CN116504387A - Self-help diagnosis method, device, equipment and storage medium - Google Patents
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Abstract
The application relates to the technical field of artificial intelligence and discloses a self-help diagnosis method, a self-help diagnosis device, self-help diagnosis equipment and a storage medium, wherein the self-help diagnosis method comprises the following steps: when receiving a patient consultation request, acquiring descriptive information from the patient consultation request; identifying keywords in the description information, and acquiring a disease information list matched with the keywords; identifying distinguishing symptom features among the various pieces of disease information in the disease information list, generating a first questionnaire according to the distinguishing symptom features, and transmitting the first questionnaire to the target patient; acquiring a first answer result of the target patient to the first questionnaire, so as to select target disease information from a disease information list according to the first answer result; acquiring a second questionnaire corresponding to the target disease information, and sending the second questionnaire to the target patient; and obtaining a second answer result of the target patient to the second questionnaire so as to judge whether the target patient suffers from the target disease corresponding to the target disease information according to the second answer result.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a self-help diagnosis method, a self-help diagnosis device, self-help diagnosis equipment and a storage medium.
Background
At present, doctor and patient diagnosis still rely on manual dialogue with patient to make inquiry, doctor need to make multi-azimuth inquiry according to the existing symptoms, historical symptoms, inspection results, disease evolution and other processes of patient, then diagnosis is carried out according to pathological development, and finally diagnosis and treatment results can be obtained. Moreover, the patient spends a lot of time going to the hospital and queuing, resulting in inefficient patient visits.
Disclosure of Invention
The main aim of the application is to provide a self-help diagnosis method, a self-help diagnosis device, self-help diagnosis equipment and a storage medium, and aims to solve the problem of low diagnosis efficiency of patients in the prior art.
In a first aspect, the present application provides a self-help consultation method, comprising:
when a patient consultation request is received, acquiring descriptive information from the patient consultation request;
identifying keywords in the description information, and acquiring a disease information list matched with the keywords;
identifying distinguishing symptom features among the respective disease information when a plurality of disease information exists in the disease information list, so as to generate a first questionnaire according to the distinguishing symptom features, and transmitting the first questionnaire to a target patient;
Acquiring a first response result of the target patient to the first questionnaire, so as to select target disease information from the disease information list according to the first response result;
acquiring a second questionnaire corresponding to the target disease information, and sending the second questionnaire to the target patient;
and obtaining a second response result of the target patient to the second questionnaire, so as to judge whether the target patient suffers from the target disease corresponding to the target disease information according to the second response result.
In a second aspect, the present application further provides a self-help diagnosis device, the self-help diagnosis device comprising:
the consultation processing module is used for acquiring descriptive information from the patient consultation request when the patient consultation request is received;
the keyword recognition module is used for recognizing keywords in the description information and acquiring a disease information list matched with the keywords;
a first questionnaire module for identifying distinguishing symptom features among the respective disease information when there are a plurality of disease information in the disease information list, generating a first questionnaire according to the distinguishing symptom features, and transmitting the first questionnaire to a target patient;
The first diagnosis module is used for obtaining a first answer result of the target patient to the first questionnaire so as to select target disease information from the disease information list according to the first answer result;
a second questionnaire module for acquiring a second questionnaire corresponding to the target disease information and sending the second questionnaire to the target patient;
and the second diagnosis module is used for acquiring a second answer result of the target patient to the second questionnaire so as to judge whether the target patient suffers from the target disease corresponding to the target disease information according to the second answer result.
In a third aspect, the present application also provides a computer device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program when executed by the processor implements the steps of the self-help consultation method described above.
In a fourth aspect, the present application also provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a self-service consultation method as described above.
