58 results about "Gestational period" patented technology

The invention discloses a gestational diabetes risk intelligent prediction method based on multi-modal data fusion, and particularly relates to the technical field of data processing, and the method comprises the steps: S1, setting a prediction window, S2, obtaining risk detection data, S3, constructing a multi-modal time sequence matrix, S4, carrying out risk analysis, S5, constructing a gestational risk change curve, and S6, carrying out visual display. According to the method, detection is carried out through physiological risk indexes and biochemical risk indexes, multi-modal time sequence matrix construction is carried out based on risk detection data, the data acquisition accuracy is met, a matrix structure of time periods and multiple indexes is formed, correlation analysis is carried out on pregnancy risks and detection time periods, and a pregnancy risk change curve is constructed from the correlation analysis; the dynamic evolution of the risk along with pregnancy is visually displayed, the risk occurrence time can be accurately positioned through inflection point detection, key detection of the risk occurrence time is facilitated, and a structured foundation is laid for the follow-up risk change trend and fluctuation amplitude.

The invention relates to the technical field of biomedicine, in particular to a gestational period premature delivery risk auxiliary prediction method and system, and the method comprises the steps: obtaining the historical body data and delivery cycle of a pregnant woman, and determining the historical premature delivery degree of the pregnant woman according to the delivery cycle; according to the premature delivery degree difference between different historical pregnant women and the deviation degree change condition difference of the same type of body data during each physical examination, determining the premature delivery correlation degree of different types of body data of the historical pregnant women; clustering the historical pregnant women according to the deviation time difference of the same type of body data among different historical pregnant women, determining the overall correlation of each type of body data according to the number of pregnant women in the cluster and the premature delivery correlation degree of the corresponding type of body data, and screening out the target type of body data; and based on the body data of the target type of the historical pregnant woman during different times of physical examination, constructing a premature delivery prediction model to perform premature delivery prediction. According to the method, the accuracy of premature delivery prediction is effectively improved.

Systems, devices, and methods are provided that control, support, and / or elongate a cervix to treat pregnancy complications and / or delay an onset of labor so that a gestational period for a fetus may be extended, thereby preventing preterm labor and delivery of the fetus. The cervical control systems may include a main body configured for placement in a vagina. The main body has in internal passageway configured for acceptance of a cervix therein and an inflatable and / or expandable inner cuff extends from portion of the internal passageway. The inner cuff may be expandable inward toward the cervix, or downward away from the uterus, or both inward and downward so that the inner cuff may engage, or grip, the cervix thereby holding the cervical control system in place while compressing the cervix closed, elongating the cervix, and / or adjusting an angle of orientation of the cervix.

The invention relates to the technical field of medical diagnosis, and particularly discloses a pregnant woman self-management ability evaluation and analysis management method, which comprises the following steps: providing a module of related knowledge points of gestational diabetes in pregnant woman terminal equipment or application, and enabling a pregnant woman to learn and test through the module to obtain the mastering condition of the pregnant woman on knowledge; real-time monitoring is carried out through a pregnant woman terminal device or application, and health data and daily management feedback of a pregnant woman are collected regularly; comprehensively calculating the self-management ability level of the pregnant woman according to the health data and the knowledge mastering condition by utilizing a machine learning algorithm, and outputting an evaluation result; the health data and daily management feedback of the pregnant woman are collected in real time, the health change of the pregnant woman can be continuously monitored, the self-management effect of the pregnant woman can be reflected in time, the knowledge mastering condition and the health data of the pregnant woman are comprehensively analyzed through a machine learning algorithm, the self-management ability of the pregnant woman is accurately evaluated, and the self-management efficiency of the pregnant woman is improved. Weak links in the management process are identified, and personalized optimization suggestions are provided.

