313 results about "Pregnancy" patented technology

The invention relates to the technical field of medical health data intelligent processing, in particular to a gestational diabetes patient family doctor management method and system based on artificial intelligence, and the method comprises the steps: 1, collecting patient data through a dynamic glucometer, a fetal heart monitor and a hospital information system; 2, aligning the multi-source data according to timestamps to construct a time sequence data set; 3, generating a complication risk score containing a ketoacidosis risk and a giant child risk; 4, generating a personalized sugar control scheme based on the risk score and a pre-constructed medical knowledge graph; 5, integrating opinions of multiple parties to generate a final edition scheme; and step 6, updating deep learning model parameters based on feedback data. According to the mode, the treatment efficiency and accuracy are improved, the control scheme can be adjusted in real time, and the health and safety of the patient in the pregnancy period are ensured. In addition, a feedback mechanism can continuously optimize the system, a closed loop is formed, and the management effect is further improved.

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.

The invention provides a gestational diabetes risk prediction system and method based on multi-modal data fusion, and the system comprises a data collection module which is used for integrating clinical indexes and medical record text data; the data preprocessing module converts the multimode data into numerical values and text variables which can be used for modeling; the variable screening module is used for extracting data features by adopting LASSO regression in combination with a recursive feature elimination algorithm and a Clinical-BERT model; the data prediction module is used for constructing a GDM risk prediction model through a dual-channel calculation unit and a fusion unit, generating an accurate risk probability and providing an interpretable clinical index in combination with an SHAP value; and a prediction result is output through the output module in the forms of a dynamic column diagram, a webpage calculator and an API interface, so that clinical operation and application are facilitated. According to the method, the limitation of a traditional prediction method is broken through, the prediction precision and the real-time monitoring capability are improved, and the development of precise medical treatment is promoted.

The invention discloses an establishment method and a verification method of a hydrogen peroxide induced mouse autism model, and relates to the technical field of medical biologication.The establishment method of the hydrogen peroxide induced mouse autism model comprises the following steps that a hydrogen peroxide solution is injected into the abdominal cavity of a pregnant mouse, hydrogen peroxide induction is conducted, and the mouse autism model is obtained. And obtaining a hydrogen peroxide induced offspring mouse, namely the mouse autism model. Hydrogen peroxide is used for near intraperitoneal injection of a female mouse during pregnancy, the oxidative stress state of the female mouse during pregnancy and the oxidative stress state of an offspring mouse embryo are induced through independent hydrogen peroxide, and obvious immune response of the female mouse is not caused, so that an ASD mouse model specifically induced by oxidative stress is formed; the obtained ASD offspring mouse has a remarkable ASD phenotype, but does not show obvious dyskinetic disorder.

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.

Disclosed herein includes a method of detecting RhD incompatibility during pregnancy, comprising: a) extracting DNA from a blood sample of a pregnant mother; b) performing qPCR on the DNA to determine Ct values of at least one RhD exon and at least one internal control gene; and c) calculating a ΔCt value between the at least one RhD exon and at least one internal control gene.

The invention relates to the technical field of data feedback control, and discloses a pregnancy health care control system based on data feedback analysis, which comprises a data acquisition module, a dynamic physiological feature mapping module, an implicit feedback channel module, a self-stabilization control module and a care execution module. By constructing the dynamic physiological feature mapping matrix, the multi-dimensional physiological data and the historical trend of the pregnant woman are deeply fused, the continuous health state vector is generated, and the limitation that in the prior art, a single threshold value is relied on, and dynamic adaptive capacity is lacked is broken through. Meanwhile, an implicit feedback and self-stabilization control mechanism is introduced into the system, the nursing strategy can be automatically adjusted when data abnormity or environment change is detected, pregnancy health management with individualized, continuous and robust coexistence is achieved, and the sensitivity, the stability and the individualized nursing capacity of the monitoring system are remarkably improved.

The invention discloses a pregnancy-related monitoring system based on an electrochemical sensing technology, and the system comprises a wearable device which is used for obtaining hormone detection data of a target monitoring user; the server is connected to the wearable device and comprises a parameter analysis module and a scheme generation module, and the parameter analysis module is used for obtaining the hormone detection data and obtaining ovulation cycle parameters of the target monitoring user through analysis according to the hormone detection data; and the scheme generation module is used for generating a pregnancy-related guidance scheme corresponding to the target monitoring user according to a pregnancy-related target corresponding to the target monitoring user and the ovulation cycle parameter. Therefore, more intelligent and efficient pregnancy-related monitoring and guidance services can be realized, the monitoring accuracy of pregnancy-related indexes is improved, and better use experience is provided for users.

The present disclosure relates to a vaginal system that prevents pregnancy comprised of segesterone acetate and ethinyl estradiol and is configured for thirteen 28-day product-use cycles.

