68 results about "High risk patients" patented technology

The invention discloses a pre-hospital emergency resource scheduling optimization method and system, and particularly relates to the technical field of resource scheduling. The method comprises the following steps: receiving a multi-source emergency call request, extracting key basic information, and realizing disease condition scoring of each request by using an emergency evaluation model fusing historical cases, emergency rules and a machine learning algorithm; then, a multi-objective optimization model including response time, scheduling cost, resource balance and illness state matching degree is constructed, the weight is dynamically adjusted, and medical oriented optimization of a scheduling decision is realized; and then an optimal dispatching scheme is generated by means of a dynamic weighted intelligent optimization algorithm, rolling updating is carried out in combination with real-time traffic and vehicle states, it is ensured that the dispatching scheme is continuous and effective, and the response priority of a dispatching system to high-risk patients and the overall operation efficiency are remarkably improved.

The invention discloses an emergency treatment high-risk patient real-time grading method based on an improved multi-mode Transform algorithm. The method comprises the following steps: S1, generating a time synchronization multi-mode event sequence; s2, obtaining a corresponding single-mode feature representation tensor; s3, obtaining a risk weighted cross-modal attention matrix; s4, in the improved multi-modal Transform fusion network, generating a fusion feature representation tensor by using a time sequence sliding window cache and incremental updating mechanism, and outputting a risk grading label and a corresponding risk grading confidence coefficient based on the fusion feature representation tensor; s5, inputting the risk grading label and the risk grading confidence into the interpretive sub-network, and generating clinical causal chain prompt information; and S6, synchronizing the risk grading label, the risk grading confidence and the clinical causal chain prompt information. According to the method, the accuracy of high-risk patient identification and the adaptability of the model to a clinical complex scene are remarkably improved, and a test result shows that the real-time identification accuracy of a high-risk case is improved compared with that of a conventional multi-modal model.

The invention relates to the technical field of medical health data analysis, in particular to a multi-modal data fusion abnormal behavior early warning system before falling of a high-risk patient. According to the method, for any patient, a plurality of gait cycles formed by time frames are obtained according to ankle coordinate distribution of different time frames; according to the ankle coordinate distribution of different time frames in each gait cycle, gait high risk of each gait cycle is obtained; according to the hip coordinate distribution of different time frames in each gait cycle, obtaining the center-of-gravity shift degree of each gait cycle, and obtaining the high-risk center-of-gravity shift reference degree of all patients in each gait cycle; for any patient, the falling probability in each sliding window is obtained according to the gravity center shift degrees of different gait cycles, the high-risk gravity center shift reference degree and various physiological parameter data at different moments; and early warning is carried out on abnormal falling behaviors. According to the invention, the accuracy of early warning of abnormal falling behaviors is improved by accurately analyzing the falling probability of the high-risk patient.

The invention provides an image-based eye lesion grading analysis method and device, a medium, a program product and a terminal, and the method comprises the steps: obtaining one or more eye lesion images, inputting the one or more eye lesion images to a pre-trained first model, and generating a corresponding eye feature vector; for a single image, calculating the similarity between the feature vector and each eye disease category, and outputting a prediction category with the highest similarity; and for a plurality of images, respectively calculating the similarity between the feature vectors and each category, and realizing adaptive weighted integration by using the second model to obtain a final prediction category. Sub-model subdivision and risk level layering are carried out according to prediction categories, and a structured risk layering report is output. The problem that in the prior art, the diagnostic value of multi-image information in complex cases is difficult to effectively evaluate is solved, the accuracy of ultrasound in serious vitreous lesion and intraocular tumor recognition is remarkably improved through a self-adaptive weighted integration mechanism, high-risk patient screening and clinical decision making are supported, and the application scene of ultrasound in ophthalmic diagnosis is expanded.

