CN112820371A - Health recommendation system and method based on medical knowledge map - Google Patents
Health recommendation system and method based on medical knowledge map Download PDFInfo
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Abstract
The application provides a health recommendation system based on a medical knowledge graph, which comprises an abnormal physical sign library; the abnormal sign library comprises medical abnormal data and time interval abnormal data; the user health portrait can be established through the medical abnormal data and the time interval abnormal data. The present application further provides a health recommendation method, including: obtaining an abnormal category feature label of a user a; acquiring the occurrence time of an i-th class abnormal feature tag AXi, setting the association time period of the i-th class abnormal feature tag AXi as delta ATi, wherein each association time period of the AXi comprises each occurrence time of the AXi; acquiring abnormal category characteristics AF occurring in all associated time periods of the abnormal category characteristics AXi; recording all abnormal category characteristics AF occurring in the association time quantum delta ATi of the abnormal category characteristics AXi as preset association characteristics of the AXi, and constructing a user health portrait according to the associated abnormal category characteristics. The present application provides an optimized health recommendation system.
Description
Technical Field
The application relates to the field of user health portraits, in particular to a health recommendation system and a health recommendation method based on a medical knowledge graph.
Background
The current economy is continuously developed, the aging of social population is more and more serious, the diseases of the old are prevalent, the chronic diseases of young and young people are also caused by greater living pressure, and in order to facilitate the statistics of users, abnormal symptoms of physical signs and tracking, a personal health medical record report aiming at each user is gradually formed in the prior art, and the information sharing is realized through the internet and big data; however, most of data in the existing personal health medical record report only relates to physical sign parameters of patient advocacy, and information such as abnormal data, living conditions, work, diet and motion found in daily life of a patient is acquired less and updated untimely, so that a user cannot be subjected to multidimensional real-time portrait in all aspects, and thus multidimensional information of the patient cannot be obtained in time. Because effective treatment and health care measures cannot be formed, the treatment timeliness reminding and effectiveness of the user are influenced; in the prior art, the standard physical characteristics of the same type of patient users are often adopted as the reference for evaluating and predicting the health state of the users, but the standard physical characteristics of the current users cannot be used as the reference, so that the guidance and intervention measures which are most suitable for the patients of the current users cannot be effectively provided for the current users, and the requirements of individuals, enterprises and health supervision units cannot be met.
Disclosure of Invention
In order to solve the problems, the health recommendation system based on the medical knowledge graph comprises a user health information base, wherein the user health information base comprises an abnormal physical sign base; the abnormal sign library comprises medical abnormal data and time interval abnormal data; the medical abnormal data comprise abnormal conditions and occurrence time of which numerical values are beyond normal indexes in the detection result of the body of the user through the medical instrument, and the time interval abnormal data comprise the abnormal conditions and the occurrence time obtained when the user checks himself in life; a user health portrait can be established through medical abnormal data and time interval abnormal data, and the user health portrait comprises user personal information and an abnormal category characteristic label of an associated abnormal condition.
Preferably, the association time periods of the abnormal category feature tags are set according to the time when the abnormal condition occurs, and the abnormal category feature tags with association occur in the same association time period.
Preferably, the user health portrait further comprises life behavior information and travel information of the user, wherein the life behavior information comprises life behaviors and behavior execution time; the travel information includes a user's travel time, a travel location, and a travel environment.
Preferably, the user health representation further includes life behavior information and travel information in a time period when the category feature tag of the correlated abnormal condition occurs.
Preferably, the system can also classify the users according to occupation, establish occupation category health images according to personal information and occupation characteristics, age characteristics or regional characteristics, and judge the health risks of the results of the category characteristic labels of the abnormal conditions through comparison.
The application also provides a recommendation method using the medical knowledge-graph-based health recommendation system according to any one of the above items, which comprises the following steps:
step S10, setting the total number of the category feature labels of the abnormal conditions in the abnormal physical sign library as n, wherein the category feature labels comprise X1，X2 ,…,Xn ；
Step S20, acquiring all data of user a in the abnormal physical sign library, and acquiring the abnormal category feature label a = { AX ] of user a1，AX2 ,…,AXmM is less than or equal to n, wherein AXiA label of the i-th type abnormal feature of the user a; obtaining an i-th abnormal feature label AXiTime of occurrence ATi={At1，At2 ,…,AtkK is an abnormal class feature label AXiThe number of occurrences;
step S30, setting the i-th abnormal class characteristic label AXiAssociated time period of Δ ATi，AXiEach associated time period of (a) includes AXiEach occurrence time of (A), AXiAssociated time period Δ ATi={△at1,△at2,△at3…△atkWhere atj=[Atj-△T1，Atj+△T2]（△T1＞0，△T2> 0) wherein, AtjIs AXiJ-th occurrence time,. DELTA.atjIs AXiIs associated with the jth time period, Δ T1、△T2A time greater than 0;
step S40, obtaining abnormal class characteristics AXiAbnormal class features AF = { AFX) occurring in all associated time periods1，AFX2 ,…,AFXcWherein c is more than or equal to 1 and less than or equal to k, AXi∉ AF; characterizing the anomaly class AXiAssociated time period Δ ATiAll anomaly class features AF occurring within are recorded as AXiThe preset correlation characteristics;
step S50, when the abnormal class characteristic AXiWhen the preset associated feature exists in the associated time period, adding 1 to the associated count of the preset associated feature, recording the associated counts of other abnormal class features as 0, and finally obtaining the AXiPreset associated feature count AL = { NUM) at all associated time periods1 ，NUM2 ，…，NUM cWherein, NUMjClass i exception class feature AX for user aiJ th preset correlation characteristic AFXjAnd AXiTotal number of correlations of
Step S60, setting the association threshold value as W, setting the weight of the abnormal class feature, and setting the abnormal class feature with the weight association count larger than the association threshold value as AX according to the weightiThe associated characteristics of (1);
and step S70, constructing the user health portrait according to the associated abnormal category characteristics, and displaying other associated abnormal type characteristics and corresponding health item recommendations to the client.
