CN102799794A - Self-service evaluation system and evaluation method thereof of organism physiological statuses - Google Patents
Self-service evaluation system and evaluation method thereof of organism physiological statuses Download PDFInfo
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Abstract
The invention discloses a self-service evaluation system of organism physiological statuses, solving the problem that the discrimination of all-terrain uniform standard suspected cases cannot be realized in the prior art by aiming at the prevention and control of novel transmitted diseases. The self-service evaluation system comprises more than one user terminal device and a remote control center, wherein the user terminal devices comprise data processing and controlling units, first input units, first output units, first transmission units, expense settlement units, data detection units and user identification units, wherein the first input units, the first output units, the first transmission units, the expense settlement units, the data detection units and the user identification units are all connected with the data processing and controlling units, and the first transmission units are connected with the remote control center. The invention also provides an evaluation method of the system. According to the evaluation system and the evaluation method, an all-terrain uniform suspected case discrimination standard can be provided for 24 hours a day, and when an epidemic situation occurs, the quick screening of suspected cases can be realized so as to reduce the epidemic situation spread risk; and users can be guided to finish medical detection and evaluation by selves so as to know the body health conditions at any time and prevent and treat diseases, and the medical cost is reduced.
Description
Technical field
The present invention relates to a kind of towards the community in urban areas medical treatment & health field, be specifically related to self-service evaluating system of a kind of life entity physiological situation and appraisal procedure thereof.
Background technology
In the society of high-tech, informationization, market economy fast development; Because psychological pressure is overweight; Work rhythm is accelerated and irregular life style, brings increasing sub-health state, and it has influenced people's work efficient and quality of life; Also weakened immunity of human body itself, ability of regulation and control and, become the latency of various chronic diseases and difficult disease the adaptive faculty of physical environment and social environment.For disease preventing and treating effectively, build up health, at first, constantly strengthen people's self health consciousness.Hope to research and develop a kind of quick, real-time, reliable health and fitness information detector, as clock and watch give the correct time, can test the physiologic information of body at any time, in time correct the biased of physiological function, make disease early detection, early stage diagnosis and treatment, prevent and treat in suffering from; Simultaneously, can make a concrete analysis of the formation reason of sub-health state through information, and the characteristic of different disease types, seek effective medical treatment method, make drug therapy, health nutrient and keep fit method more rationally, effectively.
Sub-health state is in the rim condition between health and the disease, often the prelude of disease.Show that mainly psychology and physiological function go down or lack of proper care.Normal tired limb acid, vexed anxiety or the notice of occurring can not be concentrated, insomnia and dreamful sleep, and palpitaition is uncomfortable in chest, and failure of memory is easy to catch cold, and hidrosis is warmed, poor appetite, symptoms such as hypogona dism.And existing Medical Instruments and experimental index; Still fail to detect and make a definite diagnosis; Mainly lean on the questionnaire mode to analyze so far, or utilization extended pattern health check-up (the standard health check-up adds psychology, physical examinations), luxury health check-up method discharge diseases such as (go up item and add ultra color, the bone density of thyroxine, sex hormone, Hemorheology, heart, exercise stress, cardio-pulmonary function); Bring very big traumatic pain to patient like this, more increased their psychological burden and financial burden.
Yet for most disease, people only show in symptom just can remove examination in hospital when obvious, often because stall for time longly, causes having lost the optimal treatment stage, the patient can only be born for a long time be tortured with a disease, even can't treat.
And since existing medical resource relatively anxiety, the cost height of seeking medical advice, regional medical level differ greatly, much diseases can not realize preventing in advance and treating.Simultaneously present medical system to the prevention and control of novel transmissible disease, can't realize that the suspected case of full region unified standard is differentiated.
Summary of the invention
The object of the present invention is to provide self-service evaluating system of a kind of life entity physiological situation and appraisal procedure thereof, solve the prevention and control that are directed against novel transmissible disease in the prior art, can't realize the problem of the suspected case differentiation of full region unified standard; Having solved existing medical system simultaneously can't provide the problem of self-medicine service for the patient.
To achieve these goals, the technical scheme of the present invention's employing is following:
The self-service evaluating system of life entity physiological situation comprises more than one user terminals device and remote control center; This user terminal apparatus comprises data processing and control module, first input block that all links to each other with data processing and control module, first output unit, first transmission unit, disbursement and sattlement unit, Data Detection unit and user identification unit; Said first transmission unit links to each other with remote control center.
Further; Described remote control center is mainly by information processing and control module; The remote control center of second transmission unit that all links to each other with information processing and control module, second input block, second output unit, data operation server, administration authority judgement unit, said second transmission unit links to each other with first transmission unit.
Further, also be connected with first storage unit and wireless transmission unit on described data processing and the control module; Also be connected with second storage unit on described information processing and the control module and expense gathers the unit.
