CN111938652A - Application of artificial intelligence diagnosis mode in early diagnosis of bronchial asthma - Google Patents
Application of artificial intelligence diagnosis mode in early diagnosis of bronchial asthma Download PDFInfo
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- CN111938652A CN111938652A CN202010856335.9A CN202010856335A CN111938652A CN 111938652 A CN111938652 A CN 111938652A CN 202010856335 A CN202010856335 A CN 202010856335A CN 111938652 A CN111938652 A CN 111938652A
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- 208000006673 asthma Diseases 0.000 title claims abstract description 125
- 238000013399 early diagnosis Methods 0.000 title claims abstract description 85
- 208000030603 inherited susceptibility to asthma Diseases 0.000 title claims abstract description 85
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 47
- 238000003745 diagnosis Methods 0.000 title abstract description 16
- 238000000034 method Methods 0.000 claims abstract description 13
- 238000004891 communication Methods 0.000 claims abstract description 7
- 238000013480 data collection Methods 0.000 claims abstract description 4
- 238000000605 extraction Methods 0.000 claims abstract description 4
- 230000004199 lung function Effects 0.000 claims description 15
- 230000002093 peripheral effect Effects 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 201000010099 disease Diseases 0.000 abstract description 4
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 abstract description 4
- 238000004393 prognosis Methods 0.000 abstract description 3
- 238000012360 testing method Methods 0.000 description 11
- 238000001514 detection method Methods 0.000 description 7
- MWUXSHHQAYIFBG-UHFFFAOYSA-N Nitric oxide Chemical compound O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 6
- 208000024891 symptom Diseases 0.000 description 6
- 210000000621 bronchi Anatomy 0.000 description 5
- 230000003110 anti-inflammatory effect Effects 0.000 description 4
- 239000003862 glucocorticoid Substances 0.000 description 4
- 208000009079 Bronchial Spasm Diseases 0.000 description 3
- 208000014181 Bronchial disease Diseases 0.000 description 3
- 206010006482 Bronchospasm Diseases 0.000 description 3
- 238000013135 deep learning Methods 0.000 description 3
- 238000013178 mathematical model Methods 0.000 description 3
- 230000000638 stimulation Effects 0.000 description 3
- 206010002091 Anaesthesia Diseases 0.000 description 2
- 206010011224 Cough Diseases 0.000 description 2
- 206010061218 Inflammation Diseases 0.000 description 2
- 238000007792 addition Methods 0.000 description 2
- 230000037005 anaesthesia Effects 0.000 description 2
- 210000004369 blood Anatomy 0.000 description 2
- 239000008280 blood Substances 0.000 description 2
- 208000023819 chronic asthma Diseases 0.000 description 2
- 208000013116 chronic cough Diseases 0.000 description 2
- 230000005284 excitation Effects 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 230000001154 acute effect Effects 0.000 description 1
- 230000009798 acute exacerbation Effects 0.000 description 1
- 239000013566 allergen Substances 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 230000001447 compensatory effect Effects 0.000 description 1
- 201000004897 cough variant asthma Diseases 0.000 description 1
- 230000034994 death Effects 0.000 description 1
- 231100000517 death Toxicity 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000003205 diastolic effect Effects 0.000 description 1
- 210000000222 eosinocyte Anatomy 0.000 description 1
- 238000011841 epidemiological investigation Methods 0.000 description 1
- 239000005556 hormone Substances 0.000 description 1
- 229940088597 hormone Drugs 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000007477 logistic regression Methods 0.000 description 1
- 210000004072 lung Anatomy 0.000 description 1
- NZWOPGCLSHLLPA-UHFFFAOYSA-N methacholine Chemical compound C[N+](C)(C)CC(C)OC(C)=O NZWOPGCLSHLLPA-UHFFFAOYSA-N 0.000 description 1
- 229960002329 methacholine Drugs 0.000 description 1
- 238000004223 overdiagnosis Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000009325 pulmonary function Effects 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 210000002345 respiratory system Anatomy 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/087—Measuring breath flow
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/091—Measuring volume of inspired or expired gases, e.g. to determine lung capacity
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- Heart & Thoracic Surgery (AREA)
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Abstract