The invention provides a kidney cancer recurrence risk prediction method based on a deep learning model, and relates to the technical field of deep learning, and the method comprises the steps: collecting an image data set for kidney cancer high recurrence risk prediction; carrying out registration on the collected multi-stage enhanced CT image; constructing and training a kidney tumor automatic detection and segmentation model; carrying out ROI positioning cutting and quality control; and constructing a deep learning model for renal cancer recurrence risk prediction based on the multi-modal convolutional neural network, and realizing renal cancer recurrence risk prediction through the constructed prediction network model. According to the method, the multi-phase enhanced CT image of the kidney cancer patient is analyzed through the deep learning model, the tumor postoperative recurrence risk is predicted, an objective basis is provided for a clinician to make an individualized follow-up visit scheme and an auxiliary treatment decision, and excessive treatment of a low-risk patient and insufficient treatment of a high-risk patient are avoided.

The invention provides a full-period follow-up visit patient risk dynamic management method based on time series data analysis. The method comprises the steps of performing standardization processing on multi-source follow-up visit data of a patient; data acquisition is dynamically optimized through an adaptive strategy based on reinforcement learning, and multi-scale time sequence features are extracted; performing dynamic risk assessment by using the attention mechanism enhanced double-layer LSTM model, and outputting a risk probability; and calculating a comprehensive risk index by integrating the risk probability, the change trend and the volatility, and realizing dynamic risk layering and automatic intervention based on a rule engine. According to the method, a traditional fixed acquisition mode is converted into a dynamic optimization process capable of responding to patient risks, equipment states and system loads in real time, on the premise that high-risk patient monitoring is guaranteed, system resource consumption is remarkably reduced, and long-term optimal balance of data quality, system performance and resource overhead is achieved.

The present invention belongs to the field of biological genetic engineering technology and relates to high-risk myelodysplastic syndrome screening genes and diagnostic kits. The present invention provides an application of detecting the expression of YTHDF2, METTL3, and NAT10 genes in bone marrow cells of high-risk MDS patients as a specific molecular marker for screening high-risk myelodysplastic syndrome. By detecting the expression levels of YTHDF2, METTL3, and NAT10 genes mRNA, it helps to screen patients with high-risk myelodysplastic syndrome and better guide the treatment and prognosis evaluation of patients with high-risk myelodysplastic syndrome. The present invention only needs to extract the patient's bone marrow fluid and collect it together when the patient undergoes bone marrow cytology examination. The patient compliance is good, and the material is taken from human bone marrow fluid mononuclear cells. The test results are more accurate and suitable for large-scale clinical application.

The present invention relates to the treatment of cancer, including colorectal cancer (CRC), by inhibiting or blocking Annexin 1. The present invention also relates to the prevention of CRC in high risk patients by inhibiting or blocking Annexin A1. The present invention also relates to the reduction of chemotherapy resistance by inhibiting or blocking Annexin A1. The present invention also relates to the detection of poor prognosis in subjects with CRC by detecting or measuring the expression level of ANXA1 and / or the protein level of Annexin 1.

The invention relates to a radiotherapy oral mucosa reaction early warning and nursing system and a use method thereof, and belongs to the technical field of oral nursing. The system comprises a data acquisition module, a risk assessment module, an early warning prompt module and a nursing scheme generation module; the data acquisition module acquires electronic medical record data, radiotherapy plan data and radiomics characteristic data of a patient; the risk assessment module is based on data acquired by the data acquisition module. In the invention, by integrating electronic medical records, radiotherapy plan parameters and dose-specific radiomics characteristics, a multi-source data fusion prediction model is established, so that the limitation that a traditional method only depends on doctor experience and simple dosimetry parameters is overcome, and particularly, dose-specific mucous membrane subregions are generated through spatial Boolean operation; radiomics features capable of reflecting tissue heterogeneity of different dose regions are extracted, the accuracy and biological significance of a prediction model are remarkably improved, and early recognition of high-risk patients is achieved.

The invention relates to the technical field of health management of diabetic foot high-risk patients, in particular to a foot sole monitoring method and system for diabetic patients based on the Internet of Things, and the system comprises a foot sole data acquisition module, a data analysis module, a dynamic correction module and a user interaction terminal. Pelma temperature, pressure and gait data are collected in real time through a sensor, abnormity is analyzed and risks are evaluated through a deep learning model, a personalized correction instruction is generated, the height of the insole and the gait angle are dynamically adjusted, and precise correction is achieved. The foot sole health state can be monitored in real time, the complication risk can be predicted, a personalized correction scheme is provided, the gait of a patient is effectively improved, the diabetic foot attack risk is reduced, and the life quality of the patient is improved.

