CN116439661B - Perinatal puerpera physiological state monitoring and evaluating method and system - Google Patents
Perinatal puerpera physiological state monitoring and evaluating method and system Download PDFInfo
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Abstract
The invention provides a method and a system for monitoring and evaluating physiological states of puerpera in perinatal period, which relate to the technical field of medical monitoring, and are characterized by obtaining information of puerpera medical files, carrying out characteristic classification marking to obtain parameter of characteristic attribute of perinatal period, constructing a puerpera risk analysis model library, matching and calling a target puerpera risk analysis model according to the parameter of characteristic attribute of perinatal period, carrying out physiological state real-time monitoring to obtain information of puerpera physiological signs, inputting the information of puerpera physiological signs into the target puerpera risk analysis model for evaluation to obtain information of puerpera risk evaluation, and carrying out puerpera perinatal intervention on target monitored puerpera to obtain a puerpera perinatal intervention scheme. The invention solves the technical problems that the physiological state of the puerpera cannot be comprehensively and timely estimated in the prior art, so that the nursing effect of the puerpera in the perinatal period is poor, realizes the comprehensive and timely physiological state estimation aiming at the target puerpera, further matches with a targeted perinatal intervention scheme, and achieves the technical effect of improving the nursing effect of the puerpera in the perinatal period.
Description
Technical Field
The application relates to the technical field of medical monitoring, in particular to a method and a system for monitoring and evaluating physiological states of puerpera in perinatal period.
Background
Perinatal refers to the entire fertility process from the beginning of pregnancy to 42 days post partum. In the perinatal period, the life safety of the pregnant women and fetuses is not guaranteed due to the continuous change of the physical state of the pregnant women and various risk factors in the delivery process, and the method is a very urgent problem. The conventional perinatal puerpera physiological state monitoring and evaluating method has certain defects, and a certain lifting space exists for perinatal puerpera physiological state monitoring and evaluating.
The prior art has the technical problems that the physiological state of the puerpera cannot be comprehensively and timely estimated, so that the nursing effect of the puerpera in the perinatal period is poor.
Disclosure of Invention
The embodiment of the application provides a method and a system for monitoring and evaluating physiological states of puerpera in perinatal period, which are used for solving the technical problems that the physiological states of puerpera cannot be comprehensively and timely evaluated in the prior art, so that nursing effects on puerpera in perinatal period are poor.
In view of the above problems, the embodiment of the application provides a method and a system for monitoring and evaluating physiological states of puerpera in perinatal period.
In a first aspect, an embodiment of the present application provides a method for monitoring and evaluating a physiological state of a parturient in perinatal period, the method comprising: acquiring perinatal medical file information of a target monitoring puerpera, wherein the perinatal medical file information comprises gestational medical information, childbirth medical information and postpartum medical information; performing feature classification marking on the surrounding production medical file information to obtain surrounding production feature attribute parameters; constructing a perinatal puerpera risk analysis model library; according to the perinatal characteristic attribute parameters, matching and calling a target puerpera risk analysis model from a perinatal puerpera risk analysis model library; the physiological state of the target monitoring puerpera is monitored in real time, and puerpera physiological sign monitoring data information is obtained; inputting the physiological sign monitoring data information of the puerpera into the target puerpera risk analysis model for evaluation to obtain puerpera risk evaluation information; and carrying out parturient perinatal intervention on the target monitoring parturient based on the women's birth expert group and the parturient risk assessment information to obtain a parturient perinatal intervention scheme.
In a second aspect, an embodiment of the present application provides a perinatal maternal physiological condition monitoring and assessment system, the system comprising: the file information acquisition module is used for acquiring the perinatal medical file information of the target monitoring puerpera, wherein the perinatal medical file information comprises gestational medical information, childbirth medical information and postpartum medical information; the feature classification marking module is used for performing feature classification marking on the surrounding medical file information to obtain surrounding feature attribute parameters; the model library construction module is used for constructing a perinatal puerpera risk analysis model library; the target model calling module is used for matching and calling a target lying-in woman risk analysis model from a lying-in woman risk analysis model library in the perinatal period according to the perinatal characteristic attribute parameters; the state real-time monitoring module is used for monitoring the physiological state of the target monitoring puerpera in real time and acquiring puerpera physiological sign monitoring data information; the monitoring data evaluation module is used for inputting the monitoring data information of the physiological signs of the puerpera into the target puerpera risk analysis model for evaluation to obtain puerpera risk evaluation information; and the parturient perinatal intervention module is used for carrying out parturient perinatal intervention on the target monitoring parturient based on the women's birth expert group and the parturient risk assessment information, so as to obtain a parturient perinatal intervention scheme.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
the embodiment of the application provides a method for monitoring and evaluating physiological states of puerpera in perinatal period, which relates to the technical field of medical monitoring, and comprises the steps of obtaining information of a puerpera medical file, carrying out characteristic classification marking, obtaining parameter of a characteristic attribute of the perinatal period, constructing a puerpera risk analysis model library, matching and calling a target puerpera risk analysis model according to the parameter of the characteristic attribute of the perinatal period, carrying out physiological state real-time monitoring, obtaining physiological sign monitoring data information of the puerpera, inputting the physiological sign monitoring data information of the puerpera into the target puerpera risk analysis model for evaluation, obtaining puerpera risk evaluation information, and carrying out puerpera perinatal intervention on a target monitored puerpera to obtain a puerpera perinatal intervention scheme. The technical problem that the physiological state of the puerpera cannot be comprehensively and timely estimated in the prior art, so that the nursing effect of the puerpera in the perinatal period is poor is solved, the comprehensive and timely physiological state estimation aiming at the target puerpera is realized, the targeted perinatal intervention scheme is matched, and the technical effect of improving the nursing effect of the puerpera in the perinatal period is achieved.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
Fig. 1 is a schematic flow chart of a method for monitoring and evaluating physiological states of a parturient in perinatal period according to an embodiment of the application;
fig. 2 is a schematic flow chart of determining perinatal characteristic attribute parameters in a method for monitoring and evaluating physiological states of parturients in perinatal period according to an embodiment of the present application;
fig. 3 is a schematic flow chart of constructing a perinatal puerpera risk analysis model library in a perinatal puerpera physiological state monitoring and evaluating method according to an embodiment of the application;
fig. 4 is a schematic structural diagram of a perinatal puerperal physiological condition monitoring and evaluating system according to an embodiment of the present application.