The invention relates to a method for early diagnosing bronchial asthma by artificial intelligence, which comprises the following steps: t1, establishing communication between the artificial intelligent early diagnosis bronchial asthma intelligent terminal and the artificial intelligent early diagnosis bronchial asthma data acquisition device; t2, instructing the artificial intelligence early diagnosis bronchial asthma intelligent terminal to acquire data by the artificial intelligence early diagnosis bronchial asthma data acquisition device; t3, inputting the collected data into the trained feature extraction by the artificial intelligence early diagnosis bronchial asthma data collection device; t4, calculating the collected data by an artificial intelligence early diagnosis bronchial asthma data collecting device and obtaining asthma information; t5, generating an artificial intelligent early diagnosis bronchial asthma report based on the asthma information by the artificial intelligent early diagnosis bronchial asthma data acquisition device, and sending the artificial intelligent early diagnosis bronchial asthma report to the artificial intelligent early diagnosis bronchial asthma intelligent terminal. The invention improves the diagnosis rate of asthma, achieves early discovery, early diagnosis and early treatment, improves the prognosis of asthma and lightens the burden of diseases.
Description
Technical Field
The invention relates to the technical field of information, in particular to application of an artificial intelligence diagnosis mode in early diagnosis of bronchial asthma.
Background
At present, the number of asthma patients is at least 3 hundred million worldwide, and the number of patients increases year by year. Recent epidemiological investigations have shown that the prevalence of asthma in adults over 20 years old in China is as high as 4.2%, and about 4570 ten thousand people, and among the asthma patients under investigation, only 28.8% of them are diagnosed, which is particularly prevalent in mild asthma accounting for 75-77% of all asthmatics.
Due to the complex lung function parameters, the sensitivity and specificity are low only by means of a single index, and even high-age specialist doctors in three-level hospitals still have insufficient reading capacity for the lung function. Meanwhile, the non-specificity of asthma symptoms and the strong compensatory ability of the lung lead to poor perception of early patients on the symptoms, which leads to difficult effective diagnosis and identification of the early patients, so that the patients often have the reduction of lung function and limited reversible degree of airflow when being diagnosed, and the serious acute attack, even life threatening, causes serious health and economic burden when the symptoms bring the reduction of life quality. Also, asthma patients have an increased risk of surgical anesthesia accidents and intraoperative bronchospasm, which studies have shown that 7% of anesthesia-related deaths are caused by bronchospasm, and 2% of american society of anesthesiologists respiratory system claims are associated with bronchospasm, of which 70% of patients die, causing a serious health economic burden.
Therefore, the difficulty of early diagnosis of asthma lies in how to effectively integrate the symptoms of patients, pulmonary function parameters, exhaled nitric oxide, imaging and blood routine and other non-invasive means to effectively identify patients at early stage, and to make sure diagnosis without over-diagnosis or missed diagnosis.
The diagnostic criteria that must currently be met in the definitive diagnosis of asthma with respect to variable airflow limitation includePositive for any one of: the bronchus stimulation test is positive, the bronchus diastole test is positive or the diastole test is positive after 1-3 months of glucocorticoid inhalation anti-inflammatory treatment, but the bronchus stimulation test cannot be carried out conventionally in many second and third hospitals due to the factors of higher risk, inconvenient operation, high quality control requirement, acute exacerbation induction risk, failure of the stimulation reagent of methacholine to pass the certification of Chinese medical supervision department and the like, and the bronchus diastole test cannot be carried out conventionally in FEV1<Positive rate was high in 70% of patients, but in FEV1The positive rate of normal mild asthma or cough variant asthma patients is less than 10%, and the positive diastolic test of anti-inflammatory treatment for 1-3 months is due to the fact that treatment follow-up is needed, glucocorticoid inhalation is needed, certain operability is lacked for most of specialists and primary doctors, and overuse of hormone is easily caused.
