CN103690240A - Medical system - Google Patents
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Abstract
The invention provides a medical system. The medical system comprises a family doctor module in handheld equipment, an electronic health record system, a doctor terminal and a medical diagnosis auxiliary system; the medical diagnosis auxiliary system is used for communicating with the electronic health record system and the family doctor module in the handheld equipment; according to life characteristic information of a patient, which is received by the family doctor module in the handheld equipment, and/or communication information of the doctor terminal, information of the patient or hospital electronic medical record information is acquired from the electronic health record system; according to the life characteristic information of the patient, the communication information of the doctor terminal and the patient information or according to the life characteristic information of the patient, the communication information of the doctor terminal and the hospital electronic medical record information, data mining is carried out. Therefore, the medical system can provide an auxiliary diagnosis result and help a family doctor to make a decision when the family doctor carries out condition diagnosis and consultation on the patient.
Description
Technical field
The application relates to medical field, particularly relates to a kind of medical system.
Background technology
Along with the raising of people's living standard and quality of life, people are also more and more higher to healthy requirement.In the past, people are in order to see a doctor,, or in order to carry out medical advice, no matter state of an illness size Dou Yaodao hospital goes to stand in a long queue, register, look for doctor to see a doctor or seek advice from, both time-consuming, take again energy, particularly, for some old peoples and handicapped patient, increased especially a lot of troubles.So people urgently wish to be stayed at home and just can be obtained medical services quick, timely, accurate, high-quality by effective method.
At present, take family as unit, the new medical model that the family doctor of take is main fusion disease control and creative management more and more obtains people and payes attention to, and under this pattern, family doctor can have office hours or regular follow-up patient.But, family doctor's work at present mainly be take manually as main, in diagnosis and treatment process, family doctor is by inquiry patient name, address, medical history etc., manual recording-related information, and provide diagnostic result according to patient's dictation and the personal experience of oneself, can find, under this pattern, family doctor can not comprehensively grasp patient information, for patient's seek medical advice experience and state of an illness information in the past, can not accomplish comprehensive grasp, thereby can not take more effectively for patient's the state of an illness, diagnosis and treatment means more accurately.
Summary of the invention
The application provides a kind of medical system, to solve the result that provides assistance in diagnosis when family doctor carries out state of an illness Diagnosis and consultation for the object of seeking medical advice, helps the problem of family doctor's decision-making.
In order to address the above problem, the application discloses a kind of medical system, comprising:
Family doctor's module in handheld device, for receiving patient's vital sign information;
Electronic health care archives economy, for providing patient information and Hospital Electronic Medical Record information;
Doctor's terminal, for the family doctor's module communication with handheld device, provides family doctor and family doctor assistant, family doctor and support staff, family doctor and expert's communicate information;
Medical diagnosis aid system, communicates for the family doctor's module with electronic health care archives economy and handheld device; Patient's the vital signs information and/or the communicate information of doctor's terminal that according to the family doctor's module in handheld device, receive, obtain patient information or Hospital Electronic Medical Record information from electronic health care archives economy; According to communicate information and the patient information of patient's vital sign information, doctor's terminal, or according to the communicate information of patient's vital sign information, doctor's terminal and Hospital Electronic Medical Record information, carry out data mining, and the medical diagnosis secondary outcome that described data mining is obtained sends the family doctor's module in handheld device to.
Preferably, described medical system, also comprises:
Database server, for communicate information and the Hospital Electronic Medical Record information of the vital sign information of store patient, patient information, doctor's terminal;
Hospital's wireless telecommunications system, for providing doctor's terminal and family doctor's module of handheld device, family doctor's module in handheld device to communicate by letter with electronic health care archives economy with medical diagnosis aid system, medical diagnosis aid system;
Media server, for storing the Voice & Video of family doctor's module communication of doctor's terminal and handheld device;
Center management server, for provide database server or media server respectively with the communicating by letter of family doctor's module of handheld device, electronic health care archives economy, doctor's terminal;
Front interface server, for storing the interface data of doctor's terminal.
Preferably, described hospital wireless telecommunications system comprises health private network, 3G/4G wireless network, hospital services center WIFI LAN and the WIFI of community medical service center LAN.
Preferably, described medical diagnosis aid system comprises base module and intelligent auxiliary engine module.
