CN111564210A - Intelligent diagnosis guiding method and device, electronic equipment and storage medium - Google Patents
Intelligent diagnosis guiding method and device, electronic equipment and storage medium Download PDFInfo
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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
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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- G06F18/22—Matching criteria, e.g. proximity measures
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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
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
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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
- G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
Abstract
The invention discloses an intelligent diagnosis guiding method, an intelligent diagnosis guiding device, electronic equipment and a storage medium, and belongs to the technical field of medical information. Wherein, the method comprises the steps of determining symptom information needing to be guided; corresponding to at least one disease to be diagnosed according to the symptom information; acquiring n dimensionality detection data of the disease to be diagnosed; comparing the n dimensional detection data of the disease to be diagnosed with the n dimensional data of the diagnosed disease respectively; and obtaining the probability of the disease diagnosis result according to the similarity matching degree. The invention fully utilizes the medical conjuncted data resources, and makes the data become one of the important reference bases for the accurate diagnosis of the disease by comparing the similarity between the data information of the disease to be diagnosed and the data information of the diagnosed disease; the intelligent department recommendation is realized, and the efficiency and the accuracy of the diagnosis guide are improved.
Description
Technical Field
The invention relates to the technical field of medical information, in particular to an intelligent diagnosis guiding method, an intelligent diagnosis guiding device, electronic equipment and a storage medium.
Background
With the development of computer technology, computing power is greatly improved at present, and the performance of the smart phone exceeds that of a PC several years ago. The artificial intelligence and the machine learning are promoted to be widely used in various fields. Meanwhile, the information construction of the hospital covers all levels of medical institutions, so that the lasting and visual diagnosis and treatment information is realized; under the promotion of policies such as grading diagnosis and treatment, internet hospitals and the like, a large amount of examination and diagnosis and treatment data are stored. However, only a huge amount of diagnosis and treatment data are saved, and no great help is brought to the subsequent clinical treatment, so that a help platform which can use the data and has breakthrough for the clinical treatment is needed.
The medical guide is also called as a medical guide, and is a process of preliminarily judging the disease of the patient according to the description of the patient on the symptoms of the patient and guiding the patient to a relevant department for medical treatment. At present, most of the diagnosis guide modes of hospitals adopt manual diagnosis guide of a diagnosis guide table without electronic records, and basically, the hospital does not have a public data set related to the diagnosis guide and does not have related data. The corresponding diseases and departments can be guided and diagnosed only manually according to the symptoms provided by the patients, and the automatic guidance and diagnosis cannot be realized. Therefore, the efficiency of the diagnosis guide is low, and the diagnosis guide depends on manual experience, so that the diagnosis guide result cannot be unified, and the accuracy of the diagnosis guide is ensured.
At present, some intelligent diagnosis guiding related applications exist in the market, but the intelligent diagnosis guiding related applications can not realize real intelligence, some symptoms and diseases are artificially associated, and the diseases and the treatments are given according to simple selection and answer of a user. These basic associations only give the user some references, and particularly require hospitalization for confirmation, and are prone to psychological stress on the actual patient. On the doctor's level, diagnosing a disease cannot be confirmed by simple association and causal relationship. The diagnosis level of a doctor is often related to clinical experience, and the clinical experience is derived from problems encountered in all medical records treated by the doctor, solutions adopted and the like.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The intelligent diagnosis guiding method, the intelligent diagnosis guiding device, the electronic equipment and the storage medium fully utilize the medical conjuncted data resources, and enable the data to become one of the important reference bases for accurate diagnosis of diseases; the method is used for improving the efficiency of the diagnosis guide and improving the accuracy of the diagnosis guide.
In order to achieve the above object, the present application adopts a technical solution that an intelligent diagnosis guiding method comprises the following steps:
determining symptom information needing to be guided;
corresponding to at least one disease to be diagnosed according to the symptom information;
acquiring n dimensionality detection data of the disease to be diagnosed;
comparing the n dimensional detection data of the disease to be diagnosed with the n dimensional data of the diagnosed disease respectively;
and obtaining the probability of the disease diagnosis result according to the similarity matching degree.
Further, according to the corresponding relation between the diagnosed diseases and departments, the diagnosis guide department of the diseases corresponding to the symptom information is determined.