The application provides a self-help consultation method, a self-help consultation device, self-help consultation equipment and a storage medium, wherein in the self-help consultation method, the self-help consultation equipment and the storage medium, when a patient consultation request is received, descriptive information is acquired from the patient consultation request; identifying keywords in the description information, and acquiring a disease information list matched with the keywords; identifying distinguishing symptom features among the various pieces of disease information in the disease information list, generating a first questionnaire according to the distinguishing symptom features, and transmitting the first questionnaire to the target patient; acquiring a first answer result of the target patient to the first questionnaire, so as to select target disease information from a disease information list according to the first answer result; acquiring a second questionnaire corresponding to the target disease information, and sending the second questionnaire to the target patient; and obtaining a second answer result of the target patient to the second questionnaire so as to judge whether the target patient suffers from the target disease corresponding to the target disease information according to the second answer result. Through the technical scheme that this application provided to the mode of artificial intelligence lets the patient realize the self-service treatment, has solved the problem that patient's treatment efficiency is low among the prior art.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic step flow diagram of a self-help diagnosis method according to an embodiment of the present application;
fig. 2 is a schematic block diagram of a self-service diagnosis device according to an embodiment of the present application;
fig. 3 is a schematic block diagram of a structure of a computer device according to an embodiment of the present application.
The realization, functional characteristics and advantages of the present application will be further described with reference to the embodiments, referring to the attached drawings.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations. In addition, although the division of the functional modules is performed in the apparatus schematic, in some cases, the division of the modules may be different from that in the apparatus schematic.
The embodiment of the application provides a self-help diagnosis method, device, equipment and storage medium. The method can be applied to terminal equipment or a server, wherein the terminal equipment can be electronic equipment such as mobile phones, tablet computers, notebook computers, desktop computers, personal digital assistants, wearable equipment and the like; the server may be a single server or a server cluster composed of a plurality of servers. The following explanation will be made taking the application of the method to a server as an example.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic step flow diagram of a self-help diagnosis method according to an embodiment of the present application.
As shown in fig. 1, the self-help diagnosis method includes steps S10 to S15.
And step S10, when a patient consultation request is received, acquiring description information from the patient consultation request.
It can be understood that, when the execution body of the application is a server, the patient consultation request may be a network request sent by the terminal device for the patient received by the server, or may be an instant communication message sent by the patient received by the server through the terminal device after the terminal device and the server are connected by full duplex communication, and of course, the patient consultation request may also be other forms of control instructions received by and identifiable by the server, which is not limited herein.
Correspondingly, when the execution subject of the application is the terminal device, the patient consultation request can be the control information captured and identified by the terminal device when the patient clicks a control such as a screen or a button of the terminal device, and likewise, the patient consultation request can be other forms of control instructions received and identified by the terminal device, which is not limited herein.
The patient consultation request carries description information of the patient for describing the condition of the patient, and when the patient consultation is received, the description information is extracted from the patient consultation, so that the patient can be analyzed for which diseases according to the description information.
Step S11, identifying keywords in the description information, and acquiring a disease information list matched with the keywords.
The keywords in the descriptive information include, but are not limited to, symptoms input by the patient aiming at discomfort of the patient, after the keywords in the descriptive information are determined, which diseases the patient possibly suffers from can be estimated according to the keywords, and disease information such as symptoms corresponding to each matched disease is extracted, so that a disease information list is obtained.
There are various kinds of keywords in the description information, and by way of example, the weights of the respective words in the description information may be calculated by a TF-IDF (term frequency-inverse document frequency) algorithm, and which words in the description information are keywords may be determined according to the calculated weights.
In some embodiments, the identifying the keyword in the description information and obtaining the disease information list matched with the keyword includes:
performing word segmentation processing on the description information to obtain a first word segmentation set;
filtering useless word segmentation in the first word segmentation set to obtain a second word segmentation set;
identifying a classification label corresponding to a second word in the second word set, and selecting the second word of the symptom label corresponding to the classification label from the second word set as a keyword, wherein the number of the keywords can be one or more;
and screening disease information of which symptoms comprise at least one keyword from a disease database to obtain a disease information list.
When the descriptive information is segmented, the method can be realized by using (NLP, natural Language Processing) natural language processing technology, and other methods can be adopted to segment the descriptive information according to situation requirements, so that the method is not limited. In addition, after the description information is subjected to word segmentation, the obtained set formed by the words is used as a first word segmentation set.
