CN116631640A - Method and platform for generating personalized demand scheme of pregnant woman - Google Patents

Method and platform for generating personalized demand scheme of pregnant woman Download PDF

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CN116631640A
CN116631640A CN202310862533.XA CN202310862533A CN116631640A CN 116631640 A CN116631640 A CN 116631640A CN 202310862533 A CN202310862533 A CN 202310862533A CN 116631640 A CN116631640 A CN 116631640A
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pregnant woman
medical
portrait
data
nutrition
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CN116631640B (en
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林娟
宋李斌
缪崇
潘琦
叶晓燕
陈星颖
林丽华
苏友智
黄伟强
徐立波
冯鑫媛
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Fujian Maternal And Child Care Service Centre
Fuzhou Comv Network Technology Co ltd
Beijing Hospital
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Fujian Maternal And Child Care Service Centre
Fuzhou Comv Network Technology Co ltd
Beijing Hospital
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    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
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    • G—PHYSICS
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    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
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    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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Abstract

The application provides a generation method and a platform of a personalized demand scheme of pregnant women, wherein the method comprises a preparation process of a medical database, a pregnant woman data collection process and a matching process of data; the preparation process of the medical database comprises the steps of establishing a medical database and a pregnancy nutrition education knowledge base, and automatically analyzing each education material to obtain an education knowledge portrait label; the pregnant woman data collection process comprises the steps of obtaining a pregnant woman nutrition portrait label through on-line release portrait questionnaires, and determining the pregnant woman diagnosis portrait label through collecting diagnosis and treatment scheme information of the pregnant woman in a plurality of systems; the matching process of the data specifically comprises the following steps: and comparing and matching the pregnant woman nutrition portrait label and the diagnosis portrait label with the medical nutrition portrait label or the medical diagnosis portrait label of each medical material and the education knowledge portrait label of each education material, thereby making a personalized requirement scheme which is most suitable for the pregnant woman and pushing the personalized requirement scheme to the pregnant woman.

Description

Method and platform for generating personalized demand scheme of pregnant woman
Technical Field
The application relates to the technical field of pregnant woman health management, in particular to a method and a platform for generating a personalized demand scheme of a pregnant woman.
Background
In recent years, along with the increasing importance and demands of people on gestational health, various medical health management systems also begin to adopt intelligent and personalized modes to provide more scientific, accurate and customized services for pregnant women. Especially in the pregnant woman health management field, more and more institutions and enterprises start to promote intelligent prenatal examination and management services, so that better pregnancy experience and health guarantee are brought to the pregnant woman.
Some pregnant woman health management APP, online pregnant woman health management platform and other products are currently on the market. The products provide management and evaluation of monitoring indexes such as weight, blood pressure, fetal B-mode and the like during pregnancy, and suggestions of pregnant women on diet, physical activities and the like. However, these products cannot provide intelligent management and personalized recommendation functions, and have the problems of low data quality, lack of scientific basis, insufficient precision of personalized recommendation and the like.
Disclosure of Invention
The application aims to solve the technical problem of providing a generation method and a platform of a personalized demand scheme for pregnant women, which can provide functions of intelligent management and personalized recommendation, and has the advantages of high data quality and more scientific and accurate personalized recommendation.
In a first aspect, the application provides a method for generating a personalized demand scheme for pregnant women, which comprises a preparation process of a medical database, a pregnant woman data collection process and a matching process of data;
the preparation process of the medical database specifically comprises the following steps:
establishing a medical database, collecting relevant medical guidelines and documents at home and abroad, screening and evaluating, finding out medical guidelines and documents conforming to pregnancy nutrition management, classifying, standardizing, encoding and quality control to obtain medical materials, and storing in the medical database; classifying or marking each medical material in the medical database by using the trained model to obtain a corresponding medical nutrition portrait label or medical diagnosis portrait label;
establishing a gestational nutrition education knowledge base, editing, applying attribute setting, format classification and coding the gestational nutrition management education knowledge according to guidance of expert team and combining logic of domestic and foreign medical guidelines and literature, obtaining education materials and storing the education materials in the gestational nutrition education knowledge base; processing the content of the educational materials by using natural language technology, and automatically analyzing the educational knowledge portrait label of each educational material according to the logic set in advance;
the pregnant woman data collection process specifically comprises the following steps:
under the guidance of expert team, combining domestic and foreign medical guidelines and logic of literature to determine the corresponding field or variable of the personalized nutrition portrait tag of pregnant women, and determining and designing the content and evaluation logic of portrait questionnaires; the portrait questionnaires comprise an international physical activity questionnaire, a FIGO questionnaire, a document building questionnaire and a questionnaire; on-line issuing of an portrait questionnaire for filling by a pregnant woman, collecting nutrition related data of the portrait questionnaire after the filling of the portrait questionnaire is completed by the pregnant woman, and calculating the nutrition related data according to evaluation logic of the portrait questionnaire to obtain a nutrition portrait label of the pregnant woman;
collecting diagnosis and treatment scheme information of pregnant women in a hospital information system, an image archiving and communication system, an electronic medical record and a hospital laboratory information management system, extracting characteristics of the collected diagnosis and treatment scheme information, converting the characteristics into a form which can be processed by a computer, and determining pregnant woman diagnosis portrait labels;
the matching process of the data specifically comprises the following steps:
after the pregnant woman nutrition portrait label and the pregnant woman diagnosis portrait label are combined, the pregnant woman nutrition portrait label and the pregnant woman diagnosis portrait label are compared and matched with the medical nutrition portrait label or the medical diagnosis portrait label of each medical material and the education knowledge portrait label of each education material, so that a personalized requirement scheme which is most suitable for the pregnant woman is formulated and pushed to the pregnant woman.