Provided herein is a method of using digital twins for medical diagnosis, prediction, and / or prognosis of a disease or condition, such as diagnosis, prediction, and / or prognosis of one or more cancers, organ-specific age prediction, predicting of female aging, predicting hospital-acquired infection, predicting dialysis time series, predicting diabetes and complications, predicting pregnancy and child outcome, predicting heart failure, predicting myopia, predicting vision in anti-VEGF treatment, or any combination thereof. A system and non-transitory computer-readable storage medium configured for performing the method is also disclosed.

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.

A data analysis method for gynaecology and obstetrics based on a knowledge graph particularly relates to the technical field of data analysis for gynaecology and obstetrics, and comprises the following steps: acquiring basic information of a current puerpera in real time by using a data acquisition module, and acquiring reference information of other puerpera in the same pregnancy period as the current puerpera and reference graph information from a knowledge graph database; based on basic information, reference information and atlas information, an analysis and diagnosis module is utilized to sequentially calculate and output a puerpera risk index FF, a complication prediction value YB and a health assessment index FJ, and based on result comparison of the health assessment index FJ and a health assessment average index FJavg, the analysis and diagnosis module is utilized to analyze and judge data. The method has the remarkable advantages of improving evaluation accuracy, achieving personalized nursing, timely finding potential risks, promoting circulation influence and optimization and the like, accurate evaluation and targeted adjustment of the health condition of the puerpera are achieved, and powerful support is provided for clinical decision making.

The invention relates to the technical field of image classification, in particular to a gestational hypertension traditional Chinese medicine tongue diagnosis image classification method based on transfer learning. Firstly, a tongue diagnosis image of a gestational hypertensive patient is obtained, a tongue body area is extracted, and an indentation degree value is calculated based on shape and pixel position features to reflect the shape of fat tongue with indentation; further extracting a tongue middle area by considering that physiological water retention during pregnancy may cause tooth marks, determining a reflective connected domain by analyzing pixel reflective characteristics before and after filtering, and extracting shape characteristics of the reflective connected domain to obtain a dispersion degree value and characterize a greasy moss degree; the tongue diagnosis image purity is obtained by combining the color fluctuation and gray scale confusion conditions of the tongue middle area and integrating the indentation degree value and the dispersion degree value, so that the greasy coating state and the spleen deficiency and liver-yang hyperactivity conditions are reflected; and finally, training a neural network by using the purity of the tongue diagnosis image and the original visual features, classifying the tongue diagnosis image, obtaining a more accurate classification result, and assisting a doctor in performing accurate judgment.

The invention provides an optimal administration method for promoting cervical maturation by misoprostol based on a PopPK model, and belongs to the technical field of drug administration scheme optimization, the method comprises the following steps: constructing the PopPK model for oral administration of misoprostol by a pregnant woman in late pregnancy, the PopPK model is a model structure containing absorption lag time zero-order-first-order absorption parallel, one-atrioventricular distribution and first-order elimination, and the PopPK model is a model structure containing absorption lag time zero-order-first-order absorption parallel, one-atrioventricular distribution and first-order elimination; the PopPK model comprises two absorption chambers and one central chamber; and based on the PopPK model, determining a staged administration scheme through simulation optimization. The limitation of a traditional fixing scheme is effectively overcome, and the safety, effectiveness and clinical practicability of drug administration are considered.

Provided herein are methods and kits for identifying a pregnancy-associated risk or condition in a subject. In embodiments, the methods include detecting levels of at least one biomarker in a substantially cell-free cervical fluid sample obtained from the subject. In embodiments, the methods include detecting levels of biomarkers in a cervical fluid sample including extravillous trophoblasts (EVT) obtained from the subject.

The invention relates to the technical field of medical health information, in particular to a pregnant and lying-in woman health record digital management system which comprises a user management and data safety unit, a record management unit, a monitoring and evaluation unit, a data analysis unit, a reservation registration unit and a health education unit. According to the invention, data access dynamic management and control and privacy protection are realized through a pregnancy stage adaptive permission system and a lightweight block chain technology in combination with zero-knowledge proof, and physiological parameter real-time acquisition and analysis and sub-second early warning are realized through medical IoT equipment and edge computing nodes. And a multi-party collaborative risk prediction model is constructed by virtue of federal learning and an interpretable AI technology, and a clinical decision basis is provided, so that full-cycle safety management and control, real-time monitoring analysis and accurate risk prediction of pregnant and lying-in woman health data are realized, and technical support is provided for improving pregnant health management efficiency and quality.

