65 results about "Hemorrhagic risk" patented technology

The invention relates to a multi-parameter integration and AI fusion postpartum hemorrhage intelligent monitoring, prevention and control method and system. The method comprises the following steps: acquiring blood flow data; the blood flow data comprises blood loss volume and blood flow velocity, and the blood flow data is obtained through measurement of an optical fiber sensor; acquiring vital sign information; the vital sign information comprises blood pressure, heart rate and oxyhemoglobin saturation, and the vital sign information is obtained through continuous monitoring of a vital sign patch; according to the blood flow data, the vital sign information and the medical history information, a bleeding risk grade evaluation result is calculated in real time through an AI risk evaluation model, and the AI risk evaluation model is used for dynamically evaluating the bleeding risk; and triggering corresponding grading early warning according to the bleeding risk grade evaluation result, wherein the grading early warning is used for indicating bleeding risk processing. By adopting the method, the early warning accuracy and treatment efficiency of postpartum hemorrhage can be remarkably improved, and complications and death risks of pregnant and lying-in women are effectively reduced.

The invention relates to the technical field of peritoneal drainage, in particular to a peritoneal drainage system based on hemoglobin real-time monitoring and intelligent early warning. According to the technical scheme, the device comprises an abdominal cavity drainage tube, an optical sensing module, a microprocessor module and a wireless communication module, wherein the microprocessor module is configured to perform moving average filtering and dynamic trend modeling on hemoglobin concentration data; and triggering early warning according to a preset multi-level alarm condition. The hemoglobin concentration in the peritoneal drainage liquid is accurately monitored in real time, intelligent early warning and a personalized response mechanism are combined, the limitation of traditional manual observation is broken through, the timeliness and accuracy of bleeding risk identification are remarkably improved, the infection risk is reduced through the closed design, the medical care working process is optimized through the system integration capacity, and the medical care efficiency is improved. Data sharing and remote cooperation are achieved, intervention time windows are shortened, patient prognosis is improved, nursing resources can be released, medical quality is improved, and a comprehensive and efficient solution is provided for postoperative monitoring.

The embodiment of the invention provides a method and a system for predicting serious postpartum hemorrhage. The method comprises the following steps of: firstly, acquiring multi-modal medical image data, and preprocessing and aligning the multi-modal medical image data; performing segmentation processing on the image through a sliding window strategy, and extracting structural features, texture features, hemodynamic features and blood vessel distribution features from a segmentation result; then the features serve as nodes to construct a graph structure, the relation between the nodes is coded through a graph convolutional network, and the relevance of the features on the topology level is calculated; and finally, fusing the features according to the relevance, inputting a prediction model, and outputting a prediction result. According to the method, through multi-feature fusion and multi-modal data integration, the prediction capability and the prediction accuracy are improved, the robustness of the model in different data environments is enhanced, the bleeding risk can be recognized in time in the middle and later periods of pregnancy of the pregnant woman, early intervention and treatment suggestions are provided, and the risk of bleeding is reduced. The occurrence rate and fatality of serious postpartum hemorrhage are obviously reduced.

The invention provides a postoperative bleeding risk monitoring method and system for a hepatobiliary patient. The method comprises the following steps: collecting real-time multi-modal data; constructing an LSTM-CNN hybrid model by using the time-frequency decomposition features, and obtaining a local tissue hypoxia index and a vasomotor function anomaly probability; the low-frequency impedance change rate and the albumin level are fused through a random forest algorithm, and the ascites occurrence probability and the effusion amount predicted value are obtained; and generating a bleeding point positioning coordinate and a thermodynamic diagram risk grade. And calculating a comprehensive bleeding risk probability and positioning a bleeding area. And generating graded early warning signals and recommending personalized treatment schemes or nursing suggestions. According to the invention, the LSTM-CNN hybrid model, the random forest algorithm and the three-dimensional convolutional neural network are adopted to deeply extract different data features, so that the limitation of single index evaluation is avoided. A causal relationship model is established through the Bayesian network, and the comprehensive risk probability calculation preciseness is improved; the early warning threshold is dynamically adjusted by combining the individual characteristics of the patient, and the traditional problem of easy misjudgment is solved.