In step S30, Δ atjPreferably including an anomaly class characteristic AXiMaximum shortest time period, air quality, Δ T1And Δ T2May be equal times or may be unequal times.
In step S60, the abnormality category feature AX isIIs associated with the abnormal feature AFXjWeight of (2)
The calculation method comprises the following steps:
wherein NUMjIs the total number of occurrences, NUM, of the exception category characteristic over the associated time period1Is the number of times the anomaly category characteristic occurs in the first associated time period,
is the sum of all the abnormal category characteristics of the user;
wherein,
is the current associated anomaly class feature AFXjThe total number of occurrences on the user,
is the total number of other anomaly class features,
is in contact with AFXjThe exception category features have an associated total number of exception features,
is in other abnormal class characteristics and AFXjTotal number of times of association.
The anomaly characteristic AFXjWeight of (2)
；
Weight correlation count
；
Wherein, in step S70, the system recommends a physical examination item, recommends a diet, or reminds of purchasing a drug in advance according to the correlated abnormality type characteristics.
In step S70, obtaining the abnormal category characteristics in the affected time period by obtaining the historical information of the previous disease in the abnormal sign library, and establishing a neural network model by using the abnormal category characteristics in the affected time period and the corresponding disease state as input and output data samples (x, y), the specific method is as follows: receiving D input vectors x = [ x ]1; x 2 ; …; x D ]
z represents a weighted sum of inputs, where w = [ w ]1; w2;…; w D]Is the weight vector of the multidimensional input, b ∈ R is the bias.
The activation function used may be a Logistic function or a ReLU function, and when a ReLU function is used:
in a multi-layer feedforward neural network, order
The feed-forward neural network continuously iterates the following formula to carry out information propagation layer by layer, wherein the formula is as follows:
the composite function is:
where W and b represent the connection weights and offsets for all layers in the network.
Is the number of layers of the neural network;
is as follows
Layer to layer
A weight matrix of the layer;
is as follows
Layer to layer
The biasing of the layers is such that,
is as follows
Net input of layer neurons;
is as follows
Output of layer neurons
Using a cross-entropy loss function, for sample (x, y) the loss function is:
wherein,
representing by a one-hot vector corresponding to y;
given a training set of
Will beEach sample
Input to the pre-neural network to obtain the network output of
The risk function on the data set is:
wherein W and b represent all weight matrices and bias vectors in the network, respectively;
is a regularization term to prevent overfitting; lambda [ alpha ]>0 is a long parameter, λ>W is closer to 0 as 0 is larger:
the way in which the parameters W and b are updated in each iteration of the gradient descent method
The beneficial effect that this application realized is as follows:
according to the method and the system, under the era background of artificial intelligence and big data, information such as life, social contact, diet and movement of a user is acquired timely, the user is subjected to multidimensional all-around real-time portrait by acquiring abnormal type characteristics with relevance, effective reminding and health care measures are formed, and guidance and intervention measures which are most suitable for the current user are effectively provided for the current user by collecting abnormal conditions with relevance.
Meanwhile, the method and the system can help the individual or enterprise user to analyze the health dynamic state and provide the recommendation of related health services; then recommending suggestions for improving or maintaining the health state for the enterprise according to the health change dynamics of the enterprise population; and finally, providing trend analysis and recommending relevant decisions for local health supervision departments according to the health change characteristics of regional people.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art according to the drawings.
FIG. 1 is a flow chart of the method of the present application.
Detailed Description
The technical solutions in the embodiments of the present application are clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As shown in fig. 1, the present application provides a health recommendation system based on a medical knowledge-graph;
the health recommendation system obtains personal basic information of a user through a user information collection page, the user can directly fill in the user information collection page, and also can perform associated collection on the personal basic information in terminal equipment in a manner of associating other applications, so that the user information of the user can be obtained, wherein the user information comprises name, gender, age, identification number, occupation category, duty, working age, geographical area and the like;
after user information is acquired, a user health information base is correspondingly established, wherein the health information base comprises an abnormal physical sign base, and the abnormal physical sign base comprises a previous disease, a family genetic disease history, medical abnormal data and time interval abnormal data;
the medical abnormal data comprises all abnormal conditions except normal indexes and the time when the abnormal conditions occur in the results of the detection (including medical examination and annual physical examination) of the body by the medical instrument performed by the user over the years; the medical abnormal condition may include electrocardiogram abnormality, liver function five-item numerical abnormality, lung X-ray abnormality, B-ultrasonic image abnormality, etc.
