452 results about "Clinical information" patented technology

The invention relates to the technical field of data processing, and discloses a preoperative risk comprehensive assessment method and system for breast cancer and a storage medium. The method comprises the following steps: collecting clinical information and carrying out digital processing; segmenting the multi-modal image by using deep learning; analyzing a pathological section based on the image features; carrying out multi-level fusion modeling to obtain a risk heat map; performing digital twinborn simulation in combination with a knowledge graph; and dynamically processing the follow-up data to form a risk adjustment mechanism. According to the method, accurate quantification and individualized treatment decision making of the preoperative risk of the breast cancer patient are realized, and the prediction accuracy is improved.

The invention discloses a mild traumatic brain injury image evaluation system based on deep learning, and relates to the technical field of medical imaging, and the system comprises a data source module which is used for obtaining head magnetic resonance imaging data as an input data source; the preprocessing module is used for carrying out de-noising processing and image registration on the skull magnetic resonance imaging data to realize standardized preprocessing; and the feature extraction module is used for extracting a local feature map from the multi-dimensional image data by constructing a multilayer convolutional neural network, and identifying a subcortical structure change and damage suspicious region. According to the mild traumatic brain injury image evaluation system based on deep learning provided by the invention, a standardized evaluation report automatically generated by the system not only integrates clinical information of a patient, but also provides detailed injury types, severity and follow-up visit suggestions, so that the efficiency and reliability of clinical decision making are greatly improved; and powerful support is provided for formulating an individualized diagnosis and treatment scheme.

The invention discloses an intestinal cancer immune response graph neural network prediction method, a medium and equipment, and the method comprises the steps: collecting pathological image information, immunodetection information and basic clinical information, extracting a tissue space distribution characteristic spectrum through a deep convolutional network, and constructing a graph neural network model in combination with an immunomarker expression characteristic matrix; spatial interaction characteristics of a tumor microenvironment are modeled by adopting a graph attention mechanism, finally a treatment response probability, an optimal treatment opportunity and an adverse reaction risk are predicted through a multi-task learning framework, and a clinical decision report containing a prediction response curve, a risk early warning threshold and a treatment time window suggestion is output. According to the method, through multi-modal data fusion and spatial interaction modeling, accurate prediction of intestinal cancer immunotherapy response is realized, and a more comprehensive reference basis is provided for clinical decision making.

The invention discloses a prostate cancer three-classification risk layering method based on an integrated learning model, and relates to the technical field of data processing and analysis. The method comprises the following steps: collecting clinical information and pathological data of a patient with increased PSA, and dividing the data into a training set and a test set; the training set is preprocessed, and prediction features are screened through LASSO regression; constructing a plurality of machine learning base models based on the features, and training and optimizing through cross validation; soft voting is constructed through an integration strategy, and an integration model is stacked; setting double thresholds according to the integrated model prediction probability, and establishing a layering rule; combining an integrated model and rules to form a three-classification model, and judging low, high and medium risks according to probabilities; and finally verifying the model diagnosis performance in the test set. According to the method, through cross-modal feature integration and ensemble learning, the method is PSAlt; accurate risk stratification is provided for 30 ng / mL people, biopsy decision-making efficiency is optimized, and excessive puncture and missed diagnosis risks are reduced.

The invention discloses an intelligent diagnosis and risk assessment method and system for cerebral apoplexy related to atrial fibrillation, and relates to the technical field of atrial fibrillation detection.The method comprises the steps that multi-dimensional data of atrial fibrillation patients in a clinical information system are integrated, a structured database is generated, and a standardized data set is output; a standardized data set is adopted to train a first machine learning model, and model performance is optimized through parameter joint search and a training set-verification set convergence dynamic monitoring mechanism; performing cross validation on a feature weight sorting result in the optimized diagnosis model and a clinical index risk association degree calculated by a second machine learning model to generate an interaction map; and based on clinical event data containing timestamps in the structured database, adopting a third machine learning model to extract time sequence features, and combining with a survival probability analysis model to generate a risk assessment report. According to the invention, through an intelligent model adjusting and optimizing mechanism, multi-dimensional medical data are effectively integrated, and the recognition precision of the atrial fibrillation related cerebral apoplexy is greatly improved.