There may be some unwanted words in the first word segmentation set, such as "may", "we", "usual", etc., which occur more frequently during the daily speaking of a person, but have little meaning to characterize. And filtering the unused words in the first word segmentation set through a preset unused word filtering model to obtain a second word segmentation set, wherein the words in the second word segmentation set are the second word segmentation. It can be appreciated that, before determining the keywords, useless word segments in the first word segment set are filtered out, so that the number of words which need to be identified and classified subsequently can be reduced, and the time for determining the keywords subsequently is reduced.
The classification labels include, but are not limited to, symptom labels, personal information labels, and disease guess labels. Wherein the symptom label corresponds to the description of the patient's own symptoms; the personal information label corresponds to the information such as the sex, age, name and the like of the patient; the disease guess label corresponds to the disease name of the disease which the patient guesses to have by his own experience. In this embodiment, the second word of the symptom label corresponding to the classification label is identified and selected as the keyword, so as to analyze according to the determined keyword, and improve the accuracy of disease diagnosis of the patient.
In addition, each disease includes one or more symptoms, and when a disease corresponding symptom includes any keyword determined by the above method, the disease may be a disease that the patient currently suffers from, and a list formed by disease information corresponding to the disease that meets the matching condition is obtained, that is, a disease information list.
In some embodiments, the identifying the class label corresponding to the second term in the second set of terms includes:
acquiring a preset classified label comparison list, wherein the classified label comparison list is a mapping information table of words to be matched and classified labels corresponding to the words to be matched;
When the words to be matched in the classified label comparison list are matched with the second word, acquiring a classified label corresponding to the words to be matched as a classified label of the second word;
when the words to be matched in the classified tag comparison list are not matched with the second word, a pre-trained vector acquisition model is used for acquiring a vector corresponding to the second word as a standard vector, and a comparison vector corresponding to each alternative word is acquired from a preset word stock;
calculating cosine similarity of the standard vector and each comparison vector, and selecting candidate words with the cosine similarity meeting a preset condition from the word stock as synonyms of the second word;
and obtaining a classification label corresponding to the synonym in the classification label comparison list as a classification label of the second segmentation.
It can be understood that the classification tag comparison list is a preset information comparison list, wherein the mapping relation between each word to be matched and the classification tag is aggregated. If the to-be-matched words corresponding to the second word exist in the classified label comparison list, the classified label corresponding to the to-be-matched words is the classified label corresponding to the second word. If the to-be-matched words corresponding to the second word do not exist in the classified label comparison list, the classified labels corresponding to the second word can be matched by identifying the synonym corresponding to the second word according to the synonym de-classified label comparison list.
In this embodiment, a vector acquisition model is used to acquire the synonyms corresponding to the second synonyms. In some embodiments, the vector obtaining model may be a Bert (Bidirectional Encoder Representation from Transformers) model, a word2vec (word casting) model, or another model that may obtain word vectors corresponding to each word, which is not limited herein.
The standard vector is a vector which can represent the semantics of the second word, and the comparison vector is a vector which can represent the semantics of the alternative word. And calculating cosine similarity of the standard vector and the comparison vector corresponding to each candidate word in the word stock, and screening synonyms corresponding to the second word according to the cosine similarity. After the synonyms corresponding to the second keywords are identified, the classification labels corresponding to the second keywords can be determined according to the synonyms and the classification label comparison list. By the technical scheme provided by the embodiment, the success rate of acquiring the classification labels of the second segmentation is improved.
In some embodiments, before the selecting, from the second word set, the second word of the symptom tag corresponding to the classification tag as the keyword, the method further includes:
Judging whether special word segmentation corresponding to the special case label exists in the second word segmentation set or not;
and prompting the target patient to go to a hospital for a doctor when the special word is included in the second word segmentation set.
It can be understood that the second word segment corresponding to the special case tag is the special word segment. When the special word is present in the second word set, it is indicated that the patient may be in a special state, and at this time, it is recommended that the patient visit the hospital more surely.