The determination methods of the self-service evaluating system of life entity physiological situation may further comprise the steps:
(a) whether judges is new user, is then to register; Not, then directly login, and identification userspersonal information P [s];
(b) select the type of assessment and judge whether the model of the type upgrades, not, then upgrade model parameter; Be then to carry out the formulation of test item;
(c) whether detecting instrument is normal, not, then sends the instrument damage alarm; Be then to carry out next step;
(d) adopt the disbursement and sattlement unit to carry out disbursement and sattlement;
(e) whether judges puts in place, not, then judges overtime situation; Be, then carry out physiological situation and detect, obtain physiological situation and detect data T [N];
(f) whether judgment data is errorless, not, then sends the error in data alarm, and returns step (e); Be then to carry out physiological status assessment or suspected case anticipation, and export the doubtful degrees of data Em of corresponding case, assessment result, the detailed description state of an illness, healthy information warning and the suggestion of seeking medical advice;
(g) preserve various data, show the assessment result and the suggestion of seeking medical advice;
(h) judge whether to carry out other assessments, be, then return step (b); Not, then do not finish this assessment, and will detect data, assessment result, information such as guides of seeking medical advice exports according to the way of output of user's selection, then various data transmission arrived remote control center;
(i) after remote control center is put the data that receive in order, the unified preservation.
Further, judge that overtime situation concrete steps are in the said step (e): judge whether overtimely, be, then carry out overtime resetting, return step (a); Not, then remind the user to put in place as early as possible, return step (e).
Again further, whether errorless concrete steps are to differentiate data in the said step (f):
(1) to physical detection data bag T [N]; According to checking algorithm check bit being carried out in valid data position in the packet that receives earlier calculates; Then with packet in the check bit data compare, whether the check bit that differentiate to calculate obtains consistent with the check bit numerical value in the packet, is; Then data transmission is errorless, gets into discriminating data step (2); Not, then data transmission is wrong, sends the error in data alarm, reads the instrument detecting data again;
(2) read each number of significant digit certificate among the physical detection data bag T [N]; It is user's each item physical detection data; Differentiating then and detect the maximum value limit and the minimum value limit whether data exceed respective items purpose human detection data in the packet, is that then Data Detection is wrong; Send the error in data alarm, return step (e) and carry out the error items Data Detection again; Not, then data are errorless, continue physiological status assessment or suspected case anticipation in the step (f).
Further, physiological status assessment or suspected case anticipation concrete steps are in the said step (f):
(1) accepts the m of system item assessment request;
(2) read m item assessment request user's physical detection data Tm [N] and personal information data Pm [S];
(3) carry out normalization and handle detecting data Tm [N], userspersonal information P [S] is carried out digitized processing, and be assessment models input data layout Bm [N+S] packing data;
(4) read the m item and assess corresponding assessment models LMBP (Bm [N+S]), assess, output assessment result Em;
(5) whether differentiating Em less than smallest evaluation threshold epsilon m, is then Em zero setting; , then do not carry out next step;
(6) for the assessment request of Em greater than ill discrimination threshold, output assessment result, and state of an illness description and the seek medical advice suggestion detailed according to the output of Em value size.
In addition, described life entity physiological status assessment and suspected case judgement model are a plurality of judgement models to different assessment demands; The different assessment models of differentiating type employing different structure are all set up an assessment models for every kind of typical disease, improve discrimination precision with this, reduce the model complexity, improve modelling and identification effect; To inferior health assessment demand, then choose a plurality of typical disease judgement models, assessment obtains a plurality of assessment results respectively, thereby analyzes the ill risk of sub-health status and various diseases.
The appraisal procedure of described life entity physiological status assessment and suspected case judgement model is that life condition detection data Tm [N], the userspersonal information Pm [S] with case is raw data; Earlier to data normalization and digitizing; Be packaged as assessment data Bm [N+S] then; Choose corresponding assessment models LM-BP (Bm [N+S]) at last and assess, the doubtful degree of output case Em; 0≤Em≤1 wherein, numerical value is big more, and the doubtful degree of expression case is high more, the state of an illness is heavy more; System is described and the suggestion of seeking medical advice according to the detailed state of an illness of Em numerical value output.
In user terminal apparatus, said data processing and control module are used for the information that physiological status is measured is handled, differentiates, classified; The Data Detection unit comprises life entity each item physiological status surveying instrument, is used to obtain each item key message of user's physiological status, as: blood pressure, blood sugar, blood fat, routine blood test, cardiogram, heart rate, eyesight, hearing, body weight, body temperature, measurements of the chest, waist and hips etc.; First input block is used for the user and carries out information input, monitoring video information input etc.; First output unit, demonstration, the data that are used for data are printed, data output copies etc., make things convenient for the user to understand own present physiological situation; First transmission unit, be used to receive the information transmission request after, by the control program requirement data are transmitted, convenient with information transmission to remote control center; User identification unit is used for different users's identity is discerned, and the different users is set different rights of using, distinguishes different users's physiological status metrical information and result; The disbursement and sattlement unit is used for user's cash and swipes the card the clearing of paying; First storage unit is used for the information of physiological status is distinguished storage; Wireless transmission unit is used for giving user's individual digital equipment with detecting information transmission such as data, assessment result, the suggestion of seeking medical advice.
In remote control center, said information processing and control module are used for data such as all life entity information is obtained, processing and display device are obtained detection data, physiological situation assessment result, the disconnected result of suspected case anticipation are stored, gathered; Second transmission unit is used for the various information transmission between remote control center and user terminal apparatus; Second input block is used for the supvr and carries out information input, monitoring video information input etc.; Second output unit, demonstration, the data that are used for data are printed, data output copies etc.; The data operation server is used for the disposal system data; The administration authority judgement unit is used for different system supvr identity is distinguished, and confirms administration authority; Second storage unit is used to store the physiology situation information after each user terminal is tested; Expense gathers the unit, is used for the statistics of business condition, understands the frequency of utilization of each user terminal apparatus.