Disclosure of Invention
The first purpose of the present invention is to provide a method for early diagnosis of bronchial asthma by artificial intelligence, which is directed to the deficiencies of the prior art.
The second purpose of the invention is to provide an intelligent terminal for early diagnosis of bronchial asthma by artificial intelligence.
In order to achieve the first purpose, the invention adopts the technical scheme that:
a method for early diagnosis of bronchial asthma by artificial intelligence comprises the following steps:
t1, establishing communication between the artificial intelligent early diagnosis bronchial asthma intelligent terminal and the artificial intelligent early diagnosis bronchial asthma data acquisition device;
t2, instructing the artificial intelligence early diagnosis bronchial asthma intelligent terminal to acquire data by the artificial intelligence early diagnosis bronchial asthma data acquisition device;
t3, inputting the collected data into the trained feature extraction by the artificial intelligence early diagnosis bronchial asthma data collection device;
t4, calculating the collected data by an artificial intelligence early diagnosis bronchial asthma data collecting device and obtaining asthma information;
t5, generating an artificial intelligent early diagnosis bronchial asthma report based on the asthma information by the artificial intelligent early diagnosis bronchial asthma data acquisition device, and sending the artificial intelligent early diagnosis bronchial asthma report to the artificial intelligent early diagnosis bronchial asthma intelligent terminal.
Further preferred scheme: the artificial intelligence early diagnosis bronchial asthma data acquisition device also comprises a lung function detection instrument.
Further preferred scheme: the data collected in the step T2 comprises sex, age, height and weight of the patient, and the physical examination parameters mainly comprise tidal volume, forced vital capacity, central airway parameters, FEV1/FVC, maximum expiratory flow rate and four peripheral airway parameters.
In order to achieve the second object mentioned above,
an artificial intelligence early diagnosis bronchial asthma intelligent terminal, includes intelligent terminal and the APP program of operation on intelligent terminal, the APP program carries out following operation:
l1, establishing communication between the artificial intelligent early diagnosis bronchial asthma intelligent terminal and the artificial intelligent early diagnosis bronchial asthma data acquisition device;
l2, logging in an artificial intelligent early diagnosis bronchial asthma comprehensive management server by the identity of a doctor to obtain patient information including the bronchial asthma information of the patient for the doctor;
l3, commanding an artificial intelligence early diagnosis bronchial asthma data acquisition device to acquire data, extracting the data based on the trained features to perform early diagnosis bronchial asthma measurement and calculation on the data and acquire asthma information;
l4, acquiring an artificial intelligence early diagnosis bronchial asthma report generated based on asthma information and sent by the artificial intelligence early diagnosis bronchial asthma data acquisition device.
Further preferred scheme: the artificial intelligence early diagnosis bronchial asthma data acquisition device also comprises a lung function detection instrument.
Further preferred scheme: the data collected in the step L3 comprises sex, age, height and weight of the patient, and the physical examination parameters mainly comprise tidal volume, forced vital capacity, central airway parameters, FEV1/FVC, maximum expiratory flow rate and four peripheral airway parameters.
The invention has the advantages that: the invention belongs to the field of clinical medical diagnosis methods, and particularly relates to early diagnosis of asthma by using artificial intelligent APP for lung function examination. The APP is software which combines a mathematical prediction model established by big data with artificial intelligence programming, and the method is suitable for early diagnosis of asthma and popularization in primary hospitals. In order to establish a more accurate and more convenient diagnosis method, an artificial intelligence diagnosis APP which is easy to popularize clinically and is economic and effective is constructed in a deep learning mode, and the combination of lung function instruments is adopted, so that the asthma diagnosis rate is improved, the early discovery, the early diagnosis and the early treatment are realized, the asthma prognosis is improved, and the disease burden is reduced.
With the increase of the incidence of asthma in recent years, the invention actually improves the early diagnosis rate of asthma, can replace the challenge test and the follow-up visit after 1-3 months of anti-inflammation, reduces the abuse of glucocorticoid, is suitable for being popularized in the primary level, lightens the burden of primary level doctors, reduces the referral of patients, and has wide clinical application prospect.
