308results about How to "Improved prognosis" patented technology

The invention provides a tumor area double-view detection method and system based on image modalities and views, and relates to the field of medical image analys.The method comprises the steps that multi-modal image data are collected from the body of a patient, and image data of different view angles at the same position are obtained according to time frames; extracting multi-modal features from the image data of different modalities and different view angles, and performing weighted fusion on the extracted multi-modal features to form a fused feature map; inputting the fused feature map to train and predict the 3D-CNN network so as to identify and position a tumor region, and marking the tumor region by using a mask output by the segmentation model; calculating the pixel number of the tumor area in each layer of image according to the marked mask, and multiplying the pixel number by the volume of each pixel to obtain the volume of the tumor area; and the segmentation results of each layer of image are superposed, and a three-dimensional model of a tumor region is generated by using 3D reconstruction, so that the limitation of single-mode and single-view image data in tumor detection is overcome.

The invention provides a rectal cancer neoadjuvant chemoradiotherapy curative effect prediction method and system based on magnetic resonance image analysis, and aims to improve accurate prediction of curative effects of patients with local advanced rectal cancer (LARC). According to the method, multi-modal magnetic resonance images (T1WI, T2WI, DWI and DCE-MRI) are obtained, qualitative and quantitative analysis is combined, tumor morphology, diffusion characteristics and microcirculation parameters are extracted, clinical and pathological data of a patient are integrated, and a multi-dimensional feature set is constructed. Curative effect prediction is performed through machine learning and a deep learning model, and the model can predict treatment results of complete remission, partial remission, ineffectiveness and the like in a classified manner. The system accurately predicts the reaction of a patient to neoadjuvant radiotherapy and chemotherapy through a non-invasive method, supports formulation of a personalized treatment scheme, reduces excessive treatment, optimizes the treatment effect, and has good clinical application value.

The invention discloses a personalized hypertensive nephropathy risk assessment method and system based on a graph convolutional network, and the method comprises the steps: collecting modal data related to hypertensive nephropathy, and carrying out the preprocessing; constructing a patient similarity graph; performing multi-source information fusion and domain knowledge integration; sparse processing is carried out on the graph structure, and standardization processing is carried out on an adjacent matrix; modeling through a time sequence diagram convolutional network and a long-short term memory network, and capturing the evolution trend of patient characteristics along with time; through a self-attention mechanism and a graph regularization item, the transmission of important features is enhanced; a multi-head attention mechanism is adopted, and different types of relation modes are captured; optimizing model parameters by using an Adam optimization algorithm and a regularization method; and a risk assessment result is optimized by adopting weighted stacking and a self-adaptive attention mechanism. According to the risk assessment method, accurate assessment of the risk of hypertensive nephropathy is realized by constructing the multi-dimensional and dynamically changing patient similarity graph.

The invention discloses a craniocerebral injury degree diagnosis auxiliary system based on a medical image, and relates to the technical field of medical treatment, a primary judgment module can accurately judge whether a patient has the risk of interstitial encephaledema or not by analyzing the accumulation degree of interstitial fluid in white matter and quantitatively calculating a penetration anomaly coefficient Syxs, the ability of early risk screening is provided, and the risk of interstitial encephaledema can be accurately judged. Potential problems can be recognized in time before disease deterioration, a clinician is prompted to intervene rapidly, and therefore the prognosis effect of a patient is remarkably improved. When the risk of interstitial encephaledema exists, the type diagnosis module further distinguishes lesion types (such as brain contusion or other injuries) by extracting related distinguishing features in a multi-modal feature set, calculates a locking coefficient Sdxs, and provides a scientific quantitative basis for diagnosis of lesion types based on specific features of the lesions. According to the accurate type diagnosis method, diagnosis errors can be reduced, and reliable support is provided for clinical decision making.

