CN112908481B - Automatic personal health assessment and management method and system - Google Patents
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Abstract
The invention relates to an automatic personal health assessment and management method and system, comprising the following steps: health monitoring and data acquisition; based on the collected results, performing health assessment; performing service plan adaptation based on the result of the evaluation; based on the adaptive result, implementing a service plan according to service start-stop time, and collecting feedback information; based on the result of the feedback information, performing parameter adjustment; based on the result of the adjustment, the relevant data is updated, and the data is executed according to the adjusted parameters when the data is executed next time. Aiming at personal health conditions, the invention automatically collects data by means of intelligent machine, automatically tracks, analyzes and judges, automatically tailors the body to a custom, generates a dynamic scale evaluation problem domain and automatically adapts to a custom care path; the cost is reduced, the efficiency is improved, the individual health assessment requirement is met, the standardization and standardization of the service are realized, and the individual health assessment and service plan adaptation self-organization are realized.
Description
Technical Field
The invention relates to the technical field of health management and service, in particular to an automatic personal health assessment and management method and system.
Background
The pension care specification is a working range formed by a series of constraint knowledge files, and the pension care path is a working flow formed by a time task sequence list. Traditional care technology is usually that a nurse with abundant experience draws up a care plan according to care standards for old people when facing a service object, and determines when and where to complete care services by whom, namely, manually changes the care standards into care paths.
The Chinese patent office discloses an invention patent (application number: 201810154809.8) of an evaluation system and a method for the care needs of the aged based on portrait labels in the year 7 and 27 of 2018. The invention provides an aged care requirement assessment system based on portrait labels, which comprises: an evaluation demand selection module; a meter extraction module; a care plan recommendation module; an evaluation information collection module; an evaluation result processing module; the evaluation scale library is used for storing a plurality of evaluation scales; a care plan library for storing a plurality of care plans; a portrayal tab system comprising a meter tab system labeled based on the rating meter library, a care plan tab system labeled based on the care plan library, and an association of a meter tab with a care plan tab. According to the evaluation system based on scale depiction, the information of the old is collected in a question and answer mode, each evaluation answer is estimated in advance in a scale mode according to experience weight, the evaluation value of the scale is calculated in an answer integral mode, the evaluation value of the scale is used as a scale label adaptation care scheme, and labeling of the evaluation process and guidance of care behaviors are achieved.
The following problems exist in the prior art to be solved:
problem 1 is that the care for the aged is costly, inefficient and not standardized.
Different service objects, complicated old people, high cost, low efficiency and incapability of standardization of the nursing path which is established by experience through manual analysis and judgment; the care path cannot be standardized, so that care charges cannot be standardized, and the nursing service charges for the aged are disordered; the artificial random and random planned nursing path is influenced by subjective mind experience capability and has a plurality of uncertainties, even prejudices, errors and privates, and the pension nursing service lacks a charging standard, so that the national planning of pension assistance policies and the planning of pension insurance schemes are hindered. Therefore, it is necessary to study how to judge and automatically customize the nursing path by means of intelligent automatic analysis of the machine, thereby reducing the cost and improving the efficiency; the method not only meets the individual health evaluation requirement, but also realizes standardized and standardized management of the service.
Problem 2 is that the solid state scale problem domain departure is virtually unreliable.
The problem domains of various evaluation scales are designed empirically in advance, and are not customized for specific evaluated people, the evaluation answers of the scales are free of objective constraint and only come from subjective expressions of the evaluated people, different evaluation results can be generated by repeated evaluation, and the uncertainty of the evaluation results can lead to the change of the care plan; the key problem is that the evaluation system based on scale depiction takes the scale as a core in practice, not takes people as a core, the problems outside the scale are not considered, the problems inside the scale are not required to be verified in a supplementary mode, the evaluation result is separated from the reality, the reliability is low due to unrepeatable, and the adaptive care scheme is only used for reference and cannot be operated. Therefore, it is necessary to study how to self-customize by means of intelligent automation of machines, generate a dynamic scale assessment problem domain, and realize personalized health assessment and service plan adaptation self-organization.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to provide an automatic personal health assessment and management method and system, aiming at personal health conditions, by means of intelligent automatic data acquisition of a machine, automatic tracking analysis and judgment, automatic body measurement and customization, generation of a dynamic scale assessment problem domain and automatic adaptation and customization of a nursing path; the cost is reduced, the efficiency is improved, the individual health assessment requirement is met, the standardization and standardization of the service are realized, and the individual health assessment and service plan adaptation self-organization are realized.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an automated personal health assessment and management method, comprising:
step 1, health monitoring and data acquisition;
step 2, based on the results of health monitoring and data acquisition, performing health assessment;
step 3, performing service plan adaptation based on the result of health evaluation;
Step 4, implementing the service plan according to the service start-stop time based on the service plan adapting result, and collecting feedback information;
step 5, based on the result of the feedback information, performing parameter adjustment, wherein the parameter adjustment comprises any one or part or all of the following:
adjusting the content of the health monitoring and data acquisition in the step 1,
adjusting the content of the health monitoring and data acquisition in the step 2 for participating in health assessment,
adjusting the content of the result of the health assessment participating in the service plan adaptation in step 3,
adjusting the service start-stop time in the step 4;
and 6, updating the related data based on the result of parameter adjustment, and implementing the steps 1-4 according to the adjusted parameters when the steps 1-4 are executed next time.