Reference numerals illustrate: the system comprises an archive information acquisition module 10, a feature classification marking module 20, a model library construction module 30, a target model calling module 40, a state real-time monitoring module 50, a monitoring data evaluation module 60 and a parturient perinatal intervention module 70.
Detailed Description
The embodiment of the application provides a method for monitoring and evaluating the physiological state of a puerpera in perinatal period, which is used for solving the technical problem that the physiological state of the puerpera cannot be comprehensively and timely evaluated in the prior art, so that the nursing effect of the puerpera in perinatal period is poor.
Example 1
As shown in fig. 1, an embodiment of the present application provides a method for monitoring and evaluating physiological states of a parturient in perinatal period, the method comprising:
Step S100: acquiring perinatal medical file information of a target monitoring puerpera, wherein the perinatal medical file information comprises gestational medical information, childbirth medical information and postpartum medical information;
specifically, the perinatal puerpera physiological state monitoring and evaluating method provided by the embodiment of the application is applied to a puerpera physiological state monitoring and evaluating system. Firstly, the perinatal period refers to an important period from 28 weeks of pregnancy to one week of postpartum delivery, and medical information of pregnant women, namely a physiological period from the period after conception to the period before delivery, namely a pregnancy period, is acquired through a medical system of a doctor's hospital, wherein the medical information of pregnant women, the medical information of delivery and the medical information of postpartum delivery are monitored, and the medical information of pregnant women is the medical information of the period; the term and the process that the fetus is separated from the mother and becomes an independent individual, and the medical information of the delivery is the medical information of the term; postpartum refers to a period of time after the pregnant woman is delivered, and postpartum medical information is medical information of the period of time. The method comprises the steps of constructing gestational medical information, childbirth medical information and postpartum medical information together into perinatal medical file information, wherein the perinatal medical file information is a record file which is established by monitoring the puerpera for perinatal health care in a hospital at home, and is initially established from the beginning of three months of pregnancy, and each examination is carried out according to the hospital and examination of five months after the gestation period is reserved.
Step S200: performing feature classification marking on the surrounding production medical file information to obtain surrounding production feature attribute parameters;
further, as shown in fig. 2, step S200 of the present application further includes:
step S210: performing feature classification on the surrounding medical file information to obtain medical file classification feature information;
step S220: constructing a puerpera archive feature coordinate system, wherein the puerpera archive feature coordinate system is a multi-dimensional coordinate system, and coordinate axes correspond to the medical archive classification feature information one by one;
step S230: carrying out regional label classification on the puerperal archive feature coordinate system to obtain a regional label classification result;
step S240: inputting the perinatal medical archive information into the puerpera archive feature coordinate system to obtain a perinatal archive feature vector;
step S250: mapping and matching are carried out according to the regional tag classification result and the surrounding file feature vector, and a surrounding feature classification tag is obtained;
step S260: and determining the surrounding characteristic attribute parameters based on the surrounding characteristic classification labels.
Specifically, the information of the perinatal medical files is primarily screened and arranged, including information such as pregnancy check records, delivery modes and the like, medical file classification features are extracted from the primarily screened and arranged perinatal medical file information, and the extracted medical file classification features are marked in a classification manner, including pregnancy weeks, pregnancy weight increase conditions, pregnancy complications, delivery modes, delivery process conditions, postpartum recovery conditions and the like.
And each feature in the medical file classification feature information is respectively used as a multidimensional coordinate axis to construct a multidimensional puerperal file feature coordinate system, so that various corresponding situations of a puerperal in different states, such as weight increase situations during pregnancy at different gestational weeks, complications at different gestational weeks and the like, can be obtained by the multidimensional puerperal file feature coordinate system. The coordinate system is divided into multiple areas, and each area corresponds to a label parameter, such as a label of the postpartum obesity onset area.
And matching the surrounding medical file information with each coordinate feature of the puerpera file feature coordinate system, respectively inputting the matched features into the respective region, such as a gestational week-gestational weight region, inputting the gestational week and gestational weight of the target puerpera into coordinates, obtaining a gestational weight feature vector, and adding the gestational weight feature vector into the surrounding file feature vector.
Traversing the surrounding file feature vector in the region label classification result, matching a corresponding first region according to the first surrounding file feature, such as matching a weight vector of the surrounding file to obtain a weight region of the surrounding file and the pregnant file, wherein the region is divided into a plurality of labels such as thin, moderate and fat, the weight vector of the surrounding file and the weight vector of the surrounding file are matched with the label classification result in the weight region of the surrounding file and the pregnant file to obtain a weight feature classification label, and the weight feature classification label is added to the surrounding file feature classification label. And determining the surrounding characteristic attribute parameters according to the surrounding characteristic classification labels.
Step S300: constructing a perinatal puerpera risk analysis model library;
further, as shown in fig. 3, step S300 of the present application further includes:
step S310: acquiring historical perinatal puerpera data information based on big data, wherein the historical perinatal puerpera data information comprises historical perinatal medical file information, physiological state information and perinatal risk information of a historical perinatal puerpera;
step S320: marking and clustering the historical perinatal medical record information according to the regional label classification result to obtain a perinatal characteristic attribute parameter clustering result;
step S330: dividing the historical perinatal period puerpera data information based on the perinatal characteristic attribute parameter clustering result to obtain a perinatal period risk training data set;
step S340: and respectively carrying out model training on each parameter type data in the perinatal risk training data set, and constructing the perinatal puerpera risk analysis model library.
