CN109817328A - A kind of method and device differentiating asthma - Google Patents
A kind of method and device differentiating asthma Download PDFInfo
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Abstract
The embodiment of the present application provides a kind of method and device for differentiating asthma, it is related to technical field of data processing, after the diagnosis information for obtaining patient, the characteristic value of multiple default features of patient is extracted from diagnosis information, then by the characteristic value input linear disaggregated model of multiple default features of patient, determine the classification of patient, therefore linear classification model is to determine whether patient suffers from asthma based on multiple features of patient, whether suffered from for asthma in compared to the prior art only in accordance with symptom or course of disease diagnosis patient, accuracy is higher.Secondly, disaggregated model can automatically determine whether patient suffers from asthma according to multiple features of patient, it is smaller by doctor's subjective impact, while improving the efficiency for differentiating asthma.
Description
Technical field
The present embodiments relate to technical field of data processing more particularly to a kind of method and devices for differentiating asthma.
Background technique
Asthma in children (infantile asthma) is the common Pulmonary Diseases of children, is a kind of performance repeated relapsing cough
It coughs, stridulates and have difficulty in breathing, and with the invertibity of airway hyperreactivity, obstructive airways diseases.Asthma is a kind of serious
The common chronic respiratory disease of children's torso health is endangered, disease incidence is high, often shows as the chronic course of disease of recurrent exerbation, sternly
The study, life and activity for affecting infant again, influence the growth and development of children.Many children with bronchial asthma disease due to treat not in time or
Malpractice finally develop for Adults Asthma and protracted course of disease, impaired lung function, part infant even completely lose physical exertion
Ability.Severe asthma attacks can be fatal if not obtaining timely and effective treatment.
For doctor in Diagnosing children asthma, often by inquiry medical history, this diagnostic method lacks clinic due to existing
Index, different doctors will appear different diagnostic results, have it is very big restricted, can not be promoted.And for each
The diagnostic method that kind tool detects the lung function of children, it may appear that the ill-matched problem of children, and the result detected
Accuracy be also not very high.In addition, there are also the Diagnosis of Children with Asthma index system announced according to some tissues, using marking plan
The method slightly diagnosed, this mode accuracy is not high, and very cumbersome, is also not suitable for promoting.
Summary of the invention
The embodiment of the present application provides a kind of method and device for differentiating asthma, to real based on the diagnostic message obtained comprehensively
Existing Diagnosing Asthma, improves the accuracy of Diagnosing Asthma.
On the one hand, the embodiment of the present application provides a kind of method for differentiating asthma, comprising:
Extract the characteristic value of multiple default features of patient；
By the characteristic value input linear disaggregated model of multiple default features of the patient, the classification of the patient is determined,
The classification of the patient includes with asthma and not suffering from asthma, and the independent variable of the linear classification model is multiple default features
Characteristic value, the dependent variable of the linear classification model are classification, and the independent variable coefficient of the linear classification model is using multiple
Training sample is fitted acquisition to the linear classification model.
Optionally, the multiple default feature includes following characteristics any combination:
Gender, the age, weight, the age of wheezing for the first time, frequency of wheezing, whether have wheezing sound, inducement, whether have it is athermic
Wheeze, whether night suppress it is awake, whether limitation of activity, whether shortness of breath, whether break out, whether have gas source allergies, whether have
Whether history of rhinitis has dermatitis/eczema history, asthma family history, rhinitis family history, dermatitis family history, whether raises pet.
Optionally, the linear classification model is Logic Regression Models, includes logistic regression in the Logic Regression Models
Function；
The characteristic value input linear disaggregated model of multiple default features by the patient, determines the class of the patient
Not, comprising:
According to the logistic regression function of the characteristic value of multiple default features of the patient and the Logic Regression Models, really
The asthma probability of the fixed patient；
According to the classification of patient described in the asthma determine the probability of the patient.
Optionally, the multiple default feature are as follows:
Weight, the age of wheezing for the first time, frequency of wheezing, whether have wheezing sound, inducement, whether have it is athermic wheeze, whether
Limitation of activity, whether shortness of breath, whether raise pet.