An improved mechanical thrombectomy system is described herein that uses artificial intelligence to acquire, extrude, segment, and / or aspirate more effectively blood clots regardless of the age, size (e.g., length, diameter, etc.), solidness, or location of the blood clots while lowering the bleeding risk during the operation. For example, the improved mechanical thrombectomy system described herein can include a catheter, one or more sensors coupled to the catheter, and a control unit coupled to the catheter and sensor(s) that uses artificial intelligence to acquire, extrude, segment, and / or aspirate clots.

The invention discloses a multi-factor risk assessment method for ovarian hyperstimulation, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: based on a multi-source baseline data packet, calculating the trend of follicle volume along with time through ovarian reaction simulation to obtain a follicle reaction curve, calculating the probability of vascular injury through puncture path bleeding simulation, and calculating the risk of ovarian hyperstimulation according to the probability of vascular injury. Obtaining a bleeding probability, and fusing the follicle reaction curve and the bleeding probability to form a patient characterization risk vector; associating the intervention suggestions in the structured strategy list with the ovarian overstimulation risk score and the bleeding risk score to generate a risk change description, and forming a risk assessment report according to the risk change description; according to the method, the patient characterization risk vector fusing the follicle dynamic response and the puncture bleeding risk is constructed, so that the safety and individuation level of assisted reproduction treatment are effectively enhanced.

The application provides application of a substance for detecting neutrophil and / or total bilirubin level in preparation of a kit for predicting early postoperative hemorrhage risk after endoscopic biliary stent implantation, and provides a related prediction model. The application firstly verifies that the substance for detecting neutrophil and / or total bilirubin level can be used for predicting early postoperative hemorrhage risk after endoscopic biliary stent implantation, and based on this, provides a related prediction model, the model has good discrimination ability, and can provide a valuable evaluation tool for preventing hemorrhage of a patient subjected to endoscopic biliary stent implantation.

The embodiment of the invention discloses a method and device for predicting digestive tract hemorrhage after coronary artery bypass transplantation and a medium. The method comprises the following steps: collecting patient data, and extracting a plurality of prediction features; the plurality of prediction features comprise patient demographic features, chronic disease history and a first hospital admission detection result, and dividing the patient data into a derivation queue and an external verification queue; performing feature screening on the derivation queue, constructing an artificial intelligence model based on the screened features, performing performance evaluation on the artificial intelligence model, and selecting an optimal model; and performing model reasoning in the verification queue, and evaluating the accuracy of a prediction result so as to evaluate the generalization ability of the model. And finally, after the characteristics of a new admission patient are input, postoperative gastrointestinal hemorrhage risk prediction and personalized explanation can be obtained, so that personalized prevention measures can be guided.

The application belongs to the technical field of biomedicine, and particularly relates to a construction system of a cerebral amyloid angiopathy recognition model, a storage medium and a kit. Based on plasma proteomic data of cerebral amyloid angiopathy patients and normal old controls, six key protein markers closely related to cerebral amyloid angiopathy are screened out. The application also provides a model for recognizing cerebral amyloid angiopathy based on a combination of six plasma protein biomarkers. The model shows excellent performance in multiple independent data sets, can accurately distinguish cerebral amyloid angiopathy patients from normal old controls, and further distinguish cerebral amyloid angiopathy patients with high-risk cerebral hemorrhage risk from those with low-risk cerebral hemorrhage risk. The application provides a new tool for early diagnosis and hemorrhage risk assessment of cerebral amyloid angiopathy.

The invention discloses a pulmonary embolism patient thrombolysis treatment bleeding and death risk fusion prediction method, which is used for effectively evaluating the bleeding and death risk of a pulmonary embolism patient after thrombolysis treatment and assisting a doctor to carry out individualized evaluation on the bleeding risk of the patient so as to formulate a most suitable treatment plan and improve the treatment accuracy. And the treatment safety of the patient is further ensured. According to the method, an efficient tree model integration algorithm XGBoost is adopted, meanwhile, EHR data of a pulmonary embolism patient receiving thrombolysis treatment is used for training and evaluating a machine learning model, the risk estimation result of bleeding and death of the pulmonary embolism patient after thrombolysis treatment can be effectively obtained in combination with evaluation indexes, the risk estimation result is used for assisting doctors in making clinical decisions, and the risk estimation accuracy is improved. The treatment accuracy is improved, and the treatment safety of a patient is guaranteed.