In a second aspect, the application provides a generation platform of a personalized demand scheme of pregnant women, which comprises a preparation module of a medical database, a pregnant woman data collection module and a matching module of data;
the preparation module of the medical database is specifically used for:
establishing a medical database, collecting relevant medical guidelines and documents at home and abroad, screening and evaluating, finding out medical guidelines and documents conforming to pregnancy nutrition management, classifying, standardizing, encoding and quality control to obtain medical materials, and storing in the medical database; classifying or marking each medical material in the medical database by using the trained model to obtain a corresponding medical nutrition portrait label or medical diagnosis portrait label;
establishing a gestational nutrition education knowledge base, editing, applying attribute setting, format classification and coding the gestational nutrition management education knowledge according to guidance of expert team and combining logic of domestic and foreign medical guidelines and literature, obtaining education materials and storing the education materials in the gestational nutrition education knowledge base; processing the content of the educational materials by using natural language technology, and automatically analyzing the educational knowledge portrait label of each educational material according to the logic set in advance;
the pregnant woman data collection module is specifically used for:
under the guidance of expert team, combining domestic and foreign medical guidelines and logic of literature to determine the corresponding field or variable of the personalized nutrition portrait tag of pregnant women, and determining and designing the content and evaluation logic of portrait questionnaires; the portrait questionnaires comprise an international physical activity questionnaire, a FIGO questionnaire, a document building questionnaire and a questionnaire; on-line issuing of an portrait questionnaire for filling by a pregnant woman, collecting nutrition related data of the portrait questionnaire after the filling of the portrait questionnaire is completed by the pregnant woman, and calculating the nutrition related data according to evaluation logic of the portrait questionnaire to obtain a nutrition portrait label of the pregnant woman;
collecting diagnosis and treatment scheme information of pregnant women in a hospital information system, an image archiving and communication system, an electronic medical record and a hospital laboratory information management system, extracting characteristics of the collected diagnosis and treatment scheme information, converting the characteristics into a form which can be processed by a computer, and determining pregnant woman diagnosis portrait labels;
the matching module of the data is specifically used for:
after the pregnant woman nutrition portrait label and the pregnant woman diagnosis portrait label are combined, the pregnant woman nutrition portrait label and the pregnant woman diagnosis portrait label are compared and matched with the medical nutrition portrait label or the medical diagnosis portrait label of each medical material and the education knowledge portrait label of each education material, so that a personalized requirement scheme which is most suitable for the pregnant woman is formulated and pushed to the pregnant woman.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages: the personalized demand scheme generating method and platform provided by the application have three processes and modules, namely, preparation of a medical database, data collection of pregnant women and matching of data, wherein the medical database is based on related medical guidelines and documents at home and abroad, and the pregnancy nutrition education knowledge base is obtained by editing, applying attribute setting, format classification and encoding of pregnancy nutrition management education knowledge according to guidance of expert team and combining logic of the medical guidelines and documents at home and abroad, so that the scientificity of the personalized demand scheme is ensured. Advanced data analysis and artificial intelligence technology are utilized to carry out deep analysis and excavation on the physical health data (including fetal health data) of the pregnant women, so that the personalized recommendation function is more in line with the individual requirements of the pregnant women and the growth and health requirements of the fetuses, and the personalized recommendation is more accurate. In a word, the application has the advantages of combining medical expertise, data analysis technology and artificial intelligence technology, and can provide more scientific, accurate and personalized health management service for vast pregnant women.
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.
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The application will be further described with reference to examples of embodiments with reference to the accompanying drawings.
FIG. 1 is a flow chart of a method according to a first embodiment of the application;
fig. 2 is a schematic diagram of a platform architecture according to a second embodiment of the application.
Detailed Description
The embodiment of the application can provide the functions of intelligent management and personalized recommendation by providing the generation method and the platform of the personalized demand scheme for the pregnant women, and the personalized recommendation is more scientific and accurate, and has high data quality.
The technical scheme in the embodiment of the application has the following overall thought: the data quality is improved through the preparation of a medical database, the recommendation is more scientific, the recommendation function is more attached to pregnant women through the pregnant women data collection, the accuracy and the reliability of scheme recommendation are further improved, and intelligent data matching is realized through portrait labels so as to intelligently improve the matching accuracy and efficiency.