The invention relates to a nitric oxide data analysis method and system for bronchial asthma assessment. According to the method, exhaled air nitric oxide and pulmonary alveolar nitric oxide of a patient, omics data and clinical information are acquired in a standardized manner, and multi-dimensional features are constructed after data cleaning and fusion; comprise an inflammation level index, an inflammation region entropy, an inflammation synergy index, a glucocorticoid response factor, an infection-smoking synergy influence factor and a heredity-symptom distribution index. And outputting initial risk assessment based on an explainable elevator EBM model, and finally generating an asthma risk probability through a weighted integration formula to realize three-level layering: low / medium / high risk. The system dynamically associates treatment decisions, for example, the drug dosage and the monitoring frequency are improved when the risk is upgraded, and the medication scheme is optimized when the risk is degraded. According to the scheme, the limitation of traditional single marker static analysis is broken through, multiple mechanisms of dissection, immunity and pharmacology are fused, the evaluation precision and clinical applicability are remarkably improved, the acute attack rate is reduced, and accurate typing treatment is guided.

The invention provides a traditional Chinese medicine electronic medical record natural language processing and knowledge graph construction method and system. The method belongs to the technical field of the crossing field of traditional Chinese medicine informatization, artificial intelligence natural language processing and knowledge engineering, and comprises the following steps: performing multi-granularity semantic unit division on the traditional Chinese medicine electronic medical record to generate traditional Chinese medicine text semantic unit data; constructing a multi-granularity semantic decoupling engine according to the traditional Chinese medicine text semantic unit data so as to construct a traditional Chinese medicine text semantic analysis framework; performing traditional Chinese medicine unstructured text deep analysis according to the traditional Chinese medicine text semantic analysis framework, and performing multi-dimensional semantic feature extraction to obtain traditional Chinese medicine text semantic feature data and medical record time sequence data; through multi-granularity semantic unit division and deep analysis, the unstructured text in the traditional Chinese medicine electronic medical record can be effectively converted into structured semantic data, and the ability to understand and process traditional Chinese medicine clinical information is further improved.

The invention belongs to the technical field of intelligent diagnosis, and particularly relates to an AI combined MRI and clinical JIA diagnosis system and a storage medium. According to the system disclosed by the invention, the early auxiliary diagnosis of the juvenile idiopathic arthritis is carried out by combining artificial intelligence with multi-dimensional and multi-modal information of multi-sequence MRI images of knee joints of children and various clinical information. The main technology of the method is child knee joint tissue segmentation based on deep learning, a multi-dimensional feature extraction strategy based on a segmentation result, and disease classification based on multi-modal feature integration and deep learning. By integrating the multi-dimensional features of the multi-sequence MRI images and fusing different modal features such as image information and clinical information, an auxiliary diagnosis result with high accuracy can be provided. The technology provided by the invention is beneficial to the realization of early diagnosis and early treatment of juvenile idiopathic arthritis, and has a very good application prospect.

The invention relates to the field of medical image analysis, in particular to a lung adenocarcinoma EGFR gene mutation detection system and method based on PET / CT deep learning, and the system comprises a data collection module which is used for obtaining PET / CT image data and clinical information of a lung adenocarcinoma patient; the image preprocessing module is connected with the data acquisition module and is used for carrying out standardization processing and ROI extraction on the PET / CT image data; the feature extraction module is connected with the image preprocessing module and used for extracting depth features, metabolic parameter features and CT sign features from the PET / CT image data; the multi-modal data fusion module is connected with the feature extraction module and used for fusing the extracted multi-modal features; and the prediction model module is connected with the multi-modal data fusion module and is used for predicting the lung adenocarcinoma EGFR gene mutation state and prognosis based on the fusion features, and the prediction accuracy is improved through multi-modal data fusion and deep learning technologies.