Assessment of life entity physiological status and suspected case judgement Model Design mainly may further comprise the steps:
(1) obtain confirmed cases data sample in the past, each data sample all comprises the information of two aspects: (1a) each item physiologic information detects data, like body temperature, blood pressure, blood sugar, body weight, electrocardio, body fluid testing result etc.; Whether (1b) individual subscriber situation descriptor is like sex, age, occupation, history, whether smoking, often drunk etc.Wherein (1a) is as major parameter, (1b) as auxiliary parameter.
(2) sample data of collecting is handled.Earlier quantize data, so that in computing machine, discern.Physiologic information detects data all with international medical science standard unit record, and userspersonal information's situation is recorded as: (2a) sex: the male sex is designated as 1, and the women is designated as 0; (2b) age is by true age record; (2c) occupation is with Arabian mathematics 1～10 record; (2d) history: be designated as 1, nothing is designated as 0; (2e) whether smoking: be to be designated as 1, be not designated as 0; (2f) whether often drunk: as to be to be designated as 1, not to be designated as 0., import as an element of one group of data successively to these data vectorizations.Every group of data can be divided into 14 fields in the data file that obtains so, and the 1st field is the case numbering; The 2nd field represented confirmed result, and 0 is sub-health state, and other is preset corresponding typical disease numbering; The 3rd～8 field representes that each item physiologic information detects data; The 9th～14 expression personal information data.
(3) from sample database, choose typical data sample N, individual sample is as the network training sample to extract M (M < N) therein immediately, and remaining all kinds of (N-M) individual sample is as the testing authentication data.
(4), set up the assessment models of corresponding construction according to the case data characteristics.
(5) model training, (5a) netinit is handled; (5b) import M sample data successively the assessment models that is designed is trained, calculate each parameter of model; (5c) write down the number of samples m that had learnt.If m＜M then goes to step (2) and continues to calculate, if m=M then finishes training; (5d) according to weights correction formula correction model parameter, calculate output according to new model parameter, if model fails to meet the requirements of precision index or m＜M, then execution in step (5b) continues training, otherwise finishes training.
(6) assessment models testing authentication after assessment models is through training, with (N-M) group test sample book data fan-in network, just can obtain corresponding output.
(7) interpretation of result compares analysis through the output result of comparison model prediction and the result who originally made a definite diagnosis, and can obtain misdiagnosis rate, and differentiate misdiagnosis rate and whether meet the demands, be then to accomplish modeling; Not, then return step (4), revise model structure, rebulid model, again training according to the model structure correction formula.
The present invention compared with prior art has the following advantages and beneficial effect:
(1) the present invention is provided with remote control center, can remote update, interpolation, deletion, the required normal physiological situation of each terminal operate as normal of recombinating with reference to detect data, suspected case physiological situation with reference to detect data, case differentiate in each item index weight parameter; Can complex data computing service be provided for each end device simultaneously; Data such as user's physiological situation testing result of in operate as normal, obtaining for each terminal, physiological situation assessment result, suspected case testing result are stored, and make things convenient for the terminal to visit at any time; And can generate statistics according to the reported data at each terminal, to particular propagation property disease, can understand the suspected case distribution situation at any time;
(2) the present invention can provide full region unified suspected case discrimination standard, when epidemic situation takes place, can realize the suspected case rapid screening, reduces epidemic situation diffusion risk; And can instruct the user in time to carry out prevention from suffering from the diseases and treatment, reduce the cost of seeking medical advice;
(3) life entity physiological situation assessment of the present invention and suspected case decision method can be asked according to the user, and the physiological situation that carries out specific project detects, other health status of specific section is assessed, the suspected case anticipation of specified disease;
(4) the present invention has adopted a plurality of assessment models structures, can realize that each suspected case trains respectively, differentiates respectively, the problem that network complexity is high, the anticipation precision is low when having avoided multiple-case anticipation simultaneously;
(5) test function of the present invention is comprehensive, and test operation is convenient, and test event is more, but down loading updating;
(6) the present invention can differentiate the physiology situation information of the specific life entity of acquired sign, obtains life entity physiological status assessment result, and can carry out the suspected case anticipation of typical disease according to physiology situation information;
(7) the present invention can directly obtain the life entity physiological state information of measurement mechanism; The life entity physiological state information of also storing in the read/write memory medium; Also can accept the numerical information and the descriptive fuzzy physiological state information of spoken and written languages of the sign life entity physiological state information of user's input simultaneously; Can handle the descriptive fuzzy physiological state information of spoken and written languages, extract key message;
(8) the present invention can provide the self-medicine service in 24 hours, can instruct the self-service completion medical treatment of user to detect and the health status assessment, helped the user to understand self health status at any time, carried out prevention from suffering from the diseases and treatment, reduced the cost of seeking medical advice.
Description of drawings
Fig. 1 is the structured flowchart of user terminal apparatus among the present invention.
Fig. 2 is a structured flowchart of the present invention.