Detailed Description
The following examples are provided to illustrate specific embodiments of the present invention.
Example 1
A method for early diagnosis of bronchial asthma by artificial intelligence comprises the following steps:
t1, establishing communication between the artificial intelligent early diagnosis bronchial asthma intelligent terminal and the artificial intelligent early diagnosis bronchial asthma data acquisition device;
t2, instructing the artificial intelligence early diagnosis bronchial asthma intelligent terminal to acquire data by the artificial intelligence early diagnosis bronchial asthma data acquisition device;
t3, inputting the collected data into the trained feature extraction by the artificial intelligence early diagnosis bronchial asthma data collection device;
t4, calculating the collected data by an artificial intelligence early diagnosis bronchial asthma data collecting device and obtaining asthma information;
t5, generating an artificial intelligent early diagnosis bronchial asthma report based on the asthma information by the artificial intelligent early diagnosis bronchial asthma data acquisition device, and sending the artificial intelligent early diagnosis bronchial asthma report to the artificial intelligent early diagnosis bronchial asthma intelligent terminal. The artificial intelligence early diagnosis bronchial asthma data acquisition device also comprises a lung function detection instrument. The data collected in the step T2 includes sex, age, height and weight of the patient, and the physical examination parameters mainly include tidal volume, forced vital capacity, central airway parameters, FEV1/FVC, maximum expiratory flow rate and four peripheral airway parameters
Example 2
An artificial intelligence early diagnosis bronchial asthma intelligent terminal, includes intelligent terminal and the APP program of operation on intelligent terminal, the APP program carries out following operation:
l1, establishing communication between the artificial intelligent early diagnosis bronchial asthma intelligent terminal and the artificial intelligent early diagnosis bronchial asthma data acquisition device;
l2, logging in an artificial intelligent early diagnosis bronchial asthma comprehensive management server by the identity of a doctor to obtain patient information including the bronchial asthma information of the patient for the doctor;
l3, commanding an artificial intelligence early diagnosis bronchial asthma data acquisition device to acquire data, extracting the data based on the trained features to perform early diagnosis bronchial asthma measurement and calculation on the data and acquire asthma information;
l4, acquiring an artificial intelligence early diagnosis bronchial asthma report generated based on asthma information and sent by the artificial intelligence early diagnosis bronchial asthma data acquisition device. The artificial intelligence early diagnosis bronchial asthma data acquisition device also comprises a lung function detection instrument. The data collected in the step L3 comprises sex, age, height and weight of the patient, and the physical examination parameters mainly comprise tidal volume, forced vital capacity, central airway parameters, FEV1/FVC, maximum expiratory flow rate and four peripheral airway parameters.
In order to establish a more accurate and more convenient diagnosis method, an artificial intelligence diagnosis APP which is easy to popularize clinically and is economic and effective is constructed in a deep learning mode, and the combination of lung function instruments is adopted, so that the asthma diagnosis rate is improved, the early discovery, the early diagnosis and the early treatment are realized, the asthma prognosis is improved, and the disease burden is reduced.
An artificial intelligence APP is developed, and by retrospectively collecting 2800 chronic cough and asthma patients who are subjected to a bronchus excitation test in 2017 and 2019 of the institute and collecting 1 st diastole test negative in a prospective way, comprehensive parameters such as lung function large/small airways and the like of 200 chronic cough and asthma patients who are subjected to diagnostic anti-inflammatory treatment for 1 month are combined with symptoms, exhaled nitric oxide, blood eosinophilic granulocyte count, allergen detection and image parameters. The detection parameters of a patient are comprehensively analyzed, a mathematical model is established by a deep learning method, the mathematical model is made into APP by a programming mode and is connected with a lung function detection instrument, and the symptoms, the blood routine, the exhaled nitric oxide and the lung function parameters of the patient are comprehensively analyzed to replace the follow-up after 1-3 months of excitation test and anti-inflammatory treatment, so that the sensitivity and the specificity of asthma diagnosis are practically improved.