Further, comparing the n dimensional detection data of the disease to be diagnosed with the n dimensional data of the diagnosed disease respectively comprises similarity comparison, specifically:
disease to be diagnosed A ═ a1,a2,...,an}
Confirmed disease B ═ B1,b2,...,bn}
Wherein, a1,a2,...,anDetecting data characteristics for each dimension of the disease to be diagnosed, b1,b2,...,bnData characteristic of each dimension of the diagnosed disease;
splitting each dimension detection data of the disease to be diagnosed into m sub-dimensions, and splitting each dimension data of the diagnosed disease into m sub-dimensions, specifically:
disease to be diagnosed A ═ an1,an2,...,anm}
Confirmed disease B ═ Bn1,bn2,…,bnm}
Wherein, an1,an2,…,anmDetecting data features for each dimension of the disease to be diagnosed, bn1,bn2,...,bnmSub-dimensional data features for each dimension of diagnosed disease;
then, carrying out similarity comparison according to the characteristics of the sub-dimension data:
further, comparing the n dimensional detection data of the disease to be diagnosed with the n dimensional data of the diagnosed disease respectively further includes: and comparing the similarity of the disease case information according to the similarity of the sub-dimensions, namely:
further, the method also comprises the following steps: and outputting a clinical treatment scheme corresponding to the disease according to the disease diagnosis result.
Further, the method also comprises the following steps:
acquiring an actual diagnosis result;
adjusting the dimensionality corresponding to the symptom information according to the actual diagnosis result;
and taking the adjusted dimension as the dimension corresponding to the symptom information.
Further, the symptom information that determines that a medical guideline is needed includes at least one of a medical record and a current symptom.
Further, acquiring symptoms, diseases, departments and historical detection data according to the medical records.
The invention also discloses an intelligent diagnosis guiding device, which comprises:
the determining module is used for determining symptom information needing to be guided;
the corresponding module is used for corresponding to at least one disease to be diagnosed according to the symptom information;
the acquisition module is used for acquiring n dimensional detection data of the disease to be diagnosed;
the comparison module is used for comparing the n dimensional detection data of the diseases to be diagnosed with the n dimensional data of the diagnosed diseases respectively;
and the result module is used for obtaining the probability of the disease diagnosis result according to the similarity matching degree.
Furthermore, the comparison module comprises a sub-dimension comparison module, which is used for splitting each dimension detection data of the disease to be diagnosed into m sub-dimensions, splitting each dimension data of the diagnosed disease into m sub-dimensions, and performing similarity comparison according to the sub-dimension data characteristics.
Further, the comparison module comprises a case information comparison module for performing similarity comparison on the disease case information according to the similarity of the sub-dimensions.
The invention also discloses an electronic device, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the intelligent referral method described above.
The invention also discloses a storage medium, on which a computer program is stored, wherein the computer program realizes the intelligent diagnosis guiding method when being executed by a processor.
Due to the adoption of the technical scheme, the invention can obtain the following technical effects: fully utilizing the medical conjuncted data resources, and comparing the similarity of the data information of the diseases to be diagnosed and the data information of the diagnosed diseases to make the data become one of the important reference bases for the accurate diagnosis of the diseases; the intelligent department recommendation is realized, and the efficiency and the accuracy of the diagnosis guide are improved.
The intelligent diagnosis guiding method and the intelligent diagnosis guiding device can be applied to mobile terminal equipment or a hospital diagnosis guiding server, most time is not wasted in the aspects of inquiring the diagnosis guiding process, searching departments and the like, and convenient services such as online inquiry, appointment check, online registration, remote consultation and the like can be directly carried out according to the intelligent diagnosis result; and the hospital can better guide the patient to see a doctor through the intelligent diagnosis guide service, so that the medical experience of the patient is improved.
Drawings
FIG. 1 is a schematic flow chart diagram of a method for intelligent medical guidance in one embodiment;
FIG. 2 is a flow diagram illustrating an application of a method for intelligent medical guidance according to an embodiment;
FIG. 3 is a flow chart of similarity comparison implemented in an intelligent approach to medical guidance according to an embodiment;
FIG. 4 is a schematic block diagram of an intelligent referral apparatus of an embodiment;
fig. 5 is a flowchart illustrating a specific example of an intelligent diagnosis guiding method according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
While the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Under the rapid development of the information society, big data has become strategic resources which are as important as natural resources. Hospital databases store a large amount of medical data, which are often used only once, resulting in data accumulation and waste. The invention can automatically realize the diagnosis of diseases by utilizing the idle medical data, thereby greatly reducing the diagnosis pressure of doctors.
The technical solutions of the embodiments of the present invention are described in detail below with reference to the accompanying drawings.