Preferably, described base module comprises:
Business is understood submodule, for defining the target of medical item and medical demand;
Gather information submodule, for collecting the communicate information of the patient's of described demand vital sign information, patient information, Hospital Electronic Medical Record information and doctor's terminal;
Association knowledge submodule, for being undertaken associated by the communicate information of the patient's of the described demand of described gather information submodule vital sign information, patient information, Hospital Electronic Medical Record information and doctor's terminal;
Analysis and prediction knowledge submodule, for analyzing the patient information of described association knowledge submodule association, the communicate information of doctor's terminal, Hospital Electronic Medical Record information and patient's vital sign information is inferred associated trend.
Preferably, described intelligent auxiliary engine module comprises:
Core algorithm submodule, for core algorithm is provided, comprises association rule algorithm, decision tree sorting algorithm, clustering analysis algorithm, artificial neural network algorithm and genetic algorithm;
Intelligent selection submodule, for extracting the data of base module, selects suitable core algorithm according to the feature of described data, the data that output core algorithm calculates;
Input and output submodule, for sending the data of base module to intelligent selection submodule;
Center-control submodule, for the communication and control between core algorithm submodule, intelligent selection submodule, input and output submodule;
Meta knowledge base, for storing the core algorithm of core algorithm submodule and the data that intelligent selection submodule core algorithm calculates.
Preferably, described doctor's terminal comprises computer and mobile terminal.
Compared with prior art, the application comprises following advantage:
The application proposes a kind of medical system, management mainly for overall flow and the medical diagnosis aid system of medical system, " from receiving patient's vital sign information and the communicate information of doctor's terminal, to medical diagnosis aid system, provide the whole process of medical diagnosis secondary outcome ", realize and provide patient's vital sign information, a series of functions such as the communicate information of doctor's terminal, the access of electronic health care archives economy.
Medical diagnosis aid system in the application recalls patient's patient information and/or Hospital Electronic Medical Record information in the past according to the communicate information of patient's vital sign information and/or doctor's terminal from electronic health care archives economy, according to patient's vital signs information, the communicate information of doctor's terminal and patient information, or according to patient's vital signs information, the communicate information of doctor's terminal and Hospital Electronic Medical Record information, in knowledge base and intelligent auxiliary engine, carry out data mining, generate medical diagnosis secondary outcome, auxiliary family doctor diagnoses conditions of patients, thereby help family doctor to take effectively for patient's the state of an illness, diagnosis and treatment accurately.
Accompanying drawing explanation
Fig. 1 is the structure chart of a kind of medical system described in the embodiment of the present application;
Fig. 2 is the structure chart of a kind of medical system described in another embodiment of the application;
Fig. 3 is the structure chart of medical diagnosis aid system described in the embodiment of the present application;
Fig. 4 is medical diagnosis aid system display interface described in the embodiment of the present application.
The specific embodiment
For the application's above-mentioned purpose, feature and advantage can be become apparent more, below in conjunction with the drawings and specific embodiments, the application is described in further detail.
The application proposes a kind of medical system, the described medical system mainly family doctor's module in handheld device, doctor's terminal and electronic health care archives economy forms, its core content be medical diagnosis aid system respectively with electronic health care archives economy, handheld device in family doctor's module communicate, patient's the vital signs information and/or the communicate information of doctor's terminal that according to the family doctor's module in handheld device, receive, obtain patient information or Hospital Electronic Medical Record information from electronic health care archives economy; According to communicate information and the patient information of patient's vital sign information, doctor's terminal, or carry out data mining according to the communicate information of patient's vital sign information, doctor's terminal and Hospital Electronic Medical Record information.
With reference to Fig. 1, show the structure chart of a kind of medical system described in the embodiment of the present application, specifically comprise:
Family doctor's module 14 in handheld device, for receiving patient's vital sign information;
Handheld device can comprise physical sign monitor, sphygomanometer, blood glucose meter, panel computer etc.
Described patient's vital sign information can comprise patient's heart rate, patient's pulse, patient's pupil, patient's body temperature, patient's breathing and patient's blood pressure etc.
Electronic health care archives economy 18, for providing patient information and Hospital Electronic Medical Record information;
Electronic health care archives economy (Electronic Health Record is called for short HER), for patient information is provided, comprises the information such as patient's essential information, medical history, diagnosis records and doctor's advice; Described patient's essential information is as name, sex, age, address etc.