The invention discloses a biomarker for accurate typing of esophageal squamous carcinoma lesion and application of the biomarker, and belongs to the technical field of molecular biology, the biomarker is SOX2 protein and KDM4B protein which are co-localized in nuclei, and the biomarker is applied to accurate typing of esophageal squamous carcinoma lesion with a non-diagnostic purpose. The cell proportion and distribution conditions of the SOX2 protein and the KDM4B protein are simultaneously displayed on the same section by adopting a double-label immunofluorescence method, and tests prove that the intranuclear co-localization of the SOX2 and the KDM4B can be used as an early warning mark and is used for identifying high-risk patients or early stages of precancerous lesions or distinguishing tumor patients, so that the missed diagnosis rate is reduced, and the diagnosis time is shortened. Therefore, a reliable basis is provided for accurate typing of esophageal squamous carcinoma lesions.

The invention discloses a pressure monitoring device and method for evaluating pressure sore risk, and relates to the technical field of medical equipment. The system comprises a pressure sensing unit used for collecting pressure distribution data between the body surface of a patient and a supporting surface; the distance measuring device is used for measuring the distance between the device and the body surface of the patient. According to the method, information of three different dimensions of pressure distribution, body surface image and deep thermal imaging is subjected to fusion analysis, a pressure sensor positions a high-risk pressed area, a high-definition color image is used for identifying macroscopic skin damage and color change, infrared thermal imaging can penetrate through a surface layer, and the image can be used for identifying the skin damage and color change. According to the method, deep tissue temperature abnormity caused by ischemia and inflammation is sensitively captured, and the three types of information are integrated into a quantitative comprehensive risk score through a weighting algorithm, so that an evaluation result does not depend on subjective experience of nursing personnel any more and is converted into objective and quantifiable accurate diagnosis, and the screening accuracy of high-risk patients is remarkably improved.

The invention discloses an infectious disease whole-process tracking intervention method and system, a server and a storage medium. The tracking intervention method comprises the following steps: S1, acquiring clinical data of a patient; s2, establishing an infectious disease monitoring closed loop; s3, constructing a time-space relationship model, and generating a patient relationship network; s4, establishing an individual case of the infectious disease patient, and storing a space-time route of the infectious disease patient; inputting information of infectious disease patients, positioning spatial positions of the infectious disease patients, tracing associated patients according to the spatial positions, and screening high-risk patients; and S5, the early warning information and the intervention plan are pushed to the risk node terminal, and the infectious disease patient is intervened. According to the tracking intervention method, the calculated amount is relatively small, positioning is more accurate, the tracking intervention system can be integrated with an existing hospital information management system, a dynamic monitoring network covering the diagnosis and treatment process of a patient is constructed, real-time tracking, real-time early warning and real-time intervention are conducted, and further transmission of infectious diseases is effectively prevented.

The application belongs to the technical field of biological detection, and particularly relates to the use of apolipoprotein A-I in the preparation of a diagnostic reagent or system for predicting the risk of severe acute hepatitis E. The application first discloses and verifies the significant correlation between the serum apolipoprotein A-I (apoA-I) level and the adverse clinical outcomes (including severe jaundice, liver failure and death) of patients with acute hepatitis E, and establishes the risk interpretation threshold (0.675 g / L and 0.435 g / L) with clinical practical value. The application can objectively and early identify high-risk patients, and provides a powerful auxiliary tool for clinical stratified management and resource optimization.

The present invention relates to methods of treating previously untreated diffuse large B-cell lymphoma (DLBCL) defined as high risk according to circulating tumor DNA (ctDNA) by administering galatifamab in combination with chemotherapy.

The invention relates to the field of tumor molecular diagnosis and precision medicine, in particular to a model for predicting early chemotherapy failure of a DLBCL patient and application of the model. By detecting the ctDNA concentration level of the patient base line and the early treatment period and / or combining clinical characteristic parameters, a multivariable risk assessment algorithm is established, the high-risk patient can be identified in the early treatment period, a reliable basis is provided for individualized treatment, and remarkable clinical and social values are achieved.