In some embodiments, when second words such as "pregnancy", "pregnancy" or "senior" exist in the second word set, their corresponding classification labels are special case labels, and these second words are special words, and of course, other words may be included in the scope of the special words according to the situation, which is not limited herein.
And step S12, when a plurality of pieces of disease information exist in the disease information list, distinguishing symptom characteristics among the pieces of disease information are identified, so that a first questionnaire is generated according to the distinguishing symptom characteristics, and the first questionnaire is sent to a target patient.
The target patient is the patient who initiates the patient consultation request. It will be appreciated that the disease information list records disease information corresponding to a disease that a patient may have, and the disease information list may include a plurality of disease information, and each symptom of the disease information includes at least one keyword in the description information, where the disease information is primarily presumed according to the description information of the patient, and is not necessarily accurate.
Therefore, when there are a plurality of pieces of disease information in the disease information list, the patient may only have a disease corresponding to one of the pieces of disease information, and at this time, it is necessary to identify which disease the patient has.
It will be appreciated that the symptom features corresponding to different diseases are necessarily different, and distinguishing symptom features among the respective disease information in the disease information list are identified, so that the first questionnaire can be generated according to the identified distinguishing symptom features. After the first questionnaire is generated, the first questionnaire can be sent to the target patient, so that the target patient replies whether the first questionnaire has the distinguishing symptom characteristic or not according to the condition of the target patient.
In some embodiments, when there are a plurality of disease information in the disease information list, identifying a distinguishing symptom feature between the respective disease information to generate a first questionnaire according to the distinguishing symptom feature, including:
Collecting symptom characteristics from each piece of disease information in the disease information list to obtain a symptom characteristic set;
sequentially selecting candidate symptom characteristics from the symptom characteristic set, and judging whether each piece of disease information in the disease information list contains the candidate symptom characteristics or not;
determining that the candidate symptom feature is a distinguishing symptom feature when at least one piece of disease information does not contain the candidate symptom feature in the disease information list;
and acquiring symptom degree options corresponding to the distinguishing symptom features, and generating a first questionnaire according to the distinguishing symptom features and the symptom degree options.
It can be understood that each piece of disease information has its corresponding symptom feature, and the symptom features of each piece of disease information in the disease information list are collected and put into a set, so as to obtain a symptom feature set.
After the candidate symptom feature is selected from the symptom feature set, if each piece of disease information in the disease information list contains the candidate symptom feature, it is indicated that the target patient cannot be distinguished from the disease in the disease information list according to the candidate symptom feature. Accordingly, if not every piece of disease information in the list of disease information contains the candidate symptom feature, the candidate symptom feature is a distinguishing symptom feature, and according to the distinguishing symptom feature, it can be further determined which disease the patient suffers from.
After the distinguishing symptom features are determined, a first questionnaire can be generated according to the distinguishing symptom features and the symptom degree options corresponding to the distinguishing symptom features.
Illustratively, it is assumed that the diseases and symptoms of the disease information list are as shown in the following table one.
A first step of,
The symptom characteristic set determined from the disease information list includes "runny nose", "cough", and "fever". Wherein "disease 1" includes the candidate symptom feature "fever", but "disease 2" does not include the candidate symptom feature "fever", and thus the candidate symptom feature "fever" is a distinguishing symptom feature.
Assuming that symptom degree options for distinguishing struggle feature "fever" include "normothermia", "low fever", "medium fever" and "high fever", a first questionnaire may be generated according to the "fever" setting question, with "normothermia", "low fever", "medium fever" and "high fever" as question options.
Step S13, a first answer result of the target patient to the first questionnaire is obtained, so that target disease information is selected from the disease information list according to the first answer result.
It can be understood that after receiving the first questionnaire, the target patient selects options for each question in the first questionnaire according to the situation of the target patient, and submits a selection result after the selection is completed.
The physical condition of the target patient can be further understood according to the first reply result so as to narrow the range of diseases possibly suffered by the target patient. And screening the disease information matched with the first reply result from the disease information list to obtain target disease information.
In some embodiments, the selecting the target disease information from the disease information list according to the first reply result includes:
determining a target symptom level selected by the target patient for the distinguishing symptom feature according to the first reply result;
and screening the disease information of which the symptom degree of the distinguishing symptom characteristic is matched with the target symptom degree from the disease information list to obtain target disease information.
