CN105975740B - A kind of medical system with intelligent diagnostics - Google Patents
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- 238000012544 monitoring process Methods 0.000 claims abstract description 30
- 238000007596 consolidation process Methods 0.000 claims abstract description 12
- 238000013528 artificial neural network Methods 0.000 claims description 46
- 230000001225 therapeutic effect Effects 0.000 claims description 39
- 238000003745 diagnosis Methods 0.000 claims description 26
- 201000010099 disease Diseases 0.000 claims description 25
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 25
- 239000010410 layer Substances 0.000 claims description 18
- HVYWMOMLDIMFJA-DPAQBDIFSA-N cholesterol Chemical compound C1C=C2C[C@@H](O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H]([C@H](C)CCCC(C)C)[C@@]1(C)CC2 HVYWMOMLDIMFJA-DPAQBDIFSA-N 0.000 claims description 16
- 238000007726 management method Methods 0.000 claims description 12
- 238000001514 detection method Methods 0.000 claims description 11
- 210000002569 neuron Anatomy 0.000 claims description 11
- 230000036772 blood pressure Effects 0.000 claims description 10
- 239000008280 blood Substances 0.000 claims description 9
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- 239000011159 matrix material Substances 0.000 claims description 9
- 208000024891 symptom Diseases 0.000 claims description 9
- 230000006870 function Effects 0.000 claims description 6
- 239000011229 interlayer Substances 0.000 claims description 6
- 230000036387 respiratory rate Effects 0.000 claims description 5
- 230000002612 cardiopulmonary effect Effects 0.000 claims description 4
- 238000013500 data storage Methods 0.000 claims description 4
- 230000002452 interceptive effect Effects 0.000 claims description 4
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- 238000009472 formulation Methods 0.000 claims description 3
- 230000000474 nursing effect Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 claims description 2
- 238000002601 radiography Methods 0.000 claims description 2
- 235000013399 edible fruits Nutrition 0.000 claims 1
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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Abstract
A kind of medical system with intelligent diagnostics, it is characterised in that：Including hospital system, home system and remote monitoring center, hospital system includes medical investigative apparatus, patient condition diversity module, intelligent diagnostics module, data consolidation server, and home system includes Portable Monitoring Set.
Description
Technical field
The invention belongs to smart machine fields, more particularly to a kind of medical department with intelligent diagnostics for Nephrology dept.
System.
Background technology
Intelligent medical system analyzed using computer, retrieved, science is calculated, is reasonable, comprehensive diagnostic result, pathology
Inspection etc. is learned, the required correlative factor of the disease is made a definite diagnosis to each disease offer of diagnostic result.But current intelligent medical system
System rests in the collection of patient's essential information and case history mostly, and diagnose and treat scheme is all to have doctor to make, working doctor
Amount is not mitigated, and is lacked in family's care monitoring.
Invention content
The technical problem to be solved by the present invention is to how by algorithm realize to the assessment of condition-inference and therapeutic scheme with
And formulate, provide a kind of medical system with intelligent diagnostics to this present invention comprising hospital system, home system and far
Range monitoring center,
Hospital system includes medical investigative apparatus, patient condition diversity module, intelligent diagnostics module, Data Integration service
Device,
Home system includes Portable Monitoring Set,
Attending physician realizes the formulation to the medical examination of patient, diagnosis and therapeutic scheme by hospital system, cures mainly
Doctor is in the real time data to nurse the sick by the acquisition of the Portable Monitoring Set of home system, Portable Monitoring Set also with
Remote monitoring center is wirelessly connected, to which reply can be taken in time when emergency occurs in patient, and emergency is same
Shi Tongzhi attending physicians and upload data consolidation server,
When patient is in hospital system, medical investigative apparatus detects the multinomial medical examination data of patient, and inputs disease
People's state diversity module, patient condition diversity module assess the state of an illness of patient using assessment algorithm, and attending physician then passes through
It crosses authentication and enters patient condition diversity module and the assessment result of division is verified, if what attending physician thought to divide
Assessment result is correct, then files the relevant information of patient and medical examination data according to assessment result, and input intelligence
Diagnostic module is determined the assessment result of patient by attending physician if attending physician thinks that the assessment result divided is incorrect,