FIG. 1 is a schematic flow diagram of a method of intelligent referral according to an embodiment of the invention.
Referring to fig. 1, the method includes:
in step 201, symptom information needing to be guided is determined, wherein the symptom information comprises at least one of medical history and current symptoms; and structuring the medical records to obtain medical record data aiming at each medical record, wherein the medical record data comprises symptoms, diseases, departments and historical detection data.
The medical records are records of medical activities such as examination, diagnosis and treatment of occurrence, development and outcome of diseases of patients by medical staff, wherein the data comprise symptoms of the patients, the diseases diagnosed according to the symptoms of the patients and departments corresponding to the medical records obtained based on the diseases. The medical record includes a plurality of diagnoses, each of which includes symptoms, diseases and departments, such that the symptoms, diseases and departments correspond to each other in a single diagnosis. And storing the statistical data into a database to realize the electronic record of hospital diagnosis.
The current symptoms may include personal information data and accompanying symptom performance data of the patient. The personal information data may include data such as sex, age, height and weight, and the accompanying symptom expression data may include data such as physical condition, suspected disease, one or more symptoms and symptom degree of the patient, and is specifically implemented as:
receiving an input natural language;
and structuring the natural language to obtain the symptom information data.
In this embodiment, the method for structuring the natural language may be a rule classifier constructed based on an intermediate state, or end-to-end automatic extraction based on a machine learning algorithm, or a text structuring method for deep learning, and the like, which is not limited thereto.
In step 202, at least one disease to be diagnosed is corresponded according to the symptom information, specifically: intercepting the keywords of symptom information, finding out keywords of symptoms and signs, searching the keywords of the symptoms and signs, finding out all cases with the same symptoms and signs, referring to the cases as suspected diseases, wherein the symptom information and the diseases are in a many-to-many relationship, each symptom corresponds to a plurality of diseases, each disease corresponds to a plurality of symptoms, and different symptom combinations can be caused by different diseases; each disease can show one or several symptoms, the symptoms are the main characteristics of suspected diseases, such as symptom information including keywords such as 'emaciation', 'polyuria' and 'weight loss', and the corresponding diseases to be diagnosed can be 'diabetes' and 'multiple myeloma'.
In step 203, acquiring n dimensional detection data of the disease to be diagnosed; each disease is affected by n dimensions, so it is split; the n dimensional detection data comprises at least one of an individual dimension, an occupation dimension, a disease dimension, and a time dimension; wherein the attribute information of the personal dimension comprises at least one of the following items: age information, height information, weight information, motion information, address information; attribute information of the occupation dimension comprises at least one of the following items: professional information, income information; attribute information of the disease dimension, including at least one of: disease information; the attribute information of the time dimension comprises at least one of the following items: the duration of the disease. This information may be obtained from step 201.
In step 204, comparing the n dimensional detection data of the disease to be diagnosed with the n dimensional data of the diagnosed disease respectively;
the diagnosed disease data may come from a local database that may obtain medical data from the hospital informatization platform, in which case the mobile terminal device or the hospital lead server having the local database may construct a trust relationship with the hospital informatization platform in advance so that the mobile terminal device or the hospital lead server has the right to obtain data from the hospital informatization platform. For example, the existing cryptography principle can be used to construct the trust relationship between the mobile terminal device or the hospital consultation server and the hospital informatization platform, specifically, the hospital informatization platform can encrypt the medical data by using a public key, and the mobile terminal device or the hospital consultation server can decrypt the secret text of the medical data by using a private key thereof after obtaining the secret text of the medical data, so as to obtain the plaintext of the medical data. In this embodiment, no special limitation is imposed on the communication connection mode between the mobile terminal device or the hospital consultation server and the hospital informatization platform. The integrated medical information platform comprises a unified medical data center: the system is used for integrating clinical data in different information systems of a hospital and performing centralized management and storage on the clinical data.
Preferably, the mobile terminal device or the hospital consultation server can perform cleaning, classification, data structuring and the like on the medical data; in addition, the mobile terminal device or the hospital consultation server can acquire medical data regularly, for example, the medical data of the previous day is acquired every morning, and the advantage of acquiring the data in the morning is that the occupancy rate of the server is small, and network transmission congestion can be avoided. In addition, the mobile terminal equipment or the hospital diagnosis guide server can also acquire medical data on line in real time so as to further process the medical data in time.
In step 205, the probability of the disease diagnosis result is obtained according to the similarity matching degree, the higher the probability value is, the higher the matching degree with the disease is, the first 1-3 probability values are selected from high to low, and the disease is possibly diagnosed.