It can be understood that the first questionnaire uses the distinguishing symptom as a question, uses various symptom degrees corresponding to the distinguishing symptom as options, and selects the target symptom degree matching the condition of the target patient as a first answer result in the process of filling the first questionnaire by the target patient.
After determining the target symptom degree of the corresponding distinguishing symptom feature of the target patient, the disease information with the symptom degree of the distinguishing symptom feature matched with the target symptom degree can be selected from the disease information of the disease information list to serve as target disease information.
Illustratively, the disease, symptoms, and degree of symptoms of the disease information list are assumed as shown in the following table two.
A second part,
The distinguishing symptom characteristic is assumed to be "fever", and the target symptom degree determined from the first reply result is assumed to be "normothermia", because the symptom of "disease 2" does not include "fever", and is thus equivalent to "normothermia", therefore, "disease 2" is determined as target disease information.
Step S14, a second questionnaire corresponding to the target disease information is acquired, and the second questionnaire is sent to the target patient.
It can be understood that after the target disease information is determined, a second questionnaire can be generated according to various symptoms corresponding to the target disease information, and the second questionnaire is sent to the target patient, so as to obtain a response of the target patient to confirm whether the target patient has the target disease corresponding to the target disease information.
In some embodiments, a questionnaire list for determining whether the target patient has a disease is recorded in the database, and a second questionnaire for determining whether the target patient has a target disease corresponding to the target disease information can be obtained from the questionnaire list according to the disease name corresponding to the target disease information; in addition, the symptoms corresponding to the target disease information are recorded in the disease information list, and a second questionnaire corresponding to the target disease information can be generated by acquiring symptom degree options corresponding to the symptoms; of course, the second questionnaire may be obtained by other means, without limitation. And sending the second questionnaire to a patient terminal device held by the target patient, and enabling the target patient to view and fill the second questionnaire through the patient terminal device.
Step S15, a second answer result of the target patient to the second questionnaire is obtained, so that whether the target patient suffers from the target disease corresponding to the target disease information is judged according to the second answer result.
It can be understood that after receiving the second questionnaire, the target patient selects options for each question in the second questionnaire according to the situation of the target patient, and submits a selection result after the selection is completed. And deducing whether the target patient suffers from the target disease corresponding to the target disease information according to the second reply result.
Illustratively, the disease, symptoms, and symptom levels of the list of disease information are assumed as shown in table two above, the target disease information selected according to the first questionnaire is assumed to be "disease 2", and the second questionnaire and the second response results are assumed to be shown in table three below.
A third step of,
Then it can be inferred that the target patient has "disease 2" based on the symptoms of the target disease information in table two with respect to "disease 2" and the second response result.
In some embodiments, the determining whether the target patient has the target disease corresponding to the target disease information according to the second response result includes:
Calculating a matching score of the target patient and the target disease corresponding to the target disease information according to the second response result;
when the matching score reaches a preset score, judging that the target patient suffers from the target disease;
generating a diagnosis result according to the second reply result, and acquiring a treatment suggestion according to the diagnosis result;
and generating a diagnosis and treatment report according to the diagnosis result and the treatment suggestion, and sending the diagnosis and treatment report to the target patient.
It will be appreciated that sometimes the second answer result does not match the target disease information exactly, possibly due to poor patient health, and that, by way of example, assuming the options in the second questionnaire are color-dependent, and if the target patient has color blindness or a color weakness problem, the option matching the situation may not be selected.
Therefore, if the option selected by the target patient in the second reply result does not completely match the target disease information, it cannot be recognized that the patient suffers from a target disease that is not corresponding to the target disease information. In this embodiment, the matching score of the target patient and the target disease corresponding to the target disease information is calculated according to the second response result, so as to determine whether the patient has the target disease according to the matching score, so that the accuracy of self-help diagnosis of the target patient can be improved.
