abnormality is enhanced by utilizing the quantitative evaluation of the individual state fluctuation stability, the identification efficiency and the monitoring timeliness of the key intervention individual are improved, and the monitoring accuracy is improved. And the continuity of thyroid function state change identification and the accuracy of intervention target identification are improved.

The invention provides a heart transplantation post-operation continuous physiological monitoring and anomaly recognition method and system based on wearable equipment, and relates to the technical field of physiological monitoring. According to the method, an individualized dynamic reference interval and a multi-index correlation coefficient matrix are constructed by collecting baseline data such as electrocardio, blood pressure and blood oxygen, abnormal state detection is achieved in combination with the Mahalanobis distance of a unified feature vector and a multi-task recognition model, and then a multi-physics field digital twin model is called to simulate hemodynamic evolution. And high-confidence abnormity is judged according to a coupling rule, so that the intellectualization and response efficiency of postoperative monitoring are improved.

The invention provides a control method and system for interaction between emergency simulation training and a real respirator, and the method comprises the steps: collecting control parameters of the respirator, and carrying out the preprocessing of the control parameters, and obtaining an equipment parameter vector; calculating a physiological response state based on the device parameter vector; the physiological response state comprises alveolar pressure and oxygenation estimation; based on the physiological response state, performing quantitative judgment on the disease stage of the patient to obtain a current disease stage, and executing a corresponding evolution instruction set; a medical specification operation reference table is constructed, a target interval is extracted from the current illness state stage and the evolution instruction set, the matching degree of the target interval and a reference interval is calculated, and the reference interval is extracted from the medical specification operation reference table; determining whether to continue evolution, enter relieving or mark training is completed based on the matching degree; the breathing gas of the patient is simulated and restored through the gas compressor and the gas mixing device and is conveyed to the real breathing machine.

The invention relates to a wearable device-based monitoring and early warning method and a wearable device, and relates to the technical field of health monitoring, and the method comprises the steps: collecting real-time data and basic information; generating a health reference model based on the basic information; generating a health benchmark interval and a health benchmark trend range based on the health benchmark model, and generating a health real-time reference value and a health real-time trend based on the real-time data; judging whether the health real-time reference value is within the health reference interval or not; if yes, trend early warning information is generated based on the health real-time trend and the health reference trend range, and the trend early warning information is output to control the wearable device to give out early warning and generate and store a health report; if not, real-time reference deviation information is generated based on the health real-time reference value and the health reference interval; and generating an early warning instruction based on the real-time reference deviation information and outputting the early warning instruction to control the wearable device to give an early warning. The method has the effect of meeting the real-time performance and accuracy of chronic disease management.

The invention provides a multi-dimensional dynamic continuous biological reference interval generation method and related equipment, and is applied to the technical field of data processing. The method comprises the steps of obtaining target user data; processing the target user data to generate to-be-monitored user data with identification information; a total training set matched with the identification information and a preset multivariate continuous biological reference interval model matched with the identification information are obtained, and the total training set comprises a training sample corresponding to a to-be-detected target item of the target user; preprocessing the total training set to generate a target training set and a target test set; training a preset multivariate continuous biological reference interval model based on the target training set and the target test set to generate a target multivariate continuous biological reference interval model; the to-be-monitored user data is processed based on the target multivariate continuous biological reference interval model, a prediction result is generated, and the prediction result comprises the detection state of the to-be-detected disease of the target user.

The application provides a complaint early warning threshold determination method and device, electronic equipment and a storage medium, comprising: determining a complaint amount reference value of each dotting according to historical complaint amount data, performing interval division on a complaint amount reference value interval, determining upper and lower limits of an early warning threshold of a reference value subinterval, and generating complaint early warning information according to a comparison result of a real-time complaint amount and the upper and lower limits of the early warning threshold of a target reference value subinterval in which the real-time complaint amount is located. The application fully considers the difference of complaint amount reference values in each period, performs interval division on the complaint amount reference interval in an indefinite section according to historical complaint amount data, and then determines whether the complaint amount of the current dotting is normal according to the upper and lower limits of the early warning threshold of each reference value subinterval. Since the complaint amount reference value interval is analyzed in a more fine-grained manner, the clustering analysis effect of the complaint amount reference value is greatly improved, and the accuracy of complaint early warning is effectively improved.

