35 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 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 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 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 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 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 intelligent prediction system and method for prenatal complications of an obstetrical department based on multi-modal fusion of sclera characteristics and clinical indexes, and is applied to the technical field of data processing. The method comprises the following steps: performing preprocessing and fusion calculation on feature information in multi-modal data to generate fusion calculation data; the prenatal complications (such as gestational diabetes mellitus, gestational hypertension, premature rupture of fetal membranes and premature delivery) of the obstetrics department are dynamically predicted, and prediction characteristic parameters are generated; performing abnormal value filtering processing on the cross-modal feature weight, dynamically optimizing the weight in combination with a clinical and image feature correlation matrix, and generating feature weight data; processing the feature weight data to generate feature importance dynamic change data and abnormal features; processing the feature importance dynamic change data based on a preset risk judgment rule and the abnormal features to generate risk judgment data; and processing the risk judgment data and the prediction characteristic parameters to generate risk early warning information and a complication intervention instruction.

The invention discloses a method and device for detecting the vitamin D level in the gestation period, and relates to the technical field of biological information or other related technical fields, the method comprises the steps that a target risk factor set of a target object in the gestation period is obtained, the target risk factor set comprises N target risk factors and corresponding characteristic values thereof, and N is a positive integer; the target risk factors comprise basic clinical factors, biomarkers and genetic locus factors, and N is an integer greater than 1; and inputting the characteristic value corresponding to each target risk factor into a risk prediction model, and outputting a detection report after the risk prediction model evaluates the vitamin D level of the target object. The technical problem that the vitamin D deficiency risk of the pregnant women cannot be accurately detected in the prior art is solved.

This invention discloses an intelligent prediction method for gestational diabetes mellitus risk based on multimodal data fusion, specifically relating to the field of data processing technology. The method includes S1: setting a prediction window; S2: acquiring risk detection data; S3: constructing a multimodal time series matrix; S4: risk analysis; S5: constructing a pregnancy risk change curve; and S6: visualization. This invention detects risks using physiological and biochemical risk indicators and constructs a multimodal time series matrix based on the risk detection data. This ensures accurate data collection and forms a matrix structure of time periods and multiple indicators. It then correlates pregnancy risk with the detection time period, constructing a pregnancy risk change curve that visually demonstrates the dynamic evolution of risk throughout pregnancy. Furthermore, inflection point detection allows for precise location of the risk occurrence time, facilitating focused detection of risk occurrence times and laying a structured foundation for subsequent risk trend and fluctuation amplitude analysis.

The present invention provides a DNA methylation segment combination and application for predicting hypertensive disorder complicating pregnancy, a method and device for predicting hypertensive disorder complicating pregnancy, wherein the method utilizes screened peripheral blood DNA methylation segments and adopts a logistic regression method to construct a risk prediction model for hypertensive disorder complicating pregnancy, calculates the risk value of hypertensive disorder complicating pregnancy, and thus can effectively predict the risk of hypertensive disorder complicating pregnancy.

The invention relates to a nutritional composition for improving nausea and vomiting in a gestation period and guaranteeing growth and development of fetuses. The health-care food is prepared from the following components in percentage by mass: 10.68 percent to 24.05 percent of carbohydrate, 17.93 percent to 36.41 percent of protein substances, 20.9 percent to 52.94 percent of fat substances, 8.55 percent to 18.37 percent of dietary fiber substances, 1.11 percent to 1.35 percent of compound vitamins, 11.17 percent to 37.27 percent of compound minerals and 1.20 percent to 2.38 percent of blueberry powder. According to the nutritional composition, various nutrients are reasonably matched and the content of each nutrient is regulated and controlled, under the synergistic effect of the nutrients, the attack frequency of nausea and vomiting in the gestation period can be remarkably reduced, nutrients needed in the gestation period can be comprehensively supplemented, the nutrition gap of pregnant women caused by vomiting is complemented, and the nutritional value of the pregnant women is improved. The feed meets the requirements of pregnant women during pregnancy, guarantees normal growth and development of fetuses, and is especially suitable for pregnant women with nausea and vomiting symptoms and needing nutrition support, so as to improve the health level of mothers and infants.