The invention belongs to the technical field of disease medicine preparation, and particularly relates to nanocellulose, a preparation method thereof and application of the nanocellulose to reduction of bile acid level. The nano-crystalline cellulose is nano-crystalline cellulose fiber or nano-crystalline cellulose crystal; wherein the nano-crystalline cellulose fibers are in a slender whisker shape, the average length of the nano-crystalline cellulose fibers is 250-400 nm, and the nano-crystalline cellulose fibers are partially gathered and wound; the nanocellulose crystal is in a short rod crystal shape, the surface of the nanocellulose crystal is connected with a sulfuric acid half ester group, and the length of the nanocellulose crystal is 50-200 nm. The nanocellulose provided by the invention can achieve the purpose of treating intrahepatic cholestasis in the gestation period by improving the TBA level and improving the filial generation live birth rate; the traditional Chinese medicine composition has no obvious influence on liver and kidney functions, blood sugar, blood fat and the like, and is high in safety; in addition, the nanocellulose does not have potential target organ poison or accumulative effect, the biological safety performance is improved, and the nanocellulose can be used for a long time and is suitable for special crowds during pregnancy.

The invention relates to the technical field of intelligent medical treatment, in particular to a pregnancy management system and method based on big data, and the system comprises an abnormal segment recognition module, a deviation feature extraction module, a risk type recognition module, an abnormal trend prediction module and a physiological data management module. According to the method, the abnormal fluctuation of the data sequence is recognized by calculating the variability of the physiological data in the cycle, the method is not limited to isolated single-point measurement any more, the dynamic stability of the index in a period of time is concerned, and then quantitative extraction is conducted on the variation trend of abnormal data segments such as body weight, blood sugar and blood pressure; for example, the change of body weight along with time is fitted into a slope, original data is converted into a structured trend feature, then the dynamic change trend of an individual is compared with a normal evolution benchmark extracted from a large-scale healthy pregnant woman group, and the severity that the current physiological state deviates from a normal track is accurately quantified.

The present disclosure provides methods and systems directed to cell-free identification and / or monitoring of pregnancy-related states. A method for identifying or monitoring a presence or elevated risk of a pregnancy-related state of a pregnant subject may comprise assaying a cell-free biological sample derived from said pregnant subject to detect a set of biomarkers, and analyzing the set of biomarkers with a trained algorithm to determine the presence or elevated risk of the pregnancy-related state.

The invention relates to the technical field of hospital number calling management, in particular to a clinical laboratory blood drawing sequence number and inspection information intelligent processing system. The method comprises the following steps: firstly, acquiring an initial number calling priority from a waiting retention degree angle and a clinical emergency angle of a pregnant woman in an initial analysis module; further, historical reference pregnant women with pre-pregnancy physiological data similar to that of the current pregnant woman are screened out in the priority correction module; further acquiring an inspection emergency coefficient according to the body weight difference of different pregnancy time; further fusing the initial number calling priority and the inspection emergency coefficient to obtain the corrected number calling priority of the current pregnant woman; and finally, calling the number on the basis of the corrected number calling priority in the checking number calling module. According to the method, the waiting retention degree of the pregnant woman and the inspection emergency degree of the pregnant woman are fully considered, the corrected number calling priority is obtained, the inspection number calling priority of each pregnant woman is quantified, the waiting risk of emergency patients is reduced, and the queuing experience of non-emergency patients is guaranteed.

1. Name of the Design Product: Convenient Lumbar Pillow. 2. Use of the Design Product: A pregnancy pillow for pregnant women to use freely in combination or separately during pregnancy. 3. Design Key Points of the Design Product: Lies in the shape. 4. Picture or Photo that Best Illustrates the Design Key Points: Combined State Diagram 1.

The invention relates to medical detection equipment, in particular to an integrated portable instant urinalysis device for pregnant women. The invention relates to a pregnant woman urinalysis device, and aims to provide an integrated portable instant pregnant woman urinalysis device so as to solve the problem that a conventional pregnant woman urinalysis method is complex and time-consuming at the present stage, realize low-cost and rapid multi-index detection and contribute to early detection of various diseases in the gestation period. According to the technical scheme, the integrated portable instant urinalysis device for the pregnant woman is characterized in that the device is formed by correspondingly assembling an upper chip layer and a lower chip layer; a sample introduction area located in a central area and a plurality of detection channels radially arranged outwards from the sample introduction area are arranged in the device, and each detection channel comprises a reaction detection area and a waste liquid area which are led out from the sample introduction area and are sequentially communicated through a shunting channel, and air holes formed above the detection channels.

The present application relates to a blood sugar management system for pregnancy, comprising terminal devices carried by target users and reference users and a cloud server in data connection with the terminal devices, the cloud server being configured to: acquire physiological data and blood sugar data of the target users from the terminal devices, and calculate characteristic indexes of the target users based on the physiological data and the blood sugar data; acquire a plurality of blood sugar data of the reference users from the terminal devices and form an overall reference data set in a manner associated with gestational weeks, and assign the target users to a predetermined characteristic index value interval in the overall reference data set based on the characteristic indexes, so as to provide personalized health management suggestions for the target users based on factors identified to affect the risk of gestational diabetes mellitus of the target users.