On the one hand, the embodiment of the present application provides a kind of device for differentiating asthma, comprising:
Preprocessing module, the characteristic value of multiple default features for extracting patient；
Categorization module, for determining institute for the characteristic value input linear disaggregated model of multiple default features of the patient
The classification of patient is stated, the classification of the patient includes with asthma and not suffering from asthma, and the independent variable of the linear classification model is
The characteristic value of multiple default features, the dependent variable of the linear classification model are classification, the independent variable of the linear classification model
Coefficient is fitted acquisition to the linear classification model using multiple training samples.
Optionally, the multiple default feature includes following characteristics any combination:
Gender, the age, weight, the age of wheezing for the first time, frequency of wheezing, whether have wheezing sound, inducement, whether have it is athermic
Wheeze, whether night suppress it is awake, whether limitation of activity, whether shortness of breath, whether break out, whether have gas source allergies, whether have
Whether history of rhinitis has dermatitis/eczema history, asthma family history, rhinitis family history, dermatitis family history, whether raises pet.
Optionally, the linear classification model is Logic Regression Models, includes logistic regression in the Logic Regression Models
Function；
The categorization module is specifically used for:
According to the logistic regression function of the characteristic value of multiple default features of the patient and the Logic Regression Models, really
The asthma probability of the fixed patient；
According to the classification of patient described in the asthma determine the probability of the patient.
Optionally, the multiple default feature are as follows:
Weight, the age of wheezing for the first time, frequency of wheezing, whether have wheezing sound, inducement, whether have it is athermic wheeze, whether
Limitation of activity, whether shortness of breath, whether raise pet.
On the one hand, the embodiment of the present application provides a kind of terminal device, including at least one processing unit and at least one
A storage unit, wherein the storage unit is stored with computer program, when described program is executed by the processing unit,
So that the processing unit executes the step of method for differentiating asthma.
On the one hand, the embodiment of the present application provides a kind of computer-readable medium, and being stored with can be executed by terminal device
Computer program, when described program is run on the terminal device so that the terminal device execute differentiate asthma method
The step of.
In the embodiment of the present application, after the diagnosis information for obtaining patient, the multiple pre- of patient is extracted from diagnosis information
If the characteristic value of feature, then by the characteristic value input linear disaggregated model of multiple default features of patient, the class of patient is determined
, therefore linear classification model is not to determine whether patient suffers from asthma based on multiple features of patient, compared to the prior art in only
Only whether suffered from for asthma according to symptom or course of disease diagnosis patient, accuracy is higher.Secondly, disaggregated model can be according to patient
Multiple features automatically determine whether patient suffers from asthma, it is smaller by doctor's subjective impact, at the same improve differentiate asthma effect
Rate.
Detailed description of the invention
To describe the technical solutions in the embodiments of the present invention more clearly, make required in being described below to embodiment
Attached drawing is briefly introduced, it should be apparent that, drawings in the following description are only some embodiments of the invention, for this
For the those of ordinary skill in field, without any creative labor, it can also be obtained according to these attached drawings
His attached drawing.
Fig. 1 is a kind of flow diagram of method for differentiating asthma provided by the embodiments of the present application；
Fig. 2 is a kind of flow diagram of method for differentiating asthma provided by the embodiments of the present application；
Fig. 3 is a kind of structural schematic diagram of device for differentiating asthma provided by the embodiments of the present application；
Fig. 4 is a kind of structural schematic diagram of terminal device provided by the embodiments of the present application.
Specific embodiment
In order to which the purpose of the present invention, technical solution and beneficial effect is more clearly understood, below in conjunction with attached drawing and implementation
Example, the present invention will be described in further detail.It should be appreciated that specific embodiment described herein is only used to explain this hair
It is bright, it is not intended to limit the present invention.
The method of differentiation asthma in the embodiment of the present application can provide auxiliary to diagnosis asthma, while can also make
For a kind of domestic medicine software or home medical equipment, ordinary user is allowed to diagnose whether oneself suffers from asthma in life.
Fig. 1 illustratively shows a kind of process of method for differentiating asthma provided by the embodiments of the present application, which can
To be executed by the device of differentiation asthma, comprising the following steps:
Step S101 extracts the characteristic value of multiple default features of patient.