Specifically, a history time is set, and illustratively, the history time is set to be 5 years in the past, and history perinatal medical archive information, physiological state information and perinatal risk information of all perinatal puerpera women in the past 5 years are called up based on big data, wherein the history perinatal medical archive information comprises history gestational medical information, history parturition medical information and history post-parturient medical information; physiological state information includes arterial blood pressure, blood volume, venous pressure, heart rate, erythrocytes, leukocytes, platelets, etc.; the perinatal risk information includes various factors related to the increased risk of perinatal infection of the parturients, such as malnutrition, diabetes, obesity, severe anemia, etc. Thus, the data information of the parturient in the history perinatal period is constructed.
And carrying out marked clustering on the history surrounding medical record information according to the region tag classification result based on a KNN algorithm, specifically, extracting points of any group of data in a coordinate system according to the history surrounding medical record information, calculating the distance between the points in the region tag classification result and the current point, sorting according to the distance increasing order, selecting k points with the smallest distance from the current point, determining the occurrence frequency of the category of the k points before, returning the category with the highest occurrence frequency of the k points before as the prediction classification of the current point, and traversing all the data in the history surrounding medical record information to obtain the surrounding characteristic attribute parameter clustering result.
And acquiring a first characteristic attribute parameter category based on the perinatal characteristic attribute parameter clustering result, extracting data information which is the same as the first characteristic attribute parameter category in the data information of the puerpera in the history perinatal period according to the first characteristic attribute parameter category, acquiring a plurality of first data information, taking the plurality of first data information as training data, and constructing a first training data set, wherein the first training data set comprises a plurality of parameters in the first characteristic attribute parameter category, such as obesity in the pregnancy period, and comprises a plurality of history pregnancy period weight data, and the parameter category and the plurality of parameters have a mapping relation. And adding the first training data set to a training data set to obtain a perinatal risk training data set.
Based on BP neural network in machine learning, constructing a network structure of a first perinatal puerpera risk analysis model, wherein input data of the first perinatal puerpera risk analysis model comprises first parameter characteristic data, and output data comprises first parameter analysis results. And performing supervised learning on the first perinatal puerpera risk analysis model according to the first training data set, adjusting and updating model parameters in the supervision learning process to obtain the first perinatal puerpera risk analysis model with accuracy meeting the requirement, wherein the preset accuracy is set to be 95% in an exemplary manner. And performing supervision training on other data in the perinatal risk training data set by adopting the same method to obtain a perinatal puerpera risk analysis model library.
The BP neural network is a multi-layer feedforward neural network trained according to an error reverse propagation algorithm, a mathematical equation of a mapping relation between input and output is not required to be determined in advance, a certain rule is learned only through self training, and a result closest to an expected output value is obtained when an input value is given. The core of the BP neural network for realizing the functions is a BP algorithm, the basic idea is a gradient descent method, and a gradient search technology is utilized to minimize the error mean square error of the actual output value and the expected output value of the network. And constructing the first perinatal puerpera risk analysis model based on the BP neural network.
Step S400: according to the perinatal characteristic attribute parameters, matching and calling a target puerpera risk analysis model from a perinatal puerpera risk analysis model library;
specifically, a plurality of attribute parameters are extracted according to the perinatal characteristic attribute parameters of the target monitoring puerpera, the plurality of attribute parameters are adopted to traverse in a perinatal puerpera risk analysis model library, and a model corresponding to the plurality of attribute parameters is matched to serve as a target puerpera risk analysis model.
Step S500: the physiological state of the target monitoring puerpera is monitored in real time, and puerpera physiological sign monitoring data information is obtained;
specifically, during pregnancy, a pregnant woman may generate a series of changes for the fetus, including cardiovascular system, blood system, digestive system, respiratory system, etc., for example, the cardiovascular system may decrease the high pressure and the low pressure of the pregnant woman may decrease further, and this change is mainly to reduce the burden of the heart (after all, to supply blood to two people), and if the blood pressure during pregnancy does not decrease and rise, there may be a possibility of pregnancy hypertension or even eclampsia. The physiological state of the puerpera is monitored in real time through various instruments, wherein the physiological state comprises various items in various body systems, such as arterial blood pressure, blood volume, venous pressure, heart rate and the like; the cardiovascular system includes erythrocytes, leukocytes, platelets, etc. And integrating the monitoring data to obtain the physiological sign monitoring data information of the puerpera.
Step S600: inputting the physiological sign monitoring data information of the puerpera into the target puerpera risk analysis model for evaluation to obtain puerpera risk evaluation information;
specifically, in the target puerpera risk analysis model, according to puerpera physiological sign monitoring data information and sample physiological state information of a plurality of historical perinatal puerperas, comparing each item respectively, setting a matching threshold, and exemplarily setting the matching threshold to be 70%, namely extracting more than 70% of comparison results, acquiring a plurality of groups of sample historical physiological state information meeting the matching threshold, taking one group meeting the matching threshold as the matching sample historical physiological state information, acquiring sample perinatal risk information corresponding to the matching sample historical physiological state information according to a mapping relation, and outputting the sample perinatal risk information as an evaluation result of the target puerpera in the risk analysis model to obtain puerpera risk evaluation information.
Step S700: and carrying out parturient perinatal intervention on the target monitoring parturient based on the women's birth expert group and the parturient risk assessment information to obtain a parturient perinatal intervention scheme.
Further, step S700 of the present application further includes:
Step S710: determining the perinatal intervention requirement of the puerpera according to the puerpera risk assessment information;
step S720: analyzing the parturient perinatal intervention requirement based on a gynaecological obstetrics expert group to obtain a parturient perinatal intervention scheme set;
step S730: evaluating each intervention scheme in the parturient perinatal intervention scheme set through a perinatal intervention knowledge graph to obtain an intervention scheme evaluation characteristic value set;
step S740: and carrying out scheme screening on the parturient perinatal intervention scheme set based on the intervention scheme evaluation characteristic value set to obtain a parturient perinatal intervention scheme, and carrying out perinatal intervention management and control on the target monitoring parturient based on the parturient perinatal intervention scheme.