The invention relates to the technical field of ultrasound contrast, in particular to an ultrasound contrast T-tube sinus tract recognition method based on deep learning. The method comprises the following steps: firstly, collecting contrast agent parameters, ultrasonic equipment parameters and clinical information, adjusting a prediction model by utilizing the contrast agent parameters, and optimizing the contrast agent parameters in combination with the clinical information; and then, acquiring an ultrasonic contrast image of the T-tube sinus tract according to the updated contrast agent parameters and the ultrasonic equipment parameters. Thirdly, preprocessing the T-tube sinus tract ultrasonic contrast image, inputting the preprocessed T-tube sinus tract ultrasonic contrast image into the multi-task ultrasonic contrast image enhancement model, and performing image enhancement and adaptive adjustment in combination with ultrasonic equipment parameters; and finally, constructing an ultrasound contrast image multi-stage classification identification model, identifying the T-tube sinus tract ultrasound contrast enhanced image to obtain a T-tube sinus tract identification result, and combining clinical information to assist medical personnel in diagnosis. According to the invention, the T-tube sinus tract identification precision and accuracy can be improved.

The invention discloses an electrocardiogram analysis method and device based on a deep learning model and a medium, and relates to the field of deep learning, and the method comprises the steps: carrying out the preprocessing of an electrocardiogram, and carrying out the noise suppression; inputting the electrocardiosignals into a pre-trained deep learning model, extracting time features and spatial features, and performing cross-modal feature fusion; synchronously executing a plurality of anomaly detection tasks, and synchronously executing a time sequence prediction task; aiming at the abnormal detection result, correcting the abnormal detection result according to the clinical information and the time sequence prediction result; and outputting an analysis result corresponding to the electrocardiogram. A complete closed loop is formed from signal processing to feature extraction, multi-task analysis and result correction, manual intervention links are reduced, electrocardiogram analysis time is remarkably shortened, result consistency is guaranteed, end-to-end automation is achieved, and efficiency is improved.

The invention is applicable to the technical field of clinical medicine, provides a clinical multi-modal cancer drug response prediction method based on feature reconstruction, constructs a clinical multi-modal model for drug response prediction of diffuse large B-cell lymphoma, and aims to predict the drug response of diffuse large B-cell lymphoma by integrating gene sequencing and clinical multi-modal data. And accurate drug reaction prediction is realized. The model adopts an end-to-end multi-stage processing flow: firstly, extracting gene features through TransP-Net, and processing multi-modal clinical data by using a clinical information encoder; then, pseudo-gene features are generated through a clinical-genome filling module to deal with the data missing problem; and finally, multi-modal deep fusion is realized through a clinical information decoder, and a prediction result is output. According to the method, data characteristics and working processes in a real clinical environment are fully considered, two conditions of complete gene data and missing gene data can be processed at the same time, and the method has a good clinical transformation prospect and application value.

Provided in the present invention are a method for constructing a pre-eclampsia risk assessment prediction model, and a prediction product, a prediction system, and a related application of the method. Specifically, the method of the present invention comprises: constructing a logistic regression analysis model on the basis of acquired clinical indexes of a pregnant woman, and obtaining the prior probability of early-onset pre-eclampsia or late-onset pre-eclampsia in the pregnant woman; performing median-of-multiples calibration on an acquired MAP, IGFBP1 and / or PLGF of the pregnant woman, and respectively constructing a multivariate normal distribution model for a sick pregnant woman and a multivariate normal distribution model for a healthy pregnant woman on the basis of an MAP MoM value and an IGFBP1 MoM value and / or a PLGF MoM value, so as to obtain the conditional probability of early-onset pre-eclampsia or late-onset pre-eclampsia in the pregnant woman; and in view of the prior probability and the conditional probability, calculating the posterior probability of early-onset pre-eclampsia or late-onset pre-eclampsia in the pregnant woman on the basis of a Bayesian model. In the present invention, the detection of related protein markers and the obtaining of maternal clinical information data are relatively easy, the accuracy of predicting early-onset pre-eclampsia and late-onset pre-eclampsia is relatively high, and clinical popularization and application are facilitated; therefore, the present invention is of great value to the prevention and early diagnosis of a disease.

