350 results about "Blood testing" patented technology

Methods and systems for predicting the onset and progression of neurodegenerative diseases using blood test data and machine learning models are disclosed. The method involves receiving blood test values for a target subject over a previous time period, extracting features from the blood test data, and applying a trained predictive model to compute a risk score indicating the probability of developing a neurodegenerative disease. The system includes a processor that executes code to perform the method and can be implemented as a standalone application, cloud-based service, or integrated with healthcare systems. The predictive models are trained using supervised learning on labeled data and can include statistical models or machine learning algorithms. The systems and methods enable early detection of neurodegenerative diseases, personalized risk prediction, and integration with existing healthcare infrastructure. By leveraging blood test data and machine learning techniques, patient outcomes may be improved.

The present invention relates to a blood testing machine comprising a loading device, a retrieving device, a batching device, a transfer device, and a testing device. After a blood sample is collected, a blood collection tube and other materials are placed in the loading device. After the loading device loads the sample, the retrieving device clamps the blood collection tube and other materials on the loading device and moves them to a set position in the batching device. The blood in the blood collection tube is sampled by the batching device and the reagents required for the test are added to form a test specimen. The transfer device transfers the test specimen to the testing device for blood testing. Compared with the prior art, the present invention substantially automates the steps of blood testing, including sample transportation, sample reception and processing, on-board testing, and sample storage, greatly improving the accuracy and speed of testing.

The invention provides a blood transfusion treatment scheme recommendation system and method based on historical information, and relates to the field of medical information.The blood transfusion treatment scheme recommendation method comprises the steps that firstly, basic information and diagnosis information of a patient, real-time blood test information and historical blood transfusion medical record information are extracted from HIS, LIS, EMR and TMIS systems; performing joint semantic analysis on the basic information, the diagnosis information and the real-time blood test information, and converting the historical blood transfusion medical record information into a dynamic vector sequence; then, the current blood index and the historical blood transfusion time sequence are dynamically matched in a frequency domain space through nonlinear spectrum analysis, and a personalized recommendation scheme is generated in combination with the implicit reasoning ability of a pre-trained medical large model, so that the limitation of a traditional system in the aspects of static parameter setting, individual adaptability and semantic mining is broken through; and the accuracy and timeliness of blood transfusion treatment decision making are remarkably improved.

The invention relates to application of pirfenidone in preparation of testicular organs, and the pirfenidone can improve one or more properties and / or functions of the testicular organs, especially improve blood testis barriers and improve spermatogenesis efficiency. The invention also provides a method, a culture medium and a kit for preparing the testicular organoid.

The invention discloses a method for predicting and judging the diarrhea risk grade of suckling piglets, and belongs to the technical field of piglet epidemic disease prevention and control. Aiming at the problems that traditional piglet diarrhea prediction depends on manual observation, multi-dimensional data relevance is insufficient, early warning lags and the like, a multi-parameter monitoring device is arranged in a piglet breeding environment to collect environment data in real time, and an excretion behavior detection module is used for triggering an excrement image collection device to obtain a high-quality image; and inputting multi-source data into a random forest model for risk level prediction by combining regular weight measurement and blood detection in a specific time period after environment adjustment. The system dynamically adjusts pig environment parameters according to a prediction result, after spraying, heating compensation is started to maintain humidity and control ammonia concentration, and closed-loop feedback regulation is formed. According to the method, through multi-modal data fusion and model iterative optimization, early-stage accurate early warning of diarrhea risks is realized, the method is suitable for intelligent management of a large-scale pig farm, the diarrhea occurrence rate can be effectively reduced, and the breeding benefits are improved.

The invention discloses a phosphatidylcholine gold nano CT (Computed Tomography) probe as well as a preparation method and application thereof, and belongs to the technical field of medical materials. The preparation method comprises the following steps: step S1, preparing a phosphatidylcholine ligand NH2-MPC; step S2, synthesis of a gold nanocluster GS-Au25 is carried out; and S3, the GS-Au25 is modified with a PC ligand, and the PC-Au25 is prepared. The ultra-small gold nano CT probe PC-Au25 with an Au25 gold nano cluster as a core and phosphatidylcholine PC as a surface ligand is obtained by optimizing and screening the core size and the surface ligand property of ultra-small gold nano particles. Wherein the 2-methacryloyloxyethyl phosphorylcholine is used as a PC ligand for the first time to modify the surfaces of the ultra-small gold nanoparticles, so that the surface modification of the ultra-small gold nanoparticles is realized, and the ultra-small gold nanoparticles can be efficiently and freely filtered through the kidney. The CT probe can be applied to the determination of the glomerular filtration rate through a CT imaging or blood test method.