Fig. 3 is the process flow diagram of user terminal apparatus data processing among the present invention.
Fig. 4 is the process flow diagram of medium-long range of the present invention control center data processing.
Fig. 5 is assessment of life entity physiological situation and suspected case anticipation process flow diagram among the present invention.
Fig. 6 is assessment of life entity physiological situation and suspected case anticipation model modeling process flow diagram among the present invention.
Fig. 7 is neural network assessment models structural representation among the present invention.
Fig. 8 is the change curve of the natural logarithm value of neural network assessment models error among the present invention with frequency of training.
Embodiment
Below in conjunction with accompanying drawing and embodiment the present invention is described further, embodiment of the present invention includes but not limited to the following example.
Embodiment
Like Fig. 1, shown in 2, the self-service evaluating system of life entity physiological situation comprises more than one user terminals device and remote control center; This user terminal apparatus comprises data processing and control module, first input block that all links to each other with data processing and control module, first output unit, first transmission unit, Data Detection unit, first storage unit and user identification unit; Said first transmission unit links to each other with remote control center.
Above-mentioned data processing and control module are used for the information that physiological status is measured is handled, differentiates, classified; As: ARM, FPGA, DSP, ADC chip, DAC chip etc.
First input block is used for the user and carries out information input, monitoring video information input etc.; As: touch-screen, keyboard, mouse, camera, voice etc.
First output unit, demonstration, the data that are used for data are printed, data output copies etc.; As: display screen, sound equipment, data file, printer etc.
First transmission unit, be used to receive the information transmission request after, by the control program requirement data are transmitted; As: internet, wireless communication networks, fiber optic network etc.
The Data Detection unit comprises life entity each item physiological status surveying instrument, is used to obtain each item key message of user's physiological status, as: blood pressure, blood sugar, blood fat, routine blood test, cardiogram, heart rate, eyesight, hearing, body weight, body temperature, measurements of the chest, waist and hips etc.; Surveying instrument is like sphygmomanometer, blood glucose meter, cardiotach ometer, blood oxygen saturation detector, routine blood test detector, chair type weighing scale, clinical thermometer, height tester, electronic grip meter, bone density detector, body fluid detector etc.
User identification unit is used for different users's identity is discerned, and the different users is set different rights of using, distinguishes different users's physiological status metrical information and result; As: ID card information, user name and password, fingerprint recognition etc.
The disbursement and sattlement unit is used for user's cash and swipes the card the clearing of paying; Automatic machine, POS machine, mobile payment etc.
First storage unit is used for discrimination model parameter, user terminal working procedure, userspersonal information, user's physiological state information etc. are distinguished storage; As: PC hard disk, mobile memory etc.
Wireless transmission unit is used for giving user's individual digital equipment with detecting information transmission such as data, assessment result, the suggestion of seeking medical advice, as: wireless transmitter module.
Said remote control center comprises information processing and control module; Second transmission unit that all links to each other with information processing and control module, second input block, second output unit, data operation server, expense gather unit, administration authority judgement unit and second storage unit and form, and said second transmission unit links to each other with first transmission unit.
Information processing and control module; Be used for that the ruuning situation to each user terminal, various assessment models are set up and more new situation, running situation control and supervise, simultaneously data such as life entity information is obtained, processing and display device are obtained detection data, physiological situation assessment result, the disconnected result of suspected case anticipation are stored, are gathered; As: ARM, FPGA, DSP etc.
Second transmission unit is used for the various information transmission between remote control center and user terminal apparatus; As: internet, wireless communication networks, fiber optic network etc. and corresponding transmission server and switch.
Second input block is used for the supvr and carries out information input, monitoring video information input etc.; First-class like: touch-screen, keyboard, mouse, voice, monitoring camera.
Second output unit, demonstration, the data that are used for data are printed, data output copies etc.; As: display screen, sound equipment, audible and visual alarm, data file, printer etc.
The data operation server is used for the disposal system data; As: large server.
The administration authority judgement unit is used for different system supvr identity is distinguished, and confirms administration authority; As: ID card information, user name and password, fingerprint recognition etc.
Second storage unit is used to store the physiology situation information after each user terminal is tested, as: the large data memory set.
Expense gathers the unit, is used for the statistics of business condition, understands the frequency of utilization of each user terminal apparatus.
As shown in Figure 3, the determination methods of the self-service evaluating system of being made up of said apparatus of life entity physiological situation may further comprise the steps:
(a) through user identification unit the user is confirmed, if new user then need register login again through first input block; If the old user then can directly login, user identification unit Direct Recognition user's personal information P [s];
(b) user selects the type of own needs assessment through first input block, and confirms through data processing and control module whether the model of the type is up-to-date, if not, then upgrade model data; If up-to-date, then can directly formulate test item;
(c) user pays through the disbursement and sattlement unit;
(d) data processing and control module detect detecting instrument, if problem then can be sent the instrument damage alarm; If detecting instrument is normal, then carry out next step;
(e) whether data processing and control module judges put in place, if do not arrive the detecting instrument place, then judge overtime situation; If put in place, then detect corresponding physiological data and obtain physiological situation detection data T [N];
(f) data processing and control module judge thereupon whether the data of detection are errorless, if wrong, will send the error in data alarm, and return step (e); If errorless, then carry out physiological status assessment or suspected case anticipation, and draw suspected case physiological characteristic weights data α [N];
(g) first storage unit is preserved each data, and shows the assessment result and the suggestion of seeking medical advice through output unit;
(h) judge whether to carry out other assessments, be, then return step (b); Not, then finish this assessment, and various data transmission are arrived remote control center through first transmission unit;
(i) remote control center receives the data after each user terminal apparatus assessment through second transmission unit, and after data are put in order, unified second storage unit that is saved in.Can select according to the user at last, whether will detect data and assessment result is sent to the individual subscriber digital device through wireless transmission unit.