The invention discloses an intelligent nursing record generation method and device, and the method comprises the steps: obtaining nursing voice data, and carrying out the noise reduction and sound enhancement processing to obtain a preprocessed voice stream; a speech recognition technology fusing ECAPA-TDNN and an x vector is adopted to recognize medical terminologies, and a text transcription result is generated; recognizing the voice of the target nurse from the preprocessed voice stream and the transcription result in combination with a speaker-independent model and a speaker condition model to obtain a voice transcription text of the voice; performing voice analysis and structured processing on the text by using a multi-modal self-supervised learning model and a hierarchical CNN-BiLSTM framework to obtain a structured text with clinical semantic features; key clinical information is extracted in combination with the medical ontology knowledge base, and a nursing record element set is obtained; and based on the nursing record element set, automatically generating a nursing record meeting the specification. According to the invention, the automatic generation from the nursing voice to the standardized nursing record is realized, and the efficiency and accuracy of the medical nursing record are improved.

The invention discloses a breast cancer lymph node metastasis prediction system based on a gene spectrum, and relates to the technical field of breast cancer prediction systems. Comprising a data acquisition module which collects gene spectrum data of a breast cancer patient and collects detailed clinical information of the patient; the preprocessing module is used for carrying out data cleaning and data normalization processing on the collected data; and the feature extraction module is used for extracting principal component features by applying principal component analysis on the basis of the gene expression data. According to the method, gene expression data are considered, various gene spectrum data such as gene mutation and copy number variation and detailed clinical information are integrated, the biological characteristics of the breast cancer can be reflected more comprehensively, and the prediction accuracy is improved.

The invention relates to a method for acquiring a biomarker for assisting CRLM early diagnosis. The method comprises the following steps: acquiring sequencing data, clinical information and metabonomics characteristics of a CRC sample; evaluating and determining a plurality of differentiated machine learning models; performing classification according to the determined machine learning model, and constructing a CRLM biomarker prediction model; screening candidate biomarkers according to the prediction model; and verifying the candidate biomarker at least according to the tissue slice and the serum sample, and determining a final biomarker.

The invention discloses a device for awareness level evaluation and a storage medium. The apparatus comprises: a processor; the device realizes the following operations: collecting clinical information of a person to be assessed and a task state electroencephalogram signal under a target stimulation normal form; extracting frequency domain characteristics of a specific frequency band and spatial-temporal characteristics of a target event related potential based on the task state electroencephalogram signal, and combining the frequency domain characteristics and the spatial-temporal characteristics into a corresponding electroencephalogram topographic map; inputting the corresponding electroencephalogram topographic map into a multi-modal large language model, and performing image feature extraction by using an image encoder to obtain electroencephalogram features; inputting clinical information into the multi-modal large language model, and performing text feature extraction by using a text encoder to obtain text features; and performing cross-modal attention calculation fusion on the electroencephalogram features and the text features by using a cross-modal fusion module to realize consciousness evaluation so as to output a consciousness evaluation result. By means of the scheme, the consciousness level of the patient can be automatically and accurately evaluated.