Specifically, according to the risk assessment information of the puerpera, the abnormal item and the abnormal parameter of the abnormal item are extracted, the abnormal parameter of the abnormal item is compared with the normal parameter of the item, and the puerpera perinatal intervention requirement is determined according to the comparison result.
The gynaecological specialist group is a team built by a plurality of gynaecological specialists, the gynaecological specialists respectively analyze the requirements of the perinatal intervention of the puerpera, such as how to regulate and treat pregnancy complications such as gestational hypertension diseases, gestational diabetes mellitus and the like, so as to obtain the intervention mode, and the frequency and the degree corresponding to the intervention mode, and different specialists have different intervention modes, so that the schemes are more diverse, a plurality of intervention schemes are obtained, and a set of the puerperal perinatal intervention schemes is built according to the plurality of intervention schemes.
The method comprises the steps of constructing a surrounding intervention knowledge graph according to an intervention mode, an intervention frequency and an intervention degree, wherein the knowledge graph is an important branch technology of artificial intelligence, is a structural semantic knowledge base and is used for describing concepts and interrelationships thereof in a physical world in a symbol form, and the basic composition units are entity-relation-entity triples and combinations of entities and related attribute values thereof, and are mutually connected through relations to form a netlike knowledge structure.
Based on the perinatal intervention knowledge graph, the intervention modes of all intervention schemes are respectively matched with the grade attributes contained in each index in the perinatal intervention knowledge graph, for example, the intervention modes comprise operation intervention, traditional Chinese medicine intervention, exercise intervention and the like, a plurality of intervention mode evaluation characteristic values are obtained, the intervention frequency, the intervention degree and the intervention index in the perinatal intervention knowledge graph are respectively matched by adopting the same method, a plurality of intervention frequency evaluation characteristic values and a plurality of intervention degree evaluation characteristic values are obtained, the intervention mode evaluation characteristic values, the intervention frequency evaluation characteristic values and the intervention degree evaluation characteristic values are taken as evaluation characteristic values of a plurality of intervention schemes, and an intervention scheme evaluation characteristic value set is obtained.
Based on the set of intervention scheme evaluation feature values, a screening standard is set, and an intervention scheme with only all feature values meeting 70% can be set as an alternative scheme, wherein any item below 70% is judged to be at risk and cannot be used, the screening standard is raised in the alternative scheme, and finally the parturient perinatal intervention scheme is obtained. And performing perinatal intervention control on the target monitoring puerpera based on the intervention mode, the intervention frequency and the intervention degree in the puerpera perinatal intervention scheme.
Further, step S730 of the present application further includes:
step S731: acquiring a perinatal intervention index set, wherein the perinatal intervention index set comprises an intervention mode, an intervention frequency and an intervention degree;
step S732: extracting intervention attribute of the perinatal intervention index set to obtain a perinatal intervention attribute set;
step S733: assigning the set of the perinatal intervention attributes to obtain a set of values of the perinatal intervention attributes;
step S734: and constructing and obtaining the perinatal intervention knowledge graph based on the perinatal intervention attribute set and the perinatal intervention attribute value set.
Specifically, the perinatal intervention indexes are constructed, wherein the intervention modes comprise an intervention mode, an intervention frequency and an intervention degree, the intervention mode is a mode for carrying out the perinatal intervention on a target puerpera, the intervention modes comprise operation intervention, traditional Chinese medicine intervention, exercise intervention and the like, the intervention frequency is the frequency of carrying out the perinatal intervention in a period of time, such as once a week and once a month, the intervention degree is the condition reached by the perinatal intervention, the influence on the puerpera is larger as the intervention degree is deeper, and the perinatal intervention index set is constructed according to the intervention mode, the intervention frequency and the intervention degree.
Grading each perinatal intervention index in the perinatal intervention index set, such as surgical intervention, traditional Chinese medicine intervention and exercise intervention in an intervention mode, wherein the surgical intervention has the greatest influence on puerpera including positive and negative influence, so that the surgical intervention grade is the highest, the traditional Chinese medicine intervention is the second most, the exercise intervention grade is the lowest, and grading results are used as surgical intervention attributes. And constructing a perinatal intervention attribute set according to the surgical intervention level, the traditional Chinese medicine intervention level and the exercise intervention level.
The value set of the perinatal intervention attribute is obtained by self-assigning the set of the perinatal intervention attribute by an expert, for example, according to an intervention effect evaluation, wherein the operation intervention assignment is larger than the traditional Chinese medicine intervention, and the traditional Chinese medicine intervention is larger than the exercise intervention, so that the assignment is performed, and the result of the assignment is that the operation intervention is 90%, the traditional Chinese medicine intervention is 70% and the exercise intervention is 40% in an exemplary manner. Wherein, the perinatal intervention attribute has a mapping relation with the value of the perinatal intervention attribute.
And taking the perinatal intervention attribute and the perinatal intervention attribute value as nodes according to the perinatal intervention attribute, the perinatal intervention attribute value and the mapping relation thereof, taking the mapping relation thereof as edges connected with the nodes, and forming a network-shaped knowledge base by the nodes and the edges to obtain the perinatal intervention knowledge graph.
Further, step S700 of the present application further includes:
step S751: real-time intervention monitoring is carried out on the target monitoring puerpera to obtain a real-time physiological intervention monitoring result;
step S752: performing intervention effect evaluation based on the real-time physiological intervention monitoring result to obtain a perinatal intervention effect coefficient;
step S753: generating a perinatal intervention regulation factor according to the difference value of the perinatal intervention effect coefficient and the preset intervention effect coefficient;
step S754: and dynamically adjusting the perinatal intervention scheme of the puerpera based on the perinatal intervention regulating factor.
