237 results about "Patient survival" patented technology

The invention relates to the technical field of medical big data intelligent analysis, and discloses a medical big data intelligent analysis method and system based on deep learning, and the method comprises the steps: obtaining patient image data, case text data and wearable equipment monitoring data, carrying out the preprocessing, and carrying out the standardized coding of each modal data; and calculating the dynamic weight of each modal data, and synthesizing a multi-level fusion weight. And in combination with the fusion weight and the clinical state parameters of the patient, constructing a modal value-clinical urgency degree two-factor resource decision model to obtain a priority score. According to the priority scores, the patients are divided into different emergency treatment levels, and different treatment strategies are provided. The model provided by the invention can maintain efficient prediction capability in various clinical scenes, improves the accuracy of clinical decisions, and enables the resource allocation of hospitals to be more scientific and reasonable. In an emergency treatment environment, the real-time priority evaluation of the patient can significantly improve the efficiency of emergency treatment, reduce the waiting time, and improve the survival rate and the treatment effect of the patient.

The invention provides a bladder cancer prognosis prediction method and system, and relates to the technical field of intelligent detection, and the method specifically comprises the steps: obtaining and preprocessing a bladder cancer hematoxylin-eosin (HE) staining pathological image to obtain an effective color block, and marking to obtain a tumor and necrosis training set; respectively training the two initial models based on the tumor training set and the necrosis training set to correspondingly obtain a tumor model and a necrosis model; acquiring a to-be-predicted bladder cancer pathological image, and performing tumor and necrosis region identification based on the tumor model and the necrosis model to correspondingly obtain a tumor region and a necrosis region; based on the space coordinate information of the tumor area and the necrotic area, carrying out numerical calculation to obtain a space distribution index of the necrotic area relative to the tumor area; and carrying out survival analysis based on the spatial distribution index to obtain survival prognosis information of the bladder cancer patient. The deep learning technology is applied to the prognosis direction of the bladder cancer, a clinician is assisted in evaluating survival prognosis of a patient, and a reference basis is provided for diagnosis and treatment decisions.

The invention provides an intelligent management system for chronic diseases and common diseases of old people. The intelligent management system comprises an old people comprehensive evaluation module, a multidisciplinary collaborative diagnosis and treatment module, a multi-medication risk prediction management module, an intelligent chronic disease follow-up diagnosis management module and a health science popularization knowledge base module, the senile comprehensive assessment module inputs the personal chronic disease map of the patient into a risk factor prediction model for prediction, and outputs a comprehensive assessment result; the multidisciplinary cooperative diagnosis and treatment module is used for the first diagnosis / receiving diagnosis doctor to coordinate multidisciplinary doctors to perform cooperative diagnosis and treatment; the multi-medication risk prediction management module is used for giving comprehensive diagnosis, multi-medication management and risk prediction to the patient through collaborative diagnosis and treatment; the chronic disease intelligent follow-up diagnosis management module carries out follow-up visit tracking, health risk prediction and review prompting on the patient; and the health science knowledge base module matches and pushes corresponding health education knowledge. The accuracy and timeliness of senile chronic disease co-disease risk assessment prediction and management are improved, and a good auxiliary effect is achieved on improvement of the life quality of patients.

The invention discloses a prognosis evaluation method and system for II-III stage colorectal cancer patients, and relates to the technical field of wisdom medicines.The prognosis evaluation method comprises the steps that variables related to survival prognosis of the patients are screened, potential variables influencing RFS and OS of the patients are obtained, and prognosis outcomes of the colorectal cancer patients are evaluated by evaluating relevance between various indexes and prognosis outcomes of the colorectal cancer patients and correcting confounding factors; screening out indexes having important value for the model; the screened indexes and machine learning algorithms are utilized to construct RFS and OS prognosis models of the CRC patients, the RFS and OS prognosis models are used for performing prognosis evaluation on the CRC patients in the II-III stages, and the machine learning algorithms comprise six types of LR, RF, XGB, SVC, MLP and GNB. By integrating clinical data and a machine learning technology, an efficient and accurate tool is provided for prognosis evaluation of the II-III stage colorectal cancer patient, and a scientific basis is provided for making clinical decision support and an individualized treatment scheme.