The invention discloses a post-stroke depression risk prediction system based on cerebral small vascular diseases and inflammatory markers, and relates to the technical field of computer-aided engineering, the post-stroke depression risk prediction system comprises: a data acquisition module acquires CSVD image data, serum inflammatory markers and clinical baseline information; the data preprocessing module processes image denoising registration, fills up marker missing values and encodes clinical information; the CSVD feature extraction module extracts image omics features and quantifies severity; the inflammation marker module calculates statistical characteristics and inflammation intensity; the multi-dimensional fusion module integrates features and eliminates redundancy; the risk prediction module is trained by using an improved attention CNN-LSTM model; the result output module visualizes risk and intervention suggestions; the model dynamic optimization module updates parameters by incremental learning. According to the method, multi-source key data are integrated, the prediction accuracy and generalization ability are improved, clinical interpretation and dynamic adaptability are achieved, early recognition of post-stroke depression is assisted, and patient prognosis is improved.

The invention discloses a traditional Chinese medicine intelligent diagnosis and treatment system and method. The diagnosis and treatment system comprises a medical thinking layer used for clinical information reasoning decision; the expert mixed framework layer is used for refined data processing, analysis and reasoning; the clinical tool layer is used for collecting and analyzing clinical information; the interaction layer is used for providing digital human, voice, image-text and other interaction modes for patients and doctors, and the medical thinking layer and the expert mixed framework layer are in communication interaction with the interaction layer and the clinical tool layer. The diagnosis and treatment system has the advantages that expert models in different fields are combined through the expert mixed framework layer to achieve refined and intelligent support and optimization of the traditional Chinese medicine diagnosis and treatment process, the traditional Chinese medicine clinical real diagnosis and treatment process is simulated, the medical thinking layer is applied to conduct clinical information reasoning and decision making, and the diagnosis and treatment efficiency is improved. And the reliability and interpretability of the traditional Chinese medicine intelligent diagnosis and treatment paradigm are improved, so that a grassroots traditional Chinese medicine doctor can be assisted to improve the differentiation accuracy, and the popularity of traditional Chinese medicine diagnosis and treatment services is promoted.

The invention provides a rare disease risk screening model training system and method, a screening system and a medium, and belongs to the technical field of medical information. According to the invention, an innovative three-stage architecture design is adopted, unified identification of all rare disease types is realized, and the blank in the prior art is filled. When only basic clinical information exists, a rare disease high-risk patient can be accurately identified, the diagnosis time is remarkably shortened, and a treatment precedent is won for the patient. The system adopts a small parameter quantity model integration strategy, reduces hardware requirements and maintenance cost, can be widely deployed in basic medical institutions, and promotes balanced distribution of medical resources. Through the technical measures of nested cross validation, algorithm comparison, threshold optimization and the like, the system has high accuracy and reliability, and the credibility of doctors to the system is enhanced.

The invention discloses a clinical intelligent decision-making method based on proxy workflow and a storage medium. Comprising the following steps: acquiring and preprocessing clinical information of a patient, and constructing a candidate disease set; and constructing a proxy directed workflow. In the retrieval stage, the diagnosis criteria corresponding to the candidate diseases are retrieved and aggregated from the diagnosis criteria library rechecked by the experts to form working memory. The preliminary diagnosis stage model node generates a preliminary candidate diagnosis set in combination with work memory and patient medical history and physical examination. And the final diagnosis stage generates a final diagnosis result based on the preliminary candidate diagnosis and the complete clinical information. And when the global confidence is lower than a threshold value, the model node pointedly checks an information source and updates reasoning and confidence. A successful reasoning track forms a demonstration set after manual auditing, the demonstration set is used for supervising a fine tuning model to obtain an initial strategy, multiple structured outputs are generated through grouping sampling, relative strategy updating is carried out in combination with reward signals and reference strategy regularization constraints, and optimization and stable improvement of the model diagnosis capability are achieved.