Judge that overtime situation concrete steps are in the said step (e): judge whether overtimely, be, then carry out overtime resetting, return step (a); Not, then remind the user to put in place as early as possible, return step (e).
And middle physiological status assessment of said step (f) or suspected case anticipation concrete steps are:
(1) accepts the m of system item assessment request;
(2) read m item assessment request user's physical detection data Tm [N] and personal information data Pm [S];
(3) carry out normalization and handle detecting data Tm [N], userspersonal information P [S] is carried out digitized processing, and be assessment models input data layout Bm [N+S] packing data;
(4) read the m item and assess corresponding assessment models LMBP (Bm [N+S]), assess, output assessment result Em;
(5) whether differentiating Em less than smallest evaluation threshold epsilon m, is then Em zero setting; , then do not carry out next step;
(6) for the assessment request of Em greater than ill discrimination threshold, output assessment result, and state of an illness description and the seek medical advice suggestion detailed according to the output of Em value size.
Flow process as shown in Figure 4, that remote control center is safeguarded user terminal apparatus, specific as follows:
(1) judging whether the keeper is new management person, is then to register; Not, then directly login and the authority differentiation;
(2) check whether the critical alarm notice is arranged, be, then carry out critical alarm and handle; , then do not carry out the selection of system maintenance type;
(3) judge whether the keeper has authority, not, then reselect the system maintenance type; Be then to carry out system status information and read;
(4) judging whether the maintenance item locks, is then to reselect the system maintenance type; Not, then send and safeguard notice, start and safeguard countdown to each user terminal apparatus;
(5) editor, submission maintenance content;
(6) judge to safeguard whether countdown finishes, not, then judge once more and safeguard whether countdown finishes; Be then to send and safeguarding notice to each user terminal apparatus;
(7) upload the updating maintenance content to each user terminal apparatus;
(8) judge whether each user terminal apparatus accomplishes renewal, not, then confirm whether to upgrade overtime; Be then updating maintenance recorded and stored service data;
(9) cancel each user terminal apparatus and safeguarding notice;
(10) confirming whether carry out other maintenances, is then to return step (2) and reselect the system maintenance type; , then do not finish this system maintenance.
Judging whether to upgrade overtime concrete steps in the said step (8) is: judge whether to upgrade overtime, be, then send and safeguard the failure alarm notification, and return step (5); , then do not return step (7).
Like Fig. 5, shown in 6, assessment of life physiological status and suspected case judgement model are a kind of based on the LM-BP neural network model in the present embodiment, and this Model Design step is:
(1) obtain confirmed cases data sample in the past, each data sample all comprises the information of two aspects: (1a) each item physiologic information detects data, like body temperature, blood pressure, blood sugar, body weight, electrocardio, body fluid testing result etc.; Whether (1b) individual subscriber situation descriptor is like sex, age, occupation, history, whether smoking, often drunk etc.Wherein (1a) is as major parameter, (1b) as auxiliary parameter.
(2) sample data of collecting is handled.Earlier quantize data, so that in computing machine, discern.Physiologic information detects data all with international medical science standard unit record, and userspersonal information's situation is recorded as: (2a) sex: the male sex is designated as 1, and the women is designated as 0; (2b) age is by true age record; (2c) occupation is with Arabian mathematics 1～10 record; (2d) history: be designated as 1, nothing is designated as 0; (2e) whether smoking: be to be designated as 1, be not designated as 0; (2f) whether often drunk: as to be to be designated as 1, not to be designated as 0., import as an element of one group of data successively to these data vectorizations.Every group of data can be divided into 14 fields in the data file that obtains so, and the 1st field is the case numbering; The 2nd field represented confirmed result, and 0 is sub-health state, and other is preset corresponding typical disease numbering; The 3rd～8 field representes that each item physiologic information detects data; The 9th～14 expression personal information data.
(3) from sample database, choose typical data sample N, individual sample is as the network training sample to extract M (M < N) therein immediately, and remaining all kinds of (N-M) individual sample is as the testing authentication data.
(4), set up the neural network of corresponding construction according to the case data characteristics.
(5) the neural metwork training step is that (5a) netinit is handled; (5b) import M sample data successively the neural network that is designed is trained, calculate each parameter of model; (5c) write down the number of samples m that had learnt.If m＜M then goes to step (2) and continues to calculate, if m=M then finishes training; (5d) according to weights correction formula correction model parameter, calculate output according to new model parameter, if model fails to meet the requirements of precision index or m＜M, then execution in step (5b) continues training, otherwise finishes training.
(6) neural network testing authentication after neural network is through training, with (N-M) group test sample book data fan-in network, just can obtain corresponding output.