The mobile terminal device may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices, and the hospital consultation server may be implemented by an independent server or a server cluster formed by a plurality of servers.
The utilization rate of the existing hospital business data is not high, and the method can call all business system data of the hospital to provide more valuable reference for intelligent diagnosis guide.
FIG. 2 is a flow diagram illustrating an application of a method for intelligent referral according to an embodiment of the invention;
referring to fig. 2, the method can be applied to, but is not limited to, the following three aspects:
in step 301, a department for diagnosis guidance of a disease corresponding to the symptom information is determined based on the correspondence between the diagnosed disease and the department. The higher the similarity degree of the disease to be diagnosed and the diagnosed disease is, the higher the possibility of suffering the disease is, so that the patient can clearly see a specific diagnosis department, and intelligent department recommendation is realized, thereby improving the registration accuracy of the patient and saving the human resources and the operation cost of the hospital.
Comparing the actual department for treatment with the recommended department in the corresponding department recommendation result, judging whether the recommended department is consistent with the actual department for treatment, when the recommended department is inconsistent with the actual department for treatment, acquiring symptom information corresponding to the department recommendation result, generating correction sample data according to the symptom information and the actual department for treatment, and adding the correction sample data into a local database; and when the recommended department is consistent with the actual clinic, generating newly increased sample data according to the recommended department and the corresponding symptom information, and adding the newly increased sample data into the local database.
In step 302, outputting a clinical treatment scheme corresponding to the disease according to the disease diagnosis result; historical diagnosis and treatment sample data acquired by the integrated medical information platform can be acquired from a local database, and the historical diagnosis and treatment sample data can be diagnosis and treatment department data of patients, patient diagnosis and treatment data and the like;
in step 303, actual diagnostic results are obtained; adjusting the dimensionality corresponding to the symptom information according to the actual diagnosis result; and taking the adjusted dimension as the dimension corresponding to the symptom information. If the actual result of the doctor is different from the disease diagnosis result in the diagnosis process, the doctor manually marks the disease to be diagnosed, supervises machine learning and then adds the disease to the database; the step can also comprise an application programming interface for providing information services or resources for an external application system or other platforms, so that doctors in lower hospitals and community health hospitals can be exposed to the diagnosis experience of the third hospital, and the academic level is promoted. Personal privacy information in the symptom information can be removed, and diagnosis and treatment data can be shared in a regional mode, a whole network mode and the like under the permission of hospitals and doctors. The medical information sharing and data mining utilization in a larger area are realized, and the medical technology level and the service capability are promoted to be rapidly improved.
FIG. 3 is a flow chart of similarity comparison implemented in an intelligent approach to referral according to an embodiment of the invention;
referring to fig. 3, in the method, n dimensional detection data of a disease to be diagnosed are respectively compared with n dimensional data of a diagnosed disease, specifically:
disease to be diagnosed A ═ a1,a2,…,an}
Confirmed disease B ═ B1,b2,…,bn}
Wherein, a1,a2,…,anDetecting data characteristics for each dimension of the disease to be diagnosed, b1,b2,…,bnData characteristic of each dimension of the diagnosed disease;
taking the procedure of diagnosing pulmonary hypertension as an example: performing an echocardiogram to examine the pulmonary hypertension probability as dimension a1And several other inspection dimensions;
in step 2041, splitting each dimension detection data of the disease to be diagnosed into m sub-dimensions, and splitting each dimension data of the disease to be diagnosed into m sub-dimensions, specifically:
disease to be diagnosed A ═ an1,an2,…,anm}
Confirmed disease B ═ Bn1,bn2,…,bnm}
Wherein, an1,an2,...,anmDetecting data features for each dimension of the disease to be diagnosed, bn1,bn2,...,bnmSub-dimensional data features for each dimension of diagnosed disease;
continuing with the example of a diagnostic procedure for pulmonary arterial hypertension: symptoms, signs, risk factors, Electrocardiogram (ECG), Pulmonary Function Test (PFT), carbon monoxide diffusion ability test (DLCO), chest radiograph, high resolution ct (hrct), and the like are examined as sub-dimensions.
In step 2042, similarity comparison is performed according to the sub-dimension data features:
continuing with the example of a diagnostic procedure for pulmonary arterial hypertension: carrying out similarity fitting comparison on the sub-dimension data of the disease to be diagnosed and the corresponding pulmonary artery high pressure sub-dimension data in the local database, if the probability is low, the probability of pulmonary artery high pressure can be eliminated in a high probability, other reasons can be considered in a processing mode, regular follow-up examination and the like can be considered, and unnecessary further examination is avoided; if the likelihood is greater, further screening is performed.