The invention discloses an acute kidney injury risk early warning system, and relates to the technical field of clinical decision support systems. In order to solve the problems that in the prior art, due to the fact that a large amount of data preprocessing and model training need long-time data processing and model operation, the early warning opportunity is not accurate, the real-time performance is low, personalized early warning prompting cannot be carried out for patient groups, and early warning fatigue and early warning desensitization are likely to be caused. Through data preprocessing, model construction and risk analysis, real-time monitoring and accurate prediction of the AKI risk of a patient are realized, the early recognition rate is improved, the early warning decision unit generates specific early warning information according to risk scores and kidney function change rules and provides targeted treatment suggestions, and meanwhile, through an early warning threshold adjustment and approach matching module, the early warning decision unit can perform early warning on the AKI risk of the patient. The early warning strategy is optimized, the response speed of medical staff and the compliance of patients are improved, and the user interaction unit continuously optimizes the early warning system through a feedback mechanism.

The invention relates to the technical field of image processing, in particular to a static plantar pressure image data processing method based on an improved YOLOV5 model. Compared with traditional floor table type equipment and desktop level equipment, the static plantar pressure diagnosis system for auxiliary diagnosis of the flatfoot based on the YOLOV5 model can remarkably improve the flatfoot diagnosis effect. The improved YOLOV5 model added with the attention mechanism module can further improve the flatfoot diagnosis effect, early diagnosis and intervention are carried out on the patient, and good prognosis is achieved. Based on an advanced deep learning model and a comprehensive data analysis method, the accuracy and efficiency of flatfoot evaluation are remarkably improved through automatic auxiliary diagnosis. Through a highly automatic process, manual intervention is not needed in the whole process from data acquisition, preprocessing to diagnosis output, personal errors are reduced, and the reliability and consistency of diagnosis are improved. A non-professional user can easily use the foot sole health management system through a friendly user interface and simplified operation steps, the foot sole health management system is widely suitable for various scenes such as families, clinics and hospitals, and intelligentization and popularization of foot sole health management are achieved.

The invention provides a nano photosensitizer modified by outer membrane vesicles of genetically engineered bacteria as well as a preparation method and application of the nano photosensitizer. The nano photosensitizer is a nano material with the particle size of 80-120 nm, and the nano photosensitizer modified by the genetically engineered bacterium outer membrane vesicle comprises dipalmitic acid phosphatidylcholine (DPPC), distearoyl phosphatidylcholine (DSPC), dioleoyl lecithin (DOPC), cholesterol, a small molecule dye and CD47nb-OMV membrane protein. The chemical structure of the small molecule dye is shown as a formula I or a formula II. The nano photosensitizer OBDlp modified by outer membrane vesicles of genetically engineered bacteria serves as a novel nano photosensitizer, tumor targeting PTT is mediated through NIR II FL / PA imaging, tumor immunogenicity is improved, antigen presenting cells are activated, and infiltration and functionalization of CD8 + T cells are caused. And OBDlp and laser are combined to induce release of a large amount of DAMPs and trigger ICD to promote maturation of cDC1s. Meanwhile, a CD47 nano antibody carried by the nano photosensitizer induces polarization of M1 phenotype macrophages by blocking a CD47-SIRP alpha signal transduction axis, and an immunosuppression tumor microenvironment is reversed.

The invention provides a risk early warning method and system for stomach cancer postoperative malnutrition, and relates to the technical field of medical treatment, and the method comprises the steps: obtaining gene expression data and clinical indexes of a stomach cancer patient based on a cancer genome map database; gastric cancer methylation data is collected, and a methylation risk scoring model is constructed to determine a methylation risk score; performing quantitative evaluation on the nutritional status of the gastric cancer patient based on a nutritional risk screening tool to obtain a nutritional risk level; determining risk characteristics related to stomach cancer postoperative dystrophy according to the gene expression data, the clinical indexes, the methylation risk score and the nutrition risk grade, and establishing a risk early warning model based on a support vector machine; and acquiring current risk data of the patient side, inputting the current risk data into the risk early warning model, outputting a risk score and a risk level, generating a risk intervention measure according to the risk level, and feeding back the risk intervention measure to the medical care side, so that accurate and comprehensive early warning can be performed on the risk of postoperative malnutrition of the gastric cancer patient, and a timely and effective nutrition intervention scheme is formulated.