Provided are a method, an IoT system, and a storage medium for maintenance management of an auxiliary component based on smart gas. The method includes: determining a reference interval based on operation data of the auxiliary component; determining an operational intensity; determining an amplification coefficient; determining a first anomaly value of the auxiliary component based on the operational intensity and the amplification coefficient; determining infrared data of the auxiliary component based on the first anomaly value; determining a second anomaly value of the auxiliary component; determining a maintenance instruction and / or a parameter adjustment instruction, sending the maintenance instruction to a monitoring component and / or sending the parameter adjustment instruction to an interactive device; instructing a staff member to manually maintain the auxiliary component; adjusting a monitoring parameter of the monitoring component before completing the maintenance; and controlling the monitoring component to monitor the operation data of the auxiliary component.

This application discloses a system for evaluating the suitability of reference intervals for medical test items. The system for evaluating the suitability of reference intervals for medical test items evaluates the abnormality rate of the reference interval and the distribution of abnormality levels of test results through a preset reference interval and a reference interval obtained by calculating based on a set of physical examination data, and also evaluates the consistency between the preset reference interval and the literature reference interval of the test item, and then generates an evaluation result on the suitability of the preset reference interval for each test item. In this way, the system for evaluating the suitability of reference intervals for medical test items can evaluate the suitability of the preset reference interval, which is of great significance for value-based medicine. The data extracted by the system for evaluating the suitability of reference intervals for medical test items comes from real-world test data, without the need to recruit additional subjects for testing, and can greatly reduce the cost of evaluating the suitability of reference intervals.

The invention relates to the technical field of physiological signal analysis, in particular to a neuropediatric disease assessment system for real-time physiological signals, which comprises a resting feature extraction module, a mark generation module, an abnormality judgment module, an abnormal fragment division module and a disease fluctuation labeling module. According to the method, by collecting the physiological signals of the child patient in the specific period, extracting the amplitude peak value of the physiological signals in the resting state and constructing the dynamic amplitude feature reference, the fluctuation range of the individual physiological baseline of the child patient can be more accurately captured, and compared with simple statistical feature matching, the method has the advantage that the accuracy is higher. According to the method, the adaptive capacity to individual differences and physiological natural variability is improved, real-time changes of physiological signals are continuously monitored, whether the multi-channel signals continuously deviate from respective reference intervals or not is judged, continuous physiological abnormal events instead of transient accidental artifacts are effectively recognized, then the signal strength in the deviation time periods is deeply analyzed, and the accuracy of the physiological abnormal events is improved. And significant anomalies in intensity can be accurately identified.

The invention discloses a network equipment health degree evaluation method based on multi-modal fusion, and relates to the technical field of new-generation information. According to the scheme, a multi-modal acquisition sequence is constructed based on multi-source operation information, a time sequence incidence matrix is established according to acquisition identification parameters, and a target time difference set is generated; forming a dynamic reference interval based on the time difference set, and performing time interval remapping on each modal sequence; performing time weighted combination processing on the corrected modal data to obtain a feature mapping set with a time association weight; constructing a multi-modal joint feature representation matrix based on the feature mapping set and performing health degree judgment operation; based on the above steps, the method can achieve the time consistency constraint of the multi-modal features in an asynchronous collection and distributed operation environment, reduces the interference of time sequence drift on the health degree evaluation, and improves the stability and reliability of the operation state evaluation of the network equipment.

The application discloses a kind of based on protein-based line and relationship field reasoning's dog tumor risk prediction method and device.Step includes: data acquisition and uniform warehousing, obtain the sample data containing dog metadata and multiple protein quantitative values;Numerical pre-processing and canine context coding, logarithmic smooth transformation is carried out to protein value, and context base function vector is constructed;Canine individualized protein-based line reconstruction;Protein function module division and module abstract construction;Protein relationship field construction, based on standardized residual, form the relationship channel of protein, generate original evidence intensity;Inflammation confounding stripping, obtain tumor-specific evidence;Multi-branch evidence accumulation and hierarchical risk determination, calculate cross-module consistency and multiple branch scores, output risk stratification conclusion through gate threshold system.The application breaks through the limitation of uniform reference interval, effectively eliminates inflammation interference, the reasoning process is strong in explainability, significantly reduces the misjudgment rate and guarantees clinical safety, and AUC is stable and can reach 0.84.

The application discloses a gender and age-based test item reference interval construction system, which comprises the following steps: obtaining a set of health examination test data from a laboratory information system; removing duplicates and performing data screening on the set of health examination test data to obtain a set of candidate health examination test data; grouping the set of candidate health examination test data based on age and gender to obtain a grouping result; performing outlier processing and data distribution description on a data subset of each candidate health examination test data in the grouping result; determining a reference interval of each data subset of the candidate health examination test data by using a big data algorithm to obtain a plurality of reference intervals; and constructing a continuous reference interval based on the plurality of reference intervals. In this way, the test item reference interval is constructed based on real-world test data, which improves feasibility and reduces cost.