On the basis of the technical scheme, the health monitoring and data acquisition specifically comprises the following steps:
and (3) continuously collecting data: continuously collecting personal health data through portable health electronic equipment directly worn on the body;
data selection and collection: based on the first abnormal information, triggering data selection and collection, and selectively collecting health index data associated with the first abnormal information through intelligent household equipment and/or wearable equipment;
Data are collected in a key way: based on the second anomaly information, data focus acquisition is triggered, and discomfort symptom index data and disease index data associated with the second anomaly information are focus acquired and verified through personal self-description and/or medical diagnosis.
Based on the technical scheme, the health evaluation is performed based on the results of health monitoring and data acquisition, and specifically comprises the following steps:
the method comprises the steps of obtaining results of health monitoring and data acquisition, wherein the results of health monitoring and data acquisition comprise any one or part or all of the following: personal health data, health index data, discomfort symptom index data, disease index data;
inputting the results of health monitoring and data acquisition into a neural network knowledge base to obtain an overall evaluation drawing board, and filling the overall evaluation drawing board according to personal conditions to obtain overall evaluation portrait data;
inputting the whole evaluation portrait data into a neural network knowledge base to obtain a special evaluation drawing board, and filling the special evaluation drawing board according to personal conditions to obtain special evaluation portrait data;
inputting the special evaluation portrait data into a neural network knowledge base, obtaining element evaluation drawing boards, and filling the element evaluation drawing boards according to personal conditions to obtain element evaluation data;
After summarizing the overall evaluation portrait data, the special evaluation portrait data and the element evaluation data, inputting the overall evaluation portrait data, the special evaluation portrait data and the element evaluation data into a neural network knowledge base again to accurately identify symptom related information and disease related information contained in the overall evaluation portrait data, and outputting an evaluation result report, wherein the evaluation result report is divided into: a report of current discomfort symptoms of the individual and a report of current disease condition information of the individual.
Based on the above technical solution, the service plan adaptation based on the result of the health assessment specifically includes:
inputting the evaluation result report into a health prescription library, and accurately identifying a solution adapted to the current discomfort symptom information and/or disease condition information of the individual;
determining a service plan based on the solution, wherein the service plan comprises service start-stop time;
the solution is divided into: life care, life service and professional care solutions for the current situations requiring assistance by the user, health education, risk prevention and first aid treatment solutions for the subsequent situations requiring assistance by the user.
On the basis of the technical scheme, when the solution adapted to the current discomfort symptom information and/or disease condition information of the individual is not the only one, the method further comprises:
Step 31, inputting the current discomfort symptom information and/or disease condition information of the individual into a neural network knowledge base to obtain discomfort symptom severity evaluation data and/or disease development stage evaluation data;
step 32, setting weights for a plurality of solutions one by one based on the discomfort symptom severity evaluation data and/or the disease development stage evaluation data;
step 33, further determining a service plan based on the weights of the plurality of solutions.
On the basis of the technical scheme, the service plan comprises: nursing diagnosis conclusion, nursing expected target, nursing measure concrete content and nursing evaluation method;
the specific content of the nursing measures comprises: care path information, task allocation information;
the care path information includes: the duration of the nursing service, the task detail before operation, the task detail in operation and the task detail after operation;
the task allocation information includes: server, service start-stop time, service result, variation remarks.
On the basis of the technical scheme, the neural network knowledge base is generated through the following steps:
the diagnosis and treatment standard documents are crawled through big data or manually input to form a document knowledge base;
Performing clustering-based rule extraction on diagnosis and treatment standard based on a document knowledge base to obtain a rule base, wherein the rule base comprises an evaluation algorithm model and a service algorithm model;
word segmentation processing is carried out based on a document knowledge base, a basic word data set and a basic word data set are obtained, and the two data sets are combined to form a corpus;
performing semantic assembly, context assembly and scene assembly on basic words and basic words in a corpus based on a rule base to obtain a health scene base and a service scene base, and summarizing the health scene base and the service scene base to obtain a neural network knowledge base;
the health prescription library is generated by the following steps:
carrying out logic association processing on the health events and the service events of the neural network knowledge base based on the rule base to obtain a health prescription base;
the logical association processing means: and associating the health event with the adapted service event, defining the health condition corresponding to a certain health event, and selecting the service operation corresponding to a certain service event.