The invention provides a myocardial amyloidosis classification prediction method and system based on cardiac magnetic resonance multi-sequence radiomics, and is applied to the technical field of data processing. The method comprises the following steps: preprocessing cardiac magnetic resonance multi-sequence image information to generate a standardized multi-sequence image data set; processing the standardized multi-sequence image data set to generate a target image omics feature vector; processing the gene detection information to generate pathogenic gene characteristics and mutation type characteristics; processing the target radiomics feature vectors and the cardiac magnetic resonance parameters based on medical knowledge graph data to generate an AL type myocardial feature associated feature set and an ATTR type myocardial feature associated feature set; processing the blood detection index information and the intestinal microflora parameter information to generate myocardial amyloidosis influence factors; and processing the data based on the target myocardial amyloidosis classification prediction model to generate a myocardial amyloidosis classification prediction result.

The invention discloses an application method of a serum Raman spectrum as a biomarker in cognitive impairment diagnosis, a cognitive impairment diagnosis system is used for diagnosis, the cognitive impairment diagnosis system comprises a Raman spectrum detection and preprocessing module, a database module and a cognitive diagnosis module, and the application method comprises the following steps: step 1, collecting a sample; step 2, pre-treating a sample; 3, collecting and preprocessing a serum sample by using a Raman spectrum detection and preprocessing module; 4, performing model construction and optimization by utilizing a database module; step 5, performing efficiency evaluation; and 6, performing cognitive grading diagnosis on the new sample by using a cognitive diagnosis module. The method has remarkable technical progress, no cognitive impairment diagnosis blood test method suitable for the whole crowd exists clinically at present, cognitive impairment can be diagnosed through the Raman spectrum of serum, detailed cognitive function grading can be carried out, and the cost of a single sample is less than 100 yuan.

A method of determining the risk of developing a known disease or condition or of identifying the presence of the known disease or condition in a subject includes obtaining subject data that includes results of blood tests. The blood tests include a basic metabolic panel (BMP) and a complete blood count (CBC) panel. The method further includes classifying the subject data with respect to the risk of the subject having or developing the known disease or condition by using the subject data in a machine learning classification system. The classification system includes a machine learning model previously trained on BMP and CBC data from a positive group of training subjects who received a diagnosis of the disease or condition and from a negative group of training subjects who were not diagnosed to have the disease or condition.

A microfluidic device (12) for applying blood samples to a sensor element (26) for protein defect diagnosis and / or for conditioning a sensor element (26) for protein defect diagnosis is proposed, as is a protein defect diagnosis device (10) with the microfluidic device (12). A method for applying blood samples to a sensor element (26) for protein defect diagnosis and / or for conditioning a sensor element (26) for protein defect diagnosis and a method for analyzing protein defects in a blood sample (212) are also disclosed.

A system for early diagnosis and prognosis prediction of sepsis, according to an embodiment of the present invention, includes: a memory for storing general blood test data of a patient; and a processor, wherein the processor may be configured to pre-process the general blood test data of the patient stored in the memory as data to be input to an artificial intelligence model, input the pre-processed data to the artificial intelligence model trained for early diagnosis and prognosis prediction of sepsis, and obtain a sepsis prediction result from the artificial intelligence model.

The present application provides a method for detecting pseudo-lesions of the fundus, comprising: performing image feature analysis of the lesion area according to preliminary lesion markings, locating and evaluating the cell density and the degree of vascular leakage, and determining the risk of disease aggravation; predicting the tortuosity and vascular fragility of new blood vessels and evaluating the probability of complications in patients according to morphological processing and feature analysis of multi-phase vascular images; predicting the risk of serious complications according to the lesion markings and vascular feature analysis of the patient, combined with blood test results, and re-identifying and excluding pseudo-lesion images that are mistaken for lesion images; comparing the feature changes of pseudo-lesions and real lesion areas according to time series analysis of fundus disease images, evaluating the disease development path, and performing early intervention on the disease; segmenting the new blood vessel area based on the use of sensitive index filtering technology, and evaluating the risk of disease aggravation through vascular feature statistics.