The present application relates to the technical field of medical data processing, in particular to a method for quickly evaluating thyroid stimulating hormone value, which comprises the following steps: S1, clinical data acquisition; S2, data quality control and feature construction; S3, double-task joint prediction; S4, age self-adaptive reference interval correction; and S5, output of explainable report. The present application compresses the TSH evaluation period from the traditional 2-4 hours to within 5 minutes, reduces the detection cost by about 80%, and greatly improves the accessibility of thyroid function abnormality screening.

This disclosure provides a method and electronic device for diagnosing bearing faults. The diagnostic method includes: acquiring historical vibration signals of the bearing in three channel directions to form a benchmark database; extracting several key influence features associated with bearing faults from each historical vibration signal in the benchmark database, and determining a reference interval corresponding to each key influence feature; acquiring actual vibration signals of the target bearing in the three channel directions; extracting several target features corresponding to the key influence features from the actual vibration signals; calculating the comprehensive deviation of the actual vibration signals based on the target features and the reference intervals; and determining the bearing fault feature vector and target fault characteristics in response to the comprehensive deviation exceeding a preset threshold. This disclosure ensures the accuracy of bearing diagnosis while meeting the real-time monitoring needs of unlabeled, multi-condition industrial applications, and can dynamically adapt to changes in operating conditions without supervision, thereby significantly improving the accuracy of bearing fault identification.

The application relates to a three-self laser inertial measurement unit fault detection method based on an interval value confidence rule base, which comprises the following steps: determining a plurality of key index information of a three-self laser inertial measurement unit, and setting a reference interval according to each key index information; activating corresponding rules in an optimized confidence rule base according to the reference interval to which each key index information belongs, so as to obtain a plurality of activated rules; fusing the plurality of activated rules according to the weight, confidence and availability of each activated rule, so as to obtain a rule synthesis result; performing utility conversion by using the rule synthesis result and the utility value of a detection result, so as to obtain a utility conversion result; judging whether the three-self laser inertial measurement unit is faulty according to the utility conversion result, so as to obtain a detection result. The fault detection method solves the problems of index combination rule quantity explosion and the uncertainty and unreliability of expert knowledge.

The present invention relates to the technical field of data analysis, and specifically discloses a Down syndrome screening system based on multiple key indicators. The system comprises: mining the risk distribution pattern of chromosomal abnormality by analyzing historical sample data, including maternal age, weight, ethnic background, pregnancy history, serum PAPP-A concentration, free β-hCG concentration, and nuchal translucency thickness; standardizing test data using principal component analysis, and grouping maternal characteristics using a clustering algorithm to determine a characteristic shift coefficient; establishing personalized indicator reference intervals based on factors such as fetal structural abnormalities, chromosomal abnormalities, and family genetic disease history; generating a personalized risk probability distribution by fusing individual characteristics with ultrasound measurement data; and calculating risk values ​​and generating a dynamically adjusted risk prediction curve based on real-time test data and the personalized reference intervals, thereby providing an accurate Down syndrome risk assessment basis for clinical decision-making.

The application discloses a coordinate conversion method and device, comprising: when finding the minimum reference line interval of a target point on a reference line, starting from the initial reference line interval, if the currently detected reference line interval is not the minimum reference line interval of the target point, calculating the projection of the target point to the first discrete point of the reference interval, determining the advancing step and the advancing direction according to the projection, and determining the new reference line interval through the advancing step and the advancing direction, and cyclically executing the above steps until the minimum reference line interval of the target point on the reference line is found. Thus, the projection of the target point to the first discrete point of the reference interval can point to the direction of the minimum reference line interval and the length of moving to the minimum reference line interval, so that the updated reference line interval is closer to the minimum reference interval of the target point on the reference line, the searching efficiency is improved, the time for finding the minimum reference line interval of the target point on the reference line is shortened, and the coordinate conversion efficiency is improved.

The invention discloses a method for establishing a reference interval of thyroid stimulating hormone in the early pregnancy stage of a pregnant woman. The method comprises the following steps: S1, collecting data and establishing a database; s2, performing data processing on the database obtained in the step S1, determining a data distribution type, and determining a reference interval; the interval combination obtained in the invention is convenient for assisting doctors in subsequent diagnosis and treatment, and in combination with the TSH reference interval, the pregnant women with thyroid diseases can be prevented from being neglected through the judgment standard.