This invention relates to a pharmaceutical composition and its application for treating acute ischemic stroke. The pharmaceutical composition for treating acute ischemic stroke comprises separately packaged tissue-type plasminogen activator citPA5 and a platelet aggregation inhibitor. Its beneficial effects include significantly improving cerebral blood flow recovery, shortening thrombolysis time, reducing infarct volume, and improving neurological function without increasing the risk of bleeding, demonstrating a good synergistic thrombolytic effect.

The invention relates to the field of medical health and intelligent optimization, and discloses a dialysis anticoagulation risk intelligent early warning method and system fused with multi-modal physiological signals, and the method comprises the steps: S1, synchronously collecting the multi-modal data of a patient in the dialysis process, and standardizing the multi-modal data into a unified time sequence matrix; s2, inputting the unified time sequence matrix into an anticoagulation risk prediction model, performing first-class feature extraction on continuous monitoring signals, performing second-class feature extraction on discrete detection signals, and extracting embedding features of patient individualization and treatment scene data; performing cross-modal fusion on the three types of extracted features to generate a fusion feature vector, and outputting a thrombus risk probability and a bleeding risk probability in a future preset time period; s3, when the risk probability exceeds the corresponding early warning threshold value, performing search optimization on the administration parameter of the current anticoagulant, and generating a dose adjustment instruction; and S4, executing the adjustment instruction, predicting the anti-coagulation risk again, and evaluating the regulation and control effect. Therefore, the safety and effectiveness of the hemodialysis process are remarkably improved.

The application discloses a polypeptide FXIa inhibitor FXIapepin with high inhibition and high specificity. Since FXIa can be used as a low hemorrhage risk anti-thrombus treatment target, the polypeptide inhibitor can be used for preparing a drug for developing a low hemorrhage risk treatment or prevention of a thrombotic disease, and has a wide application prospect.

The invention provides an aneurysm embolism auxiliary device, and belongs to the technical field of medical instruments. The device comprises a conveying catheter, an operating handle, a balloon and a filling unit, wherein the conveying catheter is used for conveying a treatment instrument to an aneurysm position; the operating handle is arranged at the near end of the conveying catheter; the balloon is arranged at the far end of the conveying catheter and is used for supporting a blood vessel after being filled; the filling unit is used for filling injection gel for treating the aneurysm into the aneurysm along the conveying catheter from the position of the operating handle, and the injection gel is used for being solidified in the aneurysm; during working, the conveying catheter conveys the balloon to the aneurysm position, the balloon supports a blood vessel for positioning after being filled, the filling unit fills injection gel into the aneurysm, and the injection gel is solidified and formed after entering the aneurysm so as to treat the aneurysm; the technical problems that in an existing aneurysm interventional therapy mode, a spring ring is prone to displacement, and bleeding risk is increased due to long-term medicine taking after stent implantation are mainly solved.

This invention provides an aneurysm embolization auxiliary device, belonging to the field of medical device technology. The device includes a delivery catheter, an operating handle, a balloon, and a filling unit. The delivery catheter delivers a treatment device to the aneurysm location. The operating handle is located at the proximal end of the delivery catheter. The balloon, after inflation, supports the blood vessel. The filling unit fills the aneurysm with an injectable gel for treating the aneurysm from the operating handle location along the delivery catheter. The injectable gel solidifies within the aneurysm. During operation, the delivery catheter delivers the balloon to the aneurysm location. After inflation, the balloon supports and positions the blood vessel. The filling unit fills the aneurysm with the injectable gel, which solidifies upon entering the aneurysm to treat it. This invention primarily addresses technical problems in existing interventional aneurysm treatment methods, such as easy displacement of coils and the increased risk of bleeding due to the need for long-term medication after stent implantation.

