18 results about "Laboratory Test Result" patented technology

The invention discloses a multi-agent large-model disease diagnosis knowledge reasoning system based on data dual drive, and relates to the technical field of artificial intelligence assisted medical diagnosis. The system collects patient symptom follow-up records, laboratory test results, observation diagnosis probabilities and expert diagnosis recommendation results in a multi-source manner; time sequence evolution characteristics are extracted, a time sequence diagnosis sensitivity coefficient is calculated, and early recognition of disease risks is achieved; in combination with anti-fact simulation and statistical reasoning, a causal consistency coefficient is obtained and is used for verifying causal reasonability of observation diagnosis and contrast results; based on agent group consensus analysis, calculating a game consistency coefficient for judging the credibility of a diagnosis conclusion; positioning and multi-level verification are carried out on abnormal reasoning steps and knowledge fragments, so that the reliability and safety of a result are guaranteed; continuous optimization of the diagnosis model is realized through a log analysis and knowledge backflow mechanism; according to the invention, the accuracy, interpretability and safety of disease diagnosis can be obviously improved.

The invention relates to the technical field of clinical medical treatment, and discloses a disease deterioration risk identification method and system based on depth time sequence clustering enhancement, and the method comprises the steps: 1, obtaining multi-modal clinical sequence data of a patient, including physiological indexes of a time sequence, a laboratory detection result, historical diseases and medication data; 2, performing feature extraction and classification on the patient sequences by adopting a knowledge enhanced sequence clustering method, and grouping the patient sequences according to future outcome distribution of the patient sequences; 3, enabling the model to quickly adapt to a prediction task of a new patient subgroup through a meta-training process; and step 4, based on the trained meta-model, carrying out rapid adaptation on the new patient subtype, and predicting the possibility that the new patient subtype has a deterioration event in a certain time window in the future. The method and the system can effectively learn the disease change mode of the patient under the condition of limited clinical data, improve the prediction accuracy of the new patient subgroup, and are especially suitable for clinical prediction scenes under the condition of small samples.

The invention discloses a carbonate rock pore retention mechanism simulation method under an ultra-deep heat-fluid-solid coupling effect, and relates to the technical field of rock physics. The method comprises the following steps: preparing a carbonate rock sample, obtaining a CT scanning slice image of the carbonate rock sample, extracting three-dimensional pore information, preparing a plunger sample in a laboratory, carrying out a quasi-triaxial experiment, calibrating mesoscopic parameters by using a laboratory test result, constructing a carbonate rock model, and arranging a measuring ball in the carbonate rock model, the method comprises the following steps: dynamically monitoring the change of porosity and pore pressure, determining a strength reduction function of carbonate rock through a laboratory experiment, applying a true triaxial compression servo environment in which a temperature field and a stress field cooperatively change to a carbonate rock model, and performing true triaxial experiment numerical simulation by using the carbonate rock model. The internal pore condition of the carbonate rock in each evolution stage is obtained, the pore retention mechanism of the carbonate rock under the ultra-deep heat-fluid-solid coupling effect is determined, and technical support is provided for research of carbonate rock reservoirs.

The present disclosure relates to techniques for preprocessing samples and using preprocessed samples and machine learning models to predict clinical diagnostic tests for a patient from their historical laboratory testing data. Particularly, aspects are directed to obtaining datasets including features and / or historical laboratory test results for subjects, filtering the datasets based on a denoise-balance scheme to obtain filtered datasets, training a machine learning model using the filtered datasets to obtain a trained machine learning model, and providing the trained machine learning model. A candidate machine learning model may be an ensemble of classifiers implemented with a boosting algorithm, and the ensemble is trained by applying base machine learning algorithms on different distributions of the filtered datasets. The ensemble is then combined into a machine learning model having the set of learned model parameters for predicting results for clinical diagnostic tests.