The time interval abnormal data includes abnormal conditions obtained when the user performs self-check in life and the time when the abnormal conditions occur, and the types of the time interval abnormal data are blood pressure abnormality, blood sugar abnormality, body temperature abnormality, heartbeat abnormality, sudden pain and the like.
Meanwhile, the life behavior information and the travel information of the user can be acquired through a network tool or other special application programs by combining with the user information of the user, wherein the life behavior information comprises life behaviors and time for executing the behaviors, and the types of the life behaviors comprise medicine taking behaviors (comprising medicine names and medicine taking time), eating behaviors (recipes, food volumes and eating time), work and rest time, exercise intensity, duration and the like;
the category of the travel information includes a vehicle (walking, riding or car, airplane) used by the user, an area traveled, an environmental condition of the traveled area, a time of traveling through different areas, and the like.
In specific implementation, for example, the abnormal situation category labels may include labels for recording senses such as joint pain, limb pain, headache, muscle pain, neuropathic pain, and the like, and in other embodiments, the abnormal situation category labels may also be provided as labels for daily physical examinations including hypertension, high heart rate, high blood sugar, high blood fat, and the like; in other embodiments, the abnormal situation category label can be set as a label with visual expression of fever, cough, dyspnea, body weakness, local red swelling and the like;
in the specific implementation process, preferably, the value range of n is [10,200], more preferably [20,50], the value of the category total n determines the size of the range for dividing the abnormal condition, when n is larger, the number of the abnormal condition tags is more, the attribute of the abnormal condition is more finely divided, for example, the joint pain can be further refined into wrist joint pain, hip joint pain and ankle joint pain, and the specialty and the pertinence of the user can be further facilitated.
In addition, in order to numerically represent the category label of the abnormal situation, the total number of abnormal categories in the abnormal physical sign library is set as n, and the abnormal category feature label comprises X1，X2 ,…,XnN is more than or equal to 1; meanwhile, as will be appreciated by those skilled in the art, the above-mentioned expression of the attribute tag is only an exemplary description, and is not a sole example for limiting the protection scope of the present invention.
The health recommendation system of the medical knowledge graph disclosed by the invention can be arranged on terminal equipment, and also can be arranged on a host and a system, and when the system starts to work, the following recommendation method of the health recommendation system based on the medical knowledge graph can be realized:
setting the total number of category feature labels of abnormal conditions in the abnormal physical sign library as n, wherein the category feature labels comprise X1，X2 ,…,Xn ；
Acquiring all the abnormal physical signs of the user a in the abnormal physical sign libraryObtaining the abnormal class feature label A = { AX of the user a1，AX2 ,…,AXmM is less than or equal to n, wherein AXiA label of the i-th type abnormal feature of the user a; obtaining the i-th abnormal feature AXiTime of occurrence ATi=[At1，At2 ,…,Atk ] TWhere k is an anomaly class feature AXiThe number of occurrences;
setting the class i exception class feature AXiAssociated time period of Δ ATi，AXiEach associated time period of (a) includes AXiEach occurrence time of (A), AXiAssociated time period Δ ATi={△at1,△at2,△at3…△atkWhere atj=[Atj-△T1，Atj+△T2]（△T1＞0，△T2> 0) wherein, AtjIs AXiJ-th occurrence time,. DELTA.atjIs AXiIs associated with the jth time period, Δ T1、△T2For times greater than 0, Δ T1、△T2May or may not be equal;
obtaining an anomaly class feature AXiAbnormal class features AF = { AFX) occurring in all associated time periods1，AFX2,…,AFXcWherein c is more than or equal to 1 and less than or equal to k, AXi∉ AF; characterizing the anomaly class AXiAssociated time period Δ ATiAll anomaly class features AF occurring within are recorded as AXiThe preset correlation characteristics;
when abnormal class feature AXiWhen the preset associated feature exists in the associated time period, adding 1 to the associated count of the preset associated feature, recording the associated counts of other abnormal class features as 0, and finally obtaining the AXiPreset associated feature count AL = { NUM) at all associated time periods1 ，NUM2 ，…，NUM cWherein, NUMjClass i exception class feature AX for user aiJ th preset correlation characteristic AFXjAnd AXiTotal number of associations.