The relevant information of patient and medical examination data are filed according to assessment result again, and input intelligent diagnostics module；Intelligence
Diagnostic module automatically generates corresponding therapeutic scheme according to the relevant information, medical examination data and assessment result of patient, main
It controls doctor and enters intelligent diagnostics module by authentication and therapeutic scheme is verified, if attending physician thinks therapeutic scheme
Correctly, then the relevant information of patient, medical examination data, therapeutic scheme and assessment result are uploaded to Data Integration service
Device reformulates therapeutic scheme if attending physician thinks that therapeutic scheme is incorrect by attending physician, then by the correlation of patient
Information, medical examination data, therapeutic scheme and assessment result are uploaded to data consolidation server；
Intelligent diagnostics module includes dynamic comprehensive database, neural network learning module, inference machine, explanation module, sample
Knowledge base, neural network structure knowledge base, clinical symptoms Description of Knowledge library, disease knowledge library, therapeutic scheme knowledge base, history note
Knowledge base, knowledge data database management module are recorded,
Expert can modify to neural network learning module and by knowledge base management module to each knowledge base into
Update is safeguarded in row adjustment management；
Explanation module is the bridge linked up between system and attending physician, is responsible for converting the diagnosis of attending physician to system
The information that can be identified, and convert the last output result of system to attending physician it will be appreciated that information；
Inference machine utilizes each knowledge base, relevant information, medical treatment inspection in conjunction with the patient provided in dynamic comprehensive database
It looks into data and assessment result makes inferences, obtain corresponding therapeutic scheme.
Inference machine includes ANN Reasoning module and rule-based reasoning module；
Neural network learning module proposes the neural network knot including the network number of plies, input, output, hidden node number
Structure organizes learning sample to be trained and Learning Algorithm, is learnt by the extraction of sample knowledge library, is weighed
Distribution value completes knowledge acquisition.
Further, neural network structure is realized by the method that fuzzy logic and neural network combine, neural network
Learning algorithm is BP algorithm.
Sample knowledge library, neural network structure knowledge base, clinical symptoms Description of Knowledge library, disease knowledge library, scheme of curing the disease
Knowledge base, historical record knowledge base store corresponding knowledge data respectively；Each knowledge base is dynamic expansion, has self study
From the function of supplement.
ANN Reasoning module is inputted according to neural network structure knowledge base, dynamic comprehensive database and explanation module
Data make inferences, and the reasoning results are exported and give rule-based reasoning module, rule-based reasoning module combines dynamic
State integrated database, clinical symptoms Description of Knowledge library, disease knowledge library, scheme of curing the disease knowledge base, historical record Analysis of Knowledge Bases Reasoning
Generate final therapeutic scheme；
Knowledge data database management module has the function of that complete database manipulation, expert pass through knowledge data database management module
Being inquired, added and being deleted and being changed to each knowledge base
Dynamic comprehensive database receives and stores the relevant information of patient, medical examination data and assessment result；
The course of work of inference machine is as follows：
The relevant information of patient, medical examination data and assessment result are carried out Fuzzy processing by step (1), and with this
Input pattern as each sub-neural network；
Step (2) reads in the weight matrix of each sub-neural network from neural network structure knowledge base；
Step (3) combines the weight matrix of each sub-neural network input layer, implicit interlayer, calculates each sub-neural network input
The output of layer neuron, and will be output as the input of hidden layer neuron；
The weight matrix that step (4) combines each sub-neural network hidden layer, exports interlayer calculates the defeated of output layer neuron
Go out value；
Step (5) is according to the output valve of output layer neuron, in conjunction with the relevant information in dynamic comprehensive database into professional etiquette
Then reasoning assigns a cause for an illness, and provides confidence level；
Whether step (6) correct according to the cause of disease that Credibility judgement reasoning obtains, if confidence level 80% hereinafter, if recognize
It is incorrect for reasoning, return to step (1), if confidence level is 80% or more, then it is assumed that reasoning is correct, enters step (7)；
Step (7) provides the therapeutic scheme corresponding to the specific cause of disease according to finally determining reason in conjunction with relevant information.