Specifically, in the process of intervention of the target monitoring puerpera according to the intervention scheme, the physiological state of the target monitoring puerpera is monitored in real time through various instruments, and a real-time physiological intervention monitoring result is obtained. Comparing the real-time physiological intervention monitoring result with corresponding items in the puerperal physiological sign monitoring data information to obtain the ratio of the change condition to the initial condition of the data in any item, wherein the ratio is used as a perinatal intervention effect coefficient, and when the ratio is a positive value, the positive effect of intervention is indicated, and when the ratio is larger, the positive effect is indicated; when the ratio is negative, this indicates that the intervention has a negative effect, and a larger absolute value of the ratio indicates a poorer effect.
The preset intervention effect coefficient is the prediction of the intervention effect by the expert group according to self experience, the difference value between the perinatal intervention effect coefficient and the preset intervention effect coefficient is calculated, when the perinatal intervention effect coefficient is larger than the preset intervention effect coefficient, the actual intervention effect is indicated to exceed the preset intervention effect, and the larger the difference value is, the better the actual intervention effect is indicated; when the surrounding intervention effect coefficient is smaller than the preset intervention effect coefficient, the intervention effect of the corresponding item is insufficient, adjustment is needed, the item with insufficient intervention effect is extracted, and the surrounding intervention regulation factor is generated according to the item with insufficient intervention effect and the corresponding difference value.
Based on the perinatal intervention regulation factors, acquiring items with insufficient intervention effects, and dynamically adjusting related items in the puerperal perinatal intervention scheme aiming at the items.
Further, the application also comprises:
step S810: if the target monitors that the puerpera is in the gestation stage and the delivery stage of the perinatal period, the fetal health management index is obtained;
step S820: performing fetal health assessment based on the fetal health management index to obtain fetal health assessment information;
step S830: performing maternal correlation analysis on the fetal health assessment information to generate maternal health influence factors;
Step S840: and carrying out gain correction on the risk index of the puerpera based on the maternal health influence factor.
Specifically, the gestation stage refers to the physiological period from the conception stage to the pre-delivery stage, and the delivery stage is mainly divided into three stages, namely a first delivery stage, a second delivery stage and a third delivery stage, wherein the first delivery stage refers to the process of the parturient from pain relief to full opening of the cervix, the second delivery stage refers to the process of the parturient from full opening of the uterus to delivery of the fetus, and the third delivery stage refers to the process of the fetus from delivery to delivery of the placenta. If the target monitoring parturient is in any of the two stages, fetal health management indexes including the length and weight of the fetus, heartbeat, movement, sheep water volume and the like are established according to the expert.
The health state data of the fetus is obtained by carrying out B ultrasonic examination and the like on the puerpera, and the health state data of the fetus is evaluated based on the health management index of the fetus, such as whether the body length and the weight reach the standard, whether the body length and the weight are too large or too small, and the like, so that the health evaluation information of the fetus is obtained.
And extracting data related to the mother in the fetal health assessment information, acquiring the association degree of the fetal data and the mother, wherein the higher the association degree is, the larger the fetal data affects the mother, and taking the association degree as a mother health influence factor.
Gain correction is performed on the risk index of the lying-in woman based on the maternal health influence factor, and the calculated result is taken as a corrected risk index of the lying-in woman, for example, if the influence factor is 0.7 and the risk index of the original woman is 0.8, the risk index after gain is 0.8+0.7x0.8.
In summary, the method and the system for monitoring and evaluating the physiological state of the parturient in perinatal period provided by the embodiment of the application have the following technical effects:
acquiring perinatal medical file information, performing feature classification marking to acquire perinatal feature attribute parameters, constructing a perinatal period puerpera risk analysis model library, matching and calling a target puerpera risk analysis model according to the perinatal feature attribute parameters, performing real-time monitoring on physiological states, acquiring puerpera physiological sign monitoring data information, inputting the puerpera physiological sign monitoring data information into the target puerpera risk analysis model for evaluation to acquire puerpera risk assessment information, and performing puerpera perinatal intervention on a target monitored puerpera to acquire a puerpera perinatal intervention scheme. The technical problem that the physiological state of the puerpera cannot be comprehensively and timely estimated in the prior art, so that the nursing effect of the puerpera in the perinatal period is poor is solved, the comprehensive and timely physiological state estimation aiming at the target puerpera is realized, the targeted perinatal intervention scheme is matched, and the technical effect of improving the nursing effect of the puerpera in the perinatal period is achieved.
Example two
Based on the same inventive concept as the method for monitoring and evaluating the physiological state of a parturient in perinatal period in the foregoing embodiments, as shown in fig. 4, the present application provides a system for monitoring and evaluating the physiological state of a parturient in perinatal period, the system comprising:
the file information acquisition module 10 is used for acquiring perinatal medical file information of the target monitoring puerpera, wherein the perinatal medical file information comprises gestational medical information, childbirth medical information and postpartum medical information;
the feature classification marking module 20 is used for performing feature classification marking on the surrounding medical file information to obtain surrounding feature attribute parameters;
the model base construction module 30 is used for constructing a perinatal puerpera risk analysis model base by the model base construction module 30;
the target model invoking module 40 is used for invoking a target lying-in woman risk analysis model in a matching manner from a lying-in woman risk analysis model library in the perinatal period according to the perinatal characteristic attribute parameter;
the state real-time monitoring module 50 is used for monitoring the physiological state of the target monitoring parturient in real time, and acquiring the physiological sign monitoring data information of the parturient;
The monitoring data evaluation module 60 is configured to input the monitoring data information of the physiological sign of the parturient into the target risk analysis model for evaluation, so as to obtain risk evaluation information of the parturient;
the parturient perinatal intervention module 70 is configured to perform parturient perinatal intervention on the target monitored parturient based on a women's obstetric expert group and the parturient risk assessment information, and obtain a parturient perinatal intervention scheme.
