254 results about "Differential diagnosis" patented technology

The invention relates to the technical field of medical diagnosis processing, and discloses an intelligent disease diagnosis and differential diagnosis system based on a knowledge graph, and the system comprises a data input module which is used for receiving and standardizing clinical symptoms, signs, laboratory examination data and historical medical record data of a patient; the knowledge graph construction and updating module is used for constructing and updating a knowledge graph of diseases and symptoms according to the medical literature and the clinical data, and the knowledge graph automatically extracts an incidence relation between the symptoms and the diseases from the medical literature through a natural language processing technology; and the reasoning and diagnosis module is used for performing intelligent disease diagnosis and differential diagnosis. According to the method, by optimizing reasoning path selection and information gain calculation, the path with the most information content is selected from multiple reasoning paths for diagnosis reasoning, and the most representative path is selected by calculating the correlation degree between each symptom and the disease.

The invention discloses an acute abdominal pain cause differential diagnosis system based on deep learning, and relates to the technical field of medical artificial intelligence, comprising: a data acquisition module acquires initial self-described text data of a patient; the standardization processing module generates a standardized symptom set through standardization processing; the cause reasoning module performs reasoning according to the standardized symptom set and the medical knowledge graph to obtain a suspected disease set; the contradiction detection module is used for detecting the contradiction between the symptom set and the self-contained text and calculating the contradiction intensity and disease cause relevancy; the priority calculation module ranks contradiction priorities according to contradiction intensity and disease cause relevancy; the contradiction processing module judges the contradiction significance score, if the contradiction significance score is lower than a threshold value, a standardized symptom set is output, and otherwise, an optimal clarification action is generated to solve the contradiction; an iteration updating module updates a symptom set according to clarification action feedback, and causes are inferred again until a termination condition is met; the method can ensure that the pathogenesis reasoning process gradually approaches the real pathogenesis, and improves the diagnosis reliability.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an artificial intelligence medical diagnosis system based on multi-dimensional information fusion. Comprising the steps that a self-adaptive diagnosis path planning module judges whether a user request belongs to a preset non-diagnosis and treatment category or not, if yes, a quick response path is activated, and a standardized answer is retrieved and returned to a user; the preliminary diagnosis module generates a candidate disease hypothesis list, verifies the candidate disease hypothesis list and outputs a verified disease hypothesis list; the dynamic knowledge enhancement module generates missing knowledge according to the disease knowledge graph and the query verification disease hypothesis list and supplements the missing knowledge into the medical knowledge graph; a composite confidence evaluation module performs confidence evaluation on each hypothesis disease in the verification disease hypothesis list, and outputs a final disease confidence; the result integration module sorts the final disease confidence in a descending order and integrates the multi-dimensional information of each hypothetical disease to generate a structured differential diagnosis report; the system and the method can assist doctors in realizing high-accuracy, high-reliability and explainable intelligent medical diagnosis.

The invention relates to a pathological section intelligent auxiliary differential diagnosis system based on multi-modal fusion, in particular to the field of clinical pathology, semantic unification of multi-modal data is achieved through meta-task construction and a cross-modal alignment technology, and transferable diagnostic knowledge is extracted by utilizing a meta-learning framework; the method combines a generative model and knowledge constraints to generate high-quality synthetic data, and finally fuses real and synthetic samples through a self-adaptive diagnosis mechanism, thereby remarkably improving the differential diagnosis capability of rare lesions, effectively solving the problem of model generalization in a training data scarcity scene, and improving the accuracy of model identification. And efficient and reliable intelligent auxiliary decision support is provided for clinical pathological diagnosis.

The invention provides an anti-human thrombopoietin antibody AF687, a kit and application thereof. The anti-human thrombopoietin antibody disclosed by the invention can specifically recognize epitopes without glycosylation modification of natural TPO protein, and has relatively high affinity for recombinant TPO protein and specific epitopes of the recombinant TPO protein. Meanwhile, the anti-human thrombopoietin antibody disclosed by the invention can accurately detect the content of human thrombopoietin in serum, is not influenced by heterogeneity of natural protein glycosylation sites, can be used for accurately identifying and diagnosing early aplastic anemia and primary immune thrombocytopenia, and has the advantages that compared with an existing detection reagent, the anti-human thrombopoietin antibody is high in sensitivity and high in sensitivity. The false positive rate can be reduced from 13.9% to 2.7%, and a more accurate detection method is provided for differential diagnosis of the primary immune thrombocytopenia.