The invention belongs to the technical field of bioinformatics and medical detection, and particularly relates to a marker combination for neuroblastoma (NB) noninvasive risk level grading, a prediction model, a prediction method and application thereof. The marker combination is used for determining the sex, determining whether the month age is greater than 18 months, determining whether plasma MYCN is amplified, determining whether tumors are metastatic, and determining the content of neuron-specific enolase and lactic dehydrogenase; a machine learning algorithm is used for constructing an NB noninvasive risk degree grading prediction model, the comprehensive performance of the random forest model is optimal, the area value under a subject working characteristic curve reaches 0.956, the sensitivity is 92.9%, the specificity is 82.1%, the accuracy rate is 87.5%, the Kappa value is 0.75, the F1 score is 0.881, and NB middle and low risk patients and NB high risk patients can be effectively distinguished; the NB non-invasive risk level grading prediction model constructed by the invention can quickly, accurately and non-invasively perform NB risk level grading, and has a relatively good clinical application value.

The invention discloses an interventional operation risk prediction method based on double-view cross-semantic interaction, and belongs to the crossing field of artificial intelligence and intelligent diagnosis and treatment of cardiovascular diseases. Aiming at the problems of feature redundancy, incomplete single-view modeling, semantic isolation and the like existing in complication prediction in the existing percutaneous coronary intervention treatment process, the method comprises the following specific implementation processes: firstly, screening key clinical features by adopting a two-stage recursive feature elimination-extreme gradient lifting mechanism; secondly, constructing a patient-index double-view module to describe patient similarity and index association; and then, an asymmetric cross-semantic interaction module is provided, the semantic consistency is ensured by designing collaborative reasoning alignment loss, and finally, dynamic information aggregation between nodes is realized. Accurate prediction and traceable attribution of complications in the percutaneous coronary intervention treatment process are achieved, and decision support is provided for early recognition of high-risk patients.

The present application relates to the technical field of medical health data analysis, in particular to a high-risk patient fall abnormal behavior early warning system based on multi-modal data fusion. The present application obtains a plurality of gait cycles constituted by time frames according to ankle coordinate distribution of time frames for any patient; obtains gait high risk of each gait cycle according to ankle coordinate distribution of time frames in each gait cycle; obtains center of gravity offset degree of each gait cycle according to hip coordinate distribution of time frames in each gait cycle, and obtains high-risk center of gravity offset benchmark of all patients in each gait cycle; obtains fall probability in each sliding window according to center of gravity offset degree, high-risk center of gravity offset benchmark of different gait cycles and a plurality of physiological parameter data of different time for any patient; and early warns fall abnormal behavior. The present application improves the accuracy of fall abnormal behavior early warning by accurately analyzing fall probability of high-risk patients.

According to the pre-warning system and method for the non-planed extubation of the trachea cannula, accurate pre-warning and quick response of the non-planed extubation risk are achieved through multi-mode sensing fusion and low-delay AI reasoning, and intelligent decision support is provided for ICU nursing. According to the method, accurate identification, timely early warning and decision support of the high-risk patient with non-planned tracheal intubation can be realized, so that a nursing intervention scheme is optimized, and the safety of the patient is guaranteed.

The application relates to the technical field of medical data processing, and discloses a construction method and system of a model for predicting the risk of metabolic syndrome caused by antipsychotic drugs, which comprises the following steps: acquiring clinical information and metabolic index data of a discovery cohort and a verification cohort respectively; acquiring genome data of the discovery cohort, screening significant SNP sites through whole genome association analysis, and calculating a polygenic risk score; acquiring proteome data of the verification cohort, screening candidate proteins through metabolic outcome association analysis; taking PRS data and / or candidate protein level data as independent variables, and taking the percentage change of metabolic indicators as a risk label to train a prediction model. The application integrates multi-omics information of genomes and proteomes to construct a prediction model capable of quantitatively evaluating the risk of metabolic syndrome caused by antipsychotic drugs for individuals, which can assist doctors in accurately identifying high-risk patients before or in the early stage, and provides an important technical tool for formulating individualized treatment plans and reducing the risk of MetS in clinical practice.

