292 results about "Differential diagnosis" patented technology

The invention provides a clinical auxiliary decision-making method based on artificial intelligence and a related product, and the method comprises the steps: building a knowledge graph covering medical entities and relationships through learning authoritative medical data and medical records; a doctor inputs patient information, and data standardization and cleaning are carried out; based on the multi-dimensional information of the patient, recommending suspected diagnosis and differential diagnosis; recommending a preset examination scheme according to the suspected diagnosis and differential diagnosis results; when a doctor's advice is issued, the system checks conflicts between inspection and inspection items in real time and provides risk prompts; similar medical records are recommended based on patient clinical information, and diagnosis and treatment reference is provided for doctors. The clinical aid decision-making method based on artificial intelligence and related products have the beneficial effects that the disease list of suspected diagnosis and definite diagnosis is generated by automatically analyzing the illness state of the patient, and examination, inspection and treatment schemes are recommended in a targeted manner, so that the problems of missed diagnosis and misdiagnosis are effectively avoided.

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 discloses a bovine rotavirus monoclonal antibody and a preparation method of a bovine rotavirus quantum dot immunochromatography test strip, and belongs to the technical field of bovine rotavirus monoclonal antibodies. According to the invention, a hybridoma cell strain BRV 3G9 is utilized, and a monoclonal antibody is prepared by adopting an ascites method; the BRV 3G9 is preserved in the China General Microbiological Culture Collection Center (CGMCC), and the preservation number is CGMCC No.46138. The BRV 3G9 is named as BRV 3G9. According to the present invention, the concentrated live virus immunized mouse is adopted to prepare the monoclonal antibody with strong immunoreaction, and the BRV quantum dot immunochromatography test strip is researched and developed on the basis of the monoclonal antibody, and has advantages of convenient use, high efficiency, high sensitivity, strong specificity and the like, and can achieve the rapid clinical differential diagnosis of calf diarrhea pathogens, the problem that grassroots veterinarians lack a clinical rapid detection technology is solved, the diagnosis and treatment level and the prevention and treatment effect are improved, and technical guarantee is provided for efficiently preventing and treating calf diarrhea and guaranteeing calf health.

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 relates to the technical field of medical biology, in particular to a method for identifying and diagnosing PEDV (Porcine Epidemic Diarrhea Virus) and PoRV-A. Colloidal gold is used as a carrier, and a monoclonal antibody (mAb) for resisting PEDV and PoRV-A and an initiator are respectively modified on colloidal gold (Au) to prepare a probe (Au-I-mAb). An ELISA hole is coated with a monoclonal antibody (mAb), when a target virus exists, a probe is added to form a double-antibody sandwich compound (mAb-antibody-mAb-Au-I), a hairpin is added, and an initiation chain on the compound recognizes the matched hairpin and induces the hairpin to cross to generate HCR. Due to the fact that the hairpins are marked by different fluorophores, when H1 is in a closed hairpin structure, fluorescence on H1 is quenched by a quenching group, after H1 is subjected to ring opening through HCR, the fluorophores are away from the quenching group, and fluorescence is recovered. Fluorescence excitation is carried out by using corresponding emission wavelengths, and two target pathogens are finally detected at the same time by detecting output signals of different fluorophores.

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.

The invention discloses a systemic lupus erythematosus molecular diagnosis marker based on neutrophil characteristics. The invention provides application of a substance for detecting the expression quantity of an IFIH1 gene and / or a CFLAR gene in preparation of a product for auxiliary diagnosis or screening or auxiliary screening of systemic lupus erythematosus. The invention also provides an application of a substance for detecting the relative expression quantity of the IFIH1 gene and the CFLAR gene and a carrier loaded with the following combined index C value model formula in preparation of a product for auxiliary diagnosis or screening or auxiliary screening of systemic lupus erythematosus. The specific gene expression marker IFIH1 and / or CFLAR of the neutrophile granulocyte is selected by focusing the unique change of the SLE neutrophile granulocyte to assist the clinical early-stage identification and diagnosis of the SLE, and an IFIH1 and CFLAR combined diagnosis model is constructed, so that the diagnosis or screening of the SLE is realized; the kit has high sensitivity and specificity.