An automated personal health assessment and management system for implementing the method described above, comprising:
the health monitoring module is used for obtaining health ground state monitoring data and specifically comprises the following steps:
A data continuous acquisition module for continuously acquiring personal health data,
a data selection and collection module for selectively collecting health index data associated with the first abnormal information,
the data key acquisition module is used for key acquisition and verification of uncomfortable symptom index data and disease index data associated with the second abnormal information;
the health portrait module is used for carrying out health assessment based on the results of health monitoring and data acquisition, and specifically comprises the following steps:
the integral evaluation module is used for inputting the results of health monitoring and data acquisition into the neural network knowledge base to obtain an integral evaluation drawing board, filling the integral evaluation drawing board according to personal conditions to obtain integral evaluation portrait data,
the special evaluation module is used for inputting the whole evaluation portrait data into a neural network knowledge base, obtaining a special evaluation drawing board, filling in the special evaluation drawing board according to the personal condition to obtain the special evaluation portrait data,
the element evaluation module is used for inputting the special evaluation portrait data into the neural network knowledge base, obtaining element evaluation drawing boards, filling the element evaluation drawing boards according to personal conditions to obtain element evaluation data,
the evaluation result module is used for gathering the overall evaluation portrait data, the special evaluation portrait data and the element evaluation data, inputting the overall evaluation portrait data, the special evaluation portrait data and the element evaluation data into the neural network knowledge base again, accurately identifying symptom related information and disease related information contained in the overall evaluation portrait data, and outputting an evaluation result report;
The health prescription module is used for performing service plan adaptation based on the result of health assessment, and specifically comprises the following steps:
the evaluation result analysis module is used for inputting the evaluation result report into the health prescription library, accurately identifying the solution adapted to the current uncomfortable symptom information and/or the disease condition information of the individual,
the service plan module is used for determining a service plan based on the solution, wherein the service plan comprises service start-stop time;
the execution and feedback module is used for implementing the service plan according to the service start-stop time based on the service plan adapting result and collecting feedback information;
the parameter adjustment module is used for performing parameter adjustment based on the result of feedback information, wherein the parameter adjustment comprises any one or part or all of the following steps:
adjusting the content of the health monitoring and data acquisition in the step 1,
adjusting the content of the health monitoring and data acquisition in the step 2 for participating in health assessment,
adjusting the content of the result of the health assessment participating in the service plan adaptation in step 3,
and (5) adjusting the service start-stop time in the step 4.
On the basis of the technical scheme, the method further comprises the following steps:
the stage evaluation module is used for inputting the current discomfort symptom information and/or disease condition information of the individual into the neural network knowledge base to acquire discomfort symptom severity evaluation data and/or disease development stage evaluation data when the solution matched with the current discomfort symptom information and/or disease condition information of the individual is not unique;
The weight setting module is used for setting weights for a plurality of solutions one by one based on the discomfort symptom severity evaluation data and/or the disease development stage evaluation data;
the service plan management module is used for further determining a service plan according to the weights of the solutions, and specifically comprises the following steps:
the system comprises a nursing diagnosis conclusion management module, a nursing expected target management module, a nursing measure specific content management module and a nursing evaluation method management module;
the specific content of the nursing measures comprises: care path information, task allocation information;
the care path information includes: the duration of the nursing service, the task detail before operation, the task detail in operation and the task detail after operation;
the task allocation information includes: server, service start-stop time, service result, variation remarks.
On the basis of the technical scheme, the method further comprises the following steps:
the neural network knowledge base generating and maintaining module is used for summarizing the health scene base and the service scene base to obtain the neural network knowledge base, and specifically comprises the following steps:
a document knowledge base generation module for crawling the diagnosis and treatment standard document or manually inputting the diagnosis and treatment standard document to form a document knowledge base,
A rule base generating module for extracting rules based on clustering to the diagnosis and treatment standard based on the document knowledge base to obtain a rule base,
the word segmentation processing module is used for carrying out word segmentation processing based on a document knowledge base to obtain a basic word data set and a basic word data set, combining the two data sets to form a corpus,
the assembly processing module is used for carrying out semantic assembly, context assembly and scene assembly on the basic words and the basic words in the corpus based on the rule base to obtain a health scene base and a service scene base;
the health scenario library is used for storing health event information,
the service scene library is used for storing service event information;
the health prescription library generation and maintenance module is used for carrying out logic association processing on the health events and service events of the neural network knowledge base based on the rule library to obtain a health prescription library, and specifically comprises the following steps:
a health event acquisition module for acquiring health event information,
a service event acquisition module for acquiring service event information,
and the association exhaustion module is used for associating the health event with the adapted service event, defining the health condition corresponding to a certain health event and selecting the service operation corresponding to a certain service event.
The automatic personal health assessment and management method and system have the following beneficial effects:
aiming at personal health conditions, automatically acquiring data by means of intelligent machine, automatically tracking, analyzing and judging, automatically customizing the body, generating a dynamic scale evaluation problem domain, and automatically adapting to a customized care path; the cost is reduced, the efficiency is improved, the individual health assessment requirement is met, the standardization and standardization of the service are realized, and the individual health assessment and service plan adaptation self-organization are realized.
Drawings
The invention has the following drawings:
the accompanying drawings are included to provide a better understanding of the invention, and are utilized to further illustrate the invention, but the embodiments in the drawings do not constitute any limitation to the invention, as other drawings may be obtained by those of ordinary skill in the art without undue effort from the following drawings. Wherein:
FIG. 1 is a flow chart of an automated personal health assessment and management method according to the present invention.