The invention belongs to the technical field of disease prognosis prediction, and provides a prognosis prediction method for multidrug-resistant tuberculosis in order to solve the problem that an existing prognosis monitoring method for a multidrug-resistant tuberculosis patient has certain limitation. By integrating handmade radiology features and deep learning derived imaging features extracted from baseline, two-month and six-month continuous CT scanning, processing data by adopting a gated recurrence unit (GRU) network, and combining with a multi-drug-resistant tuberculosis prognosis prediction system, a treatment result of a multi-drug-resistant tuberculosis patient is predicted. According to the prediction method provided by the invention, the high-risk patient can be accurately identified in the early stage of the treatment process, and powerful support is provided for personalized treatment decision making, intervention opportunity optimization and reasonable public health resource allocation. Moreover, the kit has higher sensitivity and specificity, and the potential in the aspects of multi-drug-resistant tuberculosis treatment result prediction and risk stratification accuracy is improved.

The invention relates to and discloses a radiation enteritis prevention method based on an AI model and image fusion, which aims at the difficulty in prediction and intervention of radiation enteritis after radiotherapy of abdominal and pelvic tumors, predicts to be incorporated into 100 radiotherapy patients through historical retrospective and prospective prediction, systematically collects multi-modal data, carries out image fusion on the multi-modal data, and carries out image fusion on the multi-modal data. Utilizing interpretable machine learning to fuse radiomics parameters, flora markers and clinical variables, constructing a radiation enteritis dynamic risk prediction model, and verifying the universality of the model through 200 historical queues; key flora / metabolic targets are further screened, a coprophilous fungi transplantation intervention scheme is designed, the regulation and control effect of coprophilous fungi is verified in 50 high-risk patients, an AI auxiliary decision-making system and a microecological preparation are finally developed, a full-chain management strategy of'image-omics prediction-targeted intervention-clinical transformation 'is formed, and an innovative solution is provided for precise prevention and control of radiotherapy complications.

This invention discloses a nursing resource allocation and management method and system based on emergency triage information. It introduces a dynamic split weight mechanism, first calculating the global correlation density of each triage feature to quantify its statistical value in the overall population, and simultaneously calculating local mutation significance factors to capture its weak but crucial discriminative information in critically ill samples. Then, the two are combined to generate dynamic split weights, which are used to optimize the split gain of features in the prediction model. This allows the gradient boosting decision tree algorithm to overcome the limitations of traditional global gain bias towards strong features during feature selection, effectively identifying those latent critically ill key features with low global importance but significant local mutations, significantly improving the prediction accuracy for latently critically ill patients. Based on accurate risk prediction results, nursing resources are dynamically allocated, realizing a shift from passive response to proactive intervention, and solving the technical problem of existing technologies failing to intervene in a timely manner due to missed identification of high-risk patients.

The invention provides an encephalopathy preoperative safety monitoring device, relates to the technical field of medical equipment, and solves the technical problem that in the prior art, high-risk patients cannot be continuously monitored in the transfer process and cannot timely find emergencies. The safety monitoring device used before the encephalopathy operation comprises a positioning mechanism, a cap body and a monitoring mechanism. The positioning mechanism comprises a frame used for fixing the head of a patient and four supports, the four supports are evenly arranged along the edge of the cap body and fixedly connected to the cap body, the length extension directions of the four supports extend in the direction away from the cap body, and the four supports are arranged in parallel; the frame is detachably installed at the ends, away from the cap body, of the four supports, and the distance between the frame and the cap body can be adjusted in the length extending direction of the supports. The monitoring mechanism is installed in the cap body and used for monitoring and detecting vital signs of a patient.

The invention belongs to the field of biological medicines, and particularly relates to application of apoptotic bodies derived from mesenchymal stem cells in preparation of medicines for preventing / treating keratitis. According to the invention, the bone marrow mesenchymal stem cells are induced by a chemical induction method, so that the bone marrow mesenchymal stem cells are subjected to apoptosis and apoptotic bodies are generated. Experiments prove that the apoptotic body derived from the bone marrow mesenchymal stem cells provided by the invention can realize inflammation regulation in bacterial keratitis by regulating polarization of macrophages, and a novel prevention and treatment method which is safer, easy to store and has a practical application effect is provided for treatment of the disease. The invention further provides a medicine for preventing / treating keratitis, the medicine takes the bone marrow mesenchymal stem cell-derived apoptotic body as an effective component, the medicine can be applied to high-risk patients who are prone to bacterial infection after corneal trauma in a pretreatment mode, and a prevention measure is provided for the high-risk exposure patients.