The invention belongs to the technical field of sow production performance evaluation, and particularly relates to a system and method for evaluating the feeding value of bamboo powder fibers based on sow production performance. By comprehensively evaluating the antenatal production performance indexes and the postpartum production performance evaluation indexes of the sows in the gestation period and the lactation period, the influence of the bamboo powder fibers on the feeding effect of the sows in different physiological stages is comprehensively considered, so that the optimal feed sample is accurately screened out, the overall production performance of the sows is improved, and the breeding cost is reduced. Firstly, the prenatal feeding value score of the sows in the gestation period is measured, feed samples conforming to an evaluation threshold value are screened out, then the lactation period performance is continuously monitored through a postpartum tracking module, and it is ensured that the selected feed still has the efficient feeding value after delivery; therefore, the optimal feed which meets the requirements of the gestation period and can continuously play a role in the lactation period is screened out, in the feeding process, the feed does not need to be frequently replaced, the feeding cost is reduced, meanwhile, nutrition balance of the sows in different physiological stages is guaranteed, and the production performance of the sows is improved.

The invention discloses application of a late pregnancy urine metabolism marker of gestational diabetes mellitus and a kit, and provides application of the late pregnancy urine metabolism marker of gestational diabetes mellitus in preparation of related drugs for prevention and / or treatment of gestational diabetes mellitus. According to the invention, three urine metabolism markers in late pregnancy, which are obviously associated with the occurrence risk of gestational diabetes mellitus, are obtained through screening, the expression of 17alpha-hydroxyprogesterone and alcadipine is up-regulated, the expression of 7-methyluric acid is down-regulated, and the three urine metabolism markers can be used as detection targets for early prediction of gestational diabetes mellitus, and can be used for early prediction of gestational diabetes mellitus. Or the compound can be used as a target for research and development of related drugs for preventing and treating gestational diabetes mellitus.

The invention discloses a biomarker composition and application thereof, and belongs to the field of biomarker analysis and detection. The invention firstly provides a group of biomarker compositions, wherein the biomarker compositions comprise a Klotho protein and a neutrophil gelatinase associated lipocalin (NGAL); the combined detection biomarker composition can be used for early assessment of the risk of hypertensive disorder in pregnancy complicated with acute kidney injury. When the marker composition is used for predicting the risk of hypertensive disorder in pregnancy complicated with acute kidney injury, the risk of acute kidney injury can be recognized in advance in the early pregnancy stage.

Use of an N-acetylneuraminic acid substance in the preparation of a food product for promoting the regulation of gut microbiota of a mother and offspring thereof via ingestion by the mother. The N-acetylneuraminic acid substance is an N-acetylneuraminic acid anhydrate, an N-acetylneuraminic acid hydrate or an N-acetylneuraminic acid salt. The mother is in a gestational period and / or lactation period, and the offspring is in a fetal stage, infant stage and / or toddler stage. The mother starting to ingest the N-acetylneuraminic acid substance in the gestational period can promote the increase of beneficial bacteria in the gut in the gestational and lactation periods, and can also increase the content of probiotics in the offspring gut, inhibit the proliferation of opportunistic pathogens in the offspring gut and reduce the relative abundance thereof, thereby further contributing to maintaining the health of the nervous system by means of the action of the gut-brain axis.

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 discloses a method for predicting gestational diabetes mellitus in early pregnancy and application, and relates to the technical field of biomedicine, and the method specifically comprises the following steps: firstly, collecting peripheral blood of a pregnant woman in 8-12 weeks of pregnancy to obtain a test sample, and then performing prediction on the test sample to obtain the expression quantity of Nrf2mRNA and HO-1mRNA, the glycosylated hemoglobin quantity and the triglyceride quantity, so as to obtain the prediction result of the gestational diabetes mellitus in early pregnancy. Combining the four substances to construct a multi-factor logistic regression model to generate a prediction model formula, calculating a risk value Y, and finally predicting the risk of the gestational diabetes in the early pregnancy according to the risk value Y. According to the method for predicting the gestational diabetes mellitus in the early pregnancy, evaluation can be completed through peripheral blood detection for 8-12 weeks of pregnancy, complex operation or special equipment is not needed, the screening cost is low, the technical process is complete, the method is 12-20 weeks earlier than traditional OGTT diagnosis for 24-28 weeks of pregnancy, accurate prediction of GDM in the early pregnancy can be achieved, and the method has good clinical application prospects.