Beneficial effects of the present invention：
(1) evaluation to patient's state of an illness is realized by assessment algorithm, to be Case treatment scheme and Case treatment ring
The selection in border provides foundation；
(2) there is home system, to ensure that the round-the-clock monitoring of patient's residential care；
(3) intelligent diagnostics mode is introduced, therapeutic scheme is automatically generated, to greatly reduce the labor intensity of doctor.
Description of the drawings
Fig. 1 is the system block diagram of the present invention；
Fig. 2 is the intelligent diagnostics module composition frame chart of the present invention；
Specific implementation mode
The present invention is further illustrated with embodiment below in conjunction with the accompanying drawings.
The embodiment of the present invention shows with reference to figure 1-2.
A kind of medical system with intelligent diagnostics comprising hospital system, home system and remote monitoring center,
Hospital system includes medical investigative apparatus, patient condition diversity module, intelligent diagnostics module, Data Integration service
Device,
Home system includes Portable Monitoring Set,
Attending physician realizes the formulation to the medical examination of patient, diagnosis and therapeutic scheme by hospital system, cures mainly
Doctor is in the real time data to nurse the sick by the acquisition of the Portable Monitoring Set of home system, Portable Monitoring Set also with
Remote monitoring center is wirelessly connected, to which reply can be taken in time when emergency occurs in patient, and emergency is same
Shi Tongzhi attending physicians and upload data consolidation server,
When patient is in hospital system, medical investigative apparatus detects the multinomial medical examination data of patient, and inputs disease
People's state diversity module, patient condition diversity module assess the state of an illness of patient using assessment algorithm, and attending physician then passes through
It crosses authentication and enters patient condition diversity module and the assessment result of division is verified, if what attending physician thought to divide
Assessment result is correct, then files the relevant information of patient and medical examination data according to assessment result, and input intelligence
Diagnostic module is determined the assessment result of patient by attending physician if attending physician thinks that the assessment result divided is incorrect,
The relevant information of patient and medical examination data are filed according to assessment result again, and input intelligent diagnostics module；Intelligence
Diagnostic module automatically generates corresponding therapeutic scheme according to the relevant information, medical examination data and assessment result of patient, main
It controls doctor and enters intelligent diagnostics module by authentication and therapeutic scheme is verified, if attending physician thinks therapeutic scheme
Correctly, then the relevant information of patient, medical examination data, therapeutic scheme and assessment result are uploaded to Data Integration service
Device reformulates therapeutic scheme if attending physician thinks that therapeutic scheme is incorrect by attending physician, then by the correlation of patient
Information, medical examination data, therapeutic scheme and assessment result are uploaded to data consolidation server；
The assessment algorithm of patient condition diversity module is specially：
Grade classification is carried out to each single item medical examination data, then
Whole detection measured value is：
Wherein, OPM is whole detection measured value, TiFor the grade point of i-th medical examination data, i=1,2 ..., N, Ti
=1,2 ..., M, M be greatest level value, M >=2, N be medical examination data total item；
Maximum entirety measured value is：
OPM_MAX is maximum whole measured value
Then the assessed value as patient's state of an illness of assessment result is：
Above-mentioned assessment calculates the state of an illness that can effectively assess patient, to carry out Put on file.
Further, M=5, N=6
Medical investigative apparatus includes glucometer, ECG detecting device, respiratory rate detector, cholesterol levels detection
Device, blood pressure instrument, X-ray production apparatus,
Medical examination data include blood sugar concentration, ECG data evaluation, respiratory rate, cholesterol levels, blood pressure conditions,
Radioscopy is evaluated,
Further,
Wherein, the opinion rating of blood sugar concentration divides as shown in the table；
| Grade | 1 | 2 | 3 | 4 | 5 |
| Blood sugar concentration (mg/DL) | ＜ 98 | 98-154 | 155-183 | 184-254 | ＞ 254 |
The opinion rating of respiratory rate divides as shown in the table；
The opinion rating of cholesterol levels divides as shown in the table；
| Grade | 1 | 2 | 3 | 4 | 5 |
| Cholesterol levels (mmolg/l) | ＜ 5.2 | 5.2-5.5 | 5.6-5.8 | 5.9-6.0 | ＞ 6.0 |
The opinion rating of blood pressure conditions divides as shown in the table；
ECG data evaluation refers to that doctor evaluates according only to the grade that electrocardiogram is made；
Radioscopy evaluation refers to that doctor evaluates according only to the grade that X-ray is made；
Intelligent diagnostics module includes dynamic comprehensive database, neural network learning module, inference machine, explanation module, sample
Knowledge base, neural network structure knowledge base, clinical symptoms Description of Knowledge library, disease knowledge library, therapeutic scheme knowledge base, history note
Knowledge base, knowledge data database management module are recorded,
Expert can be adjusted pipe to neural network learning module and by knowledge base management module to each knowledge base
Reason safeguards update；
Explanation module is the bridge linked up between system and attending physician, is responsible for converting the diagnosis of attending physician to system
The information that can be identified, and convert the last output result of system to attending physician it will be appreciated that information；
Inference machine utilizes each knowledge base, relevant information, medical treatment inspection in conjunction with the patient provided in dynamic comprehensive database
It looks into data and assessment result makes inferences, obtain corresponding therapeutic scheme.