This invention presents a multisensor-connected, AI-enabled telehealth system for assisting healthcare providers with differential diagnosis and patients with early health concern detection. The system comprises a multi-sensor medical device with at least seven sensors, a secure cloud-based platform, and an interactive telehealth module. The device preprocesses and securely transmits patient information to the cloud platform, where an ensemble of deep learning models analyzes the data to generate ranked potential diagnoses with likelihood scores. The telehealth module facilitates communication between providers, patients, and the cloud platform, presenting visualizations and receiving feedback. The system continuously updates and fine-tunes its models using incremental learning algorithms, adapting to new data while retaining previous knowledge. It also generates alerts for providers and patients when deviations from normal physiological patterns are detected, accompanied by explainable AI visualizations.

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 belongs to the technical field of biology, and particularly relates to a biomarker for differential diagnosis of pulmonary tuberculosis in different stages. The marker provided by the invention is an SCF expression level, and active tuberculosis patients can be screened from healthy people and tuberculosis patients; patients in two different stages, namely an incubation stage and an active stage, can be screened from pulmonary tuberculosis patients. And when the SCF protein expression level is less than or equal to 9.712 pg / ml, judging that the subject is an active tuberculosis patient. In the screening group, LTBI vs TB has an area under an ROC curve of 0.9006, a sensitivity of 88.46% and a specificity of 83.33%. In a verification group, LTBI vs TB, the area under an ROC curve is 0.9432, the sensitivity is 90.91%, and the specificity is 100.00%. The marker detection method is convenient to operate, simple in process, low in cost and good in sensitivity and specificity, and can be put into use in the market.

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.

The utility model is suitable for the technical field of diagnostic kits, and provides a low-grade endometrial interstitial sarcoma diagnostic kit. The low-grade endometrial interstitial sarcoma diagnostic kit comprises a kit body and a kit cover, the kit body comprises a first unit and a second unit; the first unit is provided with a test tube box containing RNase-Free water, a test tube box containing 2 * qPCR Mix and a test tube box containing a 5 * all in one reverse transcription reagent; the second unit is provided with a test tube box containing an internal standard primer, a test tube box containing a JAZF1-SUZ12 primer and a test tube box containing a positive cDNA standard substance. According to the utility model, the JAZF1-SUZ12 fusion gene can be detected, and the JAZF1-SUZ12 fusion variation has specificity and exclusiveness in the LGESS, so that the JAZF1-SUZ12 fusion gene detection kit has very high value for differential diagnosis of the LGESS; an RT-qPCR method is adopted, whether the fusion gene exists in a patient sample or not is judged according to whether a positive signal exists in a reaction tube or not and a ct value of qPCR or not, the method is rapid, simple and convenient, and no operation is needed after the PCR reaction.

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.

The invention discloses application of a reagent for detecting an esophageal squamous cell carcinoma marker in preparation of an esophageal squamous cell carcinoma auxiliary diagnosis or diagnosis product, and relates to the technical field of esophageal squamous cell carcinoma diagnosis. The esophageal squamous cell carcinoma marker is selected from at least one of CST1, IBSP, HOXA11, METTL11B, PRDM13, GBX2 and TSBP1. The biomarker provided by the invention has the technical advantages of simple operation, high diagnosis sensitivity and strong specificity in auxiliary diagnosis of esophageal squamous cell carcinoma, and has good diagnosis characteristics. Compared with imaging detection, the diagnostic detection cost provided by the invention is lower, the detection period is short, the esophageal squamous cell carcinoma can be rapidly identified and diagnosed by detecting the biomarker, and the kit is suitable for large-scale clinical diagnosis of the esophageal squamous cell carcinoma in people.

The invention relates to a depression disease management system based on targeted metabonomics and a machine learning model, and the system is characterized in that the system comprises an intelligent diagnosis model module, a differential diagnosis model module, a grading diagnosis model module, an accompanying diagnosis model module, a treatment endpoint outcome prediction model module, a recurrence prediction model module, and a comprehensive judgment module. Through the system provided by the invention, the problems in the fields of current clinical depression diagnosis, treatment outcome, relapse and the like can be solved, accurate diagnosis and timely treatment are realized, and the mental health level of the whole people is improved.

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.