For example,let us assume the first-class anomaly characteristic AX of user a1Occurring 3 times in total, namely 7/2/2001, 7/12/2001, and 7/15/2001, 4 associated abnormal features are respectively AX in abnormal time periods including 7/2/20014、AX6、 AX7, AX11There are 3 associated abnormalities characterized in the abnormal time period including 7, 12 and 2001: are each AX4、AX5、AX8(ii) a There are 2 associated abnormal features in the abnormal time period including 7, 12 and 2001, each of which is AX4、AX8(ii) a The first class anomaly class feature AX can be obtained1Is preset associated with the anomaly characteristic AX4、AX5、AX6、AX7、AX8、AX11(ii) a And the association count is {3,1,1,1,2,1}
Wherein the anomaly class feature AXIIs associated with the abnormal feature AFXjWeight of (2)
The calculation method comprises the following steps:
wherein NUMjIs the total number of occurrences, NUM, of the exception category characteristic over the associated time period1Is the number of times the anomaly category characteristic occurs in the first associated time period,
is the sum of all the abnormal category characteristics of the user;
wherein,
is the current associated anomaly class feature AFXjThe total number of occurrences on the user,
is the total number of other anomaly class features,
is in contact with AFXjThe exception category features have an associated total number of exception features,
is in other abnormal class characteristics and AFXjTotal number of times of association.
The anomaly characteristic AFXjWeight of (2)
；
Weight association count:
setting the association threshold value as W, and taking the abnormal class characteristic with the weight association count larger than the association threshold value as AX according to the weightiThe associated features of (1).
Through the scheme, the system can construct the abnormal physical sign portrait of the user according to the medical abnormal data and the abnormal time period data of the user, and when the abnormal type characteristics of the client occur, the system prompts other related abnormal conditions which may occur simultaneously to the client, and asks the client to pay attention to the related detection, diet attention, advance medicine purchase and the like.
In addition, the system can also acquire abnormal category characteristics in an affected time period by acquiring historical information of diseases suffered by a user in an abnormal sign library, and establish a neural network model by taking the abnormal category characteristics in the affected time period and corresponding conditions as input and output data samples (x, y), wherein the specific method comprises the following steps: receiving D input vectors x = [ x ]1; x 2 ; …; x D ]
z represents a weighted sum of inputs, where w = [ w ]1; w2;…; w D]Is the weight vector of the multidimensional input, b ∈ R is the bias.
The activation function used may be a Logistic function or a ReLU function, and when a ReLU function is used:
in a multi-layer feedforward neural network, order
The feed-forward neural network continuously iterates the following formula to carry out information propagation layer by layer, wherein the formula is as follows:
the composite function is:
where W and b represent the connection weights and offsets for all layers in the network. Is the number of layers of the neural network;
is as follows
A layer-to-l-th layer weight matrix;
for the bias from layer l-1 to layer l,
net input to layer i neurons;
as output by l, ll layer neurons
Using a cross-entropy loss function, for sample (x, y) the loss function is:
wherein,
representing by a one-hot vector corresponding to y;
given a training set of
Each sample is sampled
Input to the pre-neural network to obtain the network output of
The risk function on the data set is:
wherein W and b represent all weight matrices and bias vectors in the network, respectively;
is a regularization term to prevent overfitting; lambda [ alpha ]>0 is a long parameter, λ>W is closer to 0 as 0 is larger:
the way in which the parameters W and b are updated in each iteration of the gradient descent method
By establishing the model, the state of illness or possible treatment directions can be judged according to the abnormal category characteristics with relevance, and a recommended rehabilitation scheme such as exercise and diet can be given to help the abnormal situations encountered by the client.
In addition, by acquiring the life behavior information and the travel information of the user in the associated time period and judging whether abnormal life behavior information and abnormal travel information exist in the current associated time period, a behavior-abnormality model of the user can be generated, and the specific method comprises the following steps: acquiring user behavior information, wherein the behavior information comprises user living behavior information and travel information, and the living behavior information comprises living behaviors and behavior execution time; the travel information comprises travel time, travel place and travel environment of the user, and behavior information data of the user a is set to be AR = { AR = }1 ,AR2 ,…,ARdThe time when the action occurs is ART = { ARt = }1 ,ARt2 ,…,ARtdIn which are ARtiAs behavior information ARiThe occurrence time of the user is obtained, and the behavior characteristic association time period delta AT of the i-th class abnormal class characteristic AXi of the user is obtained i＇={△at1＇,△at2＇,△at3＇…△atk' where Δ atj' is AXiIs associated with the time period, Δ at, of the jth behavioral characteristic ofj＇=[ Atj-△T1-△T3，Atj+△T2-△T4]（△T3＞0，△T4> 0), known as Δ atj=[Atj-△T1，Atj+△T2]Is AXiIs said AX, isiIs associated with the j-th behavior feature of (1) by the time period Δ atjOccurring in a feature association period Δ atjBefore, or including a feature association time period Δ atjFor a period of time. Delta T3、△T4For times greater than 0, Δ T3、△T4May or may not be equal.