The invention specifically relates to a clinical deterioration risk prediction and early warning system and method based on machine learning, and relates to the technical field of medical artificial intelligence and clinical informatics, and the method comprises the steps: obtaining multi-dimensional time series data in real time; constructing a dynamic feature engineering vector; machine learning risk prediction; judging a risk threshold value and triggering early warning; and interpretation and suggestion generation driven by the large language model. According to the method, multi-dimensional time sequence data is continuously acquired in real time, multi-sliding window statistical features and standardized clinical deterioration scores are extracted in combination with dynamic feature engineering, and accurate quantitative risk prediction of multiple disease deterioration types such as sepsis and respiratory failure is realized by means of machine learning models which are specifically trained by XGBoost, LSTM and the like. The problem that traditional early warning depends on manual judgment and is high in hysteresis is effectively solved; medical staff can be helped to quickly grasp the core inducement of disease deterioration, and a standardized and landing action scheme is provided.

The invention discloses a structured information extraction system for a mental disorder electronic medical record based on a knowledge graph and a natural language, and belongs to the technical field of medical information processing. According to the system, firstly, core concepts such as diseases, symptoms and drugs are extracted from an authoritative guide to construct a mental disorder knowledge graph, and a standardized clinical knowledge base is established; then pre-training and fine-tuning the deep learning model on a large number of biomedical texts and desensitized medical records to enable the deep learning model to have a medical language understanding ability; performing preprocessing, named entity recognition and entity linking on the electronic medical record text, and mapping spoken expressions to standard medical terms; inference is carried out by utilizing a relation extraction model and combining with a knowledge graph to complement implicit clinical information; and finally, structured data output meeting the standards of FHIR and the like is generated. According to the method, through deep fusion of knowledge driving and data driving, the problems of insufficient semantic understanding and weak generalization ability of a traditional method are effectively solved, and the accuracy and clinical value of electronic medical record structured information extraction are remarkably improved.

The invention discloses an intracranial aneurysm rupture risk assessment method, device and equipment and a storage medium, and the method comprises the steps: firstly, generating a time sequence image containing flow field information based on the maximum density projection and center line extraction processing of a two-dimensional angiography image sequence in combination with contrast agent concentration change curve analysis; performing three-dimensional reconstruction, region-of-interest extraction and opening extension processing by using the three-dimensional image sequence to obtain detailed morphological parameters and a region-of-interest model after extension processing; secondly, displaying a time sequence image with flow field information and a region-of-interest model of which an opening is not prolonged in an overlapping manner, further optimizing setting of boundary conditions and performing hydrodynamic simulation by calculating a flow field vector in an observation ball and determining an actual blood flow direction, and calculating key hemodynamic parameters; and finally, in combination with the morphological parameters, the hemodynamic parameters and clinical information of the patient, obtaining an evaluation result of the aneurysm rupture risk.

The invention belongs to the technical field of medical image processing, and discloses a medical image diagnosis system based on image and text feature fusion, and the system comprises an image processing module which is responsible for carrying out the enhancement, segmentation and feature extraction of a medical image; the text analysis module is responsible for extracting key clinical information from the electronic medical record; the semantic alignment and feature fusion module is used for embedding image features and text features into a unified vector space to realize fusion of multi-modal data; the model training and optimizing module is responsible for training and optimizing a medical diagnosis system model; and the clinical application module is responsible for applying the trained medical diagnosis system model to an actual medical environment to assist doctors in disease diagnosis. According to the medical image diagnosis system based on image and text feature fusion, by combining image enhancement, morphological processing, NLP analysis and multi-modal feature fusion, the illness state of a patient can be understood more comprehensively, the diagnosis accuracy is improved, and a more reliable auxiliary decision making basis is provided for doctors.

The invention belongs to the technical field of medical image intelligent diagnosis, and provides a breast cancer focus benign and malignant discrimination method based on a gated multi-expert mechanism. The method comprises the following steps: firstly, carrying out standardization and semantic preprocessing on a mammary gland X-ray image, a BI-RADS imaging report and structured clinical data, embedding age, mammary gland density and focus position information into a text template in a natural language form, and realizing unified expression of multi-modal input; secondly, extracting image features by utilizing a ResNet network and a simplified CLIP model, obtaining a text semantic vector by adopting a Bio-ClinicalBERT model, and establishing two sub-paths of a lump expert and a calcification expert in a Transform structure; further, an expert weight is dynamically generated through a gating routing mechanism, and soft routing fusion is executed; and finally, outputting benign and malignant results of the breast cancer focus by the binary classification module. According to the method, deep fusion and dynamic collaboration of the mammary gland X-ray image, the BI-RADS text and the clinical information are realized, and the accuracy and interpretability of breast cancer discrimination can be remarkably improved.