The invention discloses a medical report interpretation method and device based on multi-modal dynamic knowledge fusion, and the method comprises the steps: recognizing a key region of a target medical report, and extracting a target physiological value index and a target text description index; inputting the baseline feature data into a physiological index interval prediction model to obtain a physiological index reference interval; calculating the physiological value index deviation degree according to the target physiological value index and the physiological index reference interval, and determining the abnormal degree of the physiological value index; key descriptors are extracted, and disease entities, standard text description and disease severity are inquired in the multi-dimensional medical knowledge graph; calculating the semantic similarity between the target text description index and the standard text description, and determining the abnormal degree of the text description index in combination with the disease severity; and determining a comprehensive anomaly score according to the physiological value index anomaly degree and the text description index anomaly degree. The method improves the accuracy and comprehensiveness of medical report interpretation, and can be widely applied to the technical field of artificial intelligence.

The invention relates to an AI posture analysis system which comprises a data acquisition module, a posture analysis module, a dynamic posture correction simulation module, a personalized scheme customization module and a user interaction module. According to the method, through algorithm design of a multi-dimensional deviation evaluation model and the like, accurate and comprehensive basic data support is provided for the system. In a feature extraction process, the module not only can capture a numerical value of a single index, but also can ensure that extracted features and healthy posture data of different ages and genders form effective contrast through a standardized reference interval and deviation calculation logic. The limitation that a traditional method only pays attention to the surface posture is avoided, and a more specific and traceable analysis basis is provided for subsequent problem diagnosis. The introduced multi-index collaborative deviation index formula breaks through the inherent mode of single-index independent evaluation in traditional diagnosis, the comprehensiveness of posture problem recognition is remarkably improved, and the diagnosis result is made to better fit the real posture health state.

The invention relates to an auxiliary diagnosis system and device for neurodegenerative diseases and a storage medium. Based on a single molecule immunoassay technology, the relevance between the ratio of a P-tau217 concentration value and an A beta 42 concentration value and the neurodegenerative disease is deeply analyzed, a reference interval is designed, and an efficient screening tool is further developed; and high-specificity, high-sensitivity and high-accuracy auxiliary screening of neurodegenerative diseases such as Alzheimer's disease, subcortical ischemic vascular dementia and frontotemporal dementia is realized.

The present application relates to the field of image analysis, and particularly relates to an artificial intelligence identification method for urodynamic examination images. The present application obtains urodynamic contrast images and monitoring point displacement information, calculates a velocity time domain curve based on the displacement information, and captures a steady state time period. The present application positions a key contrast frame through time sequence alignment, selects adjacent continuous frames to construct a reference frame group through a sliding window, identifies a reference frame group organization contour to generate a superimposed thermal map, determines a superimposed main contour through frequency threshold screening and contour closure processing, calculates the interval width perpendicular to the contour based on displacement extreme values, constructs a strip-shaped dynamic reference interval along the main contour, and performs local enhancement on the images in the tissue contour reference interval. The present application specifically considers dynamic interference caused by physiological movement, improves the key area development quality, improves the image processing efficiency, and helps doctors to improve the diagnosis speed and efficiency.

The invention relates to the technical field of power dispatching, in particular to a power dispatching-oriented multi-source data fusion analysis system, which comprises a reference interval construction module, a parameter offset identification module, a channel collaborative judgment module, an anomaly traceability analysis module and a grading alarm output module. According to the method, working condition characteristics are dynamically identified according to boundaries and periodic fluctuation trends of real-time and historical data sets, collaborative trend classification of parameter behaviors among different scheduling hierarchies is realized, and upper and lower layer traceability precision of abnormal parameters is improved by combining trend changes between parameters and nodes, subdividing offset types and collaborative marks; the change directions of power and current are globally and synergistically discriminated among power generation, interconnection lines and power distribution nodes, so that abnormal signals are not limited to local response, scheduling behaviors and historical operation records for mutual verification, and automatic tracking of a causal chain is realized by associating parameter abnormal time periods and operation event time sequences.

The invention is suitable for the technical field of artificial intelligence and document intelligent analysis, and provides an AI-based bid-invitation purchasing document cross difference analysis method and system, and the method comprises the steps: judging whether the current reverse retrieval triggering frequency of an auditor is located in a reference interval, and maintaining the current operation if the current reverse retrieval triggering frequency is located in the reference interval; if not, intervention is executed according to the reference interval, so that the reviewer keeps the reverse retrieval triggering frequency matched with the reference interval in the review of the target to-be-checked item. Compared with the prior art, the method disclosed by the invention not only reveals the internal association between the behavior parameters and the auditing performance, but also constructs an auditing mechanism capable of closed-loop learning and intelligent regulation and control, and provides a new technical path and theoretical basis for AI auxiliary document auditing.