The invention discloses an AIE type fluorescent probe with tumor targeting as well as preparation and application thereof, and belongs to the technical field of medicines, the probe can accurately target HDAC6 high-expression tumor cells, the specific binding capacity of the probe reduces interference to normal cells, and the detection accuracy is improved. The probe is fused with AIE performance dye molecules and emits strong fluorescence during aggregation, so that the imaging sensitivity and definition are remarkably enhanced. By optimizing the structure and synthesis conditions, the fluorescence intensity and stability of the probe are improved, the sensitivity of early tumor detection is ensured, and the survival rate and life quality of patients are improved. The preparation method is simple, convenient, feasible, low in cost and beneficial to practical application. The probe can be used for preparing tumor detection products, assisting in tumor diagnosis and classification, monitoring treatment changes and evaluating the curative effect. High-precision imaging can be realized in the aspect of tumor imaging, the method can be used for evaluating curative effect and guiding formulation of subsequent treatment schemes, and the application prospect is wide.

An apparatus for determining a patient survival profile using artificial intelligence-enabled electrocardiogram (ECG), the apparatus includes a processor and a memory containing instructions configuring the processor to receive a plurality of patient profiles, wherein each patient profile includes ECG data, define a plurality of cohort labels, wherein defining the plurality of cohort labels includes generating a condition score for each patient profile of the plurality of patient profiles and defining the plurality of cohort labels as a function of the condition score, assign the plurality of cohort labels to the plurality of patient profiles, generate condition training data by correlating the plurality of patient profiles with a plurality of condition identifiers, generate a condition evaluation model and a machine-learning algorithm using the condition training data, determine a patient survival profile for a user-inputted patient profile using the condition evaluation model, and display the patient survival profile at a display device.

The invention discloses a breast cancer neoadjuvant chemotherapy pyroptosis related prognosis model construction method which comprises the following steps: obtaining an original data set, and preprocessing to obtain an annotated whole-gene expression matrix and a survival clinical information matrix; screening out a gene database having significant differential expression on patient survival; obtaining a pyroptosis related gene database; taking an intersection of the pyroptosis related gene database and the gene database having significant differential expression on the survival of the patient to obtain a list of differential expression genes related to the survival of the pyroptosis; and carrying out disease survival prediction model batch construction on the differential expression gene list, screening out two optimal disease survival prediction models as sub-models, and weighting to obtain a prognosis model. According to the method, the problem that gene pathways of a prognosis model of the breast cancer neoadjuvant chemotherapy patient are deficient is solved, and the prognosis prediction capability of the breast cancer patient receiving the neoadjuvant chemotherapy is expected to be improved.

The invention belongs to the technical field of biomedicine, and particularly relates to application of ceramide as a marker in prediction of hepatocellular carcinoma microvascular invasion risk. The invention finds that the preoperative plasma ceramide CER (d18: 1 / 16: 0) level can be used for predicting the MVI of HBV-HCC patients and evaluating the postoperative prognosis of the patients for the first time. The invention provides a preoperative non-invasive detection method based on plasma ceramide CER (d18: 1 / 16: 0), and trauma of traditional histopathology detection is avoided. According to the method, the occurrence risk of the MVI can be effectively reduced by simply detecting a blood sample of a patient. According to the method, the MVI risk of a patient is evaluated through a multi-factor prediction model, and a basis is provided for personalized treatment. The method is not only suitable for preoperative MVI risk assessment, but also can assist in making postoperative follow-up visit and treatment plans, and helps to reduce the postoperative recurrence rate and improve the survival rate of patients.