The invention discloses a rock physical forward modeling method applicable to formation pore fluid of carbon dioxide, and relates to the technical field of petroleum and natural gas engineering. The method comprises the steps of firstly obtaining a carbon dioxide physical property constraint condition based on a state equation of carbon dioxide under a reservoir temperature and pressure condition, and then based on the constraint condition and according to logging information of a target well, core analysis and a laboratory test result, carrying out formation evaluation to calculate reservoir parameters. Then, based on reservoir parameters, the sandstone content is obtained through calculation, a rock physical volume model of a target well is constructed, and finally, based on the model, corresponding rock physical forward modeling is conducted according to different carbon dioxide saturations by means of the equivalent medium theory to generate stratum elastic attribute parameters. And the change relation between the corresponding saturation and the stratum elastic attribute parameters is quantitatively represented, so that the CO2 quantitative monitoring precision and reliability can be remarkably improved by comprehensively considering the special physical properties of CO2, the fluid replacement mechanism and the seismic wave propagation characteristics.

The invention relates to an infectious tophus ulcer wound nursing scheme assessment method and system, and the method comprises the steps: collecting the standardized electronic medical record data of a patient through a medical information system, including demostatistics, wound characteristics, laboratory inspection index sequences and nursing records; after structured processing, timestamp unification and missing value filling are conducted on the data, the data are input into a pre-trained deep learning model, expected wound healing probabilities, predicted healing time and complication risk scores corresponding to different nursing schemes are output, then a comprehensive assessment report is generated, the report is stored in a database, and a visual query interface is provided. And meanwhile, collecting actual nursing result data for continuous optimization and updating of the model. According to the invention, quantitative evaluation and prediction of the nursing scheme are realized, clinical decision making is effectively assisted, and the accuracy of nursing management and the resource utilization efficiency are improved.

The present application relates to the field of oil and gas field development engineering, disclose a kind of intelligent optimization method for discrete element contact parameters of proppant particle and flexible fiber mixture, comprising: based on the principle of injection method angle of repose experiment, build micro funnel device, reduce the scale of experiment under the premise of keeping the original size of material, improve the efficiency of calculation;Adopt "ball column chain + joint" fiber model, coupling JKR adhesion model represents the interfacial adhesion between particles, establish high-precision discrete element simulation model;Through Plackett-Burman, steepest climbing experiment screening key influencing factors, for the problem that each factor and angle of repose is nonlinear mapping relationship, construct GA-BP model, combine GA algorithm optimization, preferred parameter combination, compare the results of discrete element simulation and laboratory test results, verify the accuracy of contact parameters.The present application solves the problem that the existing method is difficult to calibrate mixture, improves the precision and efficiency of parameter calibration, and is helpful to the research and development of hydraulic fracturing process.

The invention discloses a cytarabine syndrome risk prediction model, a training method thereof and a system adopting the same. The model training method comprises the following steps: constructing an artificial intelligence model; wherein the input of the artificial intelligence model comprises the weight and the heating duration of the to-be-evaluated object; the output of the artificial intelligence model is the risk probability that the to-be-evaluated object belongs to the cytarabine syndrome fever; large sample data are adopted to train the artificial intelligence model, cases with fever of the cytarabine syndrome are positive samples, and other fever cases are negative samples. According to the present invention, the identification standard of the cytarabine syndrome fever and the infected fever is established, and the risk probability of the cytarabine syndrome of the object to be evaluated can be evaluated by analyzing the difference of the two detection results in the laboratory, such that the unreasonable use of the antibacterial agent is reduced, the medical cost is reduced, and the drug resistance risk of the antibacterial agent is reduced.

This invention provides a method for calculating the critical point of fatigue damage in full-thickness asphalt pavement, relating to the field of road engineering technology. The invention first divides the full-thickness asphalt pavement into multiple sub-layers, then obtains its annual temperature field and annual axle load spectrum, determines the temperature model of the full-thickness asphalt pavement and the number of equivalent axle load applications per hour, and combines laboratory test results to obtain the dynamic modulus calculation equations and multi-temperature fatigue equations for each sub-layer. The temperature of each sub-layer is obtained using the full-thickness asphalt pavement temperature model, and the dynamic modulus value of each sub-layer is determined using the dynamic modulus calculation equations. Then, the flexural strain response of each sub-layer is calculated based on its dynamic modulus value, and its fatigue life is calculated using the multi-temperature fatigue equations for each sub-layer. Combined with the number of equivalent axle load applications per hour, the fatigue damage value of each sub-layer is calculated and accumulated to obtain the cumulative fatigue damage of each sub-layer in the full-thickness asphalt pavement, accurately obtaining the critical damage point of the full-thickness asphalt pavement.