Example 1
As shown in fig. 2, the present embodiment provides a method for generating a personalized demand plan for pregnant women, which includes a preparation process of a medical database, a data collection process of the pregnant women, and a matching process of data;
the preparation process of the medical database specifically comprises the following steps:
establishing a medical database, collecting relevant medical guidelines and documents at home and abroad, screening and evaluating, finding out medical guidelines and documents conforming to pregnancy nutrition management, classifying, standardizing, encoding and quality control to obtain medical materials, and storing in the medical database; classifying or marking each medical material in the medical database by using the trained model to obtain a corresponding medical nutrition portrait label or medical diagnosis portrait label;
examples: guidelines and documents concerning gestational nutrition management, such as the national and international guidelines for the group of "abnormal weight gain women diet guidance" issued by the Chinese nutrition society and the national standard of "body weight supervision and evaluation of the baby of Chinese women", and the national and obstetrical doctor Association (ACOG) Obesity in Pregnancy, are collected, screened and evaluated, and the guidelines and documents meeting the actual demands are found.
When the medical database is established, the medical guideline and the literature are classified mainly as follows: the classification is based on body composition data, physical examination data, fetal imaging data, basic information data, lifestyle data, current pregnancy status data, pregnancy history data, past history data, menstrual history data, family history data, medication status data, diagnostic results, treatment regimens, and dietary nutrient indicators.
Establishing a gestational nutrition education knowledge base, editing the gestational nutrition management education knowledge, setting application attributes (doctor end/pregnant woman end), classifying formats (courses, videos, rich texts, audios and cartoon) and coding according to guidance of expert teams and combining logic of domestic and foreign medical guidelines and literature, so as to obtain education materials and storing the education materials in the gestational nutrition education knowledge base; processing the content of the educational materials by using natural language technology, and automatically analyzing each educational material according to preset logic to obtain an educational knowledge portrait label;
for example: educational knowledge name: the iron deficiency anemia in gestation is obtained by screening and evaluating in a built medical database through guidance of expert team and collection of related medical guidelines and documents at home and abroad. In the preparation process, the knowledge is edited, the application attribute is set to pregnant woman side, the format is classified as rich text, and encoding processing is performed so that processing of natural language technology can be performed. For the educational knowledge, natural language technology analyzes the content of the educational knowledge, and automatically extracts corresponding educational knowledge image labels, such as the labels of 'gestational nutrition', 'iron deficiency anemia', 'diet conditioning', 'nutrition supplement', and the like, according to preset logic. These tags can help the user find the desired educational knowledge more quickly and accurately, and can facilitate searching, screening, and recommending.
Iron deficiency anemia in gestation refers to anemia in gestation caused by iron deficiency, and has a hemoglobin concentration of <110 g/L. Reference is made to:
1. the medical science of Chinese society of perinatal medical science, diagnosis and treatment guidelines for iron deficiency and iron deficiency anemia in gestation [ J ]. J2014,17 (7): 451-454;
2. chinese society of nutrition, chinese resident dietary guidelines (2022).
Clinical classification: the blood-glucose level is divided into mild anemia (100-109 g/L), moderate anemia (70-99 g/L), severe anemia (40-69 g/L) and severe anemia (< 40 g/L). Clinical significance: pregnancy-associated anemia can have both near-term and long-term effects on the mother, fetus, and neonate, and can increase the risk of pregnancy-associated hypertension, premature rupture of membranes, puerperal infection, and post-partum depression; the composition can increase the morbidity risk of fetal growth restriction, fetal hypoxia, amniotic fluid reduction, dead fetus, dead birth, premature birth, neonatal asphyxia and neonatal ischemic hypoxic encephalopathy for the fetus and the neonate. Clinical treatment: 1. the treatment of mild and moderate anemia is mainly carried out by taking iron agent orally, and the diet is improved, and the iron-rich food is eaten. 2. The iron preparation can be orally taken or injected for treating severe anemia, and the concentrated red blood cells can be infused for a plurality of times in small quantity. 3. The pregnant women with the iron deficiency anemia, which are diagnosed clearly, are required to be supplemented with 100-200 mg/day of elemental iron, and after 2 weeks of treatment, hemoglobin is rechecked to evaluate the curative effect.
Educational knowledge representation label: the portrait label of the 'gestational iron deficiency anemia' is: pregnant women, gestation period, iron deficiency, anemia, iron deficiency anemia.