A blood testing device for detecting hemolysis in a blood sample is described. The blood testing device comprises an housing for containing the blood sample. The housing has a treatment window and an optical zone formed therein. The blood testing device further includes an acoustic transducer positioned to selectively generate acoustic forces directed into the treatment window of the housing and a control unit for selectively actuating and deactuating the acoustic transducer to permit colorimetric analysis of plasma within the blood sample.

The invention provides a metabolism-related fatty liver disease prediction method based on a self-attention mechanism, and the method comprises the steps: obtaining blood test and four diagnosis instrument data of a patient, and constructing a fatty liver disease data set; according to the same index data of the same patient in different periods, adding time sequence information codes to form an optimized fatty liver disease data set with sequence features; constructing and training a fatty liver detection model, wherein the fatty liver detection model comprises a convolutional neural network, a layered attention module and a feedforward neural network layer; and inputting patient data into the trained fatty liver detection model to obtain the metabolism-related fatty liver disease prediction grade of the patient. According to the invention, the method achieves the refined reasoning of the time sequence indexes of the patient through multi-scale convolution and hierarchical attention, and improves the prediction precision of the model.

The invention discloses a personalized rehabilitation scheme recommendation system for movement cognitive impairment of old people. The system comprises a data acquisition module which is used for collecting case data about a patient; the blood test data processing module is used for performing embedded representation on the blood test data of the patient; the chief complaint text processing module is used for performing embedded representation on the patient chief complaint text; the rehabilitation scheme acquisition module is used for characterizing a single rehabilitation measure, completing characterization of a composite rehabilitation scheme, predicting the rehabilitation measure to perform event classification based on patient personalization, simulating interaction of multiple rehabilitation measures and obtaining characterization of the rehabilitation scheme; and the rehabilitation scheme recommendation module is used for recommending a personalized rehabilitation scheme for the motor cognitive impairment of the old people by utilizing the trained rehabilitation measure scoring model. According to the method, the problems of slow convergence process and even over-fitting caused by lack of an embedding process for users and articles in a current recommendation algorithm are solved; and patient personalization is not considered in single rehabilitation measure recommendation.

A biological test system has receptacles for a variety of removable electro-mechanical biological blood test-based elements having multiple features such as lancets or strips. Testing is through an analysis biological test processor that provides results to a user test output with all designed to be contained in a pocket-transportable user biological test form factor that houses all elements. Biological test elements can be interchanged for multiple sensing modalities with automatic activation based on cartridge insertion of a glucose test element, an A1C test element, a ketone test element, or the like. Designs can include a user sampler configuration selector for a user sampling selection that may be analyzed so a user-personalizable, biological sampler control processor can automatically disable operation for test safety if a user sampling selection is determined to be unacceptable. The system can also provide a user cartridge action prompt, a user reselection prompt, or other action to clear the disable.

The invention relates to the technical field of medical systems, and particularly discloses a method for constructing a cancer malignant fluid prognosis composite index CCAR based on a creatinine and CAR combination, and the method comprises the following steps: S1, obtaining serum biomarker data of a cancer malignant fluid patient, including creatinine, C-reactive protein and albumin; s2, calculating the ratio CAR of the C reactive protein to the albumin, wherein CAR = CRP / Albumin; s3, taking the CAR and the Cr as input features, and constructing a nonlinear composite index CCAR through a random forest machine learning model; s4, developing a network calculator system; the CCAR index constructed by the invention can comprehensively reflect the inflammation burden, the metabolic state and the immune level of the cancer and malignant fluid patients, and compared with the traditional index, the CCAR index can provide more accurate survival prognosis prediction; creatinine, the ratio of C-reactive protein to albumin and other parameters contained in the CCAR index are all clinical common blood examination items, the obtaining approach is simple and convenient, and wide clinical application is facilitated.

The invention discloses a bone defect repair material suitability prediction method based on deep learning, and the method comprises the steps: carrying out the overlapping and uniform partitioning of a bone defect region and a host bone region in three-dimensional CT image data, carrying out the position coding and feature extraction of a plurality of sub-blocks through a three-dimensional visual Transform model of multi-scale position coding, and obtaining the anatomical and functional features; calculating a bone metabolism activity score and a systemic inflammation score based on blood detection data, and then constructing pathophysiological features; performing multi-round interaction on the anatomical and functional features, the pathophysiological features, the parameter data and the multidirectional multi-head cross attention layer, in each round, selecting one modal feature as a query vector, and selecting any modal feature in the rest modal features as a key vector and a value vector; and fusing the interaction features of each round, and determining the suitability score and the risk level of the bone repair material by combining the attention weight and the bone metabolism activity score in the multidirectional multi-head cross attention layer.