The invention discloses a method for constructing a differential diagnosis model for lupus nephritis and membranous nephropathy, and belongs to the technical field of intelligent medical treatment. The modeling method comprises the following steps: S1, respectively collecting flow cytometry detection data of lupus nephritis patients and healthy control personnel; s2, performing data cleaning and conversion on the flow cytometry detection data, and converting non-numerical features into digits; carrying out implication on the missing value by adopting a k nearest neighbor algorithm from an implication packet; s3, random sampling is carried out on the cleaned and converted data set, and samples are divided into a training set and a verification set according to the proportion of 7: 3; s4, dividing a training subset and a test set from the training set, and iteratively selecting the types of cells incorporated into the constructed model as pDC, CD4T, effector CD4T, Th2, CD8T, CD38 + HLA-DR + CD8T, and CD38 + PD-1 + CD8T by adopting an RFE method, wherein the types of the cells incorporated into the constructed model are pDC, CD4T, effector CD4T, Th2, CD8T, CD38 + HLA-DR + CD8T and CD38 + PD-1 + CD8T; and S5, performing a classification task by adopting TabPFNClassifier, performing training from features selected from the training data set, then evaluating the performance of the model, performing model training by utilizing detection data, and performing evaluation to construct a lupus nephritis prediction model with high accuracy.

An AI-based system and method for supporting differential diagnosis and standard of care in healthcare. The method involves receiving patient information from various sources, including patient-reported symptoms, physician notes, and sensor data from medical devices. The patient information is preprocessed and analyzed using deep learning models to generate a ranked list of potential diagnoses, each associated with likelihood scores and key contributing factors. The potential diagnoses are provided to physicians via an interactive interface, and physician feedback is collected to fine-tune the AI models using reinforcement learning. The method aims to enhance physician decision-making, improve diagnostic efficiency, and ensure adherence to the standard of care by leveraging AI's ability to analyze vast amounts of data more effectively than human physicians.

The invention discloses an AD diagnosis method fusing electroencephalogram frequency spectrum standardization deviation and a brain network. The AD diagnosis method comprises the steps that S1, band-pass filtering and fragment segmentation are conducted on multi-channel EEG signals of five brain regions; s2, for each segment, calculating brain region-frequency band characteristics of five different brain regions; s3, performing standardized modeling by adopting a GAMLSS model, identifying individual deviation information of a disease group about a specific brain region rPSD, training a LightGBM classifier by utilizing the individual deviation information, and extracting a structured feature vector by utilizing an internal structure of the classifier; s4, dividing each segment into a plurality of non-overlapping frequency boxes according to frequency resolution, calculating an FCN graph in a matrix form of each frequency box, constructing a three-dimensional image according to the FCN graph, and extracting a visual feature vector by using a Swin transformer network; and S5, constructing a connection vector, and inputting the connection vector into a classifier for classification. The method provides a new technical approach for accurate diagnosis and differential diagnosis of neurodegenerative diseases.

Various processes, algorithms, and systems are provided herein for assisting physicians in distinguishing among related diseases, such as distinguishing connective tissue associated interstitial lung disease from idiopathic pulmonary fibrosis. Methods for generating such processes, algorithms, and systems are also disclosed. In some embodiments, a preliminary diagnosis of a set of possible diseases is obtained, along with protein count information from a patient's blood sample. Additional, patient-specific information (e.g., age, sex, etc.) may also be obtained. The data is processed by a trained machine learning algorithm, to output a differential diagnosis of which of the set of possible diseases is present for that patient. Based on the diagnosis, a treatment course can be selected, and further information can be tracked regarding the patient's outcome.