The invention provides an interventional operation planning method and system based on anatomical structure modeling, and the method comprises the steps: carrying out the anatomical structure modeling of an interventional operation target focus and surrounding tissues and organs, and obtaining an anatomical structure three-dimensional model; based on the anatomical structure three-dimensional model, acquiring initial puncture entry candidate points of the interventional operation; screening the initial puncture entry candidate points of the interventional operation to obtain final puncture entry candidate points of the interventional operation; and carrying out joint visualization on the anatomical structure three-dimensional model, the plurality of final puncture entry candidate points of the interventional operation and the quantitative information of the puncture indexes of the puncture entry candidate points so as to provide puncture entry point simulation and reference for the interventional operation and complete interventional operation planning. According to the method, interpretable reference is provided for decision making of interventional operation planning based on multi-dimensional quantitative indexes and visual risk prompts, the pulmonary nodule interventional operation process can be optimized, and then the risk of the interventional operation is reduced.

The invention provides application of a PDLIM4 gene in preparation of a medicine for inhibiting non-small cell lung cancer proliferation, belongs to the technical field of cancer suppressor gene research, and finds that the PDLIM4 gene is highly expressed in non-small cell lung cancer tumors, and the prognosis of a patient with the highly expressed PDLIM4 gene is good; the proliferation of the non-small cell lung cancer cells overexpressed with the PDLIM4 gene is inhibited, and the proliferation of the non-small cell lung cancer cells knocked down with the PDLIM4 gene is accelerated, so that the reagent capable of realizing the overexpression of the PDLIM4 gene is confirmed to be of great significance for inhibiting the proliferation of the non-small cell lung cancer cells. The overexpression of the PDLIM4 gene in the non-small cell lung cancer cells can enhance the chemotherapy effect of the chemotherapy drug adriamycin, and can be used as a chemotherapy sensitizer. The BRD4 is an upstream regulatory factor for increasing the expression of the PDLIM4 gene in the non-small cell lung cancer, and can be used for promoting the expression of the PDLIM4 gene.

The invention discloses an application of a biomarker in preparation of a kit for a prognosis risk early warning method of an acute ST segment elevation type myocardial infarction patient. The biomarker comprises an HBP (Hepatitis Binding Protein). By monitoring the change of the HBP protein level, whether a patient suffering from the acute ST segment elevation type myocardial infarction has poor prognosis within 30 days after a PCI operation can be effectively evaluated, so that early recognition and timely clinical intervention on high-risk patients are realized, the treatment response speed and effect are improved, the occurrence rate of adverse cardiovascular events is reduced, and the treatment effect of the patient suffering from the acute ST segment elevation type myocardial infarction is improved. And the prognosis of the patient is finally improved.

The invention discloses a sugar net recognition system based on a block chain and federal learning, and the system comprises a data collection module which is used for collecting fundus image data and related medical data; the federal learning module is connected with the data acquisition module, the federal learning module comprises a plurality of participant nodes, and each participant node performs model training based on local data; the block chain module is connected with the federated learning module and used for storing model parameters, training data abstracts and data access record key information in the federated learning process; the sugar network identification module is used for receiving the global sugar network identification model trained by the federal learning module; and the result output and feedback module is connected with the sugar net identification module. According to the method, the data collaborative training model is integrated through federal learning, so that the sugar net recognition accuracy is improved, misdiagnosis and missed diagnosis are reduced, the block chain ensures the data to be safe and credible, the innovative formula assists optimization, the system application is wide, clinical and remote medical treatment is facilitated, and the sugar net diagnosis service quality is improved.

The invention relates to the field of acute lung injury caused by sepsis, in particular to application of lactobacillus rhamnosus exosome in preparation of medicine for treating and / or preventing acute lung injury caused by sepsis of subjects, and the acute lung injury caused by sepsis is acute complication of sepsis. The sepsis is caused by bacterial infection, and the particle size of the lactobacillus rhamnosus exosome ranges from 50 nm to 120 nm. The lactobacillus rhamnosus exosome provided by the invention can prevent and / or timely and effectively prevent the development of the disease course of a subject at the initial stage of sepsis acute lung injury, and finally, the treatment success rate of the subject is increased, and the prognosis of the subject is improved.