The invention provides application of acetylcholine and 1-carboxyl-6-hydroxy-3, 4-dihydro-beta-carboline (the code in the attached drawing of the abstract is CHEMBL461889) in early pregnancy diagnosis, non-targeted metabonomics detection and analysis are carried out on the urine of pregnant and non-pregnant sows by virtue of a liquid chromatography-mass spectrometry technology; the two metabolic markers capable of being used for early pregnancy diagnosis of the sows are screened out by introducing two deep learning models for joint analysis, the AUC values of ROC curves of the two metabolic markers are 1, the accuracy is very high, and the pregnancy state of the sows can be accurately judged only 18 days after the sows are bred. The invention provides a novel effective sow early pregnancy diagnosis method for the pig breeding industry, and has important significance for reducing the breeding cost.

The invention discloses a pregnant and lying-in woman depression screening information system, which relates to the technical field of depression risk screening, and has the technical key points that the system comprises five core modules, namely a pregnant and lying-in woman information management module, a screening recording module, a referral management module, a reception management module and a data statistics and analysis module, and the whole-course management of pregnant and lying-in woman psychological health is realized through multi-dimensional data integration. The system creatively adopts a self-adaptive screening tool matching mechanism in the pregnancy and birth stage, and supports intelligent analysis of screening results and risk early warning. Through a hierarchical authority management system, differentiated function configuration of pregnant and lying-in women, screening personnel, reception doctors and regional managers is realized, and information intercommunication between women and children and mental health institutions is promoted while data safety is guaranteed. The system has the functions of data visual analysis, automatic referral tracking and reception closed-loop management, the screening efficiency and data timeliness are remarkably improved, intelligent support is provided for early discovery and intervention of pregnant and puerperal depression, and the health risk of mothers and infants is effectively reduced.

The application provides a misoprostol cervical ripening optimization dosing method based on a PopPK model, belongs to the technical field of drug dosing scheme optimization, and comprises the following steps: constructing a PopPK model of oral misoprostol for pregnant women in the third trimester of pregnancy, the PopPK model is a model structure containing zero-order-first-order absorption in parallel, one-compartment distribution and first-order elimination, the PopPK model comprises two absorption compartments and one central compartment; and determining a phased dosing scheme based on the PopPK model through simulation optimization. The method effectively overcomes the limitations of traditional fixed schemes, and takes into account the safety, effectiveness and clinical practicability of dosing.

The invention provides an application of metabolites in sow vaginal secretion in early pregnancy diagnosis, and finds that three metabolic molecules can be used as markers for the early pregnancy diagnosis of sows for the first time on the basis of non-targeted metabonomics and machine learning conjoint analysis. According to the present invention, the metabolic markers are respectively INDOLEPROPIONIC ACID (indolepropionic acid), cis-Amonitate (cis-Amonitate) and 4-p-Coumaroylquinic acid (4-p-coumaroylquinic acid), the ROC curve AUC values of the metabolic markers are respectively 0.88, 0.92 and 0.77, and the metabolic markers have excellent diagnosis performance during the combination diagnosis. The pregnancy diagnosis is carried out by adopting the vaginal secretions of the sows 18 days after hybridization, the effective pregnancy diagnosis time is advanced by 5-8 days on the basis of a wave diagnosis method, and the method has important significance for reducing cost and increasing ultrasonic efficiency in live pig feeding.

The invention discloses an amniotic epithelial cell-derived exosome for treating gestational hypertension and application thereof, and belongs to the technical field of biological medicines. The exosome (hAECs-exos) is obtained by secreting and extracting human amniotic epithelial cells (hAECs), the particle size distribution of the exosome (hAECs-exos) is 30-150 nm, and the exosome (hAECs-exos) expresses exosome specific markers CD9, CD63 and CD81. The invention also provides a preparation method of the exosome. The preparation method comprises the following steps: isolated culture of hAECs, and ultracentrifugal extraction and identification of the exosome. Animal experiments prove that when the exosome is applied to a gestational hypertension disease model animal in an intravenous injection mode, the blood pressure and urine protein level of the gestational hypertension disease model animal can be remarkably reduced, and the fetal rat dysplasia condition is improved. The invention provides a brand-new and effective biological preparation candidate scheme for the treatment of the gestational hypertension disease.

Disclosed herein are methods and kits for assessing the risk of preterm birth (PTB) and hypertensive disorders of pregnancy (HDPs) by analyzing immune responsiveness in a subject.