The invention relates to the technical field of biological detection, relates to a multiple ddPCR detection method for porcine intestinal coronavirus, and in particular relates to a monitoring and quantitative detection method for samples with relatively low porcine intestinal coronavirus content or complex matrix, such as feeds, animal tissues and animal-derived products. The droplet digital PCR detection method for the porcine intestinal coronavirus established in the invention has the advantages of strong specificity, high sensitivity, good repeatability and strong anti-interference capability, can be used for absolute quantification of the copy concentration of the porcine acute diarrhea syndrome coronavirus (SADS-CoV), the porcine epidemic diarrhea virus (PEDV), the porcine delta coronavirus (PDCoV) and the porcine transmissible gastroenteritis virus (TGEV) in an actual sample, and can be used for the detection of the porcine intestinal coronavirus. The invention provides a novel and reliable technical method for trace early warning and monitoring of early-stage infection of four porcine intestinal coronavirus, differential diagnosis of other common viruses, research of propagation rules of the viruses, epidemiological risk assessment and other systematic applications.

The invention relates to the field of medical artificial intelligence, in particular to a mucus-like brain tumor multi-source data auxiliary differential diagnosis system based on deep learning, and the system collects pathological images, medical images, immunohistochemical data and molecular biology data through a multi-source heterogeneous data collection and standardization module, and carries out standardization processing; a multi-scale feature extraction module extracts key features from the data to generate multi-modal feature data; a cross-modal correlation analysis module analyzes correlation among the feature data and generates attention mapping data and feature correlation data; the diagnosis reasoning and decision-making module identifies the tumor type by using the data, carries out differential diagnosis and calculates the uncertainty of a diagnosis result; the interpretability display module is used for generating a structured diagnosis report which comprises multi-level attention visualization data and positioning a key diagnosis area; and through multi-source data fusion and correlation analysis, the diagnosis accuracy is remarkably improved.

The invention discloses a marker gene composition for identifying and diagnosing white feces syndrome of prawns and application of the marker gene composition. According to the marker gene composition, transcriptome high-throughput sequencing is carried out on intestinal tissues of collected prawns suffering from the white stool syndrome and healthy prawns, and after transcript expression level data is obtained, the marker gene composition capable of being used for identifying and diagnosing the prawn white stool syndrome is obtained by utilizing a random forest algorithm. According to the marker gene composition, transcript expression level data of each gene in the marker gene composition is utilized, a model and a system for identifying and diagnosing the prawn white feces syndrome are also constructed in combination with a random forest algorithm, prawns suffering from the white feces syndrome can be accurately predicted by utilizing the model and the system, and therefore identification and diagnosis of the prawn white feces syndrome are achieved. Based on the marker gene composition, the accuracy of constructing a model and identifying and diagnosing the prawn white feces syndrome by using the marker gene composition is high, the result is stable and reliable, the prevention and treatment of the prawn white feces syndrome are facilitated, and the healthy development of the prawn culture industry is guaranteed.

The invention discloses an application of a sugar chain marker combination in preparation of a product for identifying drug-induced liver injury and autoimmune hepatitis. The marker combination comprises a combination of more than one of the following N-sugar chain markers: NGA2F, NGA2FB, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4 and NA4Fb; wherein the NG1A2F-1 and the NG1A2F-2 are in the form of an isomeride. According to the invention, non-invasive serum detection is adopted, so that the risk of invasive examination such as liver biopsy is avoided, and the kit has high detection rate and good clinical application value. According to the invention, early differential diagnosis of DILI and AIH can be realized, a new technical means is provided for accurate diagnosis and glycomics research of liver diseases, and the kit has important clinical significance and application potential.

The present application relates to the field of veterinary diagnostic technology, and specifically to a probe composition, a primer composition, and a triple fluorescence PCR kit and related methods for differential diagnosis of three genes of different strains of African swine fever composed of the above-mentioned probe composition and primer composition. The kit includes primer probes specific to the B646L gene, the CD2v gene, and the I177L gene. The present application designs fluorescent PCR primers and probes for the B646L gene, the CD2v gene, and the I177L gene, respectively, and establishes a triple fluorescence PCR detection method, which can quickly identify African swine fever virus strains and gene-deficient strains. At the same time, it has no cross-reaction with swine fever virus, porcine reproductive and respiratory syndrome virus, pseudorabies virus, porcine epidemic diarrhea virus, porcine circovirus, porcine parvovirus, healthy whole blood, oropharyngeal swabs, nasal swabs, anal swabs, environmental swabs, pig tissues, etc., and has good specificity.