Obtaining Δ AT in a behavioral association time period iOf the behavioral characteristics that occurred, abnormal behavioral characteristics AQ = { AQX = [, ]1，AQX2 ,…,AQXdD is more than or equal to 1 and less than or equal to k; characterizing the anomaly class AXiTime interval Δ AT associated with behavior characteristics iAll abnormal behaviour characteristics occurring in' AQ are recorded as AXiPresetting associated behavior characteristics;
when abnormal class feature AXiWhen the behavior associated time period has abnormal behavior characteristics, the association count of the preset associated behavior characteristics is added with 1, the association counts of other behavior characteristics are marked as 0, and finally the AX is obtainediPreset associated feature count AL' = { NUM) at all behavior associated time periods1＇ ，NUM2 ＇，…，NUM c', wherein, NUMj' is the i-th type exception category feature AX of user aiJ < th > preset associated behavior feature AQXjAnd AXiTotal number of associations.
For example, in the time period of 6-7 months in 2014, the abnormal type feature [ topical red rash ] of the user occurs 3 times, namely 12 days in 6-6 months in 2014, 28 days in 6-28 months in 2014 and 19 days in 7-19 months in 2014; the abnormal type characteristics with relevance are [ fever ], [ running nose ], abnormal information in the life behavior information, such as [ business trip ], and [ business trip place ] [ environmental condition ] in the business trip information are checked in the life behavior information and the trip information of the user in the time period, whether people with the relevant abnormal type characteristics exist in the business trip place or not is checked in the period of 6 months to 7 months in the abnormal physical sign library, after a certain amount of same type abnormal condition data is found, the trip abnormal information is recorded, when the customer triggers the trip abnormal information again, namely the business trip arrives at the place, the relevant abnormal type characteristics are prompted, and suitable clothes, external medicines, food contraindications and the like are recommended.
In addition, the system can classify users according to occupation, establish an occupation category comparison model according to personal information and occupation characteristics, age characteristics or regional characteristics, compare abnormal physical sign images of the person undergoing physical examination with the occupation category comparison model in the system, judge whether the result of the abnormal characteristics has a health risk or not, if so, send the health risk to the person through a recommendation system, and provide a proposal for improving the risk.
Setting an abnormal value threshold according to personal conditions such as age, work and race, physical conditions and the like, and alarming under a high risk condition when the slope of an abnormal value curve is greater than a safety slope threshold; when the maximum value of the abnormal numerical value is larger than the safety threshold value, alarming under the high risk condition; and when the person has the behavior which can cause the abnormal data to continue to deteriorate, the person is warned;
if the trend of the numerical value of the abnormal data at this time is mild compared with the numerical value of the historical abnormal data, recommending the personnel to continue using the recommendation scheme;
in addition, an enterprise physical examination information portrait can be further constructed through abnormal physical sign pictures, and an enterprise models from multiple dimensions by acquiring the abnormal physical sign picture information of all current employees of the enterprise and considering the attribute information of the enterprise, the environmental data of the region where the enterprise is located and the like, so as to construct a comprehensive abnormal type characteristic model.
According to the enterprise physical examination information picture, the physical examination results with the same abnormal data value and the persons with similar personal information and behavior information can be independently established as an abnormal type characteristic model;
for example, a 15 male employee at a position 35-38 years old may have cardiac function index data that exceeds a first health risk value; reminding to push employees who fit this type of feature, such as employees who are "35-38 years old," male "," management position "," work time per month over 260 hours ", the current behavioral features may result in health risks and prompt from which behaviors should be improved.
When the physical examination is carried out again, when the personal information of the staff comprises the characteristic types, the physical examination items are preferentially added to the numerical value of the heart function direction of the staff;
meanwhile, other behavior information of the staff is added, such as daily movement, walking step number, rest interval time, sleeping time, diet and the like, and is added into the characteristic types according to the occurrence times and the weight;
when pushing, the situation that some diseases are improved by staff, and other numerical values are out of limits is also considered, and whether the improvement scheme influences the numerical values or not is considered.
And acquiring an overproof data curve graph of the personnel in the historical physical examination data, comparing each overproof data with the current physical examination data, watching the development condition of the overproof data of the historical physical examination data center, and if the standard data is better, improving the recommended score in the improvement scheme of the personnel, and optimizing and adjusting the improvement scheme.
If the situation is not improved or continues to deteriorate, the recommended score in the improvement scheme is reduced, and the improvement scheme of the overproof numerical value of the person is adjusted.
Similarly, a medical unit physical examination image can be established by the abnormal physical sign image, and the medical unit physical examination image expresses all the health condition change rules of the medical unit physical examination person, namely the medical unit physical examination person reflects the health condition of a region. The composition of the medical unit physical examination image is more complex than that of the enterprise physical examination information image. When constructing the portrait, the physical examination data of workers engaged in different health industries needs to be comprehensively considered, and the accurate physical examination information portrait of the medical unit needs to be generated by combining the data of local environment and the like.
In addition, according to different classifications of physical examination information images, personalized health service recommendations with different directions are also provided, and after the physical examination information figure image is constructed, the related figure image needs to be analyzed and applied. When generating health service recommendations for individual users, the explicit needs and implicit needs of the individual users need to be analyzed in depth. Where explicit needs refer to things that a user subjectively thinks about, and implicit needs are things that a user does not realize but is important to. The following application scenarios are given:
for an individual user, each abnormal physical sign information such as physical examination data is collected, even if physical examinations are all in a normal value range, certain single values fluctuate, the data fluctuation conditions can be deeply mined, the living state of the current user is presumed from the fluctuation conditions, and relevant health recommendation services such as diet work and rest suggestions are generated for the user, so that the user can be guaranteed to successfully pass through the physical examinations next time.