Electronic health care archives economy (Electronic Health Record is called for short HER), for Hospital Electronic Medical Record information is provided, comprises patient's essential information, medical history, doctor's advice, progress note, inspection assay, operation record and nurses' notes etc.
Described progress note refers to after RAN, the seriality record that conditions of patients and diagnosis and treatment process are carried out.Content comprises that make the rounds of the wards suggestion, consultation of doctors suggestion, doctor of patient's change of illness state situation, important auxiliary examination result and clinical meaning, higher level doctor analyze the material particular suggestion, the diagnosis and treatment measure of taking and effect, doctor's advice change and reason being discussed, being informed to patient and relatives thereof etc.
Doctor's terminal 12, for the family doctor's module communication with handheld device, provides family doctor and family doctor assistant, family doctor and support staff, family doctor and expert's communicate information;
Described expert comprises the expert of regional center hospital and the expert of front three hospital.
Doctor's terminal can comprise computer and mobile terminal, and described mobile terminal can comprise mobile phone, panel computer etc.
Described doctor's terminal is mainly used by family doctor assistant, support staff and expert; Family doctor's module in described handheld device is mainly used by family doctor, and wherein, family doctor also can be called general practitioner.
Medical diagnosis aid system 16, communicates for the family doctor's module with electronic health care archives economy and handheld device; Patient's the vital signs information and/or the communicate information of doctor's terminal that according to the family doctor's module in handheld device, receive, obtain patient information or Hospital Electronic Medical Record information from electronic health care archives economy; According to communicate information and the patient information of patient's vital sign information, doctor's terminal, or according to the communicate information of patient's vital sign information, doctor's terminal and Hospital Electronic Medical Record information, carry out data mining, and the medical diagnosis secondary outcome that described data mining is obtained sends the family doctor's module in handheld device to.
Described doctor's terminal is for the family doctor's module communication with handheld device, obtain family doctor and family doctor assistant, family doctor and support staff, family doctor and expert's communicate information, send the communicate information of described doctor's terminal to family doctor's module in handheld device, the communicate information of described doctor's terminal is mainly comprised of Voice & Video.
In patient's the vital signs information that described family doctor's module according in handheld device receives and/or the communicate information of doctor's terminal and/or can comprise following several situation:
The first, the patient's who receives according to the family doctor's module in handheld device vital signs information, from electronic health care system acquisition patient information or Hospital Electronic Medical Record information.
The second, according to the communicate information of doctor's terminal of the family doctor's module in handheld device, from electronic health care system acquisition patient information or Hospital Electronic Medical Record information.
The 3rd, patient's the vital signs information and the communicate information of doctor's terminal that according to the family doctor's module in handheld device, receive, from electronic health care system acquisition patient information or Hospital Electronic Medical Record information.
Describedly from electronic health care archives economy, obtain patient information or Hospital Electronic Medical Record information, if electronic health care archives economy only has patient information, just from electronic health care system acquisition patient information; If electronic health care system only has Hospital Electronic Medical Record information, just from electronic health care system acquisition Hospital Electronic Medical Record information; If the existing patient information of electronic health care archives economy has again Hospital Electronic Medical Record information, that had now just both obtained patient information from electronic health care archives economy and had also obtained Hospital Electronic Medical Record information.
The aspects such as described medical diagnosis aid system can be applied in basic diagnosis and treatment management, diagnosis management, prescription typing, toll administration, data acquisition, the consultation of doctors, chronic disease management, chronic disease archives, follows up a case by regular visits to, assesses, guidance, old people's health check-up, auxiliary examination, also may operate in doctor's handheld device Duan He data center, can also independent operating.
In sum, a kind of medical system described in the embodiment of the present application mainly comprises following advantage:
Medical diagnosis aid system in the application recalls patient's patient information or Hospital Electronic Medical Record information in the past according to the communicate information of patient's vital sign information and/or doctor's terminal from electronic health care archives economy, according to patient's vital signs information, the communicate information of doctor's terminal and patient information, or according to patient's vital signs information, the communicate information of doctor's terminal and Hospital Electronic Medical Record information, in knowledge base and intelligent auxiliary engine, carry out data mining, generate medical diagnosis secondary outcome, auxiliary family doctor diagnoses conditions of patients, thereby help family doctor to take effectively for patient's the state of an illness, diagnosis and treatment means accurately.