Further, the system further comprises:
the feature classification module is used for carrying out feature classification on the surrounding medical file information to obtain medical file classification feature information;
the coordinate system construction module is used for constructing a puerperal archive feature coordinate system which is a multi-dimensional coordinate system, and coordinate axes are in one-to-one correspondence with the medical archive classification feature information;
the regional labeling classification module is used for carrying out regional labeling classification on the puerperal archive feature coordinate system to obtain a regional labeling classification result;
the characteristic vector acquisition module is used for inputting the perinatal medical archive information into the puerpera archive characteristic coordinate system to obtain a perinatal archive characteristic vector;
The mapping matching module is used for carrying out mapping matching according to the regional tag classification result and the surrounding file feature vector to obtain a surrounding feature classification tag;
and the attribute parameter acquisition module is used for determining the surrounding characteristic attribute parameters based on the surrounding characteristic classification labels.
Further, the system further comprises:
the system comprises a historical data acquisition module, a data processing module and a data processing module, wherein the historical data acquisition module is used for acquiring historical perinatal puerpera data information based on big data, and the historical perinatal puerpera data information comprises historical perinatal medical file information, physiological state information and perinatal risk information of a historical perinatal puerpera;
the marking and clustering module is used for marking and clustering the historical perinatal medical archive information according to the regional label classification result to obtain a perinatal characteristic attribute parameter clustering result;
the dividing module is used for dividing the data information of the parturient in the historical perinatal period based on the perinatal characteristic attribute parameter clustering result to obtain a perinatal period risk training data set;
and the model training module is used for respectively carrying out model training on each parameter type data in the perinatal risk training data set and constructing the perinatal puerpera risk analysis model library.
Further, the system further comprises:
the intervention requirement determining module is used for determining the perinatal intervention requirement of the puerpera according to the puerperal risk assessment information;
the intervention requirement analysis module is used for analyzing the parturient perinatal intervention requirement based on a gynaecological specialist group to obtain a parturient perinatal intervention scheme set;
the intervention scheme evaluation module is used for evaluating each intervention scheme in the parturient perinatal intervention scheme set through a perinatal intervention knowledge graph to obtain an intervention scheme evaluation characteristic value set;
and the scheme screening module is used for carrying out scheme screening on the parturient perinatal intervention scheme set based on the intervention scheme evaluation characteristic value set to obtain a parturient perinatal intervention scheme, and carrying out perinatal intervention management and control on the target monitoring parturient based on the parturient perinatal intervention scheme.
Further, the system further comprises:
the intervention index acquisition module is used for acquiring a perinatal intervention index set, wherein the perinatal intervention index set comprises an intervention mode, an intervention frequency and an intervention degree;
the intervention attribute extraction module is used for extracting the intervention attribute of the perinatal intervention index set to obtain a perinatal intervention attribute set;
The assignment module is used for assigning the set of the perinatal intervention attributes to obtain a set of values of the perinatal intervention attributes;
the knowledge graph construction module is used for constructing and obtaining the perinatal intervention knowledge graph based on the perinatal intervention attribute set and the perinatal intervention attribute value set.
Further, the system further comprises:
the real-time intervention monitoring module is used for carrying out real-time intervention monitoring on the target monitoring puerpera to obtain a real-time physiological intervention monitoring result;
the coefficient acquisition module is used for evaluating the intervention effect based on the real-time physiological intervention monitoring result to acquire a perinatal intervention effect coefficient;
the regulatory factor acquisition module is used for generating a peripheral intervention regulatory factor according to the difference value of the peripheral intervention effect coefficient and the preset intervention effect coefficient;
and the dynamic adjustment module is used for dynamically adjusting the perinatal intervention scheme of the puerpera based on the perinatal intervention regulating factor.
Further, the system further comprises:
the health management index acquisition module is used for acquiring a fetal health management index if the target monitors that the puerpera is in a gestation stage and a delivery stage in a perinatal period;
the health evaluation information acquisition module is used for carrying out fetal health evaluation based on the fetal health management index to acquire fetal health evaluation information;
The maternal correlation analysis module is used for performing maternal correlation analysis on the fetal health assessment information to generate maternal health influence factors;
and the gain correction module is used for carrying out gain correction on the risk index of the puerpera based on the maternal health influence factor.