The invention relates to the field of otological disease prediction, in particular to an otological disease prediction method based on multi-modal data fusion and confidence evaluation. According to the technical scheme, the method comprises the following steps: acquiring an otoendoscope digital image, broadband tympanic image measurement data and structured or unstructured clinical information; carrying out integrity verification and quality verification on the collected data, and checking whether the resolution of the digital image of the otoendoscope meets the minimum pixel requirement or not and whether the information is complete or not; preprocessing the otology data and extracting multi-modal features, and extracting an ear endoscope digital image feature vector, a feature vector representing broadband tympanic chamber image data and a clinical information feature vector; fusion is carried out after feature extraction, and confidence evaluation and recurrence risk prediction are carried out after fusion. According to the method, the three-mode data of the image, the physiological signal and the clinical text are deeply fused through the attention mechanism, so that the prediction accuracy of the otological diseases is remarkably improved. The method is suitable for otological disease prediction.

The invention relates to the technical field of computer medical detection, in particular to a thyroid nodule benign and malignant classification method based on clinical information, radiomics and gene detection. According to the method, pathological diagnosis of thyroid nodules is used as a dependent variable, detection data including radiomics characteristics and molecular omics are used as continuous variables, and the detection data of at least three genes CLDN10, HMGA2 and LANM3 are selected as the data of the molecular omics; meanwhile, in combination with part of clinical pathological characteristics including gender, age and BRAF V600E mutation condition factors, a thyroid nodule preoperative diagnosis prediction model based on radiomics and molecular omics is constructed through an SVM modeling method. Gene detection, radiomics and clinical basic information are combined, and a thyroid nodule diagnosis model based on the combination is established on the basis of Chinese population. The method is used for assisting clinicians in distinguishing benign and malignant thyroid nodules and guiding clinical decisions

The invention provides a glycolipid metabolism related gene-based endometrial cancer prognosis prediction model construction method, which comprises the following steps of 1, acquiring data containing gene expression and clinical information, and preprocessing the data; 2, differential expression and prognosis gene screening; 3, constructing a prognosis model; 4, analyzing model gene enrichment; 5, evaluating the immunocompetence of the two GLRG related dangerous groups; and step 6, statistical analysis. According to the technical scheme, more accurate and reliable prognosis evaluation is provided for endometrial cancer by comprehensively analyzing multi-dimensional information such as gene expression, immune characteristics, mutation characteristics and drug sensitivity.

The invention relates to the technical field of data synchronization, in particular to a digestive system department clinical information acquisition and synchronization system, which comprises a multi-source heterogeneous acquisition module for generating a standardized diagnosis and treatment sequence, a characteristic spectrum construction module for mapping a text and an image into vector nodes and constructing a dynamic diagnosis and treatment characteristic spectrum, the state fingerprint verification module calculates a map fingerprint Hamming distance to position a difference feature node, and the incremental collaborative synchronization module constructs an incremental data packet and sends the incremental data packet to the central server. According to the method, the multi-dimensional characteristic spectrum based on the diagnosis and treatment time sequence is constructed, discrete images and texts are converted into topological association units, heterogeneous data semantic level alignment and integrity verification are achieved, version conflicts and information faults are eliminated, meanwhile, a dynamic hash fingerprint difference comparison strategy is adopted, accurate recognition is achieved, and only substantial change nodes are transmitted; and the network load is reduced, and high real-time consistency and zero-loss circulation of whole-flow information are ensured.