The invention relates to gestational weight gain prediction. The disclosed concepts are directed to providing protocols, solutions, concepts, designs, methods and systems related to improved prediction of gestational weight gain (GWG). Specifically, during the early stage of pregnancy (e.g., during the first 30 days of pregnancy, or during the first three months of pregnancy), a data sequence describing the weight of a pregnant subject at least twice a day (e.g., after waking up and before falling asleep) is used to determine the historical GWG curve of the subject. Since the energy balance in pregnancy is defined as energy intake equal to energy expenditure plus dynamic energy storage, a particular GWG prediction model is used to generate historical GWG curves, rather than a universal weight gain model. From the historical GWG curve, a future GWG value of the subject may be predicted. The future GWG value may be used to assess pregnancy risk scores, and / or to generate lifestyle recommendations for the subject.

The invention relates to a self-management behavior auxiliary system for gestation and epilepsy patients, and belongs to the technical field of medical monitoring and diagnosis. The auxiliary system comprises a detection unit used for obtaining physiological parameters, related to pregnancy combined epilepsy, of a patient; the processing unit is used for analyzing and processing the data obtained by the detection unit so as to evaluate the health trend of the patient, and the physiological parameters obtained by the detection unit comprise the blood concentration, the liver enzyme activity and the blood sugar level. The processing unit can calculate and obtain a gestational epilepsy management index used for representing the health trend of the patient based on the physiological parameters so as to judge the epilepsy risk condition of the patient in the current gestational period.

The present invention relates to the field of biomedical technology, and more specifically to a method and system for assisting in predicting the risk of premature birth during pregnancy. The method comprises obtaining the physical data and delivery cycles of historical pregnant women, determining the degree of premature birth of the historical pregnant women based on the delivery cycles, and determining the degree of premature birth correlation of different types of physical data of the historical pregnant women based on the differences in the degree of premature birth between different historical pregnant women and the differences in the deviation changes of the same type of physical data during each physical examination. The historical pregnant women are clustered based on the deviation time differences of the same type of physical data between different historical pregnant women, and the overall correlation of each type of physical data is determined based on the number of pregnant women in the cluster and the degree of premature birth correlation of the corresponding type of physical data, thereby screening out the physical data of the target type. A premature birth prediction model is constructed based on the physical data of the target type during different physical examinations of the historical pregnant women to perform premature birth prediction. The present invention effectively improves the accuracy of premature birth prediction.

The invention relates to the technical field of medical instrument detection, in particular to gestational hypertension related hormone rapid detection equipment which comprises a gestational hypertension monitor body, and a cleaning module is connected to the outer portion of the gestational hypertension monitor body in a hooked mode; the cleaning module comprises a mounting structure, a driving mechanism connected with the mounting structure and a water spraying mechanism fixed to the mounting structure and used in cooperation with the driving mechanism. The driving mechanism comprises a connecting shaft connected with the mounting structure, a sliding seat arranged outside the mounting structure in a sliding manner, and a swinging structure mounted between the connecting shaft and the sliding seat; the water spraying mechanism comprises a liquid storage tank fixed to the outer surface of the mounting structure, a water delivery pump and a pressure adjusting pipe, the water delivery pump communicates with the liquid storage tank and the pressure adjusting pipe through water delivery pipes, and an arc-shaped abutting seat abutting against the water delivery pump is mounted on the outer surface of the sliding seat. The device has the advantages of high efficiency, automation, excellent cleaning effect, high adjusting flexibility, compact and stable structure, energy conservation, environment friendliness, easiness in maintenance and operation and the like.