Mathematical models between asthma and metadata and between asthma and physical examination parameters are established and are analyzed and compared in parallel, the metadata mainly comprise sex, age, height and weight of a patient, and the physical examination parameters mainly comprise tidal volume, forced vital capacity, central airway parameters, FEV1/FVC, maximum expiratory flow rate and four peripheral airway parameters. The body examination parameters are brought into a regression model by constructing a multi-factor Logistic regression model, so that the regression coefficient of each parameter is estimated, the prediction precision of the regression model reaches 92.59%, and a sub-group with the optimal characteristics (forced vital capacity, central airway parameters, peripheral airway parameters A, peripheral airway parameters B, peripheral airway parameters C and peripheral airway parameters D) is found out according to the regression coefficient, so that the model prediction can reach the optimal value; finally, the metadata and the physical examination parameter data are combined, so that the prediction accuracy of the support vector machine model is 97.56%, and the disease prediction accuracy obtained by the support vector machine model is gradually improved under the background of a large amount of data.
With the increase of the incidence of asthma in recent years, the invention actually improves the early diagnosis rate of asthma, can replace the challenge test and the follow-up visit after 1-3 months of anti-inflammation, reduces the abuse of glucocorticoid, is suitable for being popularized in the primary level, lightens the burden of primary level doctors, reduces the referral of patients, and has wide clinical application prospect.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and additions can be made without departing from the method of the present invention, and these modifications and additions should also be regarded as the protection scope of the present invention.
Claims (6)
1. An artificial intelligence early diagnosis method of bronchial asthma is characterized by comprising the following steps:
t1, establishing communication between the artificial intelligent early diagnosis bronchial asthma intelligent terminal and the artificial intelligent early diagnosis bronchial asthma data acquisition device;
t2, instructing the artificial intelligence early diagnosis bronchial asthma intelligent terminal to acquire data by the artificial intelligence early diagnosis bronchial asthma data acquisition device;
t3, inputting the collected data into the trained feature extraction by the artificial intelligence early diagnosis bronchial asthma data collection device;
t4, calculating the collected data by an artificial intelligence early diagnosis bronchial asthma data collecting device and obtaining asthma information;
t5, generating an artificial intelligent early diagnosis bronchial asthma report based on the asthma information by the artificial intelligent early diagnosis bronchial asthma data acquisition device, and sending the artificial intelligent early diagnosis bronchial asthma report to the artificial intelligent early diagnosis bronchial asthma intelligent terminal.
2. The method for early diagnosing bronchial asthma of claim 1, wherein the data collecting device for early diagnosing bronchial asthma of artificial intelligence further comprises a lung function detecting instrument.
3. The method of claim 1, wherein said step T2 comprises collecting data including sex, age, height and weight of patient, and physical examination parameters mainly include tidal volume, forced vital capacity, central airway parameters, FEV1/FVC, maximum expiratory flow, four peripheral airway parameters.
4. The utility model provides an artificial intelligence early diagnosis bronchial asthma intelligent terminal, includes intelligent terminal and the APP program of operation on intelligent terminal, its characterized in that the APP program carries out following operation:
l1, establishing communication between the artificial intelligent early diagnosis bronchial asthma intelligent terminal and the artificial intelligent early diagnosis bronchial asthma data acquisition device;
l2, logging in an artificial intelligent early diagnosis bronchial asthma comprehensive management server by the identity of a doctor to obtain patient information including the bronchial asthma information of the patient for the doctor;
l3, commanding an artificial intelligence early diagnosis bronchial asthma data acquisition device to acquire data, extracting the data based on the trained features to perform early diagnosis bronchial asthma measurement and calculation on the data and acquire asthma information;
l4, acquiring an artificial intelligence early diagnosis bronchial asthma report generated based on asthma information and sent by the artificial intelligence early diagnosis bronchial asthma data acquisition device.
5. The intelligent terminal for early diagnosing bronchial asthma of claim 4, wherein the data collecting device for early diagnosing bronchial asthma of artificial intelligence further comprises a lung function detecting instrument.