The management personnel of the enterprise need to stand at the height of the enterprise to make relevant decisions according to the health data of the employees. The following application scenarios are given: for the enterprise manager, the health fluctuation condition of the current employee is fed back to the enterprise manager through the related abnormal condition data of the employee, for example, although all the employees are qualified in the health certificate physical examination project, the employees present the same change rules on the data of certain indexes, and the change rules may be related to the working system of the employees, such as night shift and the like. In order to ensure that the staff can pass the examination in the next health examination, it is necessary to recommend relevant analysis conditions and health services to the enterprise manager. And for enterprises with similar structure and function, health safety accidents of the enterprises with similar structure and function can be recommended to play a role in warning.
The health unit considers the whole health condition of a region and makes a relevant health policy according to the local health data change condition. The following application scenarios are given: from the perspective of health regulatory agencies, they need to comprehensively consider the health changes of people in each area and give changes to the environment or other relevant policies. For example, there may be a significant difference in physical examination data between two persons in adjacent management areas, and the reason for the difference may be the environment. Therefore, by analyzing the health data of the whole administrative district, relevant result analysis and recommendation can be generated for health supervision units.
In the process of establishing the abnormal characteristic model, the data acquisition, the data cleaning and the data analysis are included.
In the data acquisition process, abnormal physical sign data can be directly acquired from relevant medical knowledge map big data, and other data such as weather data can be directly captured from the Internet by using a crawler tool.
In the process of data cleaning, the obtained data is preprocessed, including processing of residual values, abnormal values and the like, so that subsequent analysis is facilitated.
In the data analysis process, the cleaned data is subjected to correlation analysis by using a correlation technology of data mining, such as clustering, PCA and the like, and the correlation among the data is found.
After the portrait is constructed, personalized recommendations need to be generated for users of different levels according to the portrait, a suitable recommendation algorithm can be adopted to model the problem, and a recommendation model is optimized by combining with an actual application scene, such as time for generating a recommendation list, so as to achieve the goal of online use.
After the research of the recommendation model is completed, a system needs to be designed for the user according to the real application scenario and corresponding services need to be provided. The development framework of the system can use the main development framework SSM and the like to ensure the normal use of users.
After the system is developed, all functional modules of the system need to be tested, and a black box testing method can be adopted to ensure the normal operation of all functions of the system. After the test is finished, the system can be deployed on line for the actual use of the user, and then more detailed adjustment can be carried out according to the feedback used by the user.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A health recommendation system based on a medical knowledge graph comprises a user health information base, wherein the user health information base comprises an abnormal physical sign base; the abnormal sign library comprises medical abnormal data and time interval abnormal data; the medical abnormal data comprise abnormal conditions and occurrence time of which numerical values are beyond normal indexes in the detection result of the body of the user through the medical instrument, and the time interval abnormal data comprise the abnormal conditions and the occurrence time obtained when the user checks himself in life; a user health portrait can be established through medical abnormal data and time interval abnormal data, and the user health portrait comprises user personal information and an abnormal category characteristic label of an associated abnormal condition.
2. The medical knowledge-graph-based health recommendation system of claim 1, wherein the association time periods of the abnormality category feature tags are set according to the time when the abnormal situation occurs, and the abnormality category feature tags having the association occur in the same association time period.
3. The medical knowledge-graph-based health recommendation system of claim 1, wherein the user health representation further comprises life behavior information and travel information of the user, the life behavior information comprising life behaviors and time to perform the behaviors; the travel information includes a user's travel time, a travel location, and a travel environment.
4. The medical knowledge-graph-based health recommendation system of claim 2, wherein the user health representation further comprises life behavior information and travel information over a period of time during which the category feature labels of the correlated abnormalities occurred.
5. The medical knowledge graph-based health recommendation system of claim 1, wherein the system is further capable of classifying users by occupation, creating occupation category health images according to occupation characteristics, age characteristics or region characteristics according to personal information, and comparing the occupation category health images to judge the health risks of the results of the category characteristic labels of abnormal situations.