The invention provides a pregnant woman gestation period disease risk prediction method based on a time sequence knowledge graph, and belongs to the technical field of medical care informatics, and the method specifically comprises the steps: obtaining the behavior data and disease pathology information data of a pregnant woman in a gestation period; based on a historical time sequence event evolution process, carrying out sub-graph event sampling on the behavior data and the disease pathology information data, and constructing a time sequence knowledge graph; obtaining graph structure features of the time sequence knowledge graph based on a graph attention mechanism, and extracting text semantic features through a text embedding model; fusing the graph structure features and the text semantic features based on a cross attention mechanism; building an entity prompt, a relation prompt and a time prompt based on the prompt project, and training the input of a pre-training language model through the fusion features; and inputting the behavior data and the disease pathology information data of the target pregnant woman in the gestation period into the prediction model to obtain a gestation period disease risk prediction result. Through the scheme of the invention, the prediction accuracy and adaptability are improved.

The invention discloses a gestational diabetes mellitus early prediction method, a gestational diabetes mellitus early prediction system and a computer readable medium, and the method comprises the steps: obtaining pregnant woman sample data used for researching GDM early prediction, the pregnant woman sample data for researching the early prediction of the GDM comprises a plurality of basic clinical characteristics of each pregnant woman before 16 weeks of pregnancy, a plurality of biomarkers related to the pathogenesis of the GDM before 16 weeks of pregnancy, and a diagnosis result whether the biomarkers are GDM or not; screening a plurality of basic clinical features and a plurality of biomarkers in the obtained pregnant woman sample data to obtain basic clinical features and biomarkers with the highest GDM prediction value; and performing GDM prediction on the pregnant woman which is insufficient for 16 weeks of pregnancy by using the basic clinical features and the biomarkers with the highest GDM prediction value, so as to obtain a prediction result that GDM appears or does not appear in 24-28 weeks of pregnancy. According to the method, the GDM high-risk crowd can be predicted in the early stage of pregnancy, the GDM high-risk crowd is intervened, and the occurrence risk of bad outcomes is reduced.

The invention provides a method and system for predicting gestational diabetes risk in the early stage of pregnancy, and the method comprises the following steps: obtaining a multi-dimensional physiological index data sample of a source pregnant woman in the early stage of pregnancy; a pre-trained biomedical field large language model is adopted, the biomedical field large language model is finely adjusted through a parameter efficient fine adjustment technology, and a fine-adjusted large language model is generated; performing data enhancement processing on the multi-dimensional physiological index data sample by utilizing the fine-tuning large language model; training a machine learning prediction model by using a training data set containing the enhanced physiological data; receiving physiological index data of the target pregnant woman; inputting the physiological index data of the target pregnant woman into a machine learning prediction model; and outputting a prediction result indicating the risk of gestational diabetes mellitus of the target pregnant woman at the early stage of pregnancy. The risk of gestational diabetes mellitus can be evaluated in the early stage of pregnancy, the accuracy and early stage of prediction are improved, and high-risk pregnant women can be recognized as early as possible and effectively managed.

The invention discloses a self-management method for pregnant women with gestational diabetes mellitus and a related device. The method comprises the following steps: collecting comprehensive health information of the pregnant women in the gestational period according to a preset period; the comprehensive health information is input into a gestational diabetes health status assessment model, and the comprehensive health status assessment result of the pregnant woman gestational diabetes is predicted, and the comprehensive health status assessment result comprises the risk probability of hyperglycemia, hypoglycemia and gestation complications; according to the gestational diabetes mellitus medical knowledge base and the comprehensive health state evaluation result, a pregnant woman personalized intervention scheme is generated and uploaded to a medical platform for an attending physician to check and confirm, and the pregnant woman personalized intervention scheme comprises diet suggestions, exercise suggestions and medicine adjustment suggestions. The risk probability of the pregnant woman suffering from hyperglycemia, hypoglycemia and pregnancy complications is predicted through the gestational diabetes health status assessment model, personalized risk assessment is realized, and the risk of complications is reduced through a personalized intervention scheme.