The invention provides a cerebral hemorrhage death rate prediction device, method, equipment, medium and program product for young adult patients, and relates to the technical field of medical biology, in the invention, wide data in a medical information intensive care database (MIMIC) and an electronic ICU cooperative research database (eICU-CRD) are utilized for research, and the young adults diagnosed as ICH and living in ICU are focused. The LassoCV and the RFECV are used for feature selection, and the synthetic minority oversampling technology-edit neighbor (SMOTEENN) is used for solving the problem of data imbalance. A machine learning model (logistic regression, random forest, multi-layer perceptron, k-nearest neighbor and XGBoost) is developed, and hyper-parameters are optimized through Bayesian. The model performance is evaluated by using sensitivity, specificity, accuracy, F1 score and AUC indexes, model interpretation is performed based on SHAP values, and a machine learning model is developed to determine key clinical indexes affecting 14-day and 30-day death rate results, so that clinicians are helped to identify high-risk patients and formulate treatment strategies to support clinical decisions.

The application discloses a biomarker related to bladder cancer and application thereof, the biomarker is tRF-1:28-chrM.Ser-TGA and / or tiRNA-1:34-Glu-CTC-1-M2, which can be used for preparing a bladder cancer early high-risk patient diagnosis and / or prognosis evaluation product, and belongs to the technical field of biological medicine. The biomarker is screened through RNA sequencing, has higher sensitivity and specificity as a biomarker, and is verified through qRT-PCR and ROC curve analysis, and it is proved that tRF-1:28-chrM.Ser-TGA and tiRNA-1:34-Glu-CTC-1-M2 can excellently distinguish bladder cancer patients from normal people, and can be used as an early diagnostic biomarker and a new treatment target of bladder cancer patients, which has great clinical value and practical significance for early clinical diagnosis of bladder cancer, related treatment target evaluation and improvement of the survival rate of patients.

The invention discloses an F-ILD acute exacerbation event prediction method and system based on quantitative image analysis. The method comprises the steps of obtaining a lung computed tomography image, lung function parameters and clinical data of a patient; carrying out quantitative analysis on the computed tomography image of the lung of the patient by adopting computer-aided quantitative analysis software; lASSO regression analysis is carried out through an R language to screen out independent influence factors of acute exacerbation events, and an exacerbation event prediction model is constructed based on the independent influence factors; and inputting to-be-predicted patient data into the exacerbation event prediction model to obtain the occurrence probability of the acute exacerbation event of the patient. The method provided by the invention is helpful for evaluating the occurrence probability of acute exacerbation of patients with fibrotic interstitial lung diseases, so that close follow-up visit to the patients of the type clinically is improved, acute exacerbation is prevented, the patients are managed in time, and deterioration of the disease of high-risk patients is delayed.

The invention provides a system for screening high risk of lower limb arteriosclerosis, and relates to the technical field of risk assessment of lower limb arteriosclerosis. The system comprises a data acquisition module, an initial risk assessment module, a CHINA-PAR assessment module, an ABI assessment module and a Medicom-ArtRiskLegFlow model assessment module. Wherein the data acquisition module is used for collecting patient information, current medical history and inspection data, and generating structured data by using a natural language processing technology. And the initial risk assessment module determines diagnosed and undiagnosed patients according to the structured data. The CHINA-PAR module performs risk assessment on undiagnosed patients, and the risk assessment is divided into low risk, medium risk and high risk. The ABI module performs ABI measurements on middle and high risk patients to determine a further assessment. And finally, the Medicom-ArtRiskLegFlow module calculates the final risk level of the undiagnosed patient according to the weight score and the type score. The system solves the problems of high cost, low precision and inspection risk in the prior art.