6. A recommendation method using the medical knowledge-graph based health recommendation system of any one of claims 1-5, comprising:
step S10, setting the total number of the category feature labels of the abnormal conditions in the abnormal physical sign library as n, wherein the category feature labels comprise X1，X2 ,…,Xn ；
Step S20, acquiring all data of user a in the abnormal physical sign library, and acquiring the abnormal category feature label a = { AX ] of user a1，AX2 ,…,AXmM is less than or equal to n, wherein AXiA label of the i-th type abnormal feature of the user a; obtaining an i-th abnormal feature label AXiTime of occurrence ATi={At1，At2 ,…,AtkK is an abnormal class feature label AXiThe number of occurrences;
step S30, setting the i-th abnormal class characteristic label AXiAssociated time period of Δ ATi，AXiEach associated time period of (a) includes AXiEach occurrence time of (A), AXiAssociated time period Δ ATi={△at1,△at2,△at3…△atkWhere atj=[Atj-△T1，Atj+△T2]（△T1＞0，△T2> 0) wherein, AtjIs AXiJ-th occurrence time,. DELTA.atjIs AXiIs associated with the jth time period, Δ T1、△T2A time greater than 0;
step S40, obtaining abnormal class characteristics AXiAbnormal class features AF = { AFX) occurring in all associated time periods1，AFX2 ,…,AFXcWherein c is more than or equal to 1 and less than or equal to k, AXi∉ AF; characterizing the anomaly class AXiAssociated time period Δ ATiAll anomaly class features AF occurring within are recorded as AXiThe preset correlation characteristics;
step S50, when the abnormal class characteristic AXiWhen the preset associated feature exists in the associated time period, adding 1 to the associated count of the preset associated feature, recording the associated counts of other abnormal class features as 0, and finally obtaining the AXiPreset associated feature count AL = { NUM) at all associated time periods1 ，NUM2 ，…，NUM cWherein, NUMjClass i exception class feature AX for user aiJ th preset correlation characteristic AFXjAnd AXiTotal number of correlations of
Step S60, setting the association threshold value as W, setting the weight of the abnormal class feature, and setting the abnormal class feature with the weight association count larger than the association threshold value as AX according to the weightiThe associated characteristics of (1);
and step S70, constructing the user health portrait according to the associated abnormal category characteristics, and displaying other associated abnormal type characteristics and corresponding health item recommendations to the client.
7. The recommendation method of the medical knowledge-graph-based health recommendation system according to claim 6, wherein in step S30, Δ atjPreferably including an anomaly class characteristic AXiMaximum shortest time period, air quality, Δ T1And Δ T2May be equal times or may be unequal times.
8. The recommendation method of the medical knowledge-graph-based health recommendation system according to claim 6, wherein in step S60, said abnormality category feature AXIIs associated with the abnormal feature AFXjThe weight dw (afxj) of (a) is calculated by: ƒ (v)1)=
Wherein NUMjIs thatTotal number of occurrences of exception category characteristics over an associated time period, NUM1Is the number of times the anomaly category characteristic occurs in the first associated time period,
is the sum of all the abnormal category characteristics of the user;
ƒ(v2)=
wherein,Nis the current associated anomaly class feature AFXjThe total number of occurrences on the user,
is the total number of other abnormal class features, djIs in contact with AFXjThe exception class features having an associated total number of exception features, dj' is in other abnormal class characteristics with AFXjThe total number of times of association;
the anomaly characteristic AFXjWeight dw (afxj) = ƒ (v)1)׃(v2)；
Weight association count Lj=NUMj×dw(AFXj)。
9. The recommendation method of the medical knowledge-graph-based health recommendation system according to claim 6, wherein in step S70, the system recommends a physical examination item, a diet or a reminder to purchase a drug in advance according to the correlated abnormality type characteristics.
10. The recommendation method of the health recommendation system based on medical knowledge-graph as claimed in claim 6, wherein in step S70, by obtaining historical information of the previous diseases of the users in the abnormal sign library, obtaining abnormal category characteristics within the affected time period, and taking the abnormal category characteristics within the affected time period and the corresponding disease conditions as input and output data samples (x, y), a neural network model is built, and the specific method is as follows: receiving D input directionsAmount x = [ x ]1; x2 ; …; xD ]；
Z=
=w T x+b，
Where z represents a weighted sum of the inputs, where w = [ w ]1; w2;…; w D]Is the weight vector of the multidimensional input, b is the bias for R;
the activation function used may be a Logistic function or a ReLU function, and when a ReLU function is used:
ReLU(x)=
=max(0，x)
in a multi-layer feedforward neural network, ordera (0) =xThe feed-forward neural network continuously iterates the following formula to carry out information propagation layer by layer, wherein the formula is as follows: α (1) = ƒ (W) l() α l(-1)+b l())
The composite function is:Φ(x;W,b)