This invention discloses a method and system for processing clinical medical laboratory data based on big data, belonging to the field of clinical data analysis technology. First, clinical laboratory data and medical record data are collected from a hospital database. The clinical laboratory data undergoes structured preprocessing to construct a word co-occurrence matrix. Based on the entity relationships between the clinical laboratory data, clinical feature vectors are generated. A knowledge graph is established based on the medical record data. Based on the knowledge graph, the clinical feature vectors are classified using an improved ConvE model to obtain laboratory test results data. Finally, a disease probability processing engine is constructed to characterize the laboratory test results data and output the final diagnostic medical record. This invention solves the problem that the model's diagnostic logic does not conform to medical common sense, making the AI's diagnostic reasoning process closer to that of human experts.

The invention relates to the technical field of rubber puncture resistance, and discloses a rubber material puncture resistance testing device and evaluation method based on pavement simulation, and the evaluation method comprises the following steps: S1, sample preparation; s2, performing a puncture test; s3, extracting parameters; s4, calculating a motion state correction coefficient of the rubber sample, and performing primary correction on the puncturing energy consumption by using the motion state correction coefficient; s5, calculating a puncture speed correction coefficient of the to-be-detected probe, and performing secondary correction on puncture energy consumption by using the puncture speed correction coefficient; and S6, replacing the to-be-tested probe or the rubber sample, and repeating the steps S2 to S5 to obtain performance data under different temperatures and different puncture forms. Therefore, the puncturing energy consumption is continuously corrected twice based on the motion state correction coefficient and the puncturing speed correction coefficient of the rubber sample, the actual working condition mapping and the rate dependence are considered, the actual working condition of the tire is simulated as much as possible, and the prediction capability of a laboratory test result on the real use performance is further effectively improved.

The application discloses a joint wellbore casing deformation-acoustic emission b-value fracture activation condition multidimensional judgment method, which comprises the following steps: based on casing deformation analysis, preliminarily statistically controlling law of fracture own properties on fracture activation, obtaining acoustic emission b-value of experimental scale critical activation point, and constructing a fracture activation condition judgment method combining casing deformation analysis and acoustic emission b-value; the application considers the influence law of multiple properties of the fracture itself on casing deformation intensity, combines wellbore casing deformation information with acoustic emission sequence b-value of the experimental scale critical activation point, and extrapolates the experimental results to geological conditions by taking the underground statistical law as a constraint, thereby solving the problem that the laboratory test results are too idealized, simultaneously considering the multi-solution of the actual geological engineering data, and realizing quantitative characterization of the fracture activation, which greatly improves the accuracy and applicability of the activation condition prediction in theory and method, and meanwhile, the wellbore casing deformation information and fracture geometric data are easy to obtain, and the application range is wide.

The application discloses a kind of simulation methods of carbonate rock pore retention mechanism under super deep thermal fluid-solid coupling, it is related to rock physics technical field.The application is prepared by preparing carbonate rock sample, obtains its CT scanning slice image extraction three-dimensional pore information, after laboratory preparation plunger sample is carried out pseudo triaxial experiment, laboratory test result is used to calibrate micro parameter after construction carbonate rock model, cooperate in carbonate rock model and layout measuring ball, dynamically monitor porosity and pore pressure variation, by laboratory experiment, determine the strength reduction function of carbonate rock after, carbonate rock model is applied temperature field and stress field collaborative change true triaxial compression servo environment, using carbonate rock model carries out true triaxial experiment numerical simulation, obtains the pore condition inside carbonate rock of each stage evolution, determines the pore retention mechanism of carbonate rock under super deep thermal fluid-solid coupling, provides technical support for carbonate rock reservoir research.