The invention discloses a monitoring method and system for prevention and treatment of renal fibrosis. The monitoring method comprises the following steps: collecting biomarker data in a urine sample of a patient in real time through a biosensing detector; transmitting the acquired biomarker data to a data processing center, and establishing a renal fibrosis risk assessment model of the patient; according to the risk assessment result, making a personalized prevention and treatment scheme, and monitoring the execution condition of the prevention and treatment scheme in real time; periodically detecting blood sample indexes of a patient, and combining with the biomarker data to obtain a comprehensive evaluation effect of renal fibrosis prevention and treatment; and feeding back the comprehensive evaluation result to a patient and medical staff, and adjusting the personalized prevention and treatment scheme according to the comprehensive evaluation result. According to the scheme, accurate and dynamic monitoring on prevention and treatment of the renal fibrosis can be achieved, the prevention and treatment effect of the renal fibrosis can be improved, and the life quality and prognosis of a patient are improved.

The shock wave mesh basket electrode catheter comprises an inner tube and an outer tube, the inner tube is arranged in the outer tube and extends out of the far end of the outer tube, the far end of the inner tube is provided with a tip, the near end of the outer tube is connected with a handle, the handle is connected with a plug through a cable, and the plug is used for being connected with an external shock wave device. A mesh basket part is arranged on the inner tube, a balloon and a shock wave generator are arranged on the mesh basket part, and the mesh basket part has self-expansion performance so that a cage-shaped structure can be formed after expansion. According to the embodiment, the risks that the artificial valve is difficult to position and fix in the operation process, the artificial valve is not suitable to release and difficult to remove and the like can be effectively avoided, the operation time is shortened, and the operation operation and prognosis effects are greatly improved.

The invention provides a traditional Chinese medicine compound for treating sicca syndrome and a preparation method and application thereof, and belongs to the technical field of traditional Chinese medicine compounds. The traditional Chinese medicine compound provided by the invention has the effects of eliminating dampness and heat, removing blood stasis and promoting salivation, can safely and effectively treat sicca syndrome by improving functions of exocrine glands and regulating immunity, belongs to a pure traditional Chinese medicine preparation, and has no toxic or side effect. The traditional Chinese medicine compound is proved to be capable of remarkably relieving the sicca syndrome, relieving dry mouth and eye symptoms of a patient, relieving fatigue, regulating and controlling a plurality of signals taking RAGE / TLR4 as a core, inhibiting excessive activation of an HMGB1-RAGE / TLR4-NF-kappa B pro-inflammatory pathway from gene and protein levels, inhibiting generation of pro-inflammatory cytokines, reducing inflammatory response and improving the immunity of the patient. The effect of treating the sicca syndrome is achieved. By acting on STAT3 phosphorylation, Tfh proliferation is inhibited, IL-21 secretion is reduced, humoral immune disorder is relieved, and the sicca syndrome is treated, so that the condition of the sicca syndrome is relieved, and the prognosis of the sicca syndrome is improved.

The invention discloses an acupoint patch as well as a preparation method and application thereof, and belongs to the technical field of traditional Chinese medicines. The acupoint application agent comprises a traditional Chinese medicine composition, and the traditional Chinese medicine composition is prepared from, by mass, 8-12 parts of raw semen brassicae, 8-12 parts of raw rhizoma pinelliae, 3-8 parts of herba asari, 8-12 parts of semen armeniacae amarae, 8-12 parts of cortex cinnamomi and 3-8 parts of euphorbia kansui. The acupoint application agent and the lung tonifying decoction are taken as a traditional Chinese medicine combined preparation for oral administration and external use and are applied to preparation of medicines for treating lung and kidney qi deficiency type COPD in the stable phase. The combined application of the acupoint application agent and the lung tonifying decoction can tonify lung and kidney, relieve cough and reduce sputum, achieve the purposes of treating COPD stable phase and improving prognosis, has the comprehensive advantages of safety, effectiveness and economy, can well solve the current situation that clinical medicine is insufficient in clinical severe acute attack light stable phase and COPD stable phase, and has wide application prospects.