Figure 2 is a flow chart of the health monitoring and data collection of the present invention.
FIG. 3 is a flow chart of the health assessment of the present invention.
Fig. 4 is a flow chart of service plan adaptation according to the present invention.
Figure 5 is a non-exclusive flow chart of the adapted solution according to the invention.
FIG. 6 is a flow chart of the neural network knowledge base generation of the present invention.
FIG. 7 is a flow chart of the health prescription library generation of the present invention.
FIG. 8 is a block diagram of an automated personal health assessment and management system according to the present invention.
FIG. 9 is an architectural relationship between a health portrayal module, a health prescription module, a neural network knowledge base, and a health prescription base.
FIG. 10 is a schematic diagram of the architectural relationships between the health prescription module, the phase assessment module, the weight setting module, and the service plan management module.
FIG. 11 is a schematic diagram of a neural network knowledge base generation and maintenance module.
FIG. 12 is a schematic diagram of a health prescription library generation and maintenance module.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings. The detailed description, while indicating exemplary embodiments of the invention, includes various details of the embodiments of the invention for the purpose of illustration only, should be considered as exemplary. Accordingly, those skilled in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
As shown in fig. 1, the invention provides an automated personal health assessment and management method, comprising the following steps:
step 1, health monitoring and data acquisition;
as shown in fig. 2, the health monitoring and data acquisition specifically includes:
and (3) continuously collecting data: continuously collecting personal health data through portable health electronic equipment directly worn on the body;
data selection and collection: based on the first abnormal information, triggering data selection and collection, and selectively collecting health index data associated with the first abnormal information through intelligent household equipment and/or wearable equipment; the first anomaly information is from anomaly analysis of personal health data;
data are collected in a key way: triggering data key acquisition based on the second abnormal information, and key acquisition and verification of uncomfortable symptom index data and disease index data associated with the second abnormal information through personal self-description and/or medical diagnosis; the second anomaly information is from anomaly analysis of health index data;
obtaining personal health data, health index data, discomfort symptom index data and disease index data through the health monitoring and data acquisition, wherein the data are collectively called health ground state monitoring data;
As an alternative embodiment, the sensor built in the portable health electronic device may be, for example, a smart bracelet, a smart ring, a smart insole, an electronic sphygmomanometer, a digital thermometer, a respiratory accelerometer, and a blood glucose tester, etc., where personal health data obtained from a user's body is collected regularly and continuously during wearing or using the device, such as: body weight, body temperature, blood pressure, pulse, respiration, blood oxygen saturation, blood glucose, etc.;
as an alternative embodiment, the smart home device is also provided with a built-in sensor, and can collect health index data more accurately, for example, the smart home device can be an intelligent mattress, a smart mirror, a sleep monitoring radar, a thermal infrared imager, a medical grade remote electrocardiograph monitor, a pulse diagnosis instrument, a body position accelerometer, a body fat scale, a meridian leveling instrument, an intelligent health monitoring integrated machine, and the like, and collect health index data associated with the abnormality of personal health data in a short term or irregular manner based on the abnormality of the health index data, for selective use;
as one of the alternative embodiments, the individual self-describing through the remote online video terminal, under the supervision of professional personnel, carrying out targeted and guided self-describing expression, and storing audio-video data and touch screen interactive operation data, wherein the professional personnel can obtain and verify uncomfortable symptom index data and disease index data associated with the abnormality of the health index data according to the audio-video data and the touch screen interactive operation data;
As one of alternative embodiments, the medical diagnosis collects a photograph or a scan of a medical receipt issued by a hospital through an image collection terminal, performs OCR processing on the photograph or the scan, and mainly acquires and verifies discomfort symptom index data and disease index data associated with abnormality of health index data through a professional or a medical receipt interpretation engine;
as one of the alternative embodiments, the medical document interpretation engine may be implemented by a data processing model based on deep learning, which is not the focus of the present invention and will not be described in detail;
step 2, based on the results of health monitoring and data acquisition, performing health assessment;
as shown in fig. 3, the health evaluation based on the results of health monitoring and data acquisition specifically includes:
the method comprises the steps of obtaining results of health monitoring and data acquisition, wherein the results of health monitoring and data acquisition comprise any one or part or all of the following: personal health data, health index data, discomfort symptom index data, disease index data;
inputting the results of health monitoring and data acquisition into a neural network knowledge base to obtain an overall evaluation drawing board, and filling the overall evaluation drawing board according to personal conditions to obtain overall evaluation portrait data;
Inputting the whole evaluation portrait data into a neural network knowledge base to obtain a special evaluation drawing board, and filling the special evaluation drawing board according to personal conditions to obtain special evaluation portrait data;
inputting the special evaluation portrait data into a neural network knowledge base, obtaining element evaluation drawing boards, and filling the element evaluation drawing boards according to personal conditions to obtain element evaluation data;
after summarizing the overall evaluation portrait data, the special evaluation portrait data and the element evaluation data, inputting the overall evaluation portrait data, the special evaluation portrait data and the element evaluation data into a neural network knowledge base again to accurately identify symptom related information and disease related information contained in the overall evaluation portrait data, and outputting an evaluation result report, wherein the evaluation result report is divided into: reporting individual current discomfort symptom information and individual current disease condition information;