6. The intelligent terminal for early diagnosis of bronchial asthma of claim 4, wherein the data collected in step L3 includes sex, age, height and weight of the patient, and the physical examination parameters mainly include tidal volume, forced vital capacity, central airway parameters, FEV1/FVC, maximum expiratory flow rate, and four peripheral airway parameters.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115089161A (en) * | 2022-05-27 | 2022-09-23 | 东莞广济医院有限公司 | Asthma control condition evaluation equipment and method for elderly bronchial asthma patients |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102231172A (en) * | 2011-06-10 | 2011-11-02 | 王坦 | Remote medical information acquisition system |
| CN108877924A (en) * | 2018-03-30 | 2018-11-23 | 杭州依图医疗技术有限公司 | A kind of asthma method of determining probability and device |
| CN109817328A (en) * | 2018-12-29 | 2019-05-28 | 上海依智医疗技术有限公司 | A kind of method and device differentiating asthma |
| CN110533658A (en) * | 2019-09-02 | 2019-12-03 | 山东大学齐鲁医院 | Intelligent pulmonary emphysema diagnostic message processing system and method, information data processing terminal |
| US20200027558A1 (en) * | 2016-09-26 | 2020-01-23 | The University Of Queensland | A method and apparatus for automatic disease state diagnosis |
| CN111000561A (en) * | 2019-12-10 | 2020-04-14 | 中山大学附属第三医院 | Thing networking lung function detector data remote interaction system |
| CN111145902A (en) * | 2019-12-06 | 2020-05-12 | 江苏大学 | Asthma diagnosis method based on improved artificial neural network |
| CN111388817A (en) * | 2020-03-25 | 2020-07-10 | 复旦大学附属中山医院 | Portable oxygen therapy equipment based on Internet of things, oxygen therapy system and using method of portable oxygen therapy system |
| CN111629663A (en) * | 2017-12-21 | 2020-09-04 | 昆士兰大学 | Method for diagnosing respiratory system disease by analyzing cough sound using disease characteristics |
| CN115910326A (en) * | 2022-12-02 | 2023-04-04 | 河南省人民医院 | Bronchial asthma auxiliary diagnosis method and system based on interpretable machine learning |
-
2020
- 2020-08-24 CN CN202010856335.9A patent/CN111938652A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102231172A (en) * | 2011-06-10 | 2011-11-02 | 王坦 | Remote medical information acquisition system |
| US20200027558A1 (en) * | 2016-09-26 | 2020-01-23 | The University Of Queensland | A method and apparatus for automatic disease state diagnosis |
| CN111629663A (en) * | 2017-12-21 | 2020-09-04 | 昆士兰大学 | Method for diagnosing respiratory system disease by analyzing cough sound using disease characteristics |
| CN108877924A (en) * | 2018-03-30 | 2018-11-23 | 杭州依图医疗技术有限公司 | A kind of asthma method of determining probability and device |
| CN109817328A (en) * | 2018-12-29 | 2019-05-28 | 上海依智医疗技术有限公司 | A kind of method and device differentiating asthma |
| CN110533658A (en) * | 2019-09-02 | 2019-12-03 | 山东大学齐鲁医院 | Intelligent pulmonary emphysema diagnostic message processing system and method, information data processing terminal |
| CN111145902A (en) * | 2019-12-06 | 2020-05-12 | 江苏大学 | Asthma diagnosis method based on improved artificial neural network |
| CN111000561A (en) * | 2019-12-10 | 2020-04-14 | 中山大学附属第三医院 | Thing networking lung function detector data remote interaction system |
| CN111388817A (en) * | 2020-03-25 | 2020-07-10 | 复旦大学附属中山医院 | Portable oxygen therapy equipment based on Internet of things, oxygen therapy system and using method of portable oxygen therapy system |
| CN115910326A (en) * | 2022-12-02 | 2023-04-04 | 河南省人民医院 | Bronchial asthma auxiliary diagnosis method and system based on interpretable machine learning |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115089161A (en) * | 2022-05-27 | 2022-09-23 | 东莞广济医院有限公司 | Asthma control condition evaluation equipment and method for elderly bronchial asthma patients |
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Application publication date: 20201117 |