The pregnant woman data collection process specifically comprises the following steps:
under the guidance of expert team, combining domestic and foreign medical guidelines and logic of literature to determine the corresponding field or variable of the personalized nutrition portrait tag of pregnant women, and determining and designing the content and evaluation logic of portrait questionnaires; the portrait questionnaires comprise an international physical activity questionnaire, a FIGO questionnaire, a document building questionnaire and a questionnaire; on-line issuing of an portrait questionnaire for filling by a pregnant woman, collecting nutrition related data of the portrait questionnaire after the filling of the portrait questionnaire is completed by the pregnant woman, and calculating the nutrition related data according to evaluation logic of the portrait questionnaire to obtain a nutrition portrait label of the pregnant woman; wherein, the liquid crystal display device comprises a liquid crystal display device,
international physical activity questionnaire (International Physical Activity Questionnaire, IPAQ for short): a tool for quantifying physical activity levels, commonly proposed by the World Health Organization (WHO) and the international health promotion and sports medical association (International Society for Physical Activity and Health);
FIGO questionnaire: one of the common prenatal screening tools developed by the world's gynaecology and obstetrics association (International Federation of Gynecology and Obstetrics, FIGO) is used to assess whether pregnant women have high risk factors during pregnancy and their health risks associated with fetuses and neonates. The system mainly comprises the contents of personal basic information of pregnant women, pregnancy history, family medical history, present diseases, pregnancy complications, vaccination history and the like, and aims to collect comprehensive information of pregnant women and to assist medical staff in early risk assessment and intervention;
filing questionnaire: comprises basic information data, life habit data, pregnancy condition data, pregnancy history data, past history data, menstrual history data and family history data;
questionnaire: including basic information, complaints, physical activity intensity ratings, daily recommended energy needs, daily protein reference intake, daily carbohydrate reference intake, daily fat reference intake, meal evaluations, fetal B-mode exam conditions, abnormal test exams, auxiliary diagnostics, diagnostic results, medical orders, next review period, next appointment date.
The design questionnaire evaluation logic examples: according to the international physical activity questionnaire rule, if the total of the high-intensity physical activities per week reaches 3 days and the score is greater than or equal to 1500Mets, or if the total of 3 or more physical activities is performed for 7 days and the score is greater than or equal to 3000Mets, the high-intensity physical activities belong to the high-intensity group. If high intensity physical activities are performed for at least 20 minutes per day, the total amount is up to 3 days; or medium intensity and walking for at least 30 minutes per day for up to 5 days in total; or 3 or more physical activities are performed for 5 days and the score is 600Mets or more, and the physical activities belong to the medium-intensity group. If there is no activity or the activity does not meet the criteria for the medium and high intensity groups, it belongs to the low intensity group.
Examples: the collecting data of the pregnancy condition in the filing questionnaire comprises the following steps: name, last menstruation, week-week of pregnancy, week-day of pregnancy, stage of pregnancy (options: early, middle, late), week-day of pregnancy, week-week of pregnancy, week-day of pregnancy, stage of pregnancy (early, middle, late), pre-term, stage of pregnancy, mode of conception (options: natural conception, assisted reproduction), mode of conception-assisted reproduction (options: pro-row, artificial insemination, test tube infant), number of fetuses of this pregnancy (options: single, twin, multiple fetuses).
Nutritional related data of the portrait questionnaire is collected, for example: the dietary records of the pregnant woman during pregnancy are collected and processed using natural language processing techniques to identify important information such as meal caloric distribution, tri-major energy duty cycle, micronutrient intake, nutrient type ingested, etc. The food ingredients table will list the nutritional ingredients of each food, including edible parts, moisture, alcohol, energy, protein, fat, carbohydrate, insoluble fiber, cholesterol, ash, total vitamin a, carotene, retinol, thiamine, riboflavin, niacin, vitamin C, vitamin E, calcium, phosphorus, potassium, sodium, magnesium, iron, zinc, selenium, copper, manganese, and the like; and determining the category and secondary classification of the food and providing corresponding codes to convert the information into a form understandable by a computer for further analysis and assessment of the presence of insufficient or excessive nutrient intake by the pregnant woman.
Collecting diagnosis and treatment scheme information of pregnant women in a hospital information system, an image archiving and communication system, an electronic medical record and a hospital laboratory information management system, extracting characteristics of the collected diagnosis and treatment scheme information, converting the characteristics into a form which can be processed by a computer, and determining pregnant woman diagnosis portrait labels; the diagnosis and treatment regimen information includes measured body composition data, physical examination data, fetal imaging data, basic information data, lifestyle data, current pregnancy status data, pregnancy history data, past history data, menstrual history data, family history data, medication status data, diagnosis results, and treatment regimen.
The method uses natural language processing technology to process text data such as medical record records, examination reports and the like, uses word segmentation, entity recognition, keyword extraction and other technologies to identify important information such as diagnosis, treatment schemes and the like, converts the important information into a form which can be understood by a computer, and identifies the important information such as diagnosis and treatment schemes.
Feature extraction is performed and converted into a computer-processable form, for example: the diagnosis of gestational diabetes mellitus is to extract the fields of sugar tolerance examination, gestation period and the like in a directional way; the diagnosis of dyslipidemia is directed to the extraction of fields such as total cholesterol, triglycerides, low density lipoprotein cholesterol, high density lipoprotein cholesterol data, etc.
Algorithm modeling is first performed when determining the pregnant woman diagnosis portrait label: based on the results of the feature extraction, various machine learning algorithms and models are applied to determine pregnant woman diagnostic portrait tags.