It is alternatively possible to extract the characteristic value of multiple default features of patient, diagnosis information from the diagnosis information of patient
It can be the Medical record record of patient, that is, doctor inquires the information of input system after patient, can be history,
It is inputted after can be doctor's on-the-spot inquiry, the embodiment of the present application is without limitation.
Multiple default features can be any combination of following characteristics:
Gender, the age, weight, the age of wheezing for the first time, frequency of wheezing, whether have wheezing sound, inducement, whether have it is athermic
Wheeze, whether night suppress it is awake, whether limitation of activity, whether shortness of breath, whether break out, whether have gas source allergies, whether have
Whether history of rhinitis has dermatitis/eczema history, asthma family history, rhinitis family history, dermatitis family history, whether raises pet.
Illustratively, set multiple default features wheeze as weight, for the first time the age, frequency of wheezing, whether have wheezing sound, be
It is no have it is athermic wheeze, whether limitation of activity, whether shortness of breath, whether raise pet.Get the diagnosis information of patient Xiao Wang
Afterwards, the characteristic value of multiple default features of Xiao Wang is extracted are as follows:
Weight: 30kg；It wheezes for the first time the age: 7 years old；It wheezes frequency: 4 times；Whether there is wheezing sound: being；Whether have and does not generate heat
Wheeze: be；Whether limitation of activity: it is no；Whether shortness of breath: it is no；Whether raise pet: being.
The characteristic value input linear disaggregated model of multiple default features of patient is determined the classification of patient by step S102,
The classification of patient includes with asthma and not suffering from asthma.
Before the characteristic value input linear disaggregated model of multiple default features of patient, need to multiple default features
Feature is to being further processed, to realize data unified standard.For example, male is 1, and female 2 for gender item.To then
No have wheezing sound/inducement/history of rhinitis/dermatitis history/eczema history/asthma family history/rhinitis family history several, and promising 1, it is not set as
0.For weight, age, height is wheezed the age for the first time, frequency of wheezing.The average value of respective items can be concentrated according to training sample
η and standard deviation sigma, by the value linear normalization of [+3 σ of η -3 σ, η] between [0,1].Item less than η -3 σ is counted as 0, greater than η -3 σ's
Item is counted as 1.If diagnosis information has the item lacked, 0 can be denoted as.It can be by the characteristic value of default feature by the above method
Normalized, realizes the unification of data, and reduces data calculation amount.
The independent variable of linear classification model is the characteristic value of multiple default features, and the dependent variable of linear classification model is class
Not, the independent variable coefficient of linear classification model is fitted acquisition to linear classification model using multiple training samples.
In specific implementation, the diagnosis information of multiple patients is acquired in advance, is then divided into the diagnosis information of multiple patients
As training set and test set, linear classification model is trained using training set, using test section to linear classification model
It is tested, the AUC (Area Under Curve) by calculating linear classification model assesses the performance of linear classification model.
In the embodiment of the present application, after the diagnosis information for obtaining patient, the multiple pre- of patient is extracted from diagnosis information
If the characteristic value of feature, then by the characteristic value input linear disaggregated model of multiple default features of patient, the class of patient is determined
, therefore linear classification model is not to determine whether patient suffers from asthma based on multiple features of patient, compared to the prior art in only
Only whether suffered from for asthma according to symptom or course of disease diagnosis patient, accuracy is higher.Secondly, disaggregated model can be according to patient
Multiple features automatically determine whether patient suffers from asthma, it is smaller by doctor's subjective impact, at the same improve differentiate asthma effect
Rate.
In a kind of possible embodiment, linear classification model is Logic Regression Models, includes in Logic Regression Models
Logistic regression function.Determine that the classification of patient specifically includes following steps using Logic Regression Models, as shown in Figure 2:
Step S201, according to the logistic regression function of the characteristic value of multiple default features of patient and Logic Regression Models,
Determine the asthma probability of patient.
The logistic regression function of Logic Regression Models can be chosen according to the actual situation.
Illustratively, shown in the logistic regression function of Logic Regression Models such as formula (1):
Wherein, y (x) is the asthma probability of patient, and x is the characteristic value of multiple default features of patient.