The invention belongs to the technical field of data processing, and particularly discloses a method and system for predicting the lifetime of a non-small cell lung cancer patient after radiotherapy, and the method comprises the steps: collecting multi-time-point imaging and hematology follow-up visit data of the patient after radiotherapy, building a longitudinal follow-up visit sequence, and mapping the longitudinal follow-up visit sequence to a unified time axis; determining a bimodal first-time significant improvement point through preset rule judgment, and constructing and standardizing an asynchronous index according to the bimodal first-time significant improvement point; then inputting a feature vector at a follow-up time point, encoding a bimodal sequence through a Transform time sequence encoder, and generating joint representation through cross-modal interaction; and finally, inputting the standardized asynchronous index, the joint representation and the clinical and radiotherapy characteristics into a DeepHit model, and outputting a patient survival distribution prediction result. According to the method, bimodal indexes can be accurately captured, time dislocation is improved, prediction accuracy and individualization degree are improved, and a reliable basis is provided for clinical prognosis evaluation.

The present invention proposes an intelligent management system for chronic comorbidities in the elderly, including a comprehensive elderly assessment module, a multidisciplinary collaborative diagnosis and treatment module, a multiple medication risk prediction management module, a chronic disease intelligent follow-up management module, and a health science knowledge base module. The comprehensive elderly assessment module inputs the patient's individual chronic disease profile into a risk factor prediction model for prediction and outputs a comprehensive assessment result. The multidisciplinary collaborative diagnosis and treatment module is used by the first-visit / admitting physician to coordinate multidisciplinary physicians for collaborative diagnosis and treatment. The multiple medication risk prediction management module is used to provide patients with comprehensive diagnosis, multiple medication management, and risk prediction through the collaborative diagnosis and treatment. The chronic disease intelligent follow-up management module performs follow-up tracking, health risk prediction, and review prompts for patients. The health science knowledge base module matches and pushes corresponding health education knowledge. The present invention improves the accuracy and timeliness of risk assessment, prediction, and management of chronic comorbidities in the elderly, and plays a good auxiliary role in improving the quality of life of patients.

The invention discloses an individualized drug dose optimization method based on causal traceability, which improves the accuracy and clinical adaptability of dose regulation and control through a multi-modal space-time alignment and feature decoupling technology, causal reasoning modeling and causal effect quantification and individualized dose optimization, can effectively reduce adverse reactions in the treatment process, and improves the treatment efficiency. The life cycle of a patient is prolonged, the life quality is improved, meanwhile, the long-term medication cost is reduced, and wide clinical application prospects and popularization values are achieved.

A computer-implemented, scalable analytics engineering platform for intelligent insights in healthcare, consisting of: • A data acquisition module configured to collect and import health data from various sources, including electronic health records (EHRs), wearable devices, diagnostic imaging, genomic data, and patient-generated health data; • A data standardization module designed to ensure the consistency and compatibility of the captured data for seamless integration and analysis; • A data curation module configured to organize, filter, and prepare standardized data for advanced analysis; • A data analytics module that uses machine learning algorithms to detect patterns, predict outcomes and provide actionable insights, including predictive analytics, anomaly detection and natural language processing; • A feedback loop mechanism that updates and retrains machine learning models based on real-world data and outcomes to improve analysis accuracy and treatment recommendations; and • A cloud-based infrastructure configured to dynamically scale computing resources, thus ensuring efficient and cost-effective data processing for varying workloads.

The invention relates to the technical field of image analysis, in particular to a breast cancer screening analysis system and method based on multi-modal image fusion, and the system comprises an information entropy weight adjustment module, a time-frequency domain feature processing module, a geometric correction fusion module, a data real-time updating module and a comprehensive analysis output module. According to the method, weight distribution is optimized by evaluating the contribution degree of each mode in a specific scene, the data processing accuracy in the diagnosis process is enhanced, time-frequency domain conversion and filtering synchronization are carried out on image data, efficient integration of different mode features is achieved, and image alignment is more accurate through multi-scale decomposition and feature matching of geometric correction; the method has the advantages of reducing the misdiagnosis rate, flexibly adjusting and keeping the timeliness and adaptability of a processing strategy according to new diagnosis data, and remarkably improving the accuracy of early detection of breast cancer by strengthening key diagnosis features and improving the expression quality of image data, thereby playing a positive role in improving the survival rate and life quality of patients.