in order to avoid the problem caused by the fixed format of the evaluation scale, the method automatically generates a more targeted evaluation drawing board based on a neural network knowledge base according to the results of health monitoring and data acquisition, and replaces the conventional conventionally used evaluation scale with a dynamically generated multi-layer logically nested, personalized and variable evaluation drawing board;
as one of the alternative embodiments, the overall evaluation drawing board is used for evaluating the current life self-care ability, mental cognition ability and environmental positioning ability of the user;
As one of the alternative embodiments, the special evaluation drawing board is used for evaluating the current physiological life condition and health condition of the user, including dominant abnormal and recessive abnormal conditions of the respiratory system, the circulatory system, the nervous system, the motor system, the digestive system, the genitourinary system and the endocrine system;
as one of alternative embodiments, the element assessment palette is used to assess symptom-related information and disease-related information, health risks, and disease risks;
step 3, performing service plan adaptation based on the result of health evaluation;
as shown in fig. 4, the service plan adaptation based on the result of the health assessment specifically includes:
inputting the evaluation result report into a health prescription library, and accurately identifying a solution adapted to the current discomfort symptom information and/or disease condition information of the individual;
determining a service plan based on the solution, wherein the service plan comprises service start-stop time;
as one of the alternative embodiments, the solution is divided into: a solution for life care, life service and professional care of the current situation of the user to be assisted, a solution for health education, risk prevention and first aid treatment of the subsequent situation of the user to be assisted;
Step 4, implementing the service plan according to the service start-stop time based on the service plan adapting result, and collecting feedback information;
step 5, based on the result of the feedback information, performing parameter adjustment, wherein the parameter adjustment comprises any one or part or all of the following:
adjusting the content of the health monitoring and data acquisition in the step 1,
adjusting the content of the health monitoring and data acquisition in the step 2 for participating in health assessment,
adjusting the content of the result of the health assessment participating in the service plan adaptation in step 3,
adjusting the service start-stop time in the step 4;
and 6, updating the related data based on the result of parameter adjustment, and implementing the steps 1-4 according to the adjusted parameters when the steps 1-4 are executed next time.
On the basis of the above technical solution, as shown in fig. 5, when the solution adapted to the current discomfort symptom information and/or disease condition information of the individual is not unique, the method further comprises:
step 31, inputting the current discomfort symptom information and/or disease condition information of the individual into a neural network knowledge base to obtain discomfort symptom severity evaluation data and/or disease development stage evaluation data;
step 32, setting weights for a plurality of solutions one by one based on the discomfort symptom severity evaluation data and/or the disease development stage evaluation data;
Step 33, further determining a service plan based on the weights of the plurality of solutions.
As one of the alternative embodiments, the service plan includes: nursing diagnosis conclusion, nursing expected target, nursing measure concrete content and nursing evaluation method;
the specific content of the nursing measures comprises: care path information, task allocation information;
the care path information includes: the duration of the nursing service, the task detail before operation, the task detail in operation and the task detail after operation;
the task allocation information includes: server, service start-stop time, service result, variation remarks.
On the basis of the above technical solution, as shown in fig. 6, the neural network knowledge base is generated by the following steps:
the diagnosis and treatment standard documents are crawled through big data or manually input to form a document knowledge base;
performing clustering-based rule extraction on diagnosis and treatment standard based on a document knowledge base to obtain a rule base, wherein the rule base comprises an evaluation algorithm model and a service algorithm model;
word segmentation processing is carried out based on a document knowledge base, a basic word data set and a basic word data set are obtained, and the two data sets are combined to form a corpus; the basic words and the basic words form a first-level language material, namely 1-level corpus;
Performing semantic assembly, context assembly and scene assembly on basic words and basic words in a corpus based on a rule base to obtain a health scene base and a service scene base, and summarizing the health scene base and the service scene base to obtain a neural network knowledge base;
the semantic assembly refers to: combining at least two basic words or at least two basic word combinations or at least two basic words and basic word combinations in the corpus to obtain a second-level language material abbreviated as 2-level corpus with a semantic word having a specific meaning; for example: toothache, dizziness, fever, etc.;
the context assembly refers to: combining at least two semantic words to obtain a third-level language material, namely 3-level corpus, composed of context words with specific meanings only in specific environments; for example, bathing, heating for three days, dizziness, headache, somnolence, etc.;
the scene assembly refers to: combining at least two context words to obtain a fourth-level language material, namely 4-level corpus, composed of scene words of specific events occurring according to specific conditions in a specific environment; for example, the toilet can not be used by oneself, the bath is assisted by other people, the dressing is assisted by other people, the history of hypertension is 2 years, and the like;
the health scenario library is used for storing health event information,
The service scene library is used for storing service event information;
as shown in fig. 7, the health prescription library is generated by:
carrying out logic association processing on the health events and the service events of the neural network knowledge base based on the rule base to obtain a health prescription base;
the logical association processing means: and associating the health event with the adapted service event, defining the health condition corresponding to a certain health event, and selecting the service operation corresponding to a certain service event.