In the diagnosis process, a clinician makes full use of multi-modal information such as chief complaint, medical images and laboratory test results. A deep learning model used for auxiliary diagnosis does not meet the requirement. In some aspects, a transformer-based representation learning model may be used as a clinical diagnostic aid for processing multi-modal inputs in a unified manner. The model does not learn modal-specific features, but uses an embedded layer to convert an image, an unstructured text and a structured text into visual lexical elements and text lexical elements. And using bi-directional blocks with intra-modal and inter-modal attention to learn an overall representation of radiographs, unstructured chief complaints and medical records, structured clinical information, such as laboratory test results and patient demographic information. The performance of the unified model in the aspect of lung disease recognition exceeds that of an only image model and that of a non-unified multi-modal diagnosis model (respectively improved by 12% and 9%), and the performance of the unified model in the aspect of COVID-19 patient adverse clinical outcome prediction exceeds that of the only image model and that of the non-unified multi-modal diagnosis model (respectively improved by 29% and 7%). A unified multi-mode transformer-based model is fully utilized, so that a patient triage process can be simplified, and a clinical decision-making process is promoted.

The invention provides a method and system for checking the sensitivity of a sensor emitting ultrahigh frequency by using an iron core, and the method comprises the following steps: carrying out a laboratory test, and determining the relation between the injection signal intensity of the iron core of a transformer and the apparent discharge capacity; a nanosecond ultrahigh-frequency pulse generator is arranged in combination with a laboratory test result; injecting an ultrahigh-frequency pulse signal into a transformer core by using the nanosecond ultrahigh-frequency pulse generator, and collecting an output signal of the ultrahigh-frequency sensor to be detected; processing the output signal; checking the sensitivity of the ultrahigh frequency sensor to be tested according to the processed output signal; and optimizing the arrangement of the ultrahigh frequency sensor to be tested or replacing the sensor according to the checking result. According to the invention, the test process is simplified through field verification, and the accuracy and practicability of sensitivity verification are significantly improved.

The application discloses a multi-agent large model disease diagnosis knowledge reasoning system based on data double driving, relates to the technical field of artificial intelligence assisted medical diagnosis, and through multi-source collection of patient symptom follow-up records, laboratory test results, observation diagnosis probability and expert diagnosis recommendation results, the system extracts time sequence evolution characteristics and calculates a time sequence diagnosis sensitivity coefficient, realizes early identification of disease risk; combined with counterfactual simulation and statistical reasoning, a causal consistency coefficient is obtained, which is used for verifying the causal rationality of observation diagnosis and control results; based on agent group consensus analysis, a game consistency coefficient is calculated, which is used for judging the credibility of the diagnosis conclusion; abnormal reasoning steps and knowledge fragments are positioned and verified in multiple levels, so that the reliability and safety of the results are guaranteed; through log analysis and knowledge backflow mechanism, the diagnosis model is continuously optimized; the application can significantly improve the accuracy, explainability and safety of disease diagnosis.

The invention discloses a pulmonary tuberculosis recognition system based on multi-modal fusion and expert-assisted optimization, which belongs to the technical field of artificial intelligence and comprises a data acquisition module, an imaging examination image, a laboratory examination result and an electronic medical record. The data preprocessing module is used for extracting a first feature by utilizing an iconography examination image, extracting a second feature by utilizing a laboratory examination result and extracting a third feature by utilizing an electronic medical record; the third feature comprises 32 sub-features and contribution degrees corresponding to the sub-features; the optimization module is used for optimizing the contribution degree based on expert diagnosis experience; and the fusion module is used for performing modal fusion on the first feature, the second feature and the optimized third feature to generate a pulmonary tuberculosis recognition result. According to the method, imaging examination images, laboratory examination results and electronic medical records are fused, the contribution degree of expert experience to the features is optimized, intelligent detection of pulmonary tuberculosis is finally achieved, and the method has good generalization ability and pulmonary tuberculosis detection precision.

This invention relates to a method and system for early disease warning and diagnosis in large-scale pig farms, belonging to the field of pig farming technology. This method utilizes knowledge graphs and abnormal features for comprehensive disease risk assessment, determining the target disease type and current disease stage of the target pigs. Based on this, an early warning is generated. An initial treatment plan is generated based on the warning information, disease type determination, and disease stage determination. On-site diagnosis and treatment are then performed on the target pigs, and samples are collected and sent for testing, forming diagnostic verification data. This invention unifies and links the initial warning record, initial diagnosis result, on-site actual treatment execution information, re-examination results, and laboratory test results of the target pigs, forming a verification data chain for individual cases. This provides a basis for subsequent verification of warning and diagnosis results, reducing the accumulation of misjudgments caused by the inability to verify model outputs alone.