The invention relates to a composition and related methods for reducing tumor volume and / or increasing the survival of a cancer patient. The composition comprises a recombinant MVA encoding a Tumor Associated Antigen (“TAA”) as well as 4-1BBL and / or CD40L and can be administered to a subject in any suitable manner, including by intravenous and / or intratumoral administration.

A method of predicting overall survivability of a patient by a prediction system based on machine learning includes receiving, by the prediction system, first modality data and second modality data corresponding to the patient; generating, by the prediction system, a first intermediate feature vector and a second intermediate feature vector based on the first and second modality data; determining, by the prediction system, a first attention score and a second attention score based on the first and second intermediate feature vectors; generating, by the prediction system, an aggregate feature vector based on the first and second intermediate feature vectors and the first and second attention scores; and generating, by the prediction system, a survivability prediction corresponding to the patient based on the aggregate feature vector.

The invention belongs to the technical field of biological information, and provides an ampulla cancer patient prognosis model based on machine learning and a construction method thereof. According to the method, clinical information of an ampulla cancer patient is obtained through a cancer statistical database, clinical characteristics independently related to survival of the patient are analyzed and positioned through single-factor and multi-factor COX, a prognosis model of the ampulla cancer is established through multiple machine learning algorithms according to the clinical characteristics, and the method can be used for survival prediction of the patient. The method has the advantages of being rapid in prediction, high in accuracy and easy and convenient to use, gene determination does not need to be specially conducted on tissue or blood sampling of the patient, accurate survival rate judgment can be achieved only through clinical information, a clinician can make a personalized treatment scheme of the patient according to the prognosis state, and therefore the survival rate of the patient is increased.

The invention relates to a method and a model for predicting the lifetime of a non-tumor peaceful patient based on clinical data. The method comprises the following steps of: collecting and sorting previous hospitalization clinical data of a non-tumor peaceful treatment death patient, and carrying out standardized pretreatment; carrying out attribute selection and reduction on the high-dimensional data from two perspectives of correlation and redundancy by using a maximum information coefficient (MIC); the method comprises the following steps: respectively carrying out attribute weighting by applying a Pearson correlation coefficient and a Spearman rank correlation coefficient; a weighted naive Bayes classification model is used for modeling; according to the method, clinical factors related to the lifetime of the non-tumor Anning treatment patient can be objectively analyzed and predicted, and guidance is provided for Anning clinical admission standard formulation and early and later treatment plans. The model can be realized in the form of a doctor workstation and the like, and the medical work efficiency and accuracy can be greatly improved.

The invention discloses a method for predicting the survival rate of a patient with heart implantation equipment, which is used for predicting the survival rate of the patient with the heart implantation equipment through a trained heart implantation prognosis model. The cardiac implantation prognosis model construction method comprises the following steps: obtaining clinical data of a patient with cardiac implantation equipment, and preprocessing the clinical equipment; selecting prediction features for predicting the survival rate of the patient with the heart implantation equipment from the existing clinical features; taking the patient data with the prediction features as a training sample set to train a prognosis model based on a multi-layer perceptron model so as to obtain the prognosis model; the prognosis model is optimized by adopting 5-fold cross validation.

A multimodal deep learning framework which is used to determine the likelihood of a particular treatment method effectively treating a patient with ovarian / kidney cancer with the goal of increasing patient survival. The framework takes into account not only large histopathology images (whole slide images), but also clinical variables to increase the scope of the data. The results demonstrate that the proposed models achieve high prediction accuracy and interpretability and can also be transferred to other cancer datasets without significant loss of performance. One of the key innovations here is the combination of pathology and clinical variables in a deep learning model to provide recommendations in therapy areas with limited information.