Based on above content, for making those skilled in the art understand better the application, a kind of structure chart of medical system of take below further illustrates the application as example, with reference to Fig. 2, the structure chart that it shows a kind of medical system described in another embodiment of the application, specifically comprises:
Hospital's wireless telecommunications system 28, for providing doctor's terminal and family doctor's module of handheld device, family doctor's module in handheld device to communicate by letter with electronic health care archives economy with medical diagnosis aid system, medical diagnosis aid system;
Preferably, described hospital wireless telecommunications system comprises health private network, 3G/4G wireless network, hospital services center WIFI LAN and the WIFI of community medical service center LAN.
Another preferred embodiment, the aid system of medical diagnosis described in the application comprises base module and intelligent auxiliary engine module, family doctor utilizes the family doctor's module in handheld device, by the communicate information of the patient's vital sign information receiving and doctor's terminal, be sent to medical diagnosis aid system, medical diagnosis aid system can recall patient's patient information and Hospital Electronic Medical Record information in the past from EHR simultaneously, in base module and intelligent auxiliary engine module, carry out data mining, base module can add patient's synthetic data in data model, generate medical diagnosis secondary outcome, auxiliary family doctor diagnoses conditions of patients, with reference to Fig. 3, it shows the structure chart of medical diagnosis aid system described in the embodiment of the present application, specifically comprise:
Described base module 34 comprises:
(1) business is understood submodule, for defining the target of medical item and medical demand.
The initial stage of project development concentrates on to be understood the target of medical item and understands medical demand from the angle of business, the target of medical item and medical demand has been converted into the preliminary project of target simultaneously.
(2) gather information submodule, for collecting the communicate information of the patient's of described demand vital sign information, patient information, Hospital Electronic Medical Record information and doctor's terminal.
From health system, collect the communicate information of the patient's of a large amount of described demands vital sign information, patient information, Hospital Electronic Medical Record information and doctor's terminal, carry out pretreatment, described pretreatment comprises data scrubbing, information is integrated and Data induction, and data scrubbing is mainly the processing of vacancy value, smooth noise data, identification deletion isolated point.
Vacancy value is processed, and the most frequently used method is to use most probable value to fill vacancy value at present.The Return Law for example, the Return Law is to rely on existing data message to infer vacancy value, make vacancy value have larger chance to keep and other attributes between contacting.If vacancy value is a lot, may mislead Result.
Smooth noise data, are random error or the deviations of measuring in noise data variable, comprise improper value or depart from the isolated point of expectation, can carry out smooth noise data by data smoothing technology, identification and delete isolated point.
Information is integrated is mainly a communicate information for the patient's of the described demand of collecting vital sign information, patient information, Hospital Electronic Medical Record information and doctor's terminal to be carried out integrated, leaves in base module.
The reduction that data reduction techniques can be used for obtaining data set represents, it is close to the integrity that keeps data, but data volume is more much smaller than former data.Compare with non-reduction data, in the data of reduction, excavate, required time and memory source still less, excavate more effectively, and produce identical or almost identical analysis result, and wherein the method for data reduction has:
1, dimension reduction reduces data volume by deleting incoherent attribute (or dimension).
2, data compression applications data encoding or conversion, obtain reduction or the compression expression of source data.Data compression is divided into lossless compress and lossy compression method, popular and effectively to damage data compression method be wavelet transformation and principal component analysis.
3, numerical value reduction is by selecting that substitute, less data representation format to reduce data volume.
(3) association knowledge submodule, for being undertaken associated by the communicate information of the patient's of the described demand of described gather information submodule vital sign information, patient information, Hospital Electronic Medical Record information and doctor's terminal.
(4) analysis and prediction knowledge submodule, for analyzing the patient information of described association knowledge submodule association, the communicate information of doctor's terminal, Hospital Electronic Medical Record information and patient's vital sign information is inferred associated trend.
In medical research, response variable is rendered as the qualitative variable (being called again property variable) of two or limited a plurality of differential responses final results mostly, and affect, disease occurs, the risk factor of development may be quantitative (as open-assembly time, the age etc.), also may be (as whether exposed, sex, race etc.) qualitatively, be likely also grade.What for this class problem, relatively commonly use is logistic regression analysis, estimates the probability that an event occurs.