This invention discloses a method, apparatus, device, and storage medium for medication risk assessment during pregnancy, relating to the interdisciplinary fields of medical information technology and clinical pharmacy. The method includes: receiving patient information and proposed medication regimen information, whereby the patient information includes gestational age and information on multiple comorbidities; calling the corresponding gestational age adaptive risk weight matrix based on gestational age, weighting the patient features to generate a weighted feature vector, and inputting this vector into a machine learning model to obtain an initial medication risk score; based on a constructed heterogeneous knowledge graph, using entities corresponding to multiple comorbidities and medication regimen information as query starting points, performing multi-hop reasoning to determine the existence and severity of conflicting paths; if a conflicting path is detected, revising the initial medication risk score according to the severity of the conflict to generate a final risk score. This application enables individualized, interpretable, and evidence-based intelligent risk assessment of proposed medication regimens for pregnant women with multiple comorbidities.

The present application relates to a terminal device for blood sugar management during pregnancy and its cloud server, the terminal device collects physiological data and blood sugar data of a target user including the gestation period, the cloud server obtains a plurality of blood sugar data of reference users from a plurality of terminal devices and forms an overall reference data set in a manner associated with gestational age, the cloud server calculates basic statistical indicators for each group in the overall reference data set, combines the specific percentile position of the user's blood sugar data in its own group distribution, judges the current blood sugar state of the user by comparing the user's blood sugar data with the preset healthy blood sugar range, and generates a graded diet suggestion accordingly, the diet suggestion optimizes the diet structure of the target user by selecting food categories with different GI values; the terminal device receives and visually displays personalized diet suggestions matching the blood sugar status of the target user to the target user.

The invention provides a computer-implemented method for predicting and assessing a health risk parameter, such as the risk of diabetes, in particular gestational diabetes and / or preeclampsia, of a woman based on at least one pregnancy-related parameter, the method comprising the steps of: if the pregnancy-related parameter satisfies a first condition; if so, providing an assessment of the health risk parameter using a first machine learning model (1); providing an assessment of the health risk parameter using a second machine learning model (2) if the pregnancy-related parameter satisfies a second condition; and the health risk parameters are made available.

The invention relates to a blood glucose management system for a gestation period, which comprises terminal equipment carried by a target user and a reference user and a cloud server in data connection with the terminal equipment, and the cloud server is configured to obtain physiological data and blood glucose data including the gestation period of the target user from the terminal equipment, calculating a characteristic index of the target user; acquiring various blood glucose data of a reference user from a plurality of terminal devices, forming an overall reference data set according to a manner associated with gestational weeks, and distributing the target user to a predetermined feature index value interval in the overall reference data set based on the feature index, and providing personalized health management suggestions for the target user based on the identified factors influencing the gestational diabetes risk of the target user.

The present application relates to a kind of based on blood glucose biosensor pregnancy auxiliary system, belong to medical management technical field, especially, it is related to the extended application of biosensor.The system includes: a plurality of user terminals, for obtaining the data information related to user pregnancy, including the blood glucose data obtained based on blood glucose biosensor and the basic information entered;With the communication connection of user terminal server, wherein, the shared database of server construction can be divided into several subgroups based on the preset group division rule, so that each user can be based on its basic information and correspond to one subgroup in the server, the processed blood glucose data obtained by user in current gestation period can be compared with the processed blood glucose data obtained by other users in the same gestation period in corresponding subgroup, to analyze the current blood glucose management of the patient by determining the ranking way, wherein, server can determine the preset group division rule based on the category of basic information.

A group of marker sets capable of effectively predicting acute pain of a patient after cesarean section is screened out, and the marker sets comprise whether the patient has preoperative anxiety or not, whether the patient is gestational diabetes mellitus or not, the age of the patient, the Pittsburgh sleep quality index (PSQI) of the patient, the gestational week age of the patient and the abdominal circumference of the patient. On the basis, a visual and quantifiable column diagram prediction model is constructed, the method can be used for evaluating the risk probability of acute pain occurring after a cesarean section patient is operated clinically before an operation, targeted and individualized pain management is provided for the patient subsequently, the pregnancy safety of the patient is improved, and physical and psychological pain of the patient after the operation is relieved.