The invention discloses an aviation medical rescue task simulation system and method, and the system comprises a task scenario module which comprises a scenario database module and a task scenario deployment interface; the scenario database module is used for providing a medical rescue aircraft equipment and medical equipment database and a patient survival model information database, and the task scenario deployment interface is used for performing scenario editing on the task; the simulation module is used for simulating an aviation medical field first-aid task, a short-distance transfer task and a middle-long-distance transfer task scene; and the scheme evaluation module is used for summarizing and processing the data after simulation deduction, and outputting multi-azimuth task evaluation including a single machine index, a task index and a comprehensive efficiency index. According to the invention, through integrating the task scenario module, the simulation deduction module and the scheme evaluation module, simulation evaluation of the aviation medical rescue task is realized, and reference is provided for improving rescue efficiency and accuracy, optimizing resource configuration and scientifically deciding.

The invention provides a glioblastoma patient lifetime prediction method and system, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: obtaining and coding a digital full-slice image of a patient; screening high-information-content image blocks based on information entropy; executing a dendritic calculation rule and a structural plasticity rule through internal learnable dendritic neurons by using a dendritic morphological neural calculation model, and extracting global and local features from the image blocks; fusing the features through feature fusion neurons to obtain depth features; and finally, predicting the total lifetime through the output neurons. By simulating hierarchical calculation and plasticity of neuron dendrites, a double-level feature learning model capable of integrating global context and local details of a full-slice image at the same time is constructed, and the technical problems that an existing prediction method is insufficient in feature integration and limited in model calculation structure are effectively solved. And more comprehensive and accurate prediction of the lifetime of the glioblastoma patient is realized.

The invention discloses an application of a PLSCR1 protein in preparation of a biomarker for evaluating early warning of sepsis. The invention determines and verifies that the PLSCR1 is obviously increased in sepsis patients for the first time. According to the invention, the PLSCR1 is used as a novel biomarker for early warning and prognosis evaluation of sepsis, so that the clinical early recognition and prognosis capability is effectively improved. The PLSCR1 shows an independent prediction value, detection of the PLSCR1 level of a suspected or diagnosed sepsis patient can provide a more accurate and more comprehensive basis for clinical decision making, and improvement of the survival outcome of the patient and optimization of the treatment effect are facilitated.

The present disclosure provides an establishment and use of index for evaluating malignancy level of tissue microenvironment, which can be used to detect the potential of an individual to develop precancerous lesions and to predict the recurrence of tumors without relying on patient survival data.

The invention provides a method and device for predicting the lifetime of a tumor patient, computing equipment and a storage medium. The method comprises the following steps: acquiring multi-dimensional medical data related to the condition of a tumor patient, wherein the multi-dimensional medical data at least comprises data of at least two dimensions; determining a similar dataset of the multi-dimensional medical data from a reference dataset, the reference dataset including reference multi-dimensional medical data and corresponding lifetime of each of a plurality of reference patients having reached the end of life at different time points; and obtaining a prediction result of the lifetime of the tumor patient based on the multi-dimensional medical data and the similar data set by using a fine-tuned large language model, the prediction result indicating the lifetime of the tumor patient and an explanatory text of information on which the prediction result is based.

The invention provides an illness state tracking and early warning system based on a knowledge database, and the system comprises a data module which is responsible for collecting and integrating clinical knowledge and patient data information in the cardiovascular field; the survival rate prediction analysis module is used for extracting pathological features from the patient model, performing survival rate prediction analysis by using a deep learning algorithm and a neural network model, and displaying a prediction result to a physician in a visual report form; and the illness state auxiliary tracking, studying and judging module monitors the illness state change of the patient in real time, analyzes the illness state development trend and risk factors in combination with a knowledge graph and a patient model, and provides treatment scheme recommendation and early warning notification functions. Through the beneficial effects of integrating medical resources, improving data security, accurately predicting the survival rate of a patient, monitoring and early warning in real time, widely applying a knowledge graph, being friendly to user interaction, feedback circulation and continuous optimization and the like, powerful support is provided for prevention, diagnosis and treatment of cardiovascular diseases.