The invention belongs to the field of tumor drugs, and discloses a radioactive hydrogel based on crosslinking of sodium alginate and metal ions and a preparation method and application thereof.The preparation method of the radioactive hydrogel comprises the following steps that lutetium chloride and metal ions are mixed, and a metal ion mixed solution is obtained; doping 177Lu radioactive isotope into the obtained metal ion mixed solution to obtain a metal ion cross-linking agent; and injecting a metal ion cross-linking agent into the sodium alginate solution under a vortex condition, centrifuging, and discarding the supernatant to obtain the radioactive hydrogel. The radioactive hydrogel (177Lu-RH) provided by the invention has good in-tumor colonization capability, can be directly injected into a tumor to realize local radiotherapy, has good radioactive stability, and can be stably retained at a tumor part for more than 120 hours, so that the stability of radioactive labeling in a treatment process is ensured.

The present disclosure relates to a method, device, equipment and medium for intelligent fusion of CTA and DSA images, the method comprising: obtaining a two-dimensional image and a three-dimensional image of a patient's coronary artery, the two-dimensional image being a digital subtraction angiography image, and the three-dimensional image being a computed tomography angiography reconstruction image; determining the projection angle of the two-dimensional image when the patient is in a preset position; performing position correction processing on the three-dimensional image to obtain a first image of the coronary artery when the patient is in the preset position; performing registration and fusion based on the two-dimensional image, the projection angle and the first image to obtain a two-dimensional fused image; and displaying the fused image. The present disclosure can display information on completely occluded lesions and plaque properties through fused images by registering and fusing the two-dimensional and three-dimensional images of the patient's coronary artery, assisting doctors in diagnosing coronary artery lesions and planning interventional pathways, improving the success rate of treatment, and improving patient prognosis.

The invention relates to the technical field of targeted intervention of chordoma, in particular to application of a medicine to treatment of chordoma of different molecular types. The invention provides application of anlotinib, an oxidative phosphorylation inhibitor and desumab in preparation of products for preventing and / or treating chordoma. The chordoma comprises interstitial epithelial transformed chordoma, mesenchymal derived chordoma and bone microenvironment dominant chordoma. The invention also provides a pharmaceutical composition, which comprises an effective dose of anlotinib, and / or an oxidative phosphorylation inhibitor, and / or desumab. Under the condition that current treatment selection is limited, proliferation, clone formation and tumor growth of tumor cells can be remarkably inhibited by applying the medicine to specific molecular subtype chordoma, and compared with existing treatment medicine, the medicine has a better treatment effect, has good safety and shows potential clinical application value.

The invention relates to the technical field of vascular repair surgeries, in particular to a jet printing combined tool for in-situ preparation of vascular surgeries and a use method of the jet printing combined tool. The jet printing combined tool comprises a sacrificial tubular stent and an annular printing nozzle, and the two ends of the sacrificial tubular stent are used for being inserted into two fractures of a vascular defect part respectively; the sacrificial tubular stent is dissolved in blood after blood circulation, and when the sacrificial tubular stent penetrates through the annular printing spray head, the moving annular printing spray head is used for spraying printing ink towards the peripheral wall of the sacrificial tubular stent; the interior of the annular printing nozzle is of a hollow tubular structure, a feeding port is formed in the outer side of the annular printing nozzle, and a plurality of discharging ports are formed in the inner side of the annular printing nozzle in the radial direction. The annular printing nozzle and the sacrificial tubular stent are simple and portable in equipment, simple in operation process, short in printing time consumption, capable of effectively shortening the operation duration, low in technical requirement, capable of promoting tissue reperfusion, reducing the amputation rate and improving prognosis and suitable for the environment with poor medical conditions.