Inference machine includes ANN Reasoning module and rule-based reasoning module；
Neural network learning module proposes the neural network knot including the network number of plies, input, output, hidden node number
Structure organizes learning sample to be trained and Learning Algorithm, is learnt by the extraction of sample knowledge library, is weighed
Distribution value completes knowledge acquisition.
Further, neural network structure is realized by the method that fuzzy logic and neural network combine, neural network
Learning algorithm is BP algorithm.
Sample knowledge library, neural network structure knowledge base, clinical symptoms Description of Knowledge library, disease knowledge library, scheme of curing the disease
Knowledge base, historical record knowledge base store corresponding knowledge data respectively；Each knowledge base is dynamic expansion, has self study
From the function of supplement.
ANN Reasoning module is inputted according to neural network structure knowledge base, dynamic comprehensive database and explanation module
Data make inferences, and the reasoning results are exported and give rule-based reasoning module, rule-based reasoning module combines dynamic
State integrated database, clinical symptoms Description of Knowledge library, disease knowledge library, scheme of curing the disease knowledge base, historical record Analysis of Knowledge Bases Reasoning
Generate final therapeutic scheme；
Knowledge data database management module has the function of that complete database manipulation, expert pass through knowledge data database management module
Being inquired, added and being deleted and being changed to each knowledge base
Dynamic comprehensive database receives and stores the relevant information of patient, medical examination data and assessment result；
The course of work of inference machine is as follows：
The relevant information of patient, medical examination data and assessment result are carried out Fuzzy processing by step (1), and with this
Input pattern as each sub-neural network；
Step (2) reads in the weight matrix of each sub-neural network from neural network structure knowledge base；
Step (3) combines the weight matrix of each sub-neural network input layer, implicit interlayer, calculates each sub-neural network input
The output of layer neuron, and will be output as the input of hidden layer neuron；
The weight matrix that step (4) combines each sub-neural network hidden layer, exports interlayer calculates the defeated of output layer neuron
Go out value；
Step (5) is according to the output valve of output layer neuron, in conjunction with the relevant information in dynamic comprehensive database into professional etiquette
Then reasoning assigns a cause for an illness, and provides confidence level；
Whether step (6) correct according to the cause of disease that Credibility judgement reasoning obtains, if confidence level 80% hereinafter, if recognize
It is incorrect for reasoning, return to step (1), if confidence level is 80% or more, then it is assumed that reasoning is correct, enters step (7)；
Step (7) provides the therapeutic scheme corresponding to the specific cause of disease according to finally determining reason in conjunction with relevant information；
Intelligent diagnostics are truly realized by intelligent diagnostics module, full and accurate therapeutic scheme can be provided for doctor, in turn
Reduce its working strength.
Data consolidation server includes interactive interface module, registration module, communication monitoring module, data storage dress
It sets, attending physician carries out grade registration by registering module, and by interactive interface module accesses data storage device
Patient information, communication monitoring module be used for and remote monitoring center carry out data exchange.
Data consolidation server realizes comprehensive integration of patient information to provide full and accurate information for treating physician.