The present invention relates to a biomarker for predicting immunotherapeutic responsiveness based on spatial transcriptome analysis and uses thereof and, in particular, to: a marker composition for predicting the responsiveness of cancer patients to immunotherapy; a composition for predicting the responsiveness of cancer patients to immunotherapy; a kit for predicting the responsiveness of cancer patients to immunotherapy, comprising the composition; a method for providing information for predicting the responsiveness of cancer patients to immunotherapy; and a method for providing information for predicting the survival prognosis of cancer patients. The biomarker for predicting immunotherapeutic responsiveness, according to the present invention, was discovered by applying spatial transcriptome technology and analyzing cell group-specific gene expression values according to location information of cells in tissue sections, and can more precisely and accurately predict the responsiveness of cancer patients to immunotherapy and the survival prognosis of patients, thus enabling suitable treatments for patient groups, which may result in improved therapeutic effects and a reduction in pain and costs for patients.

The invention discloses an HIV (human immunodeficiency virus) combined hepatocellular carcinoma risk stratification model and a construction method thereof, the construction method comprises the following steps: obtaining clinical data of an HIC-HCC patient, the clinical data comprising demographic information, clinical feature information and treatment and outcome information; taking the total lifetime of the patient as an outcome, performing single-factor analysis on the clinical data, and screening out a plurality of risk factors influencing the total lifetime of the patient; performing multi-factor analysis on the plurality of risk factors to obtain independent prognosis factors of the total lifetime of the patient; the independent prognosis factors comprise organ fatigue and multiple tumors; and establishing a risk layering model based on the independent prognosis factors.

The application discloses a cancer survival prediction method and device based on whole-slice pathological images, and relates to the technical field of medical artificial intelligence and digital pathological image analysis. The method comprises the following steps: acquiring whole-slice pathological images and patient survival information, and performing pretreatment; performing cell detection on the whole-slice pathological images to obtain cell structure data; dividing the whole-slice pathological images into a plurality of image regions, and determining regions of interest; screening the cell structure data in each region of interest to construct a cell subset; sorting the cell structure data according to the distribution relationship of cells in space to construct a cell structure sequence; constructing a cell structure modeling model, performing feature modeling on the cell structure sequence, and obtaining tissue structure features capable of representing the microstructure of tumor tissues; and inputting the tissue structure features into a survival prediction model to predict the survival risk of the patient. The application can improve the accuracy and stability of cancer survival prediction.

The application belongs to the technical field of tumor immunotherapy, and particularly relates to a composition for enhancing tumor immunogenicity and application thereof. The composition for enhancing tumor immunogenicity comprises, in terms of mass fraction, 10-15 parts of radix stephaniae tetrandrae, 10-20 parts of radix astragali, 3-9 parts of glycyrrhiza uralensis, and 3-12 parts of atractylodes macrocephala. The composition for enhancing tumor immunogenicity provided by the application is a natural traditional Chinese medicine prescription, has good biocompatibility, low toxicity and side effects, and is suitable for long-term treatment. The composition enhances immunogenicity by up-regulating MHC-I expression of tumor cells, activates CD8 + T cell-mediated anti-tumor effect, realizes autologous immunotherapy, and can avoid related side effects of exogenous immunotherapy. The composition can also be used in combination with a PD-1 blocker to synergistically improve anti-tumor efficacy and prolong patient survival.

The present invention belongs to the technical field of medical diagnosis. Provided are a marker and a kit for the clinical diagnosis of prostate cancer and the use. By means of integrating the expression or variation of multiple genes, the marker can more comprehensively reflect the biological characteristics of prostate cancer, thereby improving the diagnostic accuracy. Since prostate cancer may present no obvious symptoms in the early stage, such a highly sensitive marker can assist physicians in detecting prostate cancer in the early stage, and can thus enhance the therapeutic efficacy and patient survival rate. The kit and system based on the marker enable the diagnosis or prediction of prostate cancer in a subject by means of acquiring detection information of the marker and combining the information with a preset threshold, thereby allowing for the efficient processing of a large volume of data and providing accurate diagnostic results. Therefore, the present invention not only contributes to the diagnosis and treatment of prostate cancer, but may also offer new insights into the pathogenesis and prevention of prostate cancer.