In step 2043, the similarity comparison is performed on the disease case information according to the similarity of the sub-dimensions, that is:
continuing with the example of a diagnostic procedure for pulmonary arterial hypertension: similarity fitting comparison is carried out on the disease case information data to be diagnosed and the corresponding pulmonary hypertension case information data in the local database, if the left heart disease or lung disease probability given by the result is low and no sign of severe Pulmonary Hypertension (PH) or Right Ventricular (RV) dysfunction exists, the possibility of pulmonary hypertension can be eliminated approximately, and the diagnosis and treatment mode can be carried out according to basic diseases; if there are signs of severe Pulmonary Hypertension (PH) or Right Ventricular (RV) dysfunction, then the diagnosis needs to be transferred to the center of a pulmonary artery specialist for further diagnosis and treatment. A further pulmonary ventilation perfusion scan (V/Q scan) is performed, and data fitting is performed according to the V/Q scan result, so that if the V/Q scan shows multiple perfusion defects, chronic thromboembolic pulmonary hypertension (CTEPH) is possible. Further confirmation of CTEPH requires CT pulmonary angiography, RHC, selective pulmonary angiography, and the like. The CT scan can be used to confirm the Pulmonary Vein Occlusion (PVOD). If the V/Q scan is normal or exhibits only a sub-segment "patch-like" perfusion defect, Pulmonary Arterial Hypertension (PAH) or other rare cases should be considered. See, for example, the following table:
| dimension 1 | Dimension 2 | Dimension 3 | Dimension 4 | Dimension n | |
| Case A to be diagnosed | 1 | 1 | 3 | 1 | |
| Confirmed case 1 | 1 | 2 | 3 | 2 | |
| Confirmed case 2 | 2 | 2 | 1 | 1 | |
| Confirmed case n |
And substituting the data in the table into a formula to calculate the similarity, and obtaining that the similarity between the case A and the case 1 is about 0.414, and the similarity between the case A and the case 2 is about 0.287, so that the probability of each diagnosis result can be obtained according to the similarity matching degree, and the diagnosis reference opinion is given.
The flow of diagnosis of pulmonary hypertension is shown in fig. 5.
It should be noted that although the various steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that these steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
FIG. 4 is a schematic block diagram of an intelligent referral apparatus according to an embodiment of the invention;
referring to fig. 5, an intelligent diagnosis guide apparatus includes:
a determining module 401, configured to determine symptom information that needs to be guided;
a corresponding module 402, configured to correspond to at least one disease to be diagnosed according to the symptom information;
an obtaining module 403, configured to obtain n dimensional detection data of the disease to be diagnosed;
a comparing module 404, configured to compare the n dimensional detection data of the disease to be diagnosed with the n dimensional data of the diagnosed disease respectively;
the comparison module 404 includes a sub-dimension comparison module, configured to split each dimension detection data of the disease to be diagnosed into m sub-dimensions, split each dimension data of the diagnosed disease into m sub-dimensions, and perform similarity calculation according to the sub-dimension data characteristics;
the comparing module 404 further comprises a case information comparing module, configured to perform similarity calculation on the disease case information according to the similarity of the sub-dimensions;
and the result module 405 is used for obtaining the probability of the disease diagnosis result according to the similarity matching degree.
For specific limitations of the intelligent diagnosis guiding apparatus, reference may be made to the above limitations of the intelligent diagnosis guiding method, which are not described herein again. The modules in the intelligent diagnosis guiding device can be wholly or partially realized by software, hardware and a combination thereof.
The embodiment of the invention also discloses an electronic device, which comprises: a memory, a processor, a bus connecting different components (including the memory and the processor); the memory stores a computer program which, when executed by the processor, implements the intelligent approach described in the embodiments of the present application.
The electronic device may also communicate with one or more external devices (e.g., keyboard, pointing device, display, etc.), one or more devices that enable a user to interact with the electronic device, and/or any devices (e.g., network card, modem, etc.) that enable the electronic device to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface. Also, the electronic device may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via a network adapter. The network adapter communicates with other modules of the electronic device over the bus. Other hardware and/or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, tape drives, and data backup storage systems, among others.
The processor executes various functional applications and data processing by executing programs stored in the memory.