About Logit, convert and Logistic regression equation:
If at independent variable (conventionally also referred to as covariant) X
1, X
2..., X
munder effect, dependent variable Y is the pathogenetic probability P of disease, and the probability of not falling ill is 1-P; P/1-P is incidence rate and the ratio of incidence rate not, is denoted as odds.If definition:
Logit(p)=ln(odds)=ln(p/1-p) (1)
Above formula (1) is called the logit conversion of p.The ln is here natural logrithm.Logistic regression equation is:
Logit(p)=ln(p/1-p)=a+b
1x
1+b
2x
2+....b
mx
m (2)
In above formula (2), a is constant term; b
j(j=l ..., m) be logistic regression coefficient.(2) formula shows when p changes from 0 → 1/2 → l, and corresponding Logit (p) changes from-∞ → 0 → ∞, like this X
1, X
2..., X
mcan in any span, change.I.e. risk factor X no matter
fbe quantitatively or qualitatively or grade (as long as the latter changes into quantitative words), the excursion of p is during from O to l, and Logit (p) changes in+∞ at-∞, and logistic regression equation is all meaningful.
In Logistic regression model, be to adopt maximum likelihood degree method to estimate the coefficient of model, this coefficient is close to observed result, and, because this Logistic returns, be nonlinear, therefore estimate that coefficient is to adopt iterative computation.
If X
f(j=1,2 ... .m) be all 0, when each risk factor does not exist, p remembered to p
0, above formula (2) formula has a=ln (p
0/ 1-p
0) be p
0/ 1-p
0=exp (a), its meaning is when each risk factor does not exist, a is incidence rate and the natural logrithm (being In (odds)) of the ratio number of the probability of not falling ill, and this is called to baseline risks than number.Logistic regression coefficient b in above formula (2)
m(j=1 ..., m) mean when other risk factors are got definite value risk factor X
fwhile changing Yi Ge unit, the change amount of Logit (p).
Described intelligent auxiliary engine module 32 comprises:
(1) core algorithm submodule, for core algorithm is provided, comprises association rule algorithm, decision tree sorting algorithm, clustering analysis algorithm, artificial neural network algorithm and genetic algorithm.
Described core algorithm submodule has been the most basic processing capacity of intelligent auxiliary engine, it is most important part in intelligent auxiliary engine, core algorithm submodule is mainly comprised of association rule algorithm, decision tree sorting algorithm, clustering analysis algorithm, artificial neural network algorithm and genetic algorithm, each own advantage and scope of application separately of these core algorithms, below the principle of article correlation rule and neural network algorithm:
The principle of association rule algorithm: the relation of association rule algorithm identification cause and effect, with " support " and " confidence level " equiprobability factor, come supported data to excavate theoretical simultaneously.The form of correlation rule is " if (conditions), then (conclusion) ", is used for making prediction or estimating Unknown Attribute Values, and this algorithm can the higher relation of Automatic Extraction probability of occurrence.
Association rule algorithm represents in base module the rule of certain incidence relation between a group objects, for example, " occur " or " from an object, can release another object " simultaneously.Association rule algorithm is exactly to find out association hiding in base module by association analysis, utilizes these correlation rules to Unknown Attribute Values, to infer according to known case.
The discovery procedure of association rule algorithm can be divided into two steps: the first step, find all sport collection, and namely support is greater than the item collection of given minimum support; Second step, concentrates and produces dependency rule from sport.The performance of data mining mainly determines by the first step, and after having determined sport collection, correlation rule is easy to intuitively obtain.
The principle of artificial neural network algorithm: artificial neural network is to be coupled to each other by being similar in a large number the neuronic processing unit of human brain the non-linear complex networks system forming, applies the value that one group of input variable, mathematics activation functions and input weight are carried out target of prediction variable; By a learning process repeatedly, neutral net constantly adjusts weight until the output of prediction is consistent with actual data; After once learning completes, this network is just as the model that is used for predicting new data value.Because artificial neural network can be applied its abstract ability insert new value in learning data, be therefore relatively suitable for the problem of successive value data mining.