Determine whether patient can be in nursing according to the opinion of assessed value C and attending physician, if assessed value for C >=
0.4, then patient need be hospitalized；If assessed value is 0 ＜ C ＜ 0.1, such patient has fully recovered substantially, without further examining
Disconnected monitoring service；If assessed value is 0.1≤C ＜ 0.4 and attending physician agrees to, patient can carry out residential care；
When patient is in home system, according to assessed value judge the required basic diagnosis service of home care patients,
High level diagnostics service, diagnosis report form, attending physician's type, monitoring period and monitoring period；
As 0.1≤C ＜ 0.2, such patient is slight patient,
Basic diagnosis service is patient's essential information, case history, electrocardiogram, blood pressure detecting；
Without high level diagnostics service；
Diagnosis report form is web page notification and mail；
Attending physician's type is the attending physician of 10 years or less experiences；
Monitoring period is 1 day；
It is 1 hour primary to monitor the period；
As 0.2≤C ＜ 0.3, such patient is moderate patient,
Basic diagnosis service is patient's essential information, case history, electrocardiogram, blood pressure detecting, blood sugar test, cardiopulmonary detection, courage
Sterol levels detect；
Without high level diagnostics service；
Diagnosis report form is web page notification and mail；
Attending physician's type is the attending physician of 10 years or less experiences；
Monitoring period is 7 days；
It is 1 hour primary to monitor the period；
As 0.3≤C ＜ 0.4, such patient is severe patient,
Basic diagnosis service is patient's essential information, case history, electrocardiogram, blood pressure detecting, blood sugar test, cardiopulmonary detection, courage
Sterol levels detect, assessment aroused in interest, heart radiography；
High level diagnostics service is virtual heart, expert consultation；
Diagnosis report form is mobile phone, web page notification and mail；
Attending physician's type is the attending physician of 10 years or more experiences；
Monitoring period is 30 days；
It is 1 minute to monitor the period；
It can be directed to the state of an illness of different patients by above-mentioned classification, different treatments and nursing care mode are taken, to close
Reason configuration medical resource, reduces medical treatment cost.
Furtherly, wherein basic diagnosis service, high level diagnostics service are realized by Portable Monitoring Set.
Embodiment described above only expresses one embodiment of the present invention, but can not therefore be interpreted as to this
The limitation of invention scope.It should be pointed out that for those of ordinary skill in the art, in the premise for not departing from present inventive concept
Under, various modifications and improvements can be made, these are all within the scope of protection of the present invention.
Claims (7)
1. a kind of medical system with intelligent diagnostics, it is characterised in that：Including hospital system, home system and remote monitoring
Center,
Hospital system includes medical investigative apparatus, patient condition diversity module, intelligent diagnostics module, data consolidation server,
Home system includes Portable Monitoring Set,
Attending physician realizes the formulation to the medical examination of patient, diagnosis and therapeutic scheme, attending physician by hospital system
Be in the real time data to nurse the sick by the acquisition of the Portable Monitoring Set of home system, Portable Monitoring Set also with remotely
Monitoring center is wirelessly connected, and to take reply in time when emergency occurs in patient, and emergency is led to simultaneously
Know attending physician and upload data consolidation server,
When patient is in hospital system, medical investigative apparatus detects the multinomial medical examination data of patient, and inputs patient's shape
State diversity module, patient condition diversity module assess the state of an illness of patient using assessment algorithm, and attending physician then passes through body
Part certification enters patient condition diversity module and verifies the assessment result of division, if attending physician thinks the assessment divided
As a result correct, then the relevant information of patient and medical examination data are filed according to assessment result, and input intelligent diagnostics
Module is determined the assessment result of patient by attending physician, then will if attending physician thinks that the assessment result divided is incorrect
The relevant information and medical examination data of patient is filed according to assessment result, and inputs intelligent diagnostics module；Intelligent diagnostics
Module automatically generates corresponding therapeutic scheme according to the relevant information, medical examination data and assessment result of patient, cures mainly doctor
Life enters intelligent diagnostics module by authentication and verifies therapeutic scheme, if attending physician thinks therapeutic scheme just
Really, then the relevant information of patient, medical examination data, therapeutic scheme and assessment result are uploaded to data consolidation server,
If attending physician thinks that therapeutic scheme is incorrect, therapeutic scheme is reformulated by attending physician, then the correlation of patient is believed