The invention discloses a secondary cesarean delivery postpartum hemorrhage risk early warning model, and relates to the technical field of gynaecology and obstetrics medical treatment. The construction method of the early warning model comprises the following steps: data collection: collecting clinical data of a puerpera in secondary cesarean section, including basic and historical information, information of the gestation period, information of the delivery period of the gestation period and an outcome variable; single factor analysis: screening significant factors of an assisted reproductive technology, a pre-pregnancy body mass index, a pre-antenatal body mass index, pregnancy and birth times, previous cesarean birth times, the number of fetuses, gestational week termination, combined hypertension, combined hysteromyoma, a placenta and uterus relationship, fetal prolapse and uterus height. According to the method, based on large sample data and Logistic regression analysis, the gestational week of gestation termination is screened out, three independent risk factors of hysteromyoma and the relationship between the placenta and the uterus are combined, and model construction has statistical significance; risk scores are quantified through column diagram design, abstract risks are converted into visual values, and the clinical decision-making efficiency is improved.

The present invention discloses a new application of proglumide in the preparation of drugs for preventing and treating venous embolism. By inhibiting the activity of P2Y 14 receptors, it can effectively relieve venous thrombosis and has no significant effect on bleeding time and platelet aggregation ability. Furthermore, it can be prepared into drugs for preventing and treating venous embolism caused by factors such as surgical operations, trauma, pregnancy, cancer, and long-term bedridden state, and significantly reduces the bleeding risk caused by traditional anticoagulant drugs. It can be used as a single-agent preparation or in combination with a compound preparation, having multiple effects of prevention and treatment and wide applications.

The application discloses a kind of multi-factor risk assessment methods for ovarian hyperstimulation, it is related to intelligent medical technical field, including, based on multi-source baseline data package, the trend of follicle volume change with time is calculated by ovarian response simulation, obtain follicle response curve, the probability of vascular injury is calculated by puncture path hemorrhage simulation, obtain bleeding probability, and the follicle response curve and bleeding probability are fused, form patient characteristic risk vector;According to the intervention suggestion in the structured strategy list is associated with ovarian hyperstimulation risk score and bleeding risk score to generate risk change description, and form risk assessment report according to risk change description;The application constructs the patient characteristic risk vector by fusing follicle dynamic response and puncture bleeding risk, effectively enhance the safety and individualization level of assisted reproductive treatment.

The invention relates to the technical field of angiography, in particular to an intelligent pressing device for angiography in the cardiovascular surgery department, and solves the problems that an existing pressing device for angiography in the cardiovascular surgery department generally carries out hemostasis operation after a catheter is pulled out, and the hemostasis timeliness is low; and the pressing device for angiocardiography in the cardiovascular surgery cannot apply downward pressure of corresponding magnitude according to different patients, and the hemostasis efficiency of the device is poor. Comprising an arm placing table, and an anti-extubation bleeding type high-adaptability pressing device is installed on the upper end face of the arm placing table. Before the catheter is pulled out, the gauze is attached to the puncture opening, and when the catheter is completely pulled out and the downward pressure is increased, the bleeding risk at the moment of catheter pulling can be effectively reduced, meanwhile, an alginate solution can be automatically sprayed on the outer surface of the gauze through the healing accelerating mechanism before pressing operation is conducted, and the healing speed is increased. Therefore, the time required by the single pressing operation of the equipment is shortened.

The invention relates to the technical field of automatic medical treatment, provides an interventional nursing process control scheme, and particularly relates to an interventional nursing process monitoring method and device and electronic equipment. According to the method, the basic medical data related to clinic is constructed, and the basic medical data is subjected to linear regression processing twice through the screening model pair formed by combining the unit linear regression model and the multiple linear regression model, so that the associated medical data highly related to the bleeding risk is obtained; and carrying out risk probability calculation on the associated medical data through the prediction model to obtain a final risk value, so that the bleeding risk in the nursing stage is determined. Compared with the prior art, according to the embodiment of the invention, the hemorrhagic high-risk patient can be automatically identified in the early stage of the nursing stage, and the occurrence of hemorrhage conditions is reduced.