Step S202, according to the classification of the asthma determine the probability patient of patient.
Specifically, when the asthma probability of patient is greater than 0.5, the classification of patient is with asthma, and the asthma probability of patient is small
When 0.5, the classification of patient is not suffer from asthma.
It optionally, can be to be set as by multiple default features when determining the classification of patient using Logic Regression Models
9 features below: weight, the age of wheezing for the first time, frequency of wheezing, whether have wheezing sound, inducement, whether have it is athermic wheeze,
Whether limitation of activity, whether shortness of breath, whether raise pet.
For the performance of verifying logic regression model, the diagnosis information of multiple patients is obtained as training set and test set,
Extract the characteristic value of above-mentioned 9 default features of each patient in training set and test set.Then it is returned using training Set Pair Logic
Return model to be trained, after training terminates, trained Logic Regression Models are tested using test set.Calculating logic
The test set AUC=0.825 of regression model therefore deduces that the classification performance of the Logic Regression Models is preferable.
It optionally, can be to be set as by multiple default features when determining the classification of patient using Logic Regression Models
19 features below: whether gender at the age, weight, at the age of wheezing for the first time, frequency of wheezing, have wheezing sound, inducement, have not
Fever wheeze, whether night suppress it is awake, whether limitation of activity, whether shortness of breath, whether break out, whether have gas source allergies,
Whether there is history of rhinitis, whether has dermatitis/eczema history, asthma family history, rhinitis family history, dermatitis family history, whether raises pet.
For the performance of verifying logic regression model, the diagnosis information of multiple patients is obtained as training set and test set,
Extract the characteristic value of above-mentioned 19 default features of each patient in training set and test set.Then it is returned using training Set Pair Logic
Return model to be trained, after training terminates, trained Logic Regression Models are tested using test set.Calculating logic
The test set AUC=0.822 of regression model therefore deduces that the classification performance of the Logic Regression Models is preferable.
In the embodiment of the present application, after the diagnosis information for obtaining patient, the multiple pre- of patient is extracted from diagnosis information
If the characteristic value of feature, then by the characteristic value input linear disaggregated model of multiple default features of patient, the class of patient is determined
, therefore linear classification model is not to determine whether patient suffers from asthma based on multiple features of patient, compared to the prior art in only
Only whether suffered from for asthma according to symptom or course of disease diagnosis patient, accuracy is higher.Secondly, disaggregated model can be according to patient
Multiple features automatically determine whether patient suffers from asthma, it is smaller by doctor's subjective impact, at the same improve differentiate asthma effect
Rate.
Based on the same technical idea, the embodiment of the invention provides a kind of devices for differentiating asthma, as shown in figure 3, should
Device 300 includes:
Preprocessing module 301, the characteristic value of multiple default features for extracting patient；
Categorization module 302, for determining the characteristic value input linear disaggregated model of multiple default features of the patient
The classification of the patient, the classification of the patient include with asthma and not suffering from asthma, the independent variable of the linear classification model
For the characteristic value of multiple default features, the dependent variable of the linear classification model is classification, and the linear classification model becomes certainly
Coefficient of discharge is fitted acquisition to the linear classification model using multiple training samples.
Optionally, the multiple default feature includes following characteristics any combination:
Gender, the age, weight, the age of wheezing for the first time, frequency of wheezing, whether have wheezing sound, inducement, whether have it is athermic
Wheeze, whether night suppress it is awake, whether limitation of activity, whether shortness of breath, whether break out, whether have gas source allergies, whether have
Whether history of rhinitis has dermatitis/eczema history, asthma family history, rhinitis family history, dermatitis family history, whether raises pet.
Optionally, the linear classification model is Logic Regression Models, includes logistic regression in the Logic Regression Models
Function；
The categorization module 302 is specifically used for:
According to the logistic regression function of the characteristic value of multiple default features of the patient and the Logic Regression Models, really
The asthma probability of the fixed patient；
According to the classification of patient described in the asthma determine the probability of the patient.