Artificial neural network algorithm is a kind of by the Nonlinear Prediction Models of training to learn, and artificial neural network for solving the advantage of data mining is: first, classification is accurate, good stability; Secondly, artificial neural network can utilize various algorithms to carry out regulation extraction, and it can complete the several data mining tasks such as classification, cluster, feature mining.Mainly contain at present feed forward type neural network model, feedback neural network model and self organizing neural network model
(2) intelligent selection submodule, for extracting the data of base module, selects suitable core algorithm according to the feature of described data, the data that output core algorithm calculates.
Intelligent selection module is responsible for selecting suitable core algorithm according to the feature of the data in base module and the occupation mode that extracts drawn data, carries out data mining, to reach optimum mining effect; Intelligent selection submodule is intelligent auxiliary engine module core intelligence module, has directly determined work efficiency and the high efficiency of intelligent auxiliary engine module.
(3) input and output submodule, for sending the data of base module to intelligent selection submodule.
Input and output submodule is responsible for receiving the data that transmission comes from base module, and by the center-control submodule in intelligent auxiliary engine module, send described data to intelligent selection submodule, make intelligent selection submodule can utilize described data to carry out work, meanwhile, input and output submodule is also responsible for sending the result of the core algorithm module in intelligent auxiliary engine module to data digging system controller.
(4) center-control submodule, for the communication and control between core algorithm submodule, intelligent selection submodule, input and output submodule.
Center-control submodule is intelligent auxiliary engine module key control unit, be responsible for the communication and control between core algorithm submodule, intelligent selection submodule, input and output submodule, center-control submodule is by control inputs output sub-module, control the interworking of intelligent auxiliary engine module, and then realize integrity and the independence of intelligent auxiliary engine module.
(5) meta knowledge base, for storing the core algorithm of core algorithm submodule and the data that intelligent selection submodule core algorithm calculates.
Meta knowledge base is for storing the core algorithm of core algorithm submodule and the data that intelligent selection submodule core algorithm calculates, center-control submodule is responsible for meta knowledge base upgrade and control, and meta knowledge base is the base unit of realizing intelligent auxiliary engine module.
Introduce below, under MATLAB, realize Logistic regression equation and be combined with the process of setting up forecast model with artificial neural network algorithm, the described process of setting up forecast model comprises foundation, artificial neural network training and the neural network prediction of artificial neural network.
The foundation of described artificial neural network comprises the following steps:
1, based on base module, set up MATLAB data set;
2, data set read in to work space and carry out standardization processing;
3, data set is divided into two parts, is respectively used to training and testing;
4, set up network and draw initial weight vector;
5, training network is also drawn weight vectors again;
6, test set data are carried out to network simulation test analyses and prediction result.
The foundation of described artificial neural network training comprises the following steps:
1, import training set data collection;
2, set up Logistic regression equation;
3, according to the various complication prediction probability of Logistic regression equation calculation p;
If 4 p are greater than 0.5, complication is 1; If p is less than 0.5, complication is 0; According to Analysis On Complications, predict the outcome.
The foundation of described neural network prediction comprises the following steps:
1, import training set data collection;
2, set up Logistic regression equation;
3, according to different complication regression equations, in MATLAB, set up corresponding data set;
4, data read in to work space and carry out standardization processing;
5, set up network and draw initial weight vector;
6, training network is also drawn weight vectors again;
7, test set data are carried out to network simulation test analytical test result.
In sum, a kind of medical system described in the embodiment of the present application mainly comprises following advantage:
The aid system of medical diagnosis described in the application comprises base module and intelligent auxiliary engine module, family doctor utilizes the family doctor's module in handheld device, by the communicate information of the patient's vital sign information receiving and doctor's terminal, be sent to medical diagnosis aid system, medical diagnosis aid system can recall patient's patient information and Hospital Electronic Medical Record information in the past from EHR simultaneously, in base module and intelligent auxiliary engine module, carry out data mining, base module can add patient's synthetic data in data model, generate medical diagnosis secondary outcome, auxiliary family doctor diagnoses conditions of patients.
Based on above content, for making those skilled in the art understand better the application, the flow process of carrying out data mining below by case introduction medical diagnosis aid system.