It should be noted that, for the implementation process and the technical principle of the electronic device of the embodiment, reference is made to the foregoing explanation of the intelligent diagnosis guiding method of the embodiment of the present application, and details are not described here again.
The present invention also provides a storage medium having a computer program stored thereon, which when executed by a processor implements the above-described intelligent diagnosis guide method.
In an alternative implementation, the embodiments may be implemented in any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CDROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present embodiment, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the consumer electronic device, partly on the consumer electronic device, as a stand-alone software package, partly on the consumer electronic device and partly on a remote electronic device, or entirely on the remote electronic device or server. In the case of remote electronic devices, the remote electronic devices may be connected to the consumer electronic device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external electronic device (e.g., through the internet using an internet service provider).
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
Claims (13)
1. An intelligent diagnosis guiding method is characterized by comprising the following steps:
determining symptom information needing to be guided;
corresponding to at least one disease to be diagnosed according to the symptom information;
acquiring n dimensionality detection data of the disease to be diagnosed;
comparing the n dimensional detection data of the disease to be diagnosed with the n dimensional data of the diagnosed disease respectively;
and obtaining the probability of the disease diagnosis result according to the similarity matching degree.
2. The intelligent diagnosis guiding method according to claim 1, wherein the diagnosis guiding department of the disease corresponding to the symptom information is determined according to the correspondence between the diagnosed disease and the department.
3. The intelligent diagnosis guiding method according to claim 1, wherein comparing the n dimensional detection data of the disease to be diagnosed with the n dimensional data of the diagnosed disease respectively comprises similarity comparison, specifically:
disease to be diagnosed A ═ a1,a2,…,an}
Confirmed disease B ═ B1,b2,…,bn}
Wherein, a1,a2,…,anDetecting data characteristics for each dimension of the disease to be diagnosed, b1,b2,…,bnData characteristic of each dimension of the diagnosed disease;
splitting each dimension detection data of the disease to be diagnosed into m sub-dimensions, and splitting each dimension data of the diagnosed disease into m sub-dimensions, specifically:
disease to be diagnosed A ═ an1,an2,…,anm}
Confirmed disease B ═ Bn1,bn2,…,bnm}
Wherein, an1,an2,…,anmDetecting data features for each dimension of the disease to be diagnosed, bn1,bn2,…,bnmSub-dimensional data features for each dimension of diagnosed disease;
then, carrying out similarity comparison according to the characteristics of the sub-dimension data:
4. the intelligent diagnosis guiding method according to claim 3, wherein comparing the n-dimensional detection data of the disease to be diagnosed with the n-dimensional data of the diagnosed disease respectively further comprises: and comparing the similarity of the disease case information according to the similarity of the sub-dimensions, namely:
5. the intelligent referral method of claim 1 further comprising: and outputting a clinical treatment scheme corresponding to the disease according to the disease diagnosis result.
6. The intelligent referral method of claim 1 further comprising:
acquiring an actual diagnosis result;
adjusting the dimensionality corresponding to the symptom information according to the actual diagnosis result;
and taking the adjusted dimension as the dimension corresponding to the symptom information.
7. The intelligent referral method of claim 1 wherein the symptom information identifying the need for referral includes at least one of medical history and current symptoms.
8. The intelligent medical guidance method of claim 7, further comprising obtaining symptoms, diseases, departments, and historical data based on medical records.
9. An intelligent diagnostic device, comprising:
the determining module is used for determining symptom information needing to be guided;
the corresponding module is used for corresponding to at least one disease to be diagnosed according to the symptom information;
the acquisition module is used for acquiring n dimensional detection data of the disease to be diagnosed;
the comparison module is used for comparing the n dimensional detection data of the diseases to be diagnosed with the n dimensional data of the diagnosed diseases respectively;
and the result module is used for obtaining the probability of the disease diagnosis result according to the similarity matching degree.
10. The intelligent diagnosis guiding device according to claim 9, wherein the comparison module comprises a sub-dimension comparison module, configured to split each dimension detection data of the disease to be diagnosed into m sub-dimensions, split each dimension data of the diagnosed disease into m sub-dimensions, and perform similarity comparison according to sub-dimension data characteristics.
11. The intelligent diagnosis guide device according to claim 9, wherein the comparison module comprises a case information comparison module for performing similarity comparison on the disease case information according to the similarity of the sub-dimensions.
12. An electronic device, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the intelligent referral method of any one of claims 1-7.
13. A storage medium having stored thereon a computer program which, when executed by a processor, implements the intelligent diagnosis guiding method according to any one of claims 1 to 7.
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