Optionally, the multiple default feature are as follows:
Weight, the age of wheezing for the first time, frequency of wheezing, whether have wheezing sound, inducement, whether have it is athermic wheeze, whether
Limitation of activity, whether shortness of breath, whether raise pet.
Based on the same technical idea, the embodiment of the present application provides a kind of terminal device, as shown in figure 4, including at least
One processor 401, and the memory 402 connecting at least one processor do not limit processor in the embodiment of the present application
Specific connection medium between 401 and memory 402 is connected as in Fig. 4 by bus between processor 401 and memory 402
Example.Bus can be divided into address bus, data/address bus, control bus etc..
In the embodiment of the present application, memory 402 is stored with the instruction that can be executed by least one processor 401, at least
The instruction that one processor 401 is stored by executing memory 402 can execute included in the aforementioned method for differentiating asthma
Step.
Wherein, processor 401 is the control centre of terminal device, can use various interfaces and connection terminal device
Various pieces, by running or executing the instruction being stored in memory 402 and calling is stored in number in memory 402
According to differentiate asthma.Optionally, processor 401 may include one or more processing units, and processor 401 can integrate application
Processor and modem processor, wherein the main processing operation system of application processor, user interface and application program etc.,
Modem processor mainly handles wireless communication.It is understood that above-mentioned modem processor can not also be integrated into
In processor 401.In some embodiments, processor 401 and memory 402 can be realized on the same chip, in some realities
It applies in example, they can also be realized respectively on independent chip.
Processor 401 can be general processor, such as central processing unit (CPU), digital signal processor, dedicated integrated
Circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array or other can
Perhaps transistor logic, discrete hardware components may be implemented or execute the application implementation for programmed logic device, discrete gate
Each method, step and logic diagram disclosed in example.General processor can be microprocessor or any conventional processor
Deng.The step of method in conjunction with disclosed in the embodiment of the present application, can be embodied directly in hardware processor and execute completion, Huo Zheyong
Hardware and software module combination in processor execute completion.
Memory 402 is used as a kind of non-volatile computer readable storage medium storing program for executing, can be used for storing non-volatile software journey
Sequence, non-volatile computer executable program and module.Memory 402 may include the storage medium of at least one type,
It such as may include flash memory, hard disk, multimedia card, card-type memory, random access storage device (Random Access
Memory, RAM), static random-access memory (Static Random Access Memory, SRAM), may be programmed read-only deposit
Reservoir (Programmable Read Only Memory, PROM), read-only memory (Read Only Memory, ROM), band
Electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory,
EEPROM), magnetic storage, disk, CD etc..Memory 402 can be used for carrying or storing have instruction or data
The desired program code of structure type and can by any other medium of computer access, but not limited to this.The application is real
Applying the memory 402 in example can also be circuit or other devices that arbitrarily can be realized store function, for storing program
Instruction and/or data.
Based on the same inventive concept, the embodiment of the present application provides a kind of computer-readable medium, and being stored with can be by end
The computer program that end equipment executes, when program is run on the terminal device, so that terminal device executes the side for differentiating asthma
The step of method.
It should be understood by those skilled in the art that, the embodiment of the present invention can provide as method or computer program product.
Therefore, complete hardware embodiment, complete software embodiment or embodiment combining software and hardware aspects can be used in the present invention
Form.It is deposited moreover, the present invention can be used to can be used in the computer that one or more wherein includes computer usable program code
The shape for the computer program product implemented on storage media (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.)
Formula.
The present invention be referring to according to the method for the embodiment of the present invention, the process of equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Although preferred embodiments of the present invention have been described, it is created once a person skilled in the art knows basic
Property concept, then additional changes and modifications may be made to these embodiments.So it includes excellent that the following claims are intended to be interpreted as
It selects embodiment and falls into all change and modification of the scope of the invention.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art
Mind and range.In this way, if these modifications and changes of the present invention belongs to the range of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to include these modifications and variations.
Claims (10)
1. a kind of method for differentiating asthma characterized by comprising
Extract the characteristic value of multiple default features of patient；
By the characteristic value input linear disaggregated model of multiple default features of the patient, the classification of the patient is determined, it is described
The classification of patient includes with asthma and not suffering from asthma, and the independent variable of the linear classification model is the feature of multiple default features
Value, the dependent variable of the linear classification model are classification, and the independent variable coefficient of the linear classification model is using multiple training
Sample is fitted acquisition to the linear classification model.