The embodiment of the invention relates to the technical field of instruments for removing obstruction in blood vessels, in particular to a coronary thrombolysis catheter and a coronary thrombolysis assembly suitable for an ultrasonic guided intervention technology. The catheter includes a catheter assembly disposed to enter the aorta and extend into the left ventricle. The medicine conveying pipe is arranged in the catheter assembly and is arranged to be capable of conveying thrombolytic medicine into the thrombus of the coronary artery through the catheter assembly. The balloon is arranged outside the catheter assembly and can expand after being filled with water, so that the space between the catheter assembly and the aorta is sealed, the thrombolytic medicine cannot flow out of the coronary artery, and thrombus is dissolved. The catheter assembly is configured to allow blood within the left ventricle to flow out through the catheter assembly. According to the catheter, the thrombolytic drug can stay in the coronary artery for a long time, so that the thrombolytic effect and targeting performance are improved, the thrombolytic drug conveyed to the coronary artery is prevented from leaking to the whole body through the aorta, the influence of the thrombolytic drug on the whole body blood coagulation mechanism is reduced, the risk of cerebral hemorrhage and bleeding of other visceral organs is reduced, and the safety of thrombolytic treatment is improved.

The present invention belongs to the field of biomedical technology and specifically relates to an anticoagulant polypeptide and its use in the preparation of a plasma kallikrein activity inhibitor. The anticoagulant polypeptide of the present invention comprises the amino acid sequence shown in SEQ ID No. 2. The anticoagulant polypeptide of the present invention can significantly inhibit plasma recalcification time and activated partial thromboplastin time, exhibits excellent anti-inflammatory thrombotic and stroke drug activity, has low bleeding risk and cytotoxicity, and is highly safe.

The present application relates to the technical field of medical artificial intelligence, and more particularly to a method for detecting critical multiple bone trauma based on AI. The method integrates physiological monitoring and image data of the patient, extracts the fracture site and constructs a whole-body bleeding risk distribution map, performs correlation analysis on hemoglobin changes to locate the bleeding source, and predicts the decompensation time window through the coagulation state trajectory. The above features are input into the decision model to simulate the physiological evolution path of different treatment schemes and calculate the risk, and finally output the treatment scheme with the lowest adverse probability and the decision time node. The present application realizes dynamic quantitative evaluation and optimized decision support of the detection results of the bleeding risk of critical multiple bone trauma.

The method of the embodiment of the present invention proposes a prediction method and system for severe postpartum hemorrhage. The method first obtains multimodal medical imaging data and then preprocesses and aligns it; then the image is segmented by a sliding window strategy, and structural features, texture features, hemodynamic features and vascular distribution features are extracted from the segmentation results; then the features are used as nodes to construct a graph structure, and the relationship between the nodes is encoded using a graph convolutional network, and the correlation of the features at the topological level is calculated; finally, the features are fused according to the correlation, input into the prediction model and the prediction results are output. The method of the present invention improves the prediction ability and accuracy of the prediction through multi-feature fusion and multimodal data integration, enhances the robustness of the model in different data environments, can timely identify the risk of bleeding in pregnant women in the middle and late stages of pregnancy, provide early intervention and treatment recommendations, and significantly reduce the incidence and lethality of severe postpartum hemorrhage.

The invention discloses a non-dominant hemorrhage risk assessment method during a perioperative period for treating intertrochanteric fracture by using proximal femur PFNA, which comprises the following steps of: (1) acquiring a patient data set, preprocessing, and dividing the data set; (2) screening the data set to obtain a multi-factor prediction factor; (3) on the basis of the training set, constructing a perioperative period recessive hemorrhage prediction model by using the screened multi-factor prediction factors; (4) drawing a calibration graph and a decision curve analysis DCA to carry out prediction performance evaluation on the constructed model; according to the method, the recessive hemorrhage condition after the intertrochanteric fracture operation is more accurately predicted and evaluated, and the prognosis of a patient is improved.