When the target patient is judged to have the target disease, the condition of the target patient with the target disease can be analyzed according to the second response result, and a diagnosis result is generated according to the condition. In addition, the database records the treatment suggestions of various diseases under various conditions, and after the corresponding treatment suggestions are obtained from the database according to the diagnosis results, diagnosis and treatment reports can be generated according to the diagnosis results and the treatment suggestions. And sending the diagnosis and treatment report to a target patient, wherein the target patient can know own illness condition and rehabilitation advice through the diagnosis and treatment report.
In some embodiments, after the calculating the matching score of the target patient and the target disease corresponding to the target disease information according to the second answer result, the method further includes:
when the matching score does not reach a preset score, removing the target disease information from the disease information list;
and re-executing the step of selecting target disease information from the disease information list according to the first reply result until the diagnosis and treatment report is generated, and sending the diagnosis and treatment report to the target patient.
It will be appreciated that if the match score does not reach the preset score, it may be determined that the probability that the target patient is currently suffering from the target disease corresponding to the target disease information is very low.
In addition, since more than one piece of disease information matching the first reply result in the disease information list may be performed when step S13 is performed, the step of selecting the target disease information from the disease information list according to the first reply result may be re-performed after the unmatched target disease information is removed from the disease information list. After the new target disease information is determined, a second questionnaire corresponding to the new target disease information is acquired, the second questionnaire is sent to the target patient, the matching score can be calculated according to the new second reply result replied by the target patient, and when the matching score reaches the preset score, a diagnosis and treatment report can be generated.
In some embodiments, the identifying a distinguishing symptom feature between each of the disease information to generate a first questionnaire based on the distinguishing symptom feature and before sending the first questionnaire to a target patient, the method further comprises:
when only one piece of disease information is contained in the disease information list, determining the disease information as target disease information;
acquiring a second questionnaire corresponding to the target disease information, and sending the second questionnaire to the target patient;
And obtaining a second response result of the target patient to the second questionnaire, so as to judge whether the target patient suffers from the target disease corresponding to the target disease information according to the second response result.
In the application, when a patient consultation request is received, descriptive information is acquired from the patient consultation request; identifying keywords in the description information, and acquiring a disease information list matched with the keywords; identifying distinguishing symptom features among the various pieces of disease information in the disease information list, generating a first questionnaire according to the distinguishing symptom features, and transmitting the first questionnaire to the target patient; acquiring a first answer result of the target patient to the first questionnaire, so as to select target disease information from a disease information list according to the first answer result; acquiring a second questionnaire corresponding to the target disease information, and sending the second questionnaire to the target patient; and obtaining a second answer result of the target patient to the second questionnaire so as to judge whether the target patient suffers from the target disease corresponding to the target disease information according to the second answer result. Through the technical scheme that this application provided to the mode of artificial intelligence lets the patient realize the self-service treatment, has solved the problem that patient's treatment efficiency is low among the prior art.
Referring to fig. 2, fig. 2 is a schematic block diagram of a self-help diagnosis device according to an embodiment of the present application.
As shown in fig. 2, the self-help diagnosis device 201 includes:
a consultation processing module 2011, configured to obtain descriptive information from a patient consultation request when the patient consultation request is received;
a keyword recognition module 2012, configured to recognize keywords in the description information and obtain a disease information list matched with the keywords;
a first questionnaire module 2013, configured to identify, when there are a plurality of pieces of disease information in the disease information list, distinguishing symptom features between the pieces of disease information, to generate a first questionnaire according to the distinguishing symptom features, and to send the first questionnaire to a target patient;
a first diagnosis module 2014, configured to obtain a first answer result of the target patient to the first questionnaire, so as to select target disease information from the disease information list according to the first answer result;
a second questionnaire module 2015, configured to obtain a second questionnaire corresponding to the target disease information, and send the second questionnaire to the target patient;
a second diagnosis module 2016, configured to obtain a second response result of the target patient to the second questionnaire, so as to determine whether the target patient has the target disease corresponding to the target disease information according to the second response result.