The invention further provides an automatic personal health assessment and management system for realizing the method, as shown in fig. 8 and 9, comprising the following steps:
the health monitoring module is used for obtaining health ground state monitoring data and specifically comprises the following steps:
a data continuous acquisition module for continuously acquiring personal health data,
a data selection and collection module for selectively collecting health index data associated with the first abnormal information,
the data key acquisition module is used for key acquisition and verification of uncomfortable symptom index data and disease index data associated with the second abnormal information;
the health portrait module is used for carrying out health assessment based on the results of health monitoring and data acquisition, and specifically comprises the following steps:
The integral evaluation module is used for inputting the results of health monitoring and data acquisition into the neural network knowledge base to obtain an integral evaluation drawing board, filling the integral evaluation drawing board according to personal conditions to obtain integral evaluation portrait data,
the special evaluation module is used for inputting the whole evaluation portrait data into a neural network knowledge base, obtaining a special evaluation drawing board, filling in the special evaluation drawing board according to the personal condition to obtain the special evaluation portrait data,
the element evaluation module is used for inputting the special evaluation portrait data into the neural network knowledge base, obtaining element evaluation drawing boards, filling the element evaluation drawing boards according to personal conditions to obtain element evaluation data,
the evaluation result module is used for gathering the overall evaluation portrait data, the special evaluation portrait data and the element evaluation data, inputting the overall evaluation portrait data, the special evaluation portrait data and the element evaluation data into the neural network knowledge base again, accurately identifying symptom related information and disease related information contained in the overall evaluation portrait data, and outputting an evaluation result report;
the health prescription module is used for performing service plan adaptation based on the result of health assessment, and specifically comprises the following steps:
the evaluation result analysis module is used for inputting the evaluation result report into the health prescription library, accurately identifying the solution adapted to the current uncomfortable symptom information and/or the disease condition information of the individual,
The service plan module is used for determining a service plan based on the solution, wherein the service plan comprises service start-stop time;
the execution and feedback module is used for implementing the service plan according to the service start-stop time based on the service plan adapting result and collecting feedback information;
the parameter adjustment module is used for performing parameter adjustment based on the result of feedback information, wherein the parameter adjustment comprises any one or part or all of the following steps:
adjusting the content of the health monitoring and data acquisition in the step 1,
adjusting the content of the health monitoring and data acquisition in the step 2 for participating in health assessment,
adjusting the content of the result of the health assessment participating in the service plan adaptation in step 3,
and (5) adjusting the service start-stop time in the step 4.
On the basis of the above technical solution, as shown in fig. 10, the method further includes:
the stage evaluation module is used for inputting the current discomfort symptom information and/or disease condition information of the individual into the neural network knowledge base to acquire discomfort symptom severity evaluation data and/or disease development stage evaluation data when the solution matched with the current discomfort symptom information and/or disease condition information of the individual is not unique;
the weight setting module is used for setting weights for a plurality of solutions one by one based on the discomfort symptom severity evaluation data and/or the disease development stage evaluation data;
The service plan management module is used for further determining a service plan according to the weights of the solutions, and specifically comprises the following steps:
the system comprises a nursing diagnosis conclusion management module, a nursing expected target management module, a nursing measure specific content management module and a nursing evaluation method management module;
the specific content of the nursing measures comprises: care path information, task allocation information;
the care path information includes: the duration of the nursing service, the task detail before operation, the task detail in operation and the task detail after operation;
the task allocation information includes: server, service start-stop time, service result, variation remarks.
On the basis of the above technical solution, as shown in fig. 11, the method further includes:
the neural network knowledge base generating and maintaining module is used for summarizing the health scene base and the service scene base to obtain the neural network knowledge base, and specifically comprises the following steps:
a document knowledge base generation module for crawling the diagnosis and treatment standard document or manually inputting the diagnosis and treatment standard document to form a document knowledge base,
a rule base generating module for extracting rules based on clustering to the diagnosis and treatment standard based on the document knowledge base to obtain a rule base,
The word segmentation processing module is used for carrying out word segmentation processing based on a document knowledge base to obtain a basic word data set and a basic word data set, combining the two data sets to form a corpus,
the assembly processing module is used for carrying out semantic assembly, context assembly and scene assembly on the basic words and the basic words in the corpus based on the rule base to obtain a health scene base and a service scene base;
the health scenario library is used for storing health event information,
the service scene library is used for storing service event information;
as shown in fig. 12, the health prescription library generating and maintaining module is configured to perform logic association processing on health events and service events of the neural network knowledge base based on a rule library, so as to obtain a health prescription library, and specifically includes:
a health event acquisition module for acquiring health event information,
a service event acquisition module for acquiring service event information,
and the association exhaustion module is used for associating the health event with the adapted service event, defining the health condition corresponding to a certain health event and selecting the service operation corresponding to a certain service event.