The invention discloses a method and a system for automatically analyzing chest respiratory muscle volume based on a deep learning network, and belongs to the field of medical images. The method for automatically analyzing the chest respiratory muscle volume based on the deep learning network comprises the steps that a CT image and a chest respiratory muscle image manually marked by a doctor are collected, and the CT image and the image manually marked by the doctor are preprocessed to obtain a standard sketching graph; performing data enhancement operation on the standard sketching picture; constructing a U-Net model, and training the constructed U-Net model by using the standard sketching graph after data enhancement; and automatically sketching the chest respiratory muscle in the medical image by using the trained U-Net model to obtain the contour of the chest respiratory muscle. A U-Net network model is constructed, an efficient, reliable and easy-to-implement solution is provided for three-dimensional medical image segmentation, the method has wide application prospects and clinical values, time consumed in clinical work is greatly shortened, respiratory muscle related information is helped to be obtained clinically and rapidly, treatment of a patient is guided, and prognosis of the patient is improved.

The invention discloses an esophageal fistula risk prediction method based on clinical data and iconomics characteristics, relates to the technical field of medical artificial intelligence, and combines deep learning and a traditional iconomics method to extract rich iconomics characteristics through multiple image processing technologies so as to predict the risk of esophageal fistula. The generalization ability and robustness of the model are ensured through a multi-stage feature selection strategy, and a plurality of prediction models based on different feature combinations are developed; comprising a clinical feature model, a manual radiomics model, a deep learning radiomics model, a clinical-deep learning radiomics model and a clinical-manual radiomics model, through cross validation and model optimization, the high efficiency of each model in clinical prediction is ensured, the performance of the models is comprehensively compared, and the accuracy of clinical prediction is improved. According to the esophageal fistula risk prediction method, the esophageal fistula risk prediction accuracy after the esophageal cancer patient receives radiotherapy or chemoradiotherapy is improved, a powerful basis is provided for treatment decision of the esophageal cancer patient, and the esophageal fistula risk prediction method has a wide clinical application prospect.

The invention discloses a PICC (Peripherally Inserted Central Catheter) dynamic CVP waveform monitoring system which comprises a pressurizing liquid injection device, a blood pressure sensor, an IBP invasive blood pressure detection module, an IBP cable and a monitor, one end of the blood pressure sensor is connected with the pressurizing liquid injection device, and the other end of the blood pressure sensor is connected with the far end of a PICC; the IBP invasive blood pressure detection module is connected with a signal interface of the blood pressure sensor through the IBP cable and is used for detecting CVP data; the monitor is connected with the IBP invasive blood pressure detection module and used for displaying CVP data monitored by the IBP invasive blood pressure detection module in a CVP oscillogram mode. According to the dynamic CVP waveform monitoring system for PICC catheterization provided by the embodiment of the invention, the tip position of the PICC catheter can be accurately positioned in real time in the PICC catheterization process, an infusion channel can be quickly and timely used, and more treatment time is won for patients. The PICC is used for replacing a CVC catheter, related complications are obviously lower than those of CVC catheterization, clinical treatment can be better guided, and prognosis is improved.

The invention provides a bionic MOF nano platform capable of double targeting cervical cancer tumor cells and cancer-related fibroblasts and co-delivering an FAK inhibitor and bismuth as well as a preparation method and application thereof, the bionic MOF nano platform comprises an MOF material loaded with the FAK inhibitor (IN10018) and bismuth (Bi), and a hybrid membrane coating the outer part of the MOF material loaded with the FAK inhibitor and the bismuth, the hybrid membrane is obtained by mixing and physically extruding a cervical cancer tumor cell (HeLa and SiHa) membrane and a cancer-related fibroblast (CAFs) membrane. According to the bionic MOF nano platform, in an acid environment, the ZIF-8 nano particles loaded with the IN10018 and the Bi disintegrate, the IN10018 is released, CAFs infiltration in tumors is reduced, meanwhile, due to addition of the Bi, the radiation absorption efficiency is improved, and the sensitivity of the tumors to radiotherapy is further enhanced through a double-targeting mechanism.