Example 1: extracting age during delivery, judging whether the age is more than 35 years old, and if the age is more than 35 years old, the label is 'senior pregnant woman'; extracting the pre-pregnancy BMI, judging whether the BMI is less than 18.5, and if the BMI is less than 18.5, judging that the diagnostic image label is 'low weight pregnant woman before pregnancy'; if the BMI is less than 24 and is not more than 18.5, the diagnosis portrait label is 'pregnant women with normal weight in gestation period'; if the pre-pregnancy BMI is more than or equal to 24 and less than 28, the diagnostic image label is 'overweight pregnant woman in gestation period'; if BMI is more than or equal to 28 before pregnancy, the diagnostic image label is "pregnant women with obesity in pregnancy".
Example 2: the gestation period is 24 weeks or less and 28 weeks or less, the sugar tolerance is 0 hour or more and is 5.1mmol/L or (and) the sugar tolerance is 1 hour or more and is 10.0mmol/L or (and) the sugar tolerance is 2 hours or more and is 8.5mmol/L, and the diagnostic image label is 'gestational diabetes'; serum 25 hydroxy vitamin D3 < 10ng/ml, the diagnostic image label is "vitamin D severely deficient"; serum 25 hydroxy vitamin D3 less than or equal to 10ng/ml and less than or equal to 20ng/ml, the diagnostic image label is 'vitamin D deficiency'; 20ng/ml < serum 25 hydroxy vitamin D3 < 30ng/ml, the diagnostic image is labeled "vitamin D deficient".
The matching process of the data specifically comprises the following steps:
after combining the pregnant woman nutrition portrait label and the diagnosis portrait label into the pregnant woman portrait label, the pregnant woman nutrition portrait label is compared and matched with the medical nutrition portrait label or the medical diagnosis portrait label of each medical material and the education knowledge portrait label of each education material, so that a personalized requirement scheme which is most suitable for the pregnant woman is formulated and pushed to the pregnant woman. The content of the personalized demand scheme of the pregnant woman comprises medication advice, nutrition management advice, blood sugar monitoring advice, blood pressure monitoring advice and knowledge education.
Wherein, the comparison and the matching are to analyze and compare the pregnant woman nutrition portrait label and the diagnosis portrait label with a medical database and a pregnancy nutrition education knowledge base respectively by means of artificial intelligence, big data, natural language processing and other technologies.
The personalized demand scheme of the pregnant woman is to convert the content into a text, picture or video format which is easy to understand and absorb by utilizing a natural language generation technology, and push the content to relevant equipment of the pregnant woman at a specific time or place according to the preference and the demand of the pregnant woman.
The method for preparing the most suitable personalized demand scheme to be pushed to the pregnant woman specifically comprises the following steps: according to domestic and foreign medical guidelines and logic of documents, determining fields or variables corresponding to the pregnant woman nutrition portrait tags and the pregnant woman diagnosis portrait tags, using the fields or variables as keywords, matching the education knowledge portrait tags, the medical nutrition portrait tags or the medical diagnosis portrait tags, screening corresponding education materials and medical materials from a pregnancy nutrition education knowledge base and a medical database, and sorting according to weights, thereby matching the content of the most suitable personalized demand scheme.
Example 1:
judging conditions:
judging condition 1: current history-selection [ pre-gestational diabetes ];
judging condition 2: pre-gestational diabetes mellitus type-selection [ type 1 diabetes mellitus ];
judging condition 3: is glycemic control achieved? -selecting [ up to standard ];
outputting a pregnant woman portrait label: pre-gestational diabetes mellitus-type 1 diabetes mellitus-blood sugar control meets the standard;
outputting a personalized demand scheme: frequency scheme for monitoring blood glucose in gestation period recommended for pregnant women: pre-gestational diabetes mellitus-type 1 diabetes mellitus-up to standard.
And (3) formulating a personalized demand scheme: and (3) formulating a personalized demand scheme suitable for the portrait label of the pregnant woman, wherein the content comprises suggestions in aspects of drug treatment, nutrition management, blood sugar monitoring, blood pressure monitoring, knowledge education and the like. The personalized management prescription is recommended according to the illness state characteristics and medical knowledge of the pregnant women, and the generated scheme is unique for each pregnant woman by combining the latest illness state of the pregnant women, the history record and the information in the medical database.
Example 2: frequency scheme of gestational blood glucose monitoring recommended for pregnant women:
scheme name: pregnancies diabetes-type 1 diabetes-up to standard
The application description is as follows: the pregnant woman with pre-diabetes meeting the blood sugar standard needs to monitor for 7 days per week
Scheme interpretation: the pre-pregnant diabetes-1 type diabetes with the blood sugar reaching the standard is required to monitor blood sugar for 3 days every week, and 18 times of blood sugar is required, namely, 2 hours after empty stomach and breakfast, 2 hours before noon and after lunch, and 2 hours before evening meal and after evening meal;
total monthly monitoring: 72 times
Total number of monitoring per quarter: 216 times
Total number of monitors per half year: 432 times
Please confirm: whether the pregnant woman has a large recent blood sugar fluctuation (average fluctuation amplitude of more than 3.9mmol/L in 24 hours and standard deviation of more than 1.4 mmol/L), or has a variety of conditions such as occurrence of hypoglycemia, adjustment period of treatment scheme or perioperative period, gestation period and the like, if the pregnant woman has the conditions, the monitoring frequency should be increased on the basis of following the blood sugar monitoring principle.
