22 results about "Gestational period" patented technology

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.

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.

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).

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.

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.

This invention provides a combination of biomarkers for predicting the risk of gestational diabetes in early pregnancy and their application. Utilizing targeted metabolomics technology, a series of biomarkers capable of predicting the risk of gestational diabetes in early pregnancy are screened. Combined with biochemical and clinical test information, a predictive model is constructed, which improves the accuracy of detecting the risk of gestational diabetes and classifies the risk of pregnant women with gestational diabetes into three levels, providing more comprehensive protection for the health of pregnant women and fetuses.

This invention belongs to the field of biodetection technology and discloses a risk biomarker for pregnancy complications and its application. The invention provides a risk biomarker for pregnancy complications, namely, serum fluorescence lifetime; the pregnancy complications include preeclampsia, gestational diabetes mellitus, and preeclampsia combined with gestational diabetes mellitus. This invention uses fluorescence lifetime imaging technology to characterize functional changes in the lipid-related microenvironment in maternal serum, enabling the detection of metabolic disturbance signals in the early stages of pregnancy before routine clinical indicators show abnormalities, thereby advancing the disease identification time window. This invention effectively overcomes the problem of difficulty in achieving effective early warning in the subclinical stage of disease in existing technologies, providing a new technical approach for early warning and individualized management of pregnancy complications.

The intrauterine hemostasis balloon comprises a guiding assembly and a liquid injection assembly, the guiding assembly comprises a guiding head, a bulb and a flexible catheter, the guiding assembly is used for guiding the intrauterine hemostasis balloon, the guiding head is fixedly connected to the top end of the flexible catheter, and the bulb is fixedly connected to the liquid injection assembly. The guide head penetrates through the bulb and is fixedly connected with the outer surface of the bulb, the interior of the bulb is hollow, the flexible guide pipe penetrates through the bulb and is fixedly connected with the bulb, the liquid injection assembly is jointly composed of a first one-way valve, a sleeve, a piston rod, a liquid injection bag and a connecting pipe, and the liquid injection assembly is used for injecting liquid into the bulb. The first one-way valve is fixedly connected to the flexible guide pipe in series, and the sleeve is connected with the piston rod in a sleeved mode. According to the device, the guide head is arranged, so that the uterus of a puerpera cannot be scratched when the device is used, and the problem that the uterus of the puerpera is stimulated due to scraping is solved.

The invention discloses a multifunctional gender specificity physiological signal monitoring system which comprises a sensing module based on a gender specificity structure, a control module, a battery module and a display module. One side of the sensing module based on the gender specific structure is adhered to the skin surface in different scenes in different composite structure forms, and the other side of the sensing module is connected with the control module; the control module is fixed on the clothes in the form of a flexible circuit board; the battery module is directly connected to the control module to supply power to the control module; the display module is connected with the mobile phone end and the control module by turning on built-in Bluetooth of the mobile phone and then turning on a power switch of the control module, and real-time changes of specific signals monitored by the sensing module are displayed at the mobile phone end. According to the invention, abdominal uterine contraction strain and local temperature change of the pregnant woman are synchronously captured, and dual-mode dynamic evaluation of abnormal uterine activity in the gestation period is realized; meanwhile, the device can also serve as a male penis erection function monitoring ring, and psychological and organic ED are distinguished through radial strain-temperature collaborative analysis.