(7) interpretation of result compares analysis through the output result of comparing cell prediction and the result who originally made a definite diagnosis, and can obtain misdiagnosis rate, and differentiate misdiagnosis rate and whether meet the demands, be then to accomplish modeling; Not, then return step (4), revise model structure, rebulid model, again training according to the model structure correction formula.
When life entity physiological status assessment and suspected case judgement model receive assessment request; At first confirm evaluation type, as: the m item, read the m item then and detect corresponding detection data Tm [N] and userspersonal information Pm [S]; And data are carried out normalization and digitized processing; With packing data is assessment data Bm [N+S], chooses the m item then and assesses corresponding neural network model LM-BP (Bm [N+S]) and assess, the doubtful degree of output case Em; 0≤Em≤1 wherein, numerical value is big more, and the doubtful degree of expression case is high more, the state of an illness is heavy more; System can describe and the suggestion of seeking medical advice according to the detailed state of an illness of Em numerical value output.
In the present embodiment, choose 100 cases, comprising coronary heart disease confirmed cases, rheumatic heart disease confirmed cases, hypertension confirmed cases, healthy personnel's (three kinds of personnel that disease does not all have) health check-up data as data sample.Information in the case data comprises length of smoking, day drinking amount, hypertension history, the ill duration of rheumatism (year), blood pressure (high pressure), blood pressure (low pressure), heart rate, WBC, RBC, palpitaition duration uncomfortable in chest (year), whether pectoralgia perspiration, whether oedema or edema, these 13 information of headache and dizzy whether, also comprise coronary heart disease whether, whether hypertension, rheumatic heart disease makes a definite diagnosis the result.
Adopt the BP neural network of three-decker in the present embodiment; Promptly comprise input layer, one latent layer, output layer; Confirm that according to data sample the input layer number is 13, output layer node number is 3, confirm that according to training result the number of hidden nodes is 13, the network structure signal is as shown in Figure 7.The natural logarithm value of model error value is as shown in Figure 8 with the change curve of frequency of training.
60 case data samples of picked at random are to the model training from 100 case data, 60 case data make a definite diagnosis result such as table 1 (wherein 1 expression is ill, and 0 expression is not ill):
Table 1
| The case sequence number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
| Coronary heart disease | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Rheumatic heart disease | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Hypertension | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 |
| The case sequence number | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 |
| Coronary heart disease | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Rheumatic heart disease | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Hypertension | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| The case sequence number | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 |
| Coronary heart disease | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Rheumatic heart disease | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| Hypertension | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 |
| The case sequence number | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 |
| Coronary heart disease | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Rheumatic heart disease | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Hypertension | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
After the neural network model completion with the training of the case data in the table 1, disease is carried out network measuring with this model to above-mentioned 60 cases once more, the result of determination such as the table 2 of last network:
Table 2
| The case sequence number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Coronary heart disease | 0.999 | 1.003 | 1.005 | 1.005 | 1.002 | 0.998 | 1.000 | 0.994 | 1.000 | 1.000 |
| Rheumatic heart disease | 0.002 | -0.001 | 0.003 | -0.008 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 | 0.007 |
| Hypertension | 0.999 | -0.004 | 0.009 | 0.003 | 1.000 | 1.000 | 0.004 | 0.006 | 0.999 | -0.001 |
| The case sequence number | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 |
| Coronary heart disease | 1.000 | 1.003 | 0.998 | 1.001 | 1.000 | 0.999 | 1.013 | 0.988 | 1.007 | 1.000 |
| Rheumatic heart disease | -0.006 | -0.001 | 0.000 | -0.001 | 0.000 | -0.003 | 0.000 | -0.002 | 0.009 | 0.000 |
| Hypertension | 0.003 | 0.999 | 1.001 | 1.000 | 0.999 | 1.000 | -0.015 | 0.015 | 1.000 | 1.000 |
| The case sequence number | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 |
| Coronary heart disease | 1.003 | 1.000 | 0.996 | 1.005 | 0.993 | 0.999 | 1.001 | 0.993 | 0.996 | 0.999 |
| Rheumatic heart disease | 0.003 | 0.004 | 0.005 | -0.006 | -0.001 | 0.004 | -0.001 | -0.008 | -0.008 | 0.003 |
| Hypertension | -0.002 | 0.002 | -0.006 | 0.007 | -0.014 | -0.007 | 0.000 | 1.002 | 0.000 | 0.003 |
| The case sequence number | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 |
| Coronary heart disease | 0.000 | -0.004 | -0.002 | 0.000 | 0.002 | -0.002 | -0.001 | 0.001 | -0.002 | 0.006 |
| Rheumatic heart disease | 0.994 | 0.995 | 1.000 | 1.004 | 1.000 | 1.005 | 1.005 | 1.005 | 0.994 | 0.997 |
| Hypertension | -0.006 | 0.000 | -0.002 | -0.006 | 0.003 | 0.007 | 0.007 | 0.002 | -0.006 | 0.000 |
| The case sequence number | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 |
| Coronary heart disease | 0.007 | -0.001 | 0.002 | -0.009 | -0.004 | -0.007 | 0.012 | 0.002 | 0.000 | 0.000 |
| Rheumatic heart disease | 0.004 | 0.003 | 0.001 | 0.000 | -0.008 | -0.006 | 0.008 | 0.000 | -0.003 | 0.003 |
| Hypertension | 0.998 | 1.004 | 1.000 | 0.997 | 0.997 | 1.001 | 0.995 | 1.005 | 1.003 | 1.001 |
| The case sequence number | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 |
| Coronary heart disease | -0.001 | 0.001 | -0.005 | 0.002 | 0.003 | 0.012 | 0.000 | -0.006 | -0.006 | 0.000 |
| Rheumatic heart disease | -0.004 | 0.002 | -0.004 | -0.005 | 0.002 | 0.004 | 0.000 | 0.005 | 0.003 | -0.003 |
| Hypertension | -0.002 | 0.004 | 0.000 | -0.006 | -0.003 | -0.006 | -0.002 | 0.009 | 0.006 | -0.001 |
Contrast case confirmed result and model prediction result; Can see that the output numerical error less than ± 1%, promptly regards as illly fully greater than 0.8 according to numerical value, numerical value promptly is judged as not ill fully less than 0.2; It is 100% that this network model detects accuracy to 60 cases, and modelling is accomplished.