There is provided a processor configured for executing code external to a ELM and interfacing with the ELM for: grounding the ELM to predefined disorder specific structured flows, determining a differential diagnosis of mental disorders, for a first mental disorder, generating prompts for guiding the ELM for conducting a structured interview with the subject via a user interface according to a first disorder specific structured flow, dynamically analyzing a response(s) for determining inconsistency with the first mental disorder and determining consistency with a second mental disorder, dynamically switching from the first disorder specific structured flow defined for the first mental disorder to a second disorder specific structured flow defined for the second mental disorder for guiding the ELM, and iterating the generating prompts for guiding the ELM, the dynamically analyzing, and the dynamically switching, for obtaining the differential diagnosis of mental disorders that the individual is most likely suffering from.

The invention provides an anti-human thrombopoietin antibody AF692, a kit and application of the anti-human thrombopoietin antibody AF692. The anti-human thrombopoietin antibody disclosed by the invention can specifically recognize epitopes without glycosylation modification of natural TPO protein, and has relatively high affinity for recombinant TPO protein and specific epitopes of the recombinant TPO protein. Meanwhile, the anti-human thrombopoietin antibody disclosed by the invention can be used for accurately detecting the content of human thrombopoietin in serum, is not influenced by natural protein glycosylation site heterogeneity, and can be used for accurately identifying and diagnosing early aplastic anemia and primary immune thrombocytopenia; and a more accurate detection method is provided for differential diagnosis of the primary immune thrombocytopenia.

An AI-based system and method for supporting differential diagnosis and standard of care in healthcare. The method involves receiving patient information from various sources, including patient-reported symptoms, physician notes, and sensor data from medical devices. The patient information is preprocessed and analyzed using deep learning models to generate a ranked list of potential diagnoses, each associated with likelihood scores and key contributing factors. The potential diagnoses are provided to physicians via an interactive interface, and physician feedback is collected to fine-tune the AI models using reinforcement learning. The method aims to enhance physician decision-making, improve diagnostic efficiency, and ensure adherence to the standard of care by leveraging AI's ability to analyze vast amounts of data more effectively than human physicians.

The application discloses a kind of sheep mycoplasma Mo, Mccp, Mmc triple PCR detection method establishment and application, by using high-throughput Mauve genome collinearity analysis the specific difference section between three kinds of sheep mycoplasma genomes each other, design and screen out the specific primer capable of differential diagnosis three kinds of sheep mycoplasma, by primer sequence amplification, sequencing, identify three new sheep mycoplasma differential diagnosis target molecule, respectively Mo differential diagnosis target molecule DprA, Mccp differential diagnosis target molecule MCCPF38_00240, Mmc differential diagnosis target molecule restriction endonuclease subunit S.Utilize new target specific section, establish the triple PCR method capable of simultaneous differential diagnosis three kinds of sheep mycoplasma (Mo, Mccp, Mmc).The PCR method of the application has the characteristics of clinical convenient, rapid diagnosis, can be widely used in the clinical diagnosis of sheep mycoplasma, with good market prospect and economic value.

The invention discloses a progressive multiple sclerosis differential diagnosis marker based on immunophenomics and application of the progressive multiple sclerosis differential diagnosis marker. The invention specifically provides an immune biomarker set for identifying progressive or recurrent remission type multiple sclerosis and a corresponding reagent and kit thereof. The immune biological marker provided by the invention comprises one or more of the following groups: a) activating a T cell subset; b) a CD28 < + > and CD28-T cell subset; c) forming a subgroup by the CD8 + T cells; and d) a MAIT cell subset category. The immune biomarker disclosed by the invention can also be applied in a multi-index combination manner, and can be applied to a) multiple sclerosis patients which are not subjected to disease modification treatment; b) a patient with multiple sclerosis treated by disease modification; and c) a very good effect of distinguishing clinical subtypes of the multiple sclerosis can be achieved in the three types of people of the multiple sclerosis patients who do not distinguish whether the multiple sclerosis patients are subjected to the disease modification treatment or not.