2. the method as described in claim 1, which is characterized in that the multiple default feature includes following characteristics any combination:
Whether gender at the age, weight, at the age of wheezing for the first time, frequency of wheezing, has wheezing sound, inducement, whether has athermic asthma
Breath, whether night suppress it is awake, whether limitation of activity, whether shortness of breath, whether break out, whether have gas source allergies, whether have nose
Whether scorching history has dermatitis/eczema history, asthma family history, rhinitis family history, dermatitis family history, whether raises pet.
3. the method as described in claim 1, which is characterized in that the linear classification model is Logic Regression Models, described to patrol
Collecting includes logistic regression function in regression model；
The characteristic value input linear disaggregated model of multiple default features by the patient, determines the classification of the patient,
Include:
According to the logistic regression function of the characteristic value of multiple default features of the patient and the Logic Regression Models, institute is determined
State the asthma probability of patient；
According to the classification of patient described in the asthma determine the probability of the patient.
4. method as claimed in claim 3, which is characterized in that the multiple default feature are as follows:
Weight, the age of wheezing for the first time, frequency of wheezing, whether have wheezing sound, inducement, whether have it is athermic wheeze, whether activity
It is limited, whether shortness of breath, whether raise pet.
5. a kind of device for differentiating asthma characterized by comprising
Preprocessing module, the characteristic value of multiple default features for extracting patient；
Categorization module, for determining the trouble for the characteristic value input linear disaggregated model of multiple default features of the patient
The classification of person, the classification of the patient include with asthma and not suffering from asthma, and the independent variable of the linear classification model is multiple
The characteristic value of default feature, the dependent variable of the linear classification model are classification, the independent variable coefficient of the linear classification model
It is that acquisition is fitted to the linear classification model using multiple training samples.
6. device as claimed in claim 5, which is characterized in that the multiple default feature includes following characteristics any combination:
Whether gender at the age, weight, at the age of wheezing for the first time, frequency of wheezing, has wheezing sound, inducement, whether has athermic asthma
Breath, whether night suppress it is awake, whether limitation of activity, whether shortness of breath, whether break out, whether have gas source allergies, whether have nose
Whether scorching history has dermatitis/eczema history, asthma family history, rhinitis family history, dermatitis family history, whether raises pet.
7. device as claimed in claim 5, which is characterized in that the linear classification model is Logic Regression Models, described to patrol
Collecting includes logistic regression function in regression model；
The categorization module is specifically used for:
According to the logistic regression function of the characteristic value of multiple default features of the patient and the Logic Regression Models, institute is determined
State the asthma probability of patient；
According to the classification of patient described in the asthma determine the probability of the patient.
8. device as claimed in claim 7, which is characterized in that the multiple default feature are as follows:
Weight, the age of wheezing for the first time, frequency of wheezing, whether have wheezing sound, inducement, whether have it is athermic wheeze, whether activity
It is limited, whether shortness of breath, whether raise pet.
9. a kind of terminal device, which is characterized in that including at least one processing unit and at least one storage unit, wherein
The storage unit is stored with computer program, when described program is executed by the processing unit, so that the processing unit
Perform claim requires the step of 1~4 any claim the method.
10. a kind of computer-readable medium, which is characterized in that it is stored with the computer program that can be executed by terminal device, when
When described program is run on the terminal device, so that the terminal device perform claim requires the step of 1~4 any the method
Suddenly.
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| WO2021203517A1 (en) * | 2020-04-10 | 2021-10-14 | 广州永士达医疗科技有限责任公司 | Oct device and apparatus for detecting spell of sickness of asthma patient, and storage medium |
| CN112002406A (en) * | 2020-07-10 | 2020-11-27 | 杭州依图医疗技术有限公司 | Nodule display method based on confidence coefficient and computing equipment |
| CN111951964A (en) * | 2020-07-30 | 2020-11-17 | 山东大学 | Method and system for rapidly detecting novel coronavirus pneumonia |
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