The present application relates to the technical field of digital medical treatment, in particular to a pregnancy-induced diabetes risk early warning method based on multi-modal data fusion. Multi-modal data such as blood glucose time series data, exercise amount, body weight and placenta thickness of pregnant women during pregnancy are acquired, the peak value and back-off characteristics of blood glucose time series are analyzed, the exercise amount is combined, the blood glucose back-off trend degree is quantified, and the dynamic metabolic control capability is accurately reflected; further, on the basis of placenta thickness data, the body weight change condition, the exercise amount and the blood glucose back-off trend degree are combined to determine the placenta thickness estimation value time series data, and the physiological correlation characteristics between various data are improved; finally, the blood glucose back-off trend degree mean value and the placenta thickness sustained increasing trend characteristics are fused to reveal the correlation characteristics of blood glucose and abnormal placenta development, a neural network model is trained to capture multi-factor nonlinear relationships, and early and accurate risk prediction of pregnancy-induced diabetes (GDM) is realized.

The present application relates to the technical field of biological medicine, and more particularly to the application of aspirin in treating uterine fibroid degeneration during pregnancy. The application of aspirin in preparing a drug or health product for treating and / or preventing uterine fibroid degeneration during pregnancy. The dosage of aspirin in the drug is 100 mg per day. The present application uses aspirin to treat and / or prevent uterine fibroid degeneration during pregnancy, and the drug is widely used during pregnancy. It has been basically confirmed that the drug can be used for a long time in pregnant women, and the drug has the functions of relieving pain, improving blood flow, not being limited to gestational weeks, and being able to be used for a long time. The application of the drug can reduce the hospitalization rate of patients. For high-risk groups with pregnancy combined with uterine fibroid degeneration, the drug can be used as a preventive drug to reduce the occurrence of uterine fibroid degeneration. The treatment of the drug can significantly reduce the patients who change to surgical treatment due to the failure of conservative treatment of myoma degeneration.

Systems, devices, and methods are provided that control, support, and / or elongate a cervix to treat pregnancy complications and / or delay an onset of labor so that a gestational period for a fetus may be extended, thereby preventing preterm labor and delivery of the fetus. The systems and devices may be made by assembling a molded component configured to have a main body with a groove therein and a flange. During manufacturing, the flange may be inserted into the groove and bonded therein to form a cervical cuff balloon and a positioning balloon may be attached to an external surface of the main body. The molded component may further include an inflation media conduit in communication with the cervical cuff balloon.

The invention relates to the technical field of digital medical treatment, in particular to a gestational diabetes risk early warning method based on multi-modal data fusion. Blood sugar time sequence data, exercise amount, body weight, placenta thickness and other multi-modal data of the pregnant woman in the gestation period are obtained, the blood sugar falling tendency degree is quantified by analyzing the peak value and falling characteristics of the blood sugar time sequence and combining the exercise amount, and the dynamic metabolism control ability is accurately reflected; further, on the basis of the placenta thickness data, determining placenta thickness estimation value time sequence data in combination with the body weight change condition, the exercise amount and the blood glucose falling tendency degree, and improving physiological related characteristics among various data; and finally, fusing the blood glucose fall-back trend mean value and the placenta thickness continuous increasing trend characteristic, revealing the correlation characteristic of blood glucose and placenta abnormal development, and training a neural network model to capture a multi-factor nonlinear relationship, thereby realizing early and accurate risk prediction of gestational diabetes mellitus (GDM).

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.