The invention relates to 4 serum peptide patterns for diagnostics of intracranial hemorrhagic stroke (ICH) in humans and differential diagnosis from acute ischemic stroke that are defined using a list of the 34 quantitative peptides that in sera of patients with ICH are increased 2 folds or more as compared with individuals without stroke and patients with acute ischemic stroke and a list of 16 qualitative peptides detectable in sera of the patients with ICH but not detectable in the sera of individuals without stroke and in sera of patients with acute ischemic stroke.

The invention discloses an emergency intelligent triage method and system based on sensing equipment, and belongs to the technical field of medical triage, and the method specifically comprises the steps: collecting vital sign data through a physiological sensing device, and constructing an initial symptom vector; retrieving disease hypotheses based on the medical knowledge graph and constructing a time sequence path tree for each hypothesis; calculating a priority score of the detection node according to the resource occupancy state and the disease association quantity; selecting an optimal first-step detection point to execute detection, and correcting the assumed confidence of each disease by adopting a confidence inversion principle based on a detection result; the process is executed through iteration, a diagnosis path is dynamically adjusted, triage is completed when the confidence coefficient of the disease hypothesis reaches a definite diagnosis threshold value, and a differential diagnosis list is output and manual triage is started when the maximum number of iterations is reached. According to the method, through resource-aware detection node optimization and confidence inversion closed-loop optimization, dynamic path planning and intelligent decision making in the emergency triage process are realized, and the triage efficiency and accuracy are effectively improved.

The invention provides a high-throughput DNA (deoxyribonucleic acid) detection method, platform, probe composition and kit for hereditary hemoglobinopathy. The high-throughput DNA detection method comprises the following steps: S1, designing probes for detecting known and unknown mutations in abnormal hemoglobinopathy related genes according to a specific rule; s2, synthesizing a probe; s3, extracting genome DNA from peripheral blood of a patient, constructing a library, hybridizing and capturing a target sequence, recovering a target region library, and performing library quality detection; s4, loading the library into a sequencing platform for high-throughput sequencing to obtain sequencing original data; and S5, performing data analysis on the sequencing original data. According to the application, a probe with a special sequence is designed, and sequencing data is subjected to information analysis, so that multiple hemoglobin mutation types can be detected at the same time, the detection efficiency and coverage are greatly improved, missing detection is avoided, and differential diagnosis can be performed on diseases with similar clinical manifestations.

The invention provides a blood marker for distinguishing lung cancer and non-lung cancer diseases and application of the blood marker, and belongs to the technical field of biomarkers. The blood marker for distinguishing lung cancer and non-lung cancer diseases comprises at least five metabolites as follows: phosphatidylethanolamine (36: 4), lysophosphatidylcholine 18: 1e, phosphatidylethanolamine 38: 7e, sphingomyelin d38: 5, dodecanedioic acid, N-acetylputrescine, L-glutamyl-L-glutamine, hippuric acid, trans-cyclohexane-1, 3, 4-triazole-1, 3, 4-triazole-1, 3, 4-triazole-1, 3, 4-triazole-1, 3, 4-triazole-1, 3, 4-triazole-1, 3-triazole-1, 3, 4- The compound is prepared from 1, 2-dicarboxylic acid, glycochenodeoxycholic acid, allantoic acid, L-valine, methyleucine and 4-guanidinobutyric acid. A diagnosis model is constructed based on the blood marker, and the area under curve (AUC) is greater than 0.77, which indicates that the diagnosis result is accurate and reliable. The invention provides an effective marker for diagnosis of lung cancer and differential diagnosis of lung cancer and non-lung cancer diseases, greatly simplifies experimental operation, and has important clinical diagnosis significance.

The present invention relates to a method for the differential diagnosis of bipolar versus unipolar disorder in a human patient during a depressive phase from a biological sample of the patient, comprising determining the relative proportions of RNA editing variants of at least the A-to-I-edited RNA ZNF267 gene. The present invention also relates to a method for monitoring the treatment of a depressed patient exhibiting bipolar or unipolar disorder. Finally, the present invention provides a kit for the differential diagnosis of bipolar versus unipolar disorder in a patient during a depressive phase.