Breath, medical examination data, therapeutic scheme and assessment result are uploaded to data consolidation server；
Intelligent diagnostics module includes dynamic comprehensive database, neural network learning module, inference machine, explanation module, sample knowledge
Library, neural network structure knowledge base, clinical symptoms Description of Knowledge library, disease knowledge library, therapeutic scheme knowledge base, historical record are known
Know library, knowledge data database management module；
Explanation module is the bridge linked up between medical system and attending physician with intelligent diagnostics, is responsible for attending physician's
Diagnosis is converted into the information that the medical system with intelligent diagnostics can identify, and the medical system with intelligent diagnostics is last
Output result be converted into attending physician it will be appreciated that information；
Inference machine utilizes each knowledge base, relevant information, medical examination number in conjunction with the patient provided in dynamic comprehensive database
According to this and assessment result makes inferences, and obtains corresponding therapeutic scheme,
Inference machine includes ANN Reasoning module and rule-based reasoning module；
Neural network learning module propose neural network structure including the network number of plies, input, output, hidden node number,
Learning sample to be trained and Learning Algorithm are organized, is learnt by the extraction of sample knowledge library, obtains weights
Knowledge acquisition is completed in distribution,
Neural network structure realizes that Learning Algorithm is calculated for BP by the method that fuzzy logic and neural network combine
Method；
Sample knowledge library, neural network structure knowledge base, clinical symptoms Description of Knowledge library, disease knowledge library, scheme of curing the disease knowledge
Library, historical record knowledge base store corresponding knowledge data respectively；Each knowledge base is dynamic expansion, and it is self-complementary to have self study
The function of filling,
The number that ANN Reasoning module is inputted according to neural network structure knowledge base, dynamic comprehensive database and explanation module
According to making inferences, and the reasoning results are exported and give rule-based reasoning module, rule-based reasoning module combines dynamic comprehensive
Close database, clinical symptoms Description of Knowledge library, disease knowledge library, scheme of curing the disease knowledge base, the generation of historical record Analysis of Knowledge Bases Reasoning
Final therapeutic scheme；
Knowledge data database management module has the function of complete database manipulation, and expert is by knowledge data database management module to each
A knowledge base is inquired, added and is deleted and changed
Dynamic comprehensive database receives and stores the relevant information of patient, medical examination data and assessment result；
The course of work of inference machine is as follows：
Step (1) by the relevant information of patient, medical examination data and assessment result carry out Fuzzy processing, and in this, as
The input pattern of each sub-neural network；
Step (2) reads in the weight matrix of each sub-neural network from neural network structure knowledge base；
Step (3) combines the weight matrix of each sub-neural network input layer, implicit interlayer, calculates each sub-neural network input layer god
Output through member, and will be output as the input of hidden layer neuron；
The weight matrix that step (4) combines each sub-neural network hidden layer, exports interlayer calculates the output of output layer neuron
Value；
Step (5) is pushed away in conjunction with the relevant information in dynamic comprehensive database into line discipline according to the output valve of output layer neuron
Reason, assigns a cause for an illness, provides confidence level；
Whether step (6) is correct according to the cause of disease that Credibility judgement reasoning obtains, if confidence level is below 80%, then it is assumed that push away
Manage incorrect, return to step (1), if confidence level is 80% or more, then it is assumed that reasoning is correct, enters step (7)；
Step (7) provides the therapeutic scheme corresponding to the specific cause of disease according to finally determining reason in conjunction with relevant information.
2. a kind of medical system with intelligent diagnostics according to claim 1, it is characterised in that：Patient condition is classified mould
The assessment algorithm of block is specially：
Grade classification is carried out to each single item medical examination data, then whole detection measured value is：
Wherein, OPM is whole detection measured value, TiFor the grade point of i-th medical examination data, i=1,2 ..., N, Ti=1,
2 ..., M, M are greatest level value, and M >=2, N are the total item of medical examination data；
Maximum entirety measured value is：
OPM_MAX is maximum whole measured value
Then the assessed value C as patient's state of an illness of assessment result is：
Above-mentioned assessment calculates the state of an illness that can effectively assess patient, to carry out Put on file.