A computer implemented method of early prediction of risk of pre-eclampsia in a pregnant obese woman is described. The method comprises the steps of inputting abundance values for a panel of obese pregnancy specific metabolite biomarkers obtained from an assayed biological sample into a computational model, in which the biological sample is obtained from an obese pregnant woman at 8 to 24 weeks of pregnancy, and inputting a patient parameter for the pregnant obese woman into the computational model selected from at least one of ethnicity, risk of gestational diabetes, fetal sex, number of pregnancies and level of obesity. The computational model is configured to select a subset comprising at least two of the obese pregnancy specific metabolite biomarkers based on the patient parameter input, correlate abundance values for the subset of obese pregnancy specific metabolite biomarkers with risk of pre-eclampsia, and output a predicted risk of pre-eclampsia for the pregnant obese woman.

The invention discloses application of ferrous sulfate in preparation of a medicine for treating intrahepatic cholestasis in a gestation period, and belongs to the field of medicine. The invention finds that ferrous sulfate can improve intrahepatic cholestasis in the gestation period and relieve liver and placenta injury, and on the basis, placenta functions and filial generation development are improved. The invention provides a new treatment means for preparing the medicine for treating the intrahepatic cholestasis in the gestation period, and provides a new means and way for treating the intrahepatic cholestasis in the gestation period by applying the ferrous sulfate to the development process of the medicine related to the intrahepatic cholestasis in the gestation period.

The application discloses a kind of classification model generation method and device for predicting gestational hypertension, and the method one specific embodiment includes: carrying label fundus image as first training sample, obtain first sample dataset;Wherein, label includes normal label and gestational hypertension label;Gestational hypertension label includes: early gestational hypertension label, mild preeclampsia label and severe preeclampsia label;Based on first sample dataset, model training is generated two-classification model;And according to the gestational hypertension classification result obtained by second classification model to first sample dataset prediction, obtain second sample dataset;Then based on second sample dataset, model training is generated three-classification model;Finally, based on two-classification model and three-classification model, gestational hypertension classification model is generated.Therefore, the difficulty of model to the recognition of gestational hypertension is effectively reduced, and the accuracy of gestational hypertension classification is improved.

This application provides a method, device, and electronic device for assessing medication risks during pregnancy. Through automated knowledge graph querying and risk calculation, a risk assessment report can be quickly generated, reducing the time and cognitive load required for doctors to manually review literature and memorize complex rules. Simultaneously, it can match the current stage of medication risk to the patient's specific gestational age and perform comprehensive, real-time risk assessment based on the pregnant patient's real-time physiological indicators. This avoids the shortcomings of risk assessments that fail to consider gestational changes and individual patient differences, improving the accuracy of risk assessment. Furthermore, by acquiring medication information and physiological indicators, it can continuously and in real-time calculate the risk of medication use based on the patient's actual medication behavior and changes in physiological state during medication. Based on the real-time risk assessment results, medication guidance can be provided to patients, enhancing their understanding of the medication regimen and reducing the risks of medication use during pregnancy.

The invention provides an application of a marker in preparation of a related product for predicting intrahepatic cholestasis in a gestation period. Wherein the marker is a gene marker; and the gene marker is one or more of SLC7A2, PXK, RNF141 or FCHO2. By adopting the marker for detection, high-specificity and high-sensitivity risk prediction of intrahepatic cholestasis in the gestation period can be realized.

The invention relates to the technical field of body data prediction of pregnant women in a gestation period, in particular to a gestation period complication early warning method and system based on big data analysis. According to data deviation of a pregnant woman at different moments, the data anomaly degree of each moment of each dimension of the pregnant woman is obtained, and the data association degree is obtained by combining with the time difference between the morbidity moment of each complication and a high-incidence time node; obtaining an overall change curve of the similar sick pregnant woman in the dimension according to the morbidity time distribution of the similar sick pregnant woman with each complication and the data of each dimension; according to the matching degree and the data association degree between the overall change curve and the predicted change curve of the pregnant woman in the dimension, obtaining a complication early warning index; and performing early warning on the pregnant woman according to the early warning index. According to the invention, the risk that the pregnant woman suffers from complications in different periods of the gestation period can be accurately predicted, and the possible risk can be early warned in time.