In some embodiments, the keyword recognition module 2012 includes, when recognizing a keyword in the description information and obtaining a disease information list matching the keyword:
performing word segmentation processing on the description information to obtain a first word segmentation set;
filtering useless word segmentation in the first word segmentation set to obtain a second word segmentation set;
identifying a classification label corresponding to a second word in the second word set, and selecting the second word of the symptom label corresponding to the classification label from the second word set as a keyword, wherein the number of the keywords can be one or more;
and screening disease information of which symptoms comprise at least one keyword from a disease database to obtain a disease information list.
In some embodiments, before selecting the second keyword of the symptom tag corresponding to the category tag from the second keyword set, the keyword recognition module 2012 further includes:
judging whether special word segmentation corresponding to the special case label exists in the second word segmentation set or not;
and prompting the target patient to go to a hospital for a doctor when the special word is included in the second word segmentation set.
In some embodiments, the first questionnaire module 2013, when identifying a distinguishing symptom feature between each of the disease information when there are a plurality of disease information in the list of disease information, to generate a first questionnaire according to the distinguishing symptom feature, comprises:
collecting symptom characteristics from each piece of disease information in the disease information list to obtain a symptom characteristic set;
sequentially selecting candidate symptom characteristics from the symptom characteristic set, and judging whether each piece of disease information in the disease information list contains the candidate symptom characteristics or not;
determining that the candidate symptom feature is a distinguishing symptom feature when at least one piece of disease information does not contain the candidate symptom feature in the disease information list;
and acquiring symptom degree options corresponding to the distinguishing symptom features, and generating a first questionnaire according to the distinguishing symptom features and the symptom degree options.
In some embodiments, the first diagnostic module 2014, when selecting the target disease information from the disease information list according to the first reply result, includes:
determining a target symptom level selected by the target patient for the distinguishing symptom feature according to the first reply result;
And screening the disease information of which the symptom degree of the distinguishing symptom characteristic is matched with the target symptom degree from the disease information list to obtain target disease information.
In some embodiments, the second diagnosis module 2016, when determining whether the target patient has the target disease corresponding to the target disease information according to the second reply result, includes:
calculating a matching score of the target patient and the target disease corresponding to the target disease information according to the second response result;
when the matching score reaches a preset score, judging that the target patient suffers from the target disease;
generating a diagnosis result according to the second reply result, and acquiring a treatment suggestion according to the diagnosis result;
and generating a diagnosis and treatment report according to the diagnosis result and the treatment suggestion, and sending the diagnosis and treatment report to the target patient.
In some embodiments, the self-help diagnosis apparatus 201 further includes a third diagnosis module 2017, after the second diagnosis module 2016 calculates the matching score of the target patient and the target disease corresponding to the target disease information according to the second answer result, the third diagnosis module 2017 is configured to remove the target disease information from the disease information list when the matching score does not reach a preset score;
And re-executing the step of selecting target disease information from the disease information list according to the first reply result until the diagnosis and treatment report is generated, and sending the diagnosis and treatment report to the target patient.
It should be noted that, for convenience and brevity of description, specific working processes of the above-described apparatus and each module and unit may refer to corresponding processes in the foregoing embodiments of the self-help diagnosis method, which are not described herein again.
The apparatus provided by the above embodiments may be implemented in the form of a computer program which may be run on a computer device as shown in fig. 3.
Referring to fig. 3, fig. 3 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device includes, but is not limited to, a server.
As shown in fig. 3, the computer device 301 includes a processor 3011, a memory, and a network interface connected via a system bus, wherein the memory may include a storage medium 3012 and an internal memory 3015, and the storage medium 3012 may be non-volatile or volatile.
The storage medium 3012 may store an operating system and computer programs. The computer programs include program instructions that, when executed, cause the processor 3011 to perform any of the self-help methods of consultation.
The processor 3011 is used to provide computing and control capabilities to support the operation of the overall computer device.
The internal memory 3015 provides an environment for the execution of a computer program in the storage medium 3012 that, when executed by the processor 3011, causes the processor 3011 to perform any self-service consultation method.
The network interface is used for network communication such as transmitting assigned tasks and the like. It will be appreciated by those skilled in the art that the structure