Blood glucose monitoring xiaoshi:
taking care of alternating blood sampling parts, the intensive and repeated monitoring of the same part is not suitable;
blood collection on the side of the finger can reduce the pain degree;
wiping fingers with soap, warm water and alcohol before blood sampling, and wiping with clean napkin or cotton ball;
when blood is collected, the fingers are not squeezed, and if the blood amount is small, the fingers can be naturally drooped for a moment and massaged before blood collection.
Delivery regimen to doctors and pregnant women: the system delivers personalized demand regimens to doctors and pregnant women, from which the doctor can formulate specific treatment plans and interpretation regimen content for the pregnant woman, while the pregnant woman can get a deeper understanding of his/her illness and advice to improve his/her health.
Results show that: using data visualization techniques such as histograms, line graphs, radar graphs, etc.; dividing into courses, videos, rich texts, audios and cartoon according to format types; according to the application attribute, the method is divided into doctor-side checking and pregnant woman-side checking; so that doctors or pregnant women can intuitively know the related knowledge of the nutritional status and the related advice.
Based on the same inventive concept, the application also provides a platform corresponding to the method in the first embodiment, and the details of the second embodiment are shown.
Example two
The embodiment provides a generation platform of a personalized demand scheme of pregnant women, which comprises a preparation module of a medical database, a pregnant woman data collection module and a matching module of data;
the preparation module of the medical database is specifically used for:
establishing a medical database, collecting relevant medical guidelines and documents at home and abroad, screening and evaluating, finding out medical guidelines and documents conforming to pregnancy nutrition management, classifying, standardizing, encoding and quality control to obtain medical materials, and storing in the medical database; classifying or marking each medical material in the medical database by using the trained model to obtain a corresponding medical nutrition portrait label or medical diagnosis portrait label;
establishing a gestational nutrition education knowledge base, editing, applying attribute setting, format classification and coding the gestational nutrition management education knowledge according to guidance of expert team and combining logic of domestic and foreign medical guidelines and literature, obtaining education materials and storing the education materials in the gestational nutrition education knowledge base; processing the content of the educational materials by using natural language technology, and automatically analyzing each educational material according to preset logic to obtain an educational knowledge portrait label;
the pregnant woman data collection module is specifically used for:
under the guidance of expert team, combining domestic and foreign medical guidelines and logic of literature to determine the corresponding field or variable of the personalized nutrition portrait tag of pregnant women, and determining and designing the content and evaluation logic of portrait questionnaires; the portrait questionnaires comprise an international physical activity questionnaire, a FIGO questionnaire, a document building questionnaire and a questionnaire; on-line issuing of an portrait questionnaire for filling by a pregnant woman, collecting nutrition related data of the portrait questionnaire after the filling of the portrait questionnaire is completed by the pregnant woman, and calculating the nutrition related data according to evaluation logic of the portrait questionnaire to obtain a nutrition portrait label of the pregnant woman;
collecting diagnosis and treatment scheme information of pregnant women in a hospital information system, an image archiving and communication system, an electronic medical record and a hospital laboratory information management system, extracting characteristics of the collected diagnosis and treatment scheme information, converting the characteristics into a form which can be processed by a computer, and determining pregnant woman diagnosis portrait labels;
the matching module of the data is specifically used for:
after the pregnant woman nutrition portrait label and the pregnant woman diagnosis portrait label are combined, the pregnant woman nutrition portrait label and the pregnant woman diagnosis portrait label are compared and matched with the medical nutrition portrait label or the medical diagnosis portrait label of each medical material and the education knowledge portrait label of each education material, so that a personalized requirement scheme which is most suitable for the pregnant woman is formulated and pushed to the pregnant woman.
When the medical database is established, the medical guideline and the literature are classified mainly as follows: the classification is based on body composition data, physical examination data, fetal imaging data, basic information data, lifestyle data, current pregnancy status data, pregnancy history data, past history data, menstrual history data, family history data, medication status data, diagnostic results, treatment regimens, and dietary nutrient indicators.
The diagnosis and treatment regimen information includes measured body composition data, physical examination data, fetal imaging data, basic information data, lifestyle data, current pregnancy status data, pregnancy history data, past history data, menstrual history data, family history data, medication status data, diagnosis results, and treatment regimen.
The content of the personalized demand scheme of the pregnant woman comprises medication advice, nutrition management advice, blood sugar monitoring advice, blood pressure monitoring advice and knowledge education;
the personalized demand scheme of the pregnant woman is to convert the content into a text, picture or video format which is easy to understand and absorb by utilizing a natural language generation technology, and push the content to relevant equipment of the pregnant woman at a specific time or place according to the preference and the demand of the pregnant woman.
The method for preparing the most suitable personalized demand scheme to be pushed to the pregnant woman specifically comprises the following steps: according to domestic and foreign medical guidelines and logic of documents, determining fields or variables corresponding to the pregnant woman nutrition portrait tags and the pregnant woman diagnosis portrait tags, using the fields or variables as keywords, matching the education knowledge portrait tags, the medical nutrition portrait tags or the medical diagnosis portrait tags, screening corresponding education materials and medical materials from a pregnancy nutrition education knowledge base and a medical database, and sorting according to weights, thereby matching the content of the most suitable personalized demand scheme.