What is not described in detail in this specification is prior art known to those skilled in the art.
The above description is merely of the preferred embodiments of the present invention, the protection scope of the present invention is not limited to the above embodiments, but all equivalent modifications or variations according to the disclosure of the present invention should be included in the protection scope of the claims.
Claims (5)
1. An automated personal health assessment and management method, comprising:
step 1, health monitoring and data acquisition, specifically comprising:
and (3) continuously collecting data: continuously collecting personal health data through portable health electronic equipment directly worn on the body;
data selection and collection: based on the first abnormal information, triggering data selection and collection, and selectively collecting health index data associated with the first abnormal information through intelligent household equipment and/or wearable equipment; the first anomaly information is from anomaly analysis of personal health data;
data are collected in a key way: triggering data key acquisition based on the second abnormal information, and key acquisition and verification of uncomfortable symptom index data and disease index data associated with the second abnormal information through personal self-description and/or medical diagnosis; the second anomaly information is from anomaly analysis of health index data;
Step 2, based on the results of health monitoring and data acquisition, performing health assessment, specifically including:
the method comprises the steps of obtaining results of health monitoring and data acquisition, wherein the results of health monitoring and data acquisition comprise any one or part or all of the following: personal health data, health index data, discomfort symptom index data, disease index data;
inputting the results of health monitoring and data acquisition into a neural network knowledge base to obtain an overall evaluation drawing board, and filling the overall evaluation drawing board according to personal conditions to obtain overall evaluation portrait data;
inputting the whole evaluation portrait data into a neural network knowledge base to obtain a special evaluation drawing board, and filling the special evaluation drawing board according to personal conditions to obtain special evaluation portrait data;
inputting the special evaluation portrait data into a neural network knowledge base, obtaining element evaluation drawing boards, and filling the element evaluation drawing boards according to personal conditions to obtain element evaluation data;
after summarizing the overall evaluation portrait data, the special evaluation portrait data and the element evaluation data, inputting the overall evaluation portrait data, the special evaluation portrait data and the element evaluation data into a neural network knowledge base again to accurately identify symptom related information and disease related information contained in the overall evaluation portrait data, and outputting an evaluation result report, wherein the evaluation result report is divided into: reporting individual current discomfort symptom information and individual current disease condition information;
The neural network knowledge base is generated by the following steps:
the diagnosis and treatment standard documents are crawled through big data or manually input to form a document knowledge base;
performing clustering-based rule extraction on diagnosis and treatment standard based on a document knowledge base to obtain a rule base, wherein the rule base comprises an evaluation algorithm model and a service algorithm model;
word segmentation processing is carried out based on a document knowledge base, a basic word data set and a basic word data set are obtained, and the two data sets are combined to form a corpus;
performing semantic assembly, context assembly and scene assembly on basic words and basic words in a corpus based on a rule base to obtain a health scene base and a service scene base, and summarizing the health scene base and the service scene base to obtain a neural network knowledge base;
step 3, based on the result of health evaluation, performing service plan adaptation, specifically including:
inputting the evaluation result report into a health prescription library, and accurately identifying a solution adapted to the current discomfort symptom information and/or disease condition information of the individual;
determining a service plan based on the solution, wherein the service plan comprises service start-stop time;
the solution is divided into: a solution for life care, life service and professional care of the current situation of the user to be assisted, a solution for health education, risk prevention and first aid treatment of the subsequent situation of the user to be assisted;
The health prescription library is generated by the following steps:
carrying out logic association processing on the health events and the service events of the neural network knowledge base based on the rule base to obtain a health prescription base;
the logical association processing means: associating the health event with the adapted service event, defining the health condition corresponding to a certain health event, and selecting the service operation corresponding to a certain service event;
step 4, implementing the service plan according to the service start-stop time based on the service plan adapting result, and collecting feedback information;
step 5, based on the result of the feedback information, performing parameter adjustment, wherein the parameter adjustment comprises any one or part or all of the following:
adjusting the content of the health monitoring and data acquisition in the step 1,
adjusting the content of the health monitoring and data acquisition in the step 2 for participating in health assessment,
adjusting the content of the result of the health assessment participating in the service plan adaptation in step 3,
adjusting the service start-stop time in the step 4;
and 6, updating the related data based on the result of parameter adjustment, and implementing the steps 1-4 according to the adjusted parameters when the steps 1-4 are executed next time.
2. The automated personal health assessment and management method of claim 1, further comprising, when the solution to the current discomfort symptom information and/or disease condition information of the individual is not unique:
Step 31, inputting the current discomfort symptom information and/or disease condition information of the individual into a neural network knowledge base to obtain discomfort symptom severity evaluation data and/or disease development stage evaluation data;
step 32, setting weights for a plurality of solutions one by one based on the discomfort symptom severity evaluation data and/or the disease development stage evaluation data;
step 33, further determining a service plan based on the weights of the plurality of solutions.