The utility model discloses a blood detection device. The blood detection device comprises a shell, test paper and a cap, the shell comprises an upper shell and a lower shell which are fixedly connected, a containing cavity is formed between the upper shell and the lower shell, the test paper is located in the containing cavity, a blood sampling head is formed at the front end of the shell, a liquid inlet is formed in the blood sampling head, and the liquid inlet is communicated with the containing cavity through a capillary structure and a premixing cavity in sequence; a hydrophobic coating belt arranged in the left-right direction is arranged on the front cavity wall of the premixing cavity, and an air hole communicated with the containing cavity is formed in the rear end of the shell; the front end of the shell is sleeved with the cap, a liquid storage bag is arranged at the bottom in the cap, and buffer liquid is stored in the liquid storage bag. The blood detection device can realize integration of sample collection and sample treatment, and has the advantages of simple structure, low manufacturing cost, convenience in use, high detection success rate, high detection accuracy and the like.

The invention relates to a sepsis detection system which evaluates the sepsis risk of a patient in real time by integrating clinical signs, various physiological parameters, laboratory detection data and biomarkers and utilizing an intelligent algorithm. The system comprises a data acquisition module, a data transmission module, a data processing module, a user display module and an alarm module. The data acquisition module monitors physiological parameters such as consciousness, body temperature, heart rate, respiratory rate, finger pulse oxygen saturation, invasive blood pressure, urine volume and skin piebald in real time, and biomarker data such as leukocyte count, neutrophile granulocyte, neutrophile granulocyte number, C-reactive protein, procalcitonin and interleukin-6 are obtained through blood detection. And the data processing module performs sepsis risk assessment by integrating the biomarkers and the physiological parameters in combination with a machine learning model, and outputs a risk level. The user display module displays the evaluation result in real time and provides treatment suggestions, and the alarm module triggers an alarm under the high-risk condition to ensure timely intervention.

The utility model belongs to the technical field of blood detection sampling devices, and particularly relates to a blood detection sampling collector which comprises a main body and two first clamping grooves, a first connecting sleeve and a second connecting sleeve are inserted into the two first clamping grooves respectively, first clamping blocks are clamped with the interiors of the first clamping grooves, and second clamping blocks are clamped with the interiors of the second clamping grooves respectively. Limiting sleeves are clamped in the two limiting frames, rectangular grooves are formed in the bottoms of the two limiting frames, compression springs are fixedly connected to the inner tops of the limiting grooves, movable semi-rings are fixedly connected to the bottoms of the compression springs, fixing rings are fixedly connected to the inner side walls of the limiting sleeves, and diaphragms are fixedly connected to the interiors of the fixing rings. By arranging the limiting frame, the compression spring, the movable semi-ring, the limiting sleeve and the diaphragm, air isolation of a needle output hole is achieved, and pollution to blood in the main body caused by long-term exposure of the needle is avoided; and by arranging a first stop block and a second stop block, the two limiting frames can be conveniently limited by the limiting block.

This application provides a diabetes assessment device and a diabetes assessment method, relating to the field of image processing technology. The diabetes assessment device includes: an image determination module configured to determine a fundus image to be assessed, wherein the fundus image is used to characterize the physiological structures of the fundus; and a diabetes assessment module configured to determine a diabetes assessment result based on the fundus image to be assessed. By setting the image determination module, the fundus image to be assessed can be determined; by setting the diabetes assessment module, the diabetes assessment result can be determined based on the fundus image to be assessed. That is, invasive examinations such as blood tests are unnecessary; the diabetes assessment result can be determined solely based on the fundus image to be assessed, without causing harm to the patient's physical or mental health.

Provided are a resistive random-access memory (RRAM)-based blood test system and method. The system comprises: a computing chip, an RRAM chip, a power supply module, and a sensor module; the computing chip is connected to the RRAM chip; the RRAM chip is connected to the sensor module; and the power supply module is connected to the computing chip and the RRAM chip; the sensor module is configured to test a substance in blood and convert test result data into a plurality of pieces of first voltage data; the RRAM chip is configured to preprocess the received plurality of pieces of first voltage data and obtain second current data; and the computing chip is configured to, based on the second current data, perform a function computation on the second current data according to a preset function computation model, and obtain a blood-based classification result.

