7 results about "Perinatal period" patented technology

The invention discloses a health data linkage management system for pregnant and lying-in women in a large perinatal period, and belongs to the technical field of intelligent data management. The system comprises a multi-source heterogeneous data access and standardization gateway which is used for connecting household equipment and a hospital system and standardizing original data through a deep cleaning engine; a data fusion and association unit of the central data processing server fuses multi-source data according to a time sequence by relying on a gestational week management unit, and a multi-modal health analysis engine is combined with a clinical rule and a machine learning model to carry out early warning, risk prediction and personalized analysis; and the multi-terminal interaction platform provides a fusion data view and a closed-loop management tool based on role permission. According to the invention, equipment and hospital information islands are broken through, and through high-quality data management and'rule + AI 'fusion analysis, accurate and foresight management and efficient cooperation of the full-cycle health state of pregnant and lying-in women are realized.

The application provides a placenta perfusion and fetal development prediction method and system based on ultrasound images, and relates to the technical field of medical image analysis and diagnosis, which comprises the following steps: obtaining a placenta ultrasound image and pre-processing and quality control; segmenting the placenta region through a deep neural network, extracting multi-dimensional features of gray scale, texture, shape and blood flow; inputting a machine learning model to predict the perfusion and fetal development state; visualizing the results and key factors, combining clinical verification and generating intervention suggestions. The system comprises image processing, segmentation, prediction, visualization and verification interpretation modules. The application realizes objective quantitative evaluation, solves AI problems, forms full-chain clinical support, improves early warning accuracy, helps precise intervention and reduces perinatal risks.

1. Name of the product in this design: Ultrasonic Doppler Fetal Monitor (FS Series). 2. Purpose of this design: For continuous monitoring of fetal heart rate, fetal movement and maternal uterine contraction pressure during the perinatal period. 3. The key design features of this product are its overall shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

This invention provides a perinatal full-cycle intelligent intervention method based on multi-source heterogeneous data fusion, comprising: step S100, simultaneously collecting subjective self-reported data from pregnant women, real-time physiological data from wearable devices, and heterogeneous clinical examination data to construct a multi-dimensional spatiotemporal data pool; step S200, establishing a unified time base axis with gestational age and absolute time as dual coordinate axes, and extracting feature vectors from the data in the multi-dimensional spatiotemporal data pool at fixed time steps; step S300, calculating the real-time global comprehensive risk index RI based on the feature vectors; step S400, when RI exceeds the first dynamic threshold T1, the system triggers a level-one warning; when RI exceeds the second dynamic threshold T2 and a preset acute-critical feature operator is identified, the system triggers a level-two warning, and simultaneously generates an admission preparation list and sends a rescue instruction to the hospital's emergency dispatch system.

The invention relates to the technical field of medical health information, in particular to a perinatal period grading management system based on AI risk assessment, which comprises a data acquisition layer, a feature engineering layer, an AI risk assessment module, a five-color dynamic grading module and an intervention push management module. According to the scheme, a multi-scale decision table is constructed by adopting a multi-scale data fusion method, modeling is carried out on data of different sources and different scales, information gain is introduced as a scale selection standard, a high-fidelity fusion feature vector is formed, discriminative information of each data source under the optimal scale is reserved, and a high-fidelity fusion feature vector is formed. High-quality input with a clear structure and a uniform scale is provided for a subsequent AI risk assessment module; an improved Transform model is designed, a time dependence matrix and a dynamic weight adjustment mechanism are embedded, deep modeling of time series data and generation of interpretable dynamic rules are achieved, online self-updating of the rules is supported, and the accuracy and real-time performance of risk assessment are improved.

The invention provides a fetal growth limitation intelligent prediction system and method based on double networks, and relates to the technical field of perinatal medical monitoring, and the system comprises a data collection and preprocessing module which is used for processing collected prenatal fetal heart related data and outputting high-quality data; the fetal heart signal feature extraction module is used for outputting fetal heart feature vectors by adopting a parallel architecture of a convolutional neural network and a long-short-term memory network; the multi-modal feature fusion module is used for outputting a fusion feature vector based on the double-tower structure architecture; the hierarchical classification prediction module is used for modeling a hierarchical structure of FGR clinical diagnosis by adopting a graph neural network GNN, and outputting prediction probabilities of FGR diagnosis, subtype classification and perinatal outcome in combination with an MC loss function optimization model; and the clinical decision support module is used for generating decision result suggestions and visual reports. And the clinical requirements of early accurate identification, subtype classification and perinatal outcome prediction are met.

The present application relates to the technical field of intelligent medical treatment and maternal and child health, and particularly relates to a pregnant woman perinatal risk intelligent evaluation method based on multi-index fusion, which acquires clinical, behavioral, environmental and real-time physiological data through multi-source acquisition and preprocessing, dynamically calculates multi-dimensional feature weights based on analytic hierarchy process and entropy weight method, uses attention mechanism and graph neural network for multi-modal fusion and risk correlation analysis, integrates data with the help of space-time alignment engine, adopts integrated learning to construct an evaluation model, carries out four-level grading early warning of low, medium, high and extremely high risk, generates and continuously optimizes personalized intervention scheme combined with case reasoning and reinforcement learning, and completes the intelligent dynamic evaluation and management closed loop of perinatal risk. The present application realizes comprehensive data integration, personalized index dynamic adjustment and intelligent risk correlation analysis of perinatal risk evaluation, effectively solves the problems of data island, rigid index and low fusion efficiency, and improves the accuracy and timeliness of the evaluation.