3. The automated personal health assessment and management method of claim 2, wherein the service plan comprises: nursing diagnosis conclusion, nursing expected target, nursing measure concrete content and nursing evaluation method;
the specific content of the nursing measures comprises: care path information, task allocation information;
the care path information includes: the duration of the nursing service, the task detail before operation, the task detail in operation and the task detail after operation;
the task allocation information includes: server, service start-stop time, service result, variation remarks.
4. An automated personal health assessment and management system, comprising:
the health monitoring module is used for obtaining health ground state monitoring data and specifically comprises the following steps:
A data continuous acquisition module for continuously acquiring personal health data,
a data selection and collection module for selectively collecting health index data associated with the first abnormal information,
the data key acquisition module is used for key acquisition and verification of uncomfortable symptom index data and disease index data associated with the second abnormal information;
the health portrait module is used for carrying out health assessment based on the results of health monitoring and data acquisition, and specifically comprises the following steps:
the integral evaluation module is used for inputting the results of health monitoring and data acquisition into the neural network knowledge base to obtain an integral evaluation drawing board, filling the integral evaluation drawing board according to personal conditions to obtain integral evaluation portrait data,
the special evaluation module is used for inputting the whole evaluation portrait data into a neural network knowledge base, obtaining a special evaluation drawing board, filling in the special evaluation drawing board according to the personal condition to obtain the special evaluation portrait data,
the element evaluation module is used for inputting the special evaluation portrait data into the neural network knowledge base, obtaining element evaluation drawing boards, filling the element evaluation drawing boards according to personal conditions to obtain element evaluation data,
the evaluation result module is used for gathering the overall evaluation portrait data, the special evaluation portrait data and the element evaluation data, inputting the overall evaluation portrait data, the special evaluation portrait data and the element evaluation data into the neural network knowledge base again, accurately identifying symptom related information and disease related information contained in the overall evaluation portrait data, and outputting an evaluation result report;
The neural network knowledge base generating and maintaining module is used for summarizing the health scene base and the service scene base to obtain the neural network knowledge base, and specifically comprises the following steps:
a document knowledge base generation module for crawling the diagnosis and treatment standard document or manually inputting the diagnosis and treatment standard document to form a document knowledge base,
a rule base generating module for extracting rules based on clustering to the diagnosis and treatment standard based on the document knowledge base to obtain a rule base,
the word segmentation processing module is used for carrying out word segmentation processing based on a document knowledge base to obtain a basic word data set and a basic word data set, combining the two data sets to form a corpus,
the assembly processing module is used for carrying out semantic assembly, context assembly and scene assembly on the basic words and the basic words in the corpus based on the rule base to obtain a health scene base and a service scene base;
the health scenario library is used for storing health event information,
the service scene library is used for storing service event information;
the health prescription module is used for performing service plan adaptation based on the result of health assessment, and specifically comprises the following steps:
the evaluation result analysis module is used for inputting the evaluation result report into the health prescription library, accurately identifying the solution adapted to the current uncomfortable symptom information and/or the disease condition information of the individual,
The service plan module is used for determining a service plan based on the solution, wherein the service plan comprises service start-stop time;
the health prescription library generation and maintenance module is used for carrying out logic association processing on the health events and service events of the neural network knowledge base based on the rule library to obtain a health prescription library, and specifically comprises the following steps:
a health event acquisition module for acquiring health event information,
a service event acquisition module for acquiring service event information,
the association exhaustion module is used for associating the health event with the adapted service event, defining the health condition corresponding to a certain health event, and selecting the service operation corresponding to a certain service event;
the execution and feedback module is used for implementing the service plan according to the service start-stop time based on the service plan adapting result and collecting feedback information;
the parameter adjustment module is used for performing parameter adjustment based on the result of feedback information, wherein the parameter adjustment comprises any one or part or all of the following steps:
adjusting the content of the health monitoring and data acquisition in the step 1,
adjusting the content of the health monitoring and data acquisition in the step 2 for participating in health assessment,
Adjusting the content of the result of the health assessment participating in the service plan adaptation in step 3,
and (5) adjusting the service start-stop time in the step 4.
5. The automated personal health assessment and management system of claim 4, further comprising:
the stage evaluation module is used for inputting the current discomfort symptom information and/or disease condition information of the individual into the neural network knowledge base to acquire discomfort symptom severity evaluation data and/or disease development stage evaluation data when the solution matched with the current discomfort symptom information and/or disease condition information of the individual is not unique;
the weight setting module is used for setting weights for a plurality of solutions one by one based on the discomfort symptom severity evaluation data and/or the disease development stage evaluation data;
the service plan management module is used for further determining a service plan according to the weights of the solutions, and specifically comprises the following steps:
the system comprises a nursing diagnosis conclusion management module, a nursing expected target management module, a nursing measure specific content management module and a nursing evaluation method management module;
the specific content of the nursing measures comprises: care path information, task allocation information;
the care path information includes: the duration of the nursing service, the task detail before operation, the task detail in operation and the task detail after operation;
The task allocation information includes: server, service start-stop time, service result, variation remarks.
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