The application belongs to the technical field of biotechnology, and particularly relates to a biomarker for differential diagnosis of severe pulmonary tuberculosis. It is found that the XO content in blood of a severe pulmonary tuberculosis patient is significantly lower than that of a non-severe subject, the area (AUC) under the ROC curve in a screening test is 0.898, and the area (AUC) under the ROC curve in a verification test is 0.904. The application is helpful for early assessment of the condition of a pulmonary tuberculosis patient, and provides a basis for early intervention and comprehensive treatment of severe patients.

The embodiment of the invention provides a multi-role adaptive interaction method and system based on a stomatology knowledge base, and the method comprises the steps: recognizing a query role when a query signal is received, and obtaining the query content; calling a preset query engine matched with the query role, accessing a pre-constructed knowledge base according to the currently called query engine and the query content, and obtaining a differential diagnosis set containing a plurality of diseases with a sorting relationship; and performing multi-dimensional differential diagnosis reasoning on the diseases in the differential diagnosis set one by one according to the sorting relationship, correspondingly evaluating the comprehensive confidence coefficient of the reasoning result, and when the comprehensive confidence coefficient is not less than a preset threshold value, taking the corresponding disease as a diagnosis disease, and outputting a query report matched with the query role. According to the invention, doctors can be assisted to make professional clinical decisions, and convenient oral common sense popularization services can be provided for patients.

This disclosure belongs to the field of medical testing technology, and specifically relates to a classification, diagnostic, and prediction method, system, and electronic device for congenital neurodevelopmental disorders. Addressing the clinical diagnostic challenges of congenital neurodevelopmental disorders, this disclosure establishes for the first time a classification, diagnostic, and prediction method for congenital neurodevelopmental disorders based on whole-genome methylation signatures and customized SVM machine learning. This method achieves one-stop differential diagnosis across multiple mechanisms and diseases, filling a gap in existing technologies and providing a novel and efficient solution for the accurate diagnosis of congenital neurodevelopmental disorders.

The invention relates to the use of a microRNA or a set of microRNAs in the in vitro differential diagnosis of forms of primary aldosteronism, wherein the microRNA is at least one microRNA selected from the following or wherein the set of microRNAs comprises at least two microRNAs selected from the following: hsa-miR-146a-5p, hsa-miR-361-5p, hsa-miR-24-3p, hsa-miR-130b-3p, hsa-miR-99b-5p, hsa-miR-151a-3p, hsa-miR-199a-3p, hsa-miR-128-3p and hsa-miR-28-3p. Preferably, the forms of primary aldosteronism are unilateral and bilateral forms, preferably unilateral aldosterone-producing adenoma (APA) and bilateral adrenal hyperplasia (BAH). The invention also relates to a method for the in vitro differential diagnosis of forms of primary aldosteronism in a subject.

The present application relates to the field of medical diagnosis, and in particular to screening biomarkers for differential diagnosis of gestational diabetes mellitus by using metabolomics, and establishing a model for diagnosing whether a pregnant individual is diabetic based on the biomarkers. The present application provides biomarkers and a diagnostic model for differential diagnosis of gestational diabetes mellitus, which can be applied to early diagnosis or prediction of gestational diabetes mellitus, and is of great significance for prevention or treatment of gestational diabetes mellitus.

The invention relates to the field of medical informatics, and provides a biomarker-based thyroid nodule benign and malignant identification method and system. The method comprises the following steps: collecting a serum sample; performing quantitative detection on the multiple markers in the serum sample to obtain concentration data of the multiple markers; inputting the multi-marker concentration data into a pre-constructed probability calculation model to obtain a prediction probability value; and according to a preset diagnosis threshold value and the prediction probability value, judging whether the thyroid nodule is benign or malignant to obtain a thyroid nodule benign or malignant identification result. By integrating multi-marker information, the biological difference between benign and malignant nodules is effectively captured, and the overall efficiency of differential diagnosis is improved.