Through the foregoing detailed description of a perinatal maternal physiological state monitoring and evaluating method, those skilled in the art can clearly know a perinatal maternal physiological state monitoring and evaluating method and system in this embodiment, and for the device disclosed in the embodiment, since the device corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (3)
1. A method for monitoring and assessing physiological status of parturient in perinatal period, the method comprising:
acquiring perinatal medical file information of a target monitoring puerpera, wherein the perinatal medical file information comprises gestational medical information, childbirth medical information and postpartum medical information;
performing feature classification marking on the surrounding production medical file information to obtain surrounding production feature attribute parameters;
constructing a perinatal puerpera risk analysis model library;
according to the perinatal characteristic attribute parameters, matching and calling a target puerpera risk analysis model from a perinatal puerpera risk analysis model library;
the physiological state of the target monitoring puerpera is monitored in real time, and puerpera physiological sign monitoring data information is obtained;
inputting the physiological sign monitoring data information of the puerpera into the target puerpera risk analysis model for evaluation to obtain puerpera risk evaluation information;
based on a women birth expert group and the puerpera risk assessment information, puerpera perinatal intervention is carried out on the target monitoring puerpera, and a puerpera perinatal intervention scheme is obtained;
wherein, the obtaining the surrounding characteristic attribute parameter comprises:
performing feature classification on the surrounding medical file information to obtain medical file classification feature information;
Constructing a puerpera archive feature coordinate system, wherein the puerpera archive feature coordinate system is a multi-dimensional coordinate system, and coordinate axes correspond to the medical archive classification feature information one by one;
carrying out regional label classification on the puerperal archive feature coordinate system to obtain a regional label classification result;
inputting the perinatal medical archive information into the puerpera archive feature coordinate system to obtain a perinatal archive feature vector;
mapping and matching are carried out according to the regional tag classification result and the surrounding file feature vector, and a surrounding feature classification tag is obtained;
determining the surrounding characteristic attribute parameters based on the surrounding characteristic classification labels;
the construction of the perinatal puerpera risk analysis model library comprises the following steps:
acquiring historical perinatal puerpera data information based on big data, wherein the historical perinatal puerpera data information comprises historical perinatal medical file information, physiological state information and perinatal risk information of a historical perinatal puerpera;
marking and clustering the historical perinatal medical record information according to the regional label classification result to obtain a perinatal characteristic attribute parameter clustering result;
dividing the historical perinatal period puerpera data information based on the perinatal characteristic attribute parameter clustering result to obtain a perinatal period risk training data set;
Respectively performing model training on each parameter type data in the perinatal risk training data set, and constructing a perinatal puerpera risk analysis model library;
the method for monitoring the parturient perinatal intervention of the target monitoring parturient based on the gynaecological specialist group and the parturient risk assessment information comprises the following steps of:
determining the perinatal intervention requirement of the puerpera according to the puerpera risk assessment information;
analyzing the parturient perinatal intervention requirement based on a gynaecological obstetrics expert group to obtain a parturient perinatal intervention scheme set;
evaluating each intervention scheme in the parturient perinatal intervention scheme set through a perinatal intervention knowledge graph to obtain an intervention scheme evaluation characteristic value set;
carrying out scheme screening on the parturient perinatal intervention scheme set based on the intervention scheme evaluation characteristic value set to obtain a parturient perinatal intervention scheme, and carrying out perinatal intervention management and control on the target monitoring parturient based on the parturient perinatal intervention scheme;
wherein, construct the perinatal intervention knowledge graph, include:
acquiring a perinatal intervention index set, wherein the perinatal intervention index set comprises an intervention mode, an intervention frequency and an intervention degree;
Extracting intervention attribute of the perinatal intervention index set to obtain a perinatal intervention attribute set;
assigning the set of the perinatal intervention attributes to obtain a set of values of the perinatal intervention attributes;
constructing and obtaining the perinatal intervention knowledge graph based on the perinatal intervention attribute set and the perinatal intervention attribute value set;
real-time intervention monitoring is carried out on the target monitoring puerpera to obtain a real-time physiological intervention monitoring result;
performing intervention effect evaluation based on the real-time physiological intervention monitoring result to obtain a perinatal intervention effect coefficient;
generating a perinatal intervention regulation factor according to the difference value of the perinatal intervention effect coefficient and the preset intervention effect coefficient;
and dynamically adjusting the perinatal intervention scheme of the puerpera based on the perinatal intervention regulating factor.
2. The method of claim 1, wherein the method comprises:
if the target monitors that the puerpera is in the gestation stage and the delivery stage of the perinatal period, the fetal health management index is obtained;
performing fetal health assessment based on the fetal health management index to obtain fetal health assessment information;
performing maternal correlation analysis on the fetal health assessment information to generate maternal health influence factors;
And carrying out gain correction on the risk index of the puerpera based on the maternal health influence factor.
3. A perinatal maternal physiological condition monitoring and assessment system, the system comprising:
the file information acquisition module is used for acquiring the perinatal medical file information of the target monitoring puerpera, wherein the perinatal medical file information comprises gestational medical information, childbirth medical information and postpartum medical information;
the feature classification marking module is used for performing feature classification marking on the surrounding medical file information to obtain surrounding feature attribute parameters;
the model library construction module is used for constructing a perinatal puerpera risk analysis model library;
the target model calling module is used for matching and calling a target lying-in woman risk analysis model from a lying-in woman risk analysis model library in the perinatal period according to the perinatal characteristic attribute parameters;
the state real-time monitoring module is used for monitoring the physiological state of the target monitoring puerpera in real time and acquiring puerpera physiological sign monitoring data information;
the monitoring data evaluation module is used for inputting the monitoring data information of the physiological signs of the puerpera into the target puerpera risk analysis model for evaluation to obtain puerpera risk evaluation information;
The parturient perinatal intervention module is used for carrying out parturient perinatal intervention on the target monitoring parturient based on a gynaecological expert group and the parturient risk assessment information, and obtaining a parturient perinatal intervention scheme;