1, for example: patient A cough is more than;
2, first family doctor gets the vital sign information such as pulse, blood pressure, heart beating of patient A by monitor, and is transferred to the family doctor's module in panel computer;
3, family doctor and pneumonopathy expert carry out video, and expert's communicate information is input to the family doctor's module in panel computer;
4, family doctor is transferred to medical diagnosis aid system by family doctor's module of panel computer by patient's vital sign information and expert's communicate information;
5, medical diagnosis aid system is connected with EHR system, and patient A case history is in the past recalled;
6, the full detail that medical diagnosis aid system obtains according to the family doctor's module in panel computer and EHR system carries out data mining, draws medical diagnosis secondary outcome: 80% pneumonia, and get rid of pulmonary tuberculosis, and provide treatment suggestion and medication is advised;
7, medical diagnosis aid system is transferred to the family doctor's module in family doctor's panel computer by above result, and family doctor provides final Therapeutic Method.
Concrete medical diagnosis aid system display interface is as shown in Figure 4: the information that medical diagnosis auxiliary system processes is crossed can be presented in family doctor's module interfaces of hand-held panel computer of family doctor, medical diagnosis aid system can be pointed out diagnostic recommendations, for example this patient obtains certain sick probability, get rid of to obtain certain disease, prompting treatment suggestion, for example, provide medication reference, health care item etc., medicine taking prompting suggestions etc., medical diagnosis aid system can provide diagnosis reference to family doctor.In sum, a kind of medical system described in the embodiment of the present application mainly comprises following advantage:
Medical diagnosis aid system in the application is according to communicate information and the patient information of patient's vital sign information, doctor's terminal, or carry out data mining according to the communicate information of patient's vital sign information, doctor's terminal and Hospital Electronic Medical Record information, and the medical diagnosis secondary outcome that described data mining is obtained sends the family doctor's module in handheld device to, for family doctor's result that provides assistance in diagnosis, thus help family doctor for patient's the state of an illness take effectively, diagnosis and treatment means accurately.
Each embodiment in this description all adopts the mode of going forward one by one to describe, and each embodiment stresses is the difference with other embodiment, between each embodiment identical similar part mutually referring to.
A kind of medical system above the application being provided, be described in detail, applied specific case herein the application's principle and embodiment are set forth, the explanation of above embodiment is just for helping to understand the application's method and core concept thereof; Meanwhile, for one of ordinary skill in the art, the thought according to the application, all will change in specific embodiments and applications, and in sum, this description should not be construed as the restriction to the application.
Claims (7)
1. a medical system, is characterized in that, comprising:
Family doctor's module in handheld device, for receiving patient's vital sign information;
Electronic health care archives economy, for providing patient information and Hospital Electronic Medical Record information;
Doctor's terminal, for the family doctor's module communication with handheld device, provides family doctor and family doctor assistant, family doctor and support staff, family doctor and expert's communicate information;
Medical diagnosis aid system, communicates for the family doctor's module with electronic health care archives economy and handheld device; Patient's the vital signs information and/or the communicate information of doctor's terminal that according to the family doctor's module in handheld device, receive, obtain patient information or Hospital Electronic Medical Record information from electronic health care archives economy; According to communicate information and the patient information of patient's vital sign information, doctor's terminal, or according to the communicate information of patient's vital sign information, doctor's terminal and Hospital Electronic Medical Record information, carry out data mining, and the medical diagnosis secondary outcome that described data mining is obtained sends the family doctor's module in handheld device to.
2. system according to claim 1, is characterized in that, also comprises:
Database server, for communicate information and the Hospital Electronic Medical Record information of the vital sign information of store patient, patient information, doctor's terminal;
Hospital's wireless telecommunications system, for providing doctor's terminal and family doctor's module of handheld device, family doctor's module in handheld device to communicate by letter with electronic health care archives economy with medical diagnosis aid system, medical diagnosis aid system;
Media server, for storing the Voice & Video of family doctor's module communication of doctor's terminal and handheld device;
Center management server, for provide database server or media server respectively with the communicating by letter of family doctor's module of handheld device, electronic health care archives economy, doctor's terminal;
Front interface server, for storing the interface data of doctor's terminal.
3. system according to claim 2, is characterized in that, described hospital wireless telecommunications system comprises health private network, 3G/4G wireless network, hospital services center WIFI LAN and the WIFI of community medical service center LAN.
4. system according to claim 1, is characterized in that, described medical diagnosis aid system comprises base module and intelligent auxiliary engine module.