Adopt remaining 40 case data that the model prediction accuracy is verified then.40 checking case data make a definite diagnosis result such as table 3 (wherein 1 expression is ill, and 0 expression is not ill):
Table 3
| The |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| |
1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| |
1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| The case sequence number | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 |
| |
1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
| |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| |
1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| The case sequence number | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 |
| |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| |
1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 |
| |
0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 |
| The case sequence number | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 |
| |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| |
1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Through prototype network above-mentioned 40 cases are detected result of determination such as table 4:
Table 4
| The |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Coronary heart disease | 0.999 | 1.010 | 1.047 | 0.991 | 1.023 | 0.970 | 0.998 | 1.003 | 1.009 | 0.999 |
| Rheumatic heart disease | 0.002 | -0.011 | -0.040 | 0.016 | -0.019 | 0.023 | 0.001 | -0.007 | 0.009 | -0.001 |
| Hypertension | 0.871 | 0.173 | 0.080 | 0.032 | 1.046 | -0.029 | -0.031 | 0.988 | 0.001 | 0.000 |
| The case sequence number | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 |
| Coronary heart disease | 1.027 | 0.917 | 1.019 | 1.214 | 0.959 | 1.008 | 1.045 | 1.024 | 1.025 | 0.102 |
| Rheumatic heart disease | -0.027 | 0.099 | -0.014 | -0.174 | 0.049 | -0.003 | -0.037 | -0.018 | -0.019 | 0.885 |
| Hypertension | 0.755 | -0.096 | 0.601 | 0.452 | -0.013 | -0.011 | 0.060 | 0.157 | 0.001 | 0.159 |
| The case sequence number | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 |
| Coronary heart disease | -0.004 | 0.021 | 0.015 | 0.005 | 0.011 | -0.032 | -0.003 | -0.007 | 0.000 | -0.002 |
| Rheumatic heart disease | 1.001 | 0.988 | 0.989 | 0.995 | 0.977 | 1.026 | -0.007 | -0.004 | 0.008 | 0.012 |
| Hypertension | -0.002 | -0.035 | -0.010 | 0.023 | 0.107 | 0.042 | 0.992 | 0.998 | 0.994 | 0.958 |
| The case sequence number | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 |
| Coronary heart disease | 0.002 | 0.006 | 0.168 | 0.063 | 0.003 | -0.005 | -0.004 | 0.002 | 0.113 | 0.005 |
| Rheumatic heart disease | -0.007 | -0.005 | 0.181 | -0.006 | 0.002 | -0.006 | -0.002 | -0.004 | 0.022 | 0.007 |
| Hypertension | 1.004 | 1.010 | 0.676 | 0.010 | 0.003 | 0.000 | -0.003 | 0.004 | 0.032 | 0.015 |
Contrast 40 case confirmed results and model result of determination, promptly think ill fully according to numerical value greater than 0.8, numerical value promptly is judged as not ill fully less than 0.2, and 3 case result of determination mistakes are then arranged, and promptly accuracy rate is 92.5%.Basically satisfy disease anticipation accuracy requirement.
Less because of the present embodiment modeling sample, model anticipation accuracy is on the low side relatively, and under the abundant prerequisite of sample, accuracy can further improve.
After checking was accomplished again, just can this model parameter be transferred to user terminal apparatus, user terminal apparatus obtained a certain user's personal information and physiological detection information such as table 5:
Table 5
The life entity state estimation is-0.048,1.007,0.0862 to coronary heart disease, rheumatic heart disease, hypertensive assessment output numerical value in the user terminal apparatus; According to decision rule; Numerical value promptly thinks ill fully greater than 0.8; Numerical value promptly is judged as not ill fully less than 0.2, then can obtain result of determination is 0,1,0, and promptly this user suffers from rheumatic heart disease.User terminal apparatus just can be differentiated the result according to this and instruct and life guide for the user recommends to seek medical advice; And data, information is sent to remote control center and stores; Select according to the user simultaneously; Whether will detect data and assessment result is sent to the individual subscriber digital device through wireless transmission unit, assessment finishes.
According to the foregoing description, just can realize the present invention well.