The invention relates to the technical field of operation risk assessment, in particular to a bleeding risk assessment method and system in general surgery resection, and the method comprises the following steps: collecting the instantaneous pressure of the outer wall of a blood vessel in the operation to extract the main frequency and energy characteristic calibration time, collecting the multi-direction strain rate to synthesize a fracture expansion rate curve, and extracting the peak value and intensity; the method comprises the following steps: acquiring a pressure value, calculating a key time point phase difference, combining with intensity to generate a fracture critical state, matching a frequency band and performing frequency conversion to judge a resonance generation response section, weighting and calculating a risk index in combination with the fracture state, and outputting a result when a threshold value is exceeded and a bleeding risk label is added. Frequency domain energy and strain rate are extracted to quantify a fracture trend, a phase difference is analyzed to judge a fracture state, resonance is identified by combining frequency conversion, a risk index is calculated by fusing multi-source signal weighting, accurate risk prediction and dynamic response are realized, and support is provided for operation decision making.

The utility model provides a nasal bronchoscope operating tube which comprises an operating tube, and the rear end of the operating tube is connected with a first connector of a tee joint in a sealing mode. A second interface, on the same axis with the first interface, of the tee joint is a bronchoscope inlet, and the second interface is detachably connected with an end cover; a third interface of the tee joint is connected with a high-flow breathing humidifying therapeutic apparatus through a high-flow catheter; the operation tube which is inserted through the nasal cavity and has the front end arranged above the epiglottis is used as an entry channel of the bronchoscope; at least one air hole is formed in the side wall of the front end of the operation tube. The nasal bronchoscope operation tube serves as a bronchoscope operation pipeline, and the bleeding risk caused by nasal mucosa injury caused by movement of a bronchoscope in the operation process is reduced; meanwhile, the device can be connected with a high-flow humidifying oxygen therapy instrument, the oxygen deficit risk of the patient in the operation process is reduced, and particularly the oxygen deficit risk caused by upper airway obstruction easily occurring to some obstructive sleep respiratory syndrome and tongue retrograde falling patients is reduced.

The invention discloses an endoscopic living body sampling device for respiration and critical diseases, and belongs to the technical field of medical instruments, the endoscopic living body sampling device for respiration and critical diseases comprises a bent pipe section and a straight pipe section, and the straight pipe section is located at one end of the bent pipe section. An inner groove body, an outer through groove and a cylindrical sliding groove are formed in the straight pipe section, a main control rod is slidably connected into the cylindrical sliding groove, a first-class control assembly is arranged in the inner groove body and controls the main control rod to slide up and down, biopsy three-jaw forceps are arranged at the bottom end of the main control rod, and a second-class control assembly is arranged in the cylindrical sliding groove and controls the main control rod to slide up and down. The second-class control assembly controls the biopsy three-jaw forceps to be opened or closed. According to the device, the sampling precision can be ensured, a etiology or pathology definite diagnosis basis is provided, the peripheral lesion diagnosis condition can be clearly observed, the device is simple to operate, compression can be performed during sampling, hemostasis is assisted by medicines, the bleeding risk is reduced, the sampling position precision is ensured, and the sample quality is ensured.

The invention relates to the technical field of bleeding risk prediction, in particular to a liver cirrhosis esophageal and gastric fundus varicose vein rupture bleeding risk intelligent prediction method which comprises the following steps: acquiring vein center line coordinates, cavity wall pressure and thickness, judging a curvature mutation section, analyzing a thickness gradient, generating difference data, establishing a coupling region in combination with pressure peak time, and calculating the risk of liver cirrhosis esophageal and gastric fundus varicose vein rupture bleeding risk. According to the method, direction consistency is extracted to form a weak zone, multi-section risk grading is completed, space mapping is executed, amplitude correction is performed to generate an optimized fracture risk prediction result, and local stress expression precision and prediction sensitivity are improved, so that a continuous evolution rule of a vein structure is better fitted, clinical interpretation decision is supported, and safety management value capacity is provided. A curvature mutation section is identified to extract cavity wall thickness change, time correlation is performed in combination with a pressure peak value, and coupling characterization of a structure and stress is constructed, so that geometric mutation of a vein section and a cavity wall weak zone are subjected to direction consistency analysis in the same space range, and a basis is provided for subsequent difference synchronous quantification.