The utility model relates to the technical field of blood, and discloses a blood vibration mixing device which comprises an operation table, a vibration assembly is arranged above the operation table, the vibration assembly comprises a vibration table, a storage shell is arranged on the vibration table, and a plurality of storage grooves are formed in the storage shell; a plurality of reset springs are jointly and fixedly connected to the lower portion of the vibration table and the upper portion of the operation table, a rotating assembly is arranged in the operation table, and the rotating assembly comprises a rotating pipe, the vibration assembly is arranged, the storage shell rotates through the rotating pipe and a rotating rod, and therefore the storage shell can drive blood test tubes in the storage groove to rotate; meanwhile, under the action of reset springs on the vibration table, after the reset springs vibrate, the vibration table can drive the storage shell to vibrate, and therefore the blood test tubes in the storage groove can rotate and vibrate, and the blood can be fully mixed.

The invention relates to a medical data analysis method and device based on artificial intelligence, equipment and a medium. The method comprises the following steps: acquiring medical data of a user; the medical data comprises blood detection data, medical image index data and basic data, and the medical data is provided with a sampling time label; extracting a change trend of the medical data to obtain a comprehensive feature vector; the comprehensive feature vector at least comprises a blood detection change vector and a medical image index change vector; calculating weighted weights of the blood detection change vector and the medical image index change vector in the comprehensive feature vector, and adjusting the comprehensive feature vector based on the weighted weights to obtain a fused feature vector; and predicting a disease type and a disease risk probability of the user based on the fusion feature vector, and generating a medical report of the user based on the disease type and the risk probability. By adopting the method, the accuracy and timeliness of cardiovascular and cerebrovascular medical data analysis can be improved.

A point-of-care testing (POCT) system comprising an analyzer, a measurement cartridge, and a calibration cartridge for calibrating the measurement cartridge is described. The measurement cartridge comprises at least one electrochemical sensor for measuring the one or more properties of the blood sample, and the calibration cartridge comprises a similar at least one electrochemical sensor and at least one sealed blister containing calibration fluid for calibrating the measurement cartridge. Examples of properties of the blood sample may be pH, blood gases, electrolytes, and metabolites like glucose and creatinine. The measurement cartridge may also comprise an optical chamber for measuring for example, bilirubin and hemoglobin species, for which the analyzer comprises stored calibration algorithms. The optical chamber may be disposed in any location in the measurement cartridge, for receiving a portion of the blood sample.

Eating only one meal a day can slow down the brain's awakening and onset of activity in the morning after sleep. [Solution] A transparent diet, in which the blood test data values ​​of the food consumed are known, is eaten in the morning or at noon, and a free meal is eaten in the evening. The contents of the free meal are then compared with the blood test data, and the diet is adjusted accordingly.

The present invention provides a computer-implemented method for creating an interpretable machine learning classification model, the method being implemented on one or more processors connected to a memory. According to an embodiment, the method includes the steps of defining the model by setting up a chained component classification (CBC) architecture (300) including several multiple chained component classification (CBC) blocks (310), training the model end-to-end with available input data (402) including forward propagating samples (401) of the input data (402) through the chained CBC architecture (300), and interpreting the output of each CBC block (310) of the chained CBC architecture (300) except for the last CBC block (310) as a likelihood vector of the concept detected in the respective CBC block (310). The present invention can be used for several anticipated medical / healthcare use cases, such as transparent disease classification by analyzing markers in blood tests and knowledge discovery for disease classification.

A tangent flow hemolysis blood testing assembly, device and method are described herein.

The present invention discloses an EBV-related gastric cancer diagnostic marker and its application, wherein the diagnostic marker is selected from at least one of LF2 protein and BFRF3 protein. The present invention is the first to screen and obtain two diagnostic markers for EBV-related gastric cancer (EBVaGC) based on proteome chips and phage display technology - LF2 protein and BFRF3 protein, which can efficiently and accurately diagnose EBVaGC. In particular, the AUC of the combined diagnosis of these two proteins can reach 0.911, which is significantly better than the AUC of the individual diagnoses. And when diagnosing EBVaGC based on these two proteins, no tissue sample is required, only a serum sample is required, and it can be detected during a routine blood test. Therefore, compared with existing diagnostic methods, the present invention has the characteristics of high accuracy, small limitations, and low cost, reduces the cost of medical treatment for patients, is conducive to the realization of universal screening of the population, simplifies the diagnosis process of EBVaGC, and significantly improves the treatment efficiency.