the feature classification unit is used for carrying out feature classification on the surrounding medical file information to obtain medical file classification feature information;
the coordinate system construction unit is used for constructing a puerperal archive feature coordinate system which is a multi-dimensional coordinate system, and coordinate axes are in one-to-one correspondence with the medical archive classification feature information;
the regional labeling classification unit is used for carrying out regional labeling classification on the puerperal archive feature coordinate system to obtain a regional label classification result;
the characteristic vector acquisition unit is used for inputting the perinatal medical archive information into the puerpera archive characteristic coordinate system to obtain a perinatal archive characteristic vector;
the mapping matching unit is used for carrying out mapping matching according to the regional tag classification result and the surrounding file feature vector to obtain a surrounding feature classification tag;
the attribute parameter acquisition unit is used for determining the surrounding characteristic attribute parameters based on the surrounding characteristic classification labels;
The system comprises a historical data acquisition unit, a data processing unit and a data processing unit, wherein the historical data acquisition unit is used for acquiring historical perinatal puerpera data information based on big data, and the historical perinatal puerpera data information comprises historical perinatal medical file information, physiological state information and perinatal risk information of a historical perinatal puerpera;
the marking and clustering unit is used for marking and clustering the history perinatal medical archive information according to the regional label classification result to obtain a perinatal characteristic attribute parameter clustering result;
the dividing unit is used for dividing the data information of the parturient in the historical perinatal period based on the perinatal characteristic attribute parameter clustering result to obtain a perinatal risk training data set;
the model training unit is used for respectively carrying out model training on each parameter type data in the perinatal risk training data set and constructing the perinatal puerpera risk analysis model library;
the intervention requirement determining unit is used for determining the perinatal intervention requirement of the puerpera according to the puerperal risk assessment information;
the intervention requirement analysis unit is used for analyzing the parturient perinatal intervention requirement based on a gynaecological specialist group to obtain a parturient perinatal intervention scheme set;
the intervention scheme evaluation unit is used for evaluating each intervention scheme in the parturient intervention scheme set through the perinatal intervention knowledge graph to obtain an intervention scheme evaluation characteristic value set;
The scheme screening unit is used for carrying out scheme screening on the parturient perinatal intervention scheme set based on the intervention scheme evaluation characteristic value set to obtain a parturient perinatal intervention scheme, and carrying out perinatal intervention management and control on the target monitoring parturient based on the parturient perinatal intervention scheme;
the intervention index acquisition unit is used for acquiring a perinatal intervention index set, wherein the perinatal intervention index set comprises an intervention mode, an intervention frequency and an intervention degree;
the intervention attribute extraction unit is used for extracting the intervention attribute of the perinatal intervention index set to obtain a perinatal intervention attribute set;
the assignment unit is used for assigning the set of the perinatal intervention attributes to obtain a set of values of the perinatal intervention attributes;
the knowledge graph construction unit is used for constructing and obtaining the perinatal intervention knowledge graph based on the perinatal intervention attribute set and the perinatal intervention attribute value set;
the real-time intervention monitoring unit is used for carrying out real-time intervention monitoring on the target monitoring puerpera to obtain a real-time physiological intervention monitoring result;
the coefficient acquisition unit is used for evaluating the intervention effect based on the real-time physiological intervention monitoring result to obtain a perinatal intervention effect coefficient;
The control factor acquisition unit is used for generating a perinatal intervention control factor according to the difference value of the perinatal intervention effect coefficient and the preset intervention effect coefficient;
and the dynamic adjustment unit is used for dynamically adjusting the perinatal intervention scheme of the puerpera based on the perinatal intervention regulating factor.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106529177A (en) * | 2016-11-12 | 2017-03-22 | 杭州电子科技大学 | Patient portrait drawing method and device based on medical big data |
| CN109171644A (en) * | 2018-06-22 | 2019-01-11 | 平安科技（深圳）有限公司 | Health control method, device, computer equipment and storage medium based on voice recognition |
| CN111685773A (en) * | 2020-06-04 | 2020-09-22 | 北京荟健科技有限公司 | Pregnant woman movement monitoring method and device, computer equipment and storage medium |
| CN113057623A (en) * | 2021-03-30 | 2021-07-02 | 上海理工大学 | Flat foot disease screening insole and flat foot disease screening gait analysis system |
| CN113380392A (en) * | 2021-06-25 | 2021-09-10 | 南通市第一人民医院 | Visit management method and system based on gynecological examination safety assessment |
| CN113871026A (en) * | 2021-10-14 | 2021-12-31 | 曹庆恒 | Medical risk assessment system and method |
| CN114121295A (en) * | 2021-11-29 | 2022-03-01 | 复旦大学附属中山医院 | Construction method of knowledge graph driven liver cancer diagnosis and treatment scheme recommendation system |
| CN114613487A (en) * | 2020-12-08 | 2022-06-10 | 皇家飞利浦有限公司 | Pregnancy decision support system and method |
| CN114652311A (en) * | 2022-03-22 | 2022-06-24 | 南通市第二人民医院 | Fine nursing method and system based on stepped nursing intervention |
| CN115223679A (en) * | 2022-08-05 | 2022-10-21 | 华中科技大学同济医学院附属同济医院 | Perioperative period risk early warning method based on machine learning |
| CN115714022A (en) * | 2022-11-04 | 2023-02-24 | 杭州市临平区妇幼保健院 | Neonatal jaundice health management system based on artificial intelligence |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101596662B1 (en) * | 2014-02-11 | 2016-02-25 | 서울대학교병원 (분사무소) | Apparatus and the method for assessing treatment response in obstructive sleep apenea |
-
2023
- 2023-03-24 CN CN202310297907.8A patent/CN116439661B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106529177A (en) * | 2016-11-12 | 2017-03-22 | 杭州电子科技大学 | Patient portrait drawing method and device based on medical big data |
| CN109171644A (en) * | 2018-06-22 | 2019-01-11 | 平安科技（深圳）有限公司 | Health control method, device, computer equipment and storage medium based on voice recognition |
| CN111685773A (en) * | 2020-06-04 | 2020-09-22 | 北京荟健科技有限公司 | Pregnant woman movement monitoring method and device, computer equipment and storage medium |
| CN114613487A (en) * | 2020-12-08 | 2022-06-10 | 皇家飞利浦有限公司 | Pregnancy decision support system and method |
| CN113057623A (en) * | 2021-03-30 | 2021-07-02 | 上海理工大学 | Flat foot disease screening insole and flat foot disease screening gait analysis system |
| CN113380392A (en) * | 2021-06-25 | 2021-09-10 | 南通市第一人民医院 | Visit management method and system based on gynecological examination safety assessment |
| CN113871026A (en) * | 2021-10-14 | 2021-12-31 | 曹庆恒 | Medical risk assessment system and method |
| CN114121295A (en) * | 2021-11-29 | 2022-03-01 | 复旦大学附属中山医院 | Construction method of knowledge graph driven liver cancer diagnosis and treatment scheme recommendation system |
| CN114652311A (en) * | 2022-03-22 | 2022-06-24 | 南通市第二人民医院 | Fine nursing method and system based on stepped nursing intervention |
| CN115223679A (en) * | 2022-08-05 | 2022-10-21 | 华中科技大学同济医学院附属同济医院 | Perioperative period risk early warning method based on machine learning |
| CN115714022A (en) * | 2022-11-04 | 2023-02-24 | 杭州市临平区妇幼保健院 | Neonatal jaundice health management system based on artificial intelligence |
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