3. a kind of medical system with intelligent diagnostics according to claim 2, it is characterised in that：M=5, N=6,
Medical investigative apparatus includes glucometer, ECG detecting device, respiratory rate detector, cholesterol levels detector, blood
Instrument, X-ray production apparatus are pressed, medical examination data include blood sugar concentration, ECG data evaluation, respiratory rate, cholesterol levels, blood pressure
Situation, radioscopy evaluation.
4. a kind of medical system with intelligent diagnostics according to claim 1, it is characterised in that：Data consolidation server
Including interactive interface module, registration module, communication monitoring module, data storage device, attending physician passes through registration
Module carries out grade registration, and by the patient information in interactive interface module accesses data storage device, communicates monitoring module
For carrying out data exchange with remote monitoring center.
5. a kind of medical system with intelligent diagnostics according to claim 2, it is characterised in that：According to assessed value C with
And the opinion of attending physician determines whether patient can be in nursing, if assessed value is C >=0.4, patient's needs are hospitalized；Such as
Fruit assessed value is 0 ＜ C ＜ 0.1, and patient has fully recovered, without further diagnosis monitoring service；If assessed value is 0.1≤C
＜ 0.4 and attending physician's agreement, then patient can carry out residential care.
6. a kind of medical system with intelligent diagnostics according to claim 5, it is characterised in that：Patient is in system of family
When in system, the required basic diagnosis service of home care patients, high level diagnostics service, diagnosis report shape are judged according to assessed value
Formula, attending physician's type, monitoring period and monitoring period；
As 0.1≤C ＜ 0.2, patient is slight patient,
Basic diagnosis service be patient's essential information, case history, electrocardiogram, blood pressure detecting,
Without high level diagnostics service,
Diagnosis report form be web page notification and mail,
Attending physician's type is the attending physician of 10 years or less experiences,
Monitoring period is 1 day,
It is 1 hour primary to monitor the period；
As 0.2≤C ＜ 0.3, patient is moderate patient,
Basic diagnosis service is patient's essential information, case history, electrocardiogram, blood pressure detecting, blood sugar test, cardiopulmonary detection, cholesterol
Level detection, no high level diagnostics service,
Diagnosis report form be web page notification and mail,
Attending physician's type is the attending physician of 10 years or less experiences,
Monitoring period is 7 days,
It is 1 hour primary to monitor the period；
As 0.3≤C ＜ 0.4, patient is severe patient,
Basic diagnosis service is patient's essential information, case history, electrocardiogram, blood pressure detecting, blood sugar test, cardiopulmonary detection, cholesterol
Level detection, assessment aroused in interest, heart radiography,
High level diagnostics service be virtual heart, expert consultation,
Diagnosis report form be mobile phone, web page notification and mail,
Attending physician's type is the attending physician of 10 years or more experiences,
Monitoring period is 30 days,
It is 1 minute 1 time to monitor the period.
7. a kind of medical system with intelligent diagnostics according to claim 6, it is characterised in that：Basic diagnosis service,
High level diagnostics service is realized by Portable Monitoring Set.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102332162A (en) * | 2011-09-19 | 2012-01-25 | 西安百利信息科技有限公司 | Method for automatic recognition and stage compression of medical image regions of interest based on artificial neural network |
| CN102438519A (en) * | 2009-08-28 | 2012-05-02 | 艾伦·约瑟夫·泽尔纳 | Characterizing Physical Abilities Through Motion Analysis |
| CN103294908A (en) * | 2013-05-20 | 2013-09-11 | 浙江大学 | Cloud-computing-based method for evaluating malignant tumor chemoradiotherapy standard execution level |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9597029B2 (en) * | 2007-06-19 | 2017-03-21 | Cardiac Pacemakers, Inc. | System and method for remotely evaluating patient compliance status |
-
2016
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102438519A (en) * | 2009-08-28 | 2012-05-02 | 艾伦·约瑟夫·泽尔纳 | Characterizing Physical Abilities Through Motion Analysis |
| CN102332162A (en) * | 2011-09-19 | 2012-01-25 | 西安百利信息科技有限公司 | Method for automatic recognition and stage compression of medical image regions of interest based on artificial neural network |
| CN103294908A (en) * | 2013-05-20 | 2013-09-11 | 浙江大学 | Cloud-computing-based method for evaluating malignant tumor chemoradiotherapy standard execution level |
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