The invention discloses injectable and degradable composite drug-loading hydrogel as well as a preparation method and application thereof. The injectable and degradable composite drug-loading hydrogel comprises sodium alginate hydrogel, piezoelectric nanofibers and triamcinolone acetonide and / or ropivacaine, wherein the piezoelectric nanofibers and the triamcinolone acetonide and / or the ropivacaine are loaded on the sodium alginate hydrogel; the piezoelectric nanofibers are poly-L-lactic acid nanofibers. Through the synergistic effect of controllable release of the medicine and piezoelectric stimulation, the dual effects of continuous analgesia and neural restoration are achieved in the PHN treatment process, more effective treatment selection is provided for a PHN patient, complications caused by a traditional implantable electrode can be avoided, a more minimally invasive, convenient and safe treatment mode can be provided for the patient, and the treatment effect is good. And a theoretical basis is provided for future clinical practice.

The invention relates to a ventilation detection device for a myasthenia gravis patient, and the device comprises a muscle strength monitoring module which is configured to monitor the swallowing muscle of the myasthenia gravis patient and the muscle activity of the near end of the limb, and obtains the muscle strength data of the myasthenia gravis patient; the respiration monitoring module is configured to detect the respiration frequency, the tidal volume of each respiration cycle and the inspiration time of each respiration cycle under the condition that mechanical assisted ventilation is carried out on the myasthenia gravis patient, and the change trend along with time is obtained; the control module is in signal connection with the muscle strength monitoring module and the respiration monitoring module, and is configured to control the respiration monitoring module when the muscle strength data of the serious myasthenia patient is lower than a preset muscle strength threshold value and the change trend of the respiration frequency, the tidal volume of each respiration cycle and the inspiration time along with the time is inconsistent with the change trend of pre-stored corresponding parameters; and determining that man-machine resistance exists in the mechanical assisted ventilation process of the myasthenia gravis patient.

The invention relates to the technical field of data processing, in particular to an intelligent data processing method and system based on surgical nursing, and the method comprises the steps: firstly obtaining food intake and physiological indexes of a patient within a preset time, comparing the food intake and physiological indexes with a standard food intake set, and generating a food intake standard score; in conjunction with the physiological metric, the system determines a real-time feeding score and a total feeding score. By comparing the real-time and total feeding scores at different time points, the system finally generates a feeding balance score of the patient. According to the method, food intake and physiological data are effectively integrated, a scientific and quantitative food intake evaluation tool is provided for surgical nursing, nutrition management of a patient is optimized, nursing quality is improved, the absolute value of the food intake is considered in the evaluation, the relevance between time distribution of food intake and physiological indexes is concerned, and the evaluation accuracy is improved. Therefore, more comprehensive and in-depth patient state information is provided for nursing personnel. Especially, due to the introduction of a feeding balance degree score, an important basis is provided for formulating a more reasonable nutrition intervention scheme.

The invention relates to the technical field of digital medical treatment, in particular to a method and a device for constructing a colorectal cancer multi-mode recurrence risk model. The construction method comprises the following steps: acquiring a panoramic digital scanning image of a pathological tissue section of a colorectal cancer patient; constructing a data set and selecting a region of interest by using the panoramic digital scanning image; constructing a pathological omics feature scoring model, a collagen feature scoring model and an immune feature scoring model through the region of interest; and according to the pathological omics feature scoring model, the collagen feature scoring model and the immune feature scoring model, constructing a multi-modal recurrence risk model. On the basis of an image analysis technology, the pathological omics feature scoring model, the collagen feature scoring model and the immune feature scoring model are constructed and integrated into the multi-mode recurrence risk model, and accurate prediction of the recurrence risk of the colorectal cancer patient is achieved.

The invention belongs to the field of medical image mode recognition, provides a sacroiliac joint bone marrow edema prediction method based on deep learning, and aims to accurately predict existence of sacroiliac joint bone marrow edema. The specific process comprises the following steps: firstly, collecting and sorting two groups of distinct magnetic resonance image data: one group is used as a healthy control group without bone marrow edema signs; and the other group clearly shows the pathological characteristics of sacroiliac joint bone marrow edema. Subsequently, we establish a region of interest focused on analysis through a precise image processing technique. And it is ensured that image data in all regions of interest reach consistent standards in the aspects of brightness, contrast and the like. Gaussian random noise is superposed in the standardized region of interest, image interference in a real scene is simulated, and model learning is promoted to be more robust feature representation. A visual and reliable reference basis is provided for clinicians, and more accurate illness state assessment is facilitated.