where W and b represent the connection weights and offsets for all layers in the network,lis the number of layers of the neural network;W(l)∈R Ml×Ml-1 is as followsl1 layer to the secondlA weight matrix of the layer; b (a), (b)l) ∈R lMIs as followsl1 layer to the secondlOffset of the layers, z: (l) ∈R Ml First, thelNet input of layer neurons; a (a)l) ∈R lMIs as followslOutput of layer neurons
Using a cross-entropy loss function, for sample (x, y) the loss function is:
L(y, ŷ) =-yTlogŷ
wherein,y∈｛0.1｝crepresenting by a one-hot vector corresponding to y;
given a training set of
Each sample x(n)Input to the pre-neural network to obtain the network output of
The risk function on the data set is:
wherein W and b represent all weight matrices and bias vectors in the network, respectively;
is a regularization term to prevent overfitting; lambda [ alpha ]>0 is a long parameter, λ>W is closer to 0 as 0 is larger:
in each iteration of the gradient descent method, the update mode of the parameters W and b is as follows:
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113221012A (en) * | 2021-05-31 | 2021-08-06 | 北京海纳数聚科技有限公司 | Community fitness activity recommendation method based on group portrait |
| CN114974508A (en) * | 2022-05-20 | 2022-08-30 | 西南大学 | Method and device for generating exercise prescription, storage medium and electronic equipment |
| CN115036034A (en) * | 2022-08-11 | 2022-09-09 | 之江实验室 | Similar patient identification method and system based on patient characterization map |
| CN117727456A (en) * | 2023-12-28 | 2024-03-19 | 江苏智慧智能软件科技有限公司 | Obstetrical psychological assessment model modeling system and method based on artificial intelligence |
| CN118155847A (en) * | 2024-03-08 | 2024-06-07 | 泰昊乐生物科技有限公司 | User real-time monitoring health management system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104462858A (en) * | 2014-12-30 | 2015-03-25 | 天津迈沃医药技术有限公司 | Health warning method based on multi-order hidden Markov model |
| WO2018045228A2 (en) * | 2016-08-31 | 2018-03-08 | Intermountain Invention Management, Llc | Interpreting genomic results and providing targeted treatment options in cancer patients |
| CN108922623A (en) * | 2018-07-12 | 2018-11-30 | 中国铁道科学研究院集团有限公司 | A kind of health risk assessment and Disease Warning Mechanism information system |
| WO2019212833A1 (en) * | 2018-04-30 | 2019-11-07 | The Board Of Trustees Of The Leland Stanford Junior University | System and method to maintain health using personal digital phenotypes |
| CN111354469A (en) * | 2020-03-31 | 2020-06-30 | 浙江禾连网络科技有限公司 | User health condition comprehensive evaluation method and system |
| CN112002429A (en) * | 2020-10-27 | 2020-11-27 | 北京梦天门科技股份有限公司 | Physical examination portrait and service recommendation system and method for public health enterprise personnel |
| CN112037934A (en) * | 2019-06-03 | 2020-12-04 | 合肥市行思拓教育科技有限公司 | Intelligent health examination system based on mobile device and learning method |
-
2021
- 2021-04-22 CN CN202110437769.XA patent/CN112820371B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104462858A (en) * | 2014-12-30 | 2015-03-25 | 天津迈沃医药技术有限公司 | Health warning method based on multi-order hidden Markov model |
| WO2018045228A2 (en) * | 2016-08-31 | 2018-03-08 | Intermountain Invention Management, Llc | Interpreting genomic results and providing targeted treatment options in cancer patients |
| WO2019212833A1 (en) * | 2018-04-30 | 2019-11-07 | The Board Of Trustees Of The Leland Stanford Junior University | System and method to maintain health using personal digital phenotypes |
| CN112639990A (en) * | 2018-04-30 | 2021-04-09 | 小利兰·斯坦福大学托管委员会 | System and method for maintaining health using personal digital phenotype |
| CN108922623A (en) * | 2018-07-12 | 2018-11-30 | 中国铁道科学研究院集团有限公司 | A kind of health risk assessment and Disease Warning Mechanism information system |
| CN112037934A (en) * | 2019-06-03 | 2020-12-04 | 合肥市行思拓教育科技有限公司 | Intelligent health examination system based on mobile device and learning method |
| CN111354469A (en) * | 2020-03-31 | 2020-06-30 | 浙江禾连网络科技有限公司 | User health condition comprehensive evaluation method and system |
| CN112002429A (en) * | 2020-10-27 | 2020-11-27 | 北京梦天门科技股份有限公司 | Physical examination portrait and service recommendation system and method for public health enterprise personnel |
Non-Patent Citations (2)
| Title |
|---|
| MOHAMMADI-SHEMIRANI: "Effects of lifelong testosterone exposure on health and disease using Mendelian randomization", 《ELIFE》 * |
| 全民健康管理系统软件在慢病预警与干预管理中的开发应用: "丁伟", 《科技成果》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113221012A (en) * | 2021-05-31 | 2021-08-06 | 北京海纳数聚科技有限公司 | Community fitness activity recommendation method based on group portrait |
| CN114974508A (en) * | 2022-05-20 | 2022-08-30 | 西南大学 | Method and device for generating exercise prescription, storage medium and electronic equipment |
| CN114974508B (en) * | 2022-05-20 | 2024-05-24 | 西南大学 | Generating device of sports prescription and electronic equipment |
| CN115036034A (en) * | 2022-08-11 | 2022-09-09 | 之江实验室 | Similar patient identification method and system based on patient characterization map |
| CN117727456A (en) * | 2023-12-28 | 2024-03-19 | 江苏智慧智能软件科技有限公司 | Obstetrical psychological assessment model modeling system and method based on artificial intelligence |
| CN117727456B (en) * | 2023-12-28 | 2024-05-24 | 江苏智慧智能软件科技有限公司 | Obstetrical psychological assessment model modeling system and method based on artificial intelligence |
| CN118155847A (en) * | 2024-03-08 | 2024-06-07 | 泰昊乐生物科技有限公司 | User real-time monitoring health management system |
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