5. system according to claim 4, is characterized in that, described base module comprises:
Business is understood submodule, for defining the target of medical item and medical demand;
Gather information submodule, for collecting the communicate information of the patient's of described demand vital sign information, patient information, Hospital Electronic Medical Record information and doctor's terminal;
Association knowledge submodule, for being undertaken associated by the communicate information of the patient's of the described demand of described gather information submodule vital sign information, patient information, Hospital Electronic Medical Record information and doctor's terminal;
Analysis and prediction knowledge submodule, for analyzing the patient information of described association knowledge submodule association, the communicate information of doctor's terminal, Hospital Electronic Medical Record information and patient's vital sign information is inferred associated trend.
6. system according to claim 4, is characterized in that, described intelligent auxiliary engine module comprises:
Core algorithm submodule, for core algorithm is provided, comprises association rule algorithm, decision tree sorting algorithm, clustering analysis algorithm, artificial neural network algorithm and genetic algorithm;
Intelligent selection submodule, for extracting the data of base module, selects suitable core algorithm according to the feature of described data, the data that output core algorithm calculates;
Input and output submodule, for sending the data of base module to intelligent selection submodule;
Center-control submodule, for the communication and control between core algorithm submodule, intelligent selection submodule, input and output submodule;
Meta knowledge base, for storing the core algorithm of core algorithm submodule and the data that intelligent selection submodule core algorithm calculates.
7. system according to claim 1, is characterized in that, described doctor's terminal comprises computer and mobile terminal.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1784170A (en) * | 2003-05-09 | 2006-06-07 | 日本电气株式会社 | Diagnosis support system and mobile terminal |
| CN101517581A (en) * | 2006-09-20 | 2009-08-26 | 皇家飞利浦电子股份有限公司 | Molecular Diagnostic Decision Support System |
| CN201663616U (en) * | 2010-03-10 | 2010-12-01 | 苏州翊高科技有限公司 | Remote medical service system adopting WLAN standard |
| CN102085116A (en) * | 2010-12-08 | 2011-06-08 | 华中科技大学 | Multifunctional remote medical care system based on multi-network fusion |
| CN102223567A (en) * | 2011-06-24 | 2011-10-19 | 中兴通讯股份有限公司 | Remote medical service system and method thereof |
| CA2771779A1 (en) * | 2011-03-11 | 2012-09-11 | The Carer's Phone Pty Ltd | A system and method of monitoring care of patients |
| US20130179195A1 (en) * | 2012-01-09 | 2013-07-11 | Mymedicalrecords, Inc. | Method and system for managing personal health records with telemedicine and health monitoring device features |
-
2013
- 2013-09-16 CN CN201310421405.8A patent/CN103690240B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1784170A (en) * | 2003-05-09 | 2006-06-07 | 日本电气株式会社 | Diagnosis support system and mobile terminal |
| CN101517581A (en) * | 2006-09-20 | 2009-08-26 | 皇家飞利浦电子股份有限公司 | Molecular Diagnostic Decision Support System |
| CN201663616U (en) * | 2010-03-10 | 2010-12-01 | 苏州翊高科技有限公司 | Remote medical service system adopting WLAN standard |
| CN102085116A (en) * | 2010-12-08 | 2011-06-08 | 华中科技大学 | Multifunctional remote medical care system based on multi-network fusion |
| CA2771779A1 (en) * | 2011-03-11 | 2012-09-11 | The Carer's Phone Pty Ltd | A system and method of monitoring care of patients |
| CN102223567A (en) * | 2011-06-24 | 2011-10-19 | 中兴通讯股份有限公司 | Remote medical service system and method thereof |
| US20130179195A1 (en) * | 2012-01-09 | 2013-07-11 | Mymedicalrecords, Inc. | Method and system for managing personal health records with telemedicine and health monitoring device features |
Cited By (36)
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|---|---|---|---|---|
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| CN108206056B (en) * | 2018-01-18 | 2022-03-08 | 中山大学 | An artificial intelligence-assisted diagnosis and treatment decision-making terminal for nasopharyngeal carcinoma |
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| CN113157640B (en) * | 2020-12-31 | 2023-05-23 | 上海明品医学数据科技有限公司 | Auxiliary inquiry device, terminal and inquiry system for family doctor |
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