Claims (10)
1. the self-service evaluating system of life entity physiological situation is characterized in that, comprises more than one user terminals device and remote control center; This user terminal apparatus comprises data processing and control module, first input block that all links to each other with data processing and control module, first output unit, first transmission unit, disbursement and sattlement unit, Data Detection unit and user identification unit; Said first transmission unit links to each other with remote control center.
2. the self-service evaluating system of life entity physiological situation according to claim 1; It is characterized in that; Described remote control center is mainly by information processing and control module; Second transmission unit that all links to each other with information processing and control module, second input block, second output unit, data operation server, administration authority judgement unit are formed, and said second transmission unit links to each other with first transmission unit.
3. the self-service evaluating system of life entity physiological situation according to claim 2 is characterized in that, also is connected with first storage unit and wireless transmission unit on described data processing and the control module.
4. the self-service evaluating system of life entity physiological situation according to claim 3 is characterized in that, also is connected with second storage unit on described information processing and the control module and expense gathers the unit.
5. by the appraisal procedure of the self-service evaluating system of each described life entity physiological situation of claim 1～4, it is characterized in that, may further comprise the steps:
(a) whether judges is new user, is then to register; Not, then directly login, and identification userspersonal information P [s];
(b) select the type of assessment and judge whether the model of the type upgrades, not, then upgrade model parameter; Be then to carry out the formulation of test item;
(c) whether detecting instrument is normal, not, then sends the instrument damage alarm; Be then to carry out next step;
(d) adopt the disbursement and sattlement unit to carry out disbursement and sattlement;
(e) whether judges puts in place, not, then judges overtime situation; Be, then carry out physiological situation and detect, obtain physiological situation and detect data T [N];
(f) whether judgment data is errorless, not, then sends the error in data alarm, and returns step (e); Be then to carry out physiological status assessment or suspected case anticipation, and export the doubtful degrees of data Em of corresponding case, assessment result, the detailed description state of an illness, healthy information warning and the suggestion of seeking medical advice;
(g) preserve various data, show the assessment result and the suggestion of seeking medical advice;
(h) judge whether to carry out other assessments, be, then return step (b); Not, then do not finish this assessment, and will detect data, assessment result, information such as guides of seeking medical advice exports according to the way of output of user's selection, then various data transmission arrived remote control center;
(i) after remote control center is put the data that receive in order, the unified preservation.
6. the appraisal procedure of the self-service evaluating system of life entity physiological situation according to claim 5 is characterized in that, judges that overtime situation concrete steps are in the said step (e): judge whether overtimely, be, then carry out overtime resetting, return step (a); Not, then remind the user to put in place as early as possible, return step (e).
7. according to the appraisal procedure of the self-service evaluating system of the said life entity physiological situation of claim 6, it is characterized in that whether errorless concrete steps are to differentiate data in the said step (f):
(1) to physical detection data bag T [N]; According to checking algorithm check bit being carried out in valid data position in the packet that receives earlier calculates; Then with packet in the check bit data compare, whether the check bit that differentiate to calculate obtains consistent with the check bit numerical value in the packet, is; Then data transmission is errorless, gets into discriminating data step (2); Not, then data transmission is wrong, sends the error in data alarm, reads the instrument detecting data again;
(2) read each number of significant digit certificate among the physical detection data bag T [N]; It is user's each item physical detection data; Differentiating then and detect the maximum value limit and the minimum value limit whether data exceed respective items purpose human detection data in the packet, is that then Data Detection is wrong; Send the error in data alarm, return step (e) and carry out the error items Data Detection again; Not, then data are errorless, continue physiological status assessment or suspected case anticipation in the step (f).
8. the appraisal procedure of the self-service evaluating system of life entity physiological situation according to claim 7 is characterized in that, physiological status assessment or suspected case anticipation concrete steps are in the said step (f):
(1) accepts the m of system item assessment request;
(2) read m item assessment request user's physical detection data Tm [N] and personal information data Pm [S];
(3) carry out normalization and handle detecting data Tm [N], userspersonal information P [S] is carried out digitized processing, and be assessment models input data layout Bm [N+S] packing data;
(4) read the m item and assess corresponding assessment models LMBP (Bm [N+S]), assess, output assessment result Em;
(5) whether differentiating Em less than smallest evaluation threshold epsilon m, is then Em zero setting; , then do not carry out next step;
(6) for the assessment request of Em greater than ill discrimination threshold, output assessment result, and state of an illness description and the seek medical advice suggestion detailed according to the output of Em value size.
9. the appraisal procedure of the self-service evaluating system of life entity physiological situation according to claim 8 is characterized in that, described life entity physiological status assessment and suspected case judgement model are a plurality of judgement models to different assessment demands.
10. the appraisal procedure of the self-service evaluating system of life entity physiological situation according to claim 9; It is characterized in that; The appraisal procedure of described life entity physiological status assessment and suspected case judgement model is that life condition detection data Tm [N], the userspersonal information Pm [S] with case is raw data, earlier to data normalization and digitizing, is packaged as assessment data Bm [N+S] then; Choose corresponding assessment models LM-BP (Bm [N+S]) at last and assess, the doubtful degree of output case Em; System is described and the suggestion of seeking medical advice according to the detailed state of an illness of Em numerical value output.
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