In a word, the generating method and the generating platform of the personalized demand scheme of the pregnant woman are used for carrying out intelligent processing and analysis in each module by applying artificial intelligence technology such as machine learning, natural language processing, data mining, deep learning and the like so as to improve efficiency and accuracy, realize intelligent management and evaluation of monitoring indexes such as weight, blood pressure, fetal B-ultrasonic and the like of the pregnant woman, provide personalized advice aiming at diet, physical activity and the like of the pregnant woman, and provide more scientific, accurate and convenient auxiliary decision-making tools for doctors, thereby improving the health level and the production smoothness of the pregnant woman.
Since the platform described in the second embodiment of the present application is a device for implementing the method described in the first embodiment of the present application, based on the method described in the first embodiment of the present application, a person skilled in the art can understand the specific structure and the deformation of the device, and thus the description thereof is omitted herein. All devices used in the method according to the first embodiment of the present application are within the scope of the present application.
While specific embodiments of the application have been described above, it will be appreciated by those skilled in the art that the specific embodiments described are illustrative only and not intended to limit the scope of the application, and that equivalent modifications and variations of the application in light of the spirit of the application will be covered by the claims of the present application.

Claims (10)

1. A method for generating a personalized demand scheme for pregnant women is characterized by comprising the following steps of: the method comprises a preparation process of a medical database, a pregnant woman data collection process and a matching process of materials;
the preparation process of the medical database specifically comprises the following steps:
establishing a medical database, collecting relevant medical guidelines and documents at home and abroad, screening and evaluating, finding out medical guidelines and documents conforming to pregnancy nutrition management, classifying, standardizing, encoding and quality control to obtain medical materials, and storing in the medical database; classifying or marking each medical material in the medical database by using the trained model to obtain a corresponding medical nutrition portrait label or medical diagnosis portrait label;
establishing a gestational nutrition education knowledge base, editing, applying attribute setting, format classification and coding the gestational nutrition management education knowledge according to guidance of expert team and combining logic of domestic and foreign medical guidelines and literature, obtaining education materials and storing the education materials in the gestational nutrition education knowledge base; processing the content of the educational materials by using natural language technology, and automatically analyzing each educational material according to preset logic to obtain an educational knowledge portrait label;
the pregnant woman data collection process specifically comprises the following steps:
under the guidance of expert team, combining domestic and foreign medical guidelines and logic of literature to determine the corresponding field or variable of the personalized nutrition portrait tag of pregnant women, and determining and designing the content and evaluation logic of portrait questionnaires; the portrait questionnaires comprise an international physical activity questionnaire, a FIGO questionnaire, a document building questionnaire and a questionnaire; on-line issuing of an portrait questionnaire for filling by a pregnant woman, collecting nutrition related data of the portrait questionnaire after the filling of the portrait questionnaire is completed by the pregnant woman, and calculating the nutrition related data according to evaluation logic of the portrait questionnaire to obtain a nutrition portrait label of the pregnant woman;
collecting diagnosis and treatment scheme information of pregnant women in a hospital information system, an image archiving and communication system, an electronic medical record and a hospital laboratory information management system, extracting characteristics of the collected diagnosis and treatment scheme information, converting the characteristics into a form which can be processed by a computer, and determining pregnant woman diagnosis portrait labels;
the matching process of the data specifically comprises the following steps:
after the pregnant woman nutrition portrait label and the pregnant woman diagnosis portrait label are combined, the pregnant woman nutrition portrait label and the pregnant woman diagnosis portrait label are compared and matched with the medical nutrition portrait label or the medical diagnosis portrait label of each medical material and the education knowledge portrait label of each education material, so that a personalized requirement scheme which is most suitable for the pregnant woman is formulated and pushed to the pregnant woman.
2. The method for generating a personalized demand regimen for a pregnant woman according to claim 1, wherein: when the medical database is established, the medical guideline and the literature are classified mainly as follows: the classification is based on body composition data, physical examination data, fetal imaging data, basic information data, lifestyle data, current pregnancy status data, pregnancy history data, past history data, menstrual history data, family history data, medication status data, diagnostic results, treatment regimens, and dietary nutrient indicators.
3. The method for generating a personalized demand regimen for a pregnant woman according to claim 1, wherein: the diagnosis and treatment regimen information includes measured body composition data, physical examination data, fetal imaging data, basic information data, lifestyle data, current pregnancy status data, pregnancy history data, past history data, menstrual history data, family history data, medication status data, diagnosis results, and treatment regimen.
4. The method for generating a personalized demand regimen for a pregnant woman according to claim 1, wherein: the content of the personalized demand scheme of the pregnant woman comprises medication advice, nutrition management advice, blood sugar monitoring advice, blood pressure monitoring advice and knowledge education;
the personalized demand scheme of the pregnant woman is to convert the content into a text, picture or video format which is easy to understand and absorb by utilizing a natural language generation technology, and push the content to relevant equipment of the pregnant woman at a specific time or place according to the preference and the demand of the pregnant woman.
5. The method for generating a personalized demand regimen for a pregnant woman according to claim 1, wherein: the method for preparing the personalized demand scheme most suitable for the pregnant women and pushing the personalized demand scheme to the pregnant women is as follows: according to domestic and foreign medical guidelines and logic of documents, determining fields or variables corresponding to the pregnant woman nutrition portrait tags and the pregnant woman diagnosis portrait tags, using the fields or variables as keywords, matching the education knowledge portrait tags, the medical nutrition portrait tags or the medical diagnosis portrait tags, screening corresponding education materials and medical materials from a pregnancy nutrition education knowledge base and a medical database, and sorting according to weights, thereby matching the content of the most suitable personalized demand scheme.
6. The utility model provides a generating platform of individualized demand scheme of pregnant woman which characterized in that: the system comprises a preparation module of a medical database, a pregnant woman data collection module and a matching module of materials;
the preparation module of the medical database is specifically used for:
establishing a medical database, collecting relevant medical guidelines and documents at home and abroad, screening and evaluating, finding out medical guidelines and documents conforming to pregnancy nutrition management, classifying, standardizing, encoding and quality control to obtain medical materials, and storing in the medical database;
establishing a gestational nutrition education knowledge base, editing, applying attribute setting, format classification and coding the gestational nutrition management education knowledge according to guidance of expert team and combining logic of domestic and foreign medical guidelines and literature to obtain an education material; processing the content of the educational materials by using natural language technology, and automatically analyzing each educational material according to preset logic to obtain an educational knowledge portrait label;
the pregnant woman data collection module is specifically used for:
under the guidance of expert team, combining domestic and foreign medical guidelines and logic of literature to determine the corresponding field or variable of the personalized nutrition portrait tag of pregnant women, and determining and designing the content and evaluation logic of portrait questionnaires; the portrait questionnaires comprise an international physical activity questionnaire, a FIGO questionnaire, a document building questionnaire and a questionnaire; on-line issuing of an portrait questionnaire for filling by a pregnant woman, collecting nutrition related data of the portrait questionnaire after the filling of the portrait questionnaire is completed by the pregnant woman, and calculating the nutrition related data according to evaluation logic of the portrait questionnaire to obtain a nutrition portrait label of the pregnant woman;
collecting diagnosis and treatment scheme information of pregnant women in a hospital information system, an image archiving and communication system, an electronic medical record and a hospital laboratory information management system, extracting characteristics of the collected diagnosis and treatment scheme information, converting the characteristics into a form which can be processed by a computer, and determining pregnant woman diagnosis portrait labels;
the matching module of the data is specifically used for:
after the pregnant woman nutrition portrait label and the pregnant woman diagnosis portrait label are combined, the pregnant woman nutrition portrait label and the pregnant woman diagnosis portrait label are compared and matched with the medical nutrition portrait label or the medical diagnosis portrait label of each medical material and the education knowledge portrait label of each education material, so that a personalized requirement scheme which is most suitable for the pregnant woman is formulated and pushed to the pregnant woman.
7. The platform for generating personalized demand patterns for pregnant women according to claim 6, wherein: when the medical database is established, the medical guideline and the literature are classified mainly as follows: the classification is based on body composition data, physical examination data, fetal imaging data, basic information data, lifestyle data, current pregnancy status data, pregnancy history data, past history data, menstrual history data, family history data, medication status data, diagnostic results, treatment regimens, and dietary nutrient indicators.
8. The platform for generating personalized demand patterns for pregnant women according to claim 6, wherein: the diagnosis and treatment regimen information includes measured body composition data, physical examination data, fetal imaging data, basic information data, lifestyle data, current pregnancy status data, pregnancy history data, past history data, menstrual history data, family history data, medication status data, diagnosis results, and treatment regimen.
9. The platform for generating personalized demand patterns for pregnant women according to claim 6, wherein: the content of the personalized demand scheme of the pregnant woman comprises medication advice, nutrition management advice, blood sugar monitoring advice, blood pressure monitoring advice and knowledge education;
the personalized demand scheme of the pregnant woman is to convert the content into a text, picture or video format which is easy to understand and absorb by utilizing a natural language generation technology, and push the content to relevant equipment of the pregnant woman at a specific time or place according to the preference and the demand of the pregnant woman.
10. The platform for generating personalized demand patterns for pregnant women according to claim 6, wherein: the method for preparing the personalized demand scheme most suitable for the pregnant women and pushing the personalized demand scheme to the pregnant women is as follows: according to domestic and foreign medical guidelines and logic of documents, determining fields or variables corresponding to the pregnant woman nutrition portrait tags and the pregnant woman diagnosis portrait tags, using the fields or variables as keywords, matching the education knowledge portrait tags, the medical nutrition portrait tags or the medical diagnosis portrait tags, screening corresponding education materials and medical materials from a pregnancy nutrition education knowledge base and a medical database, and sorting according to weights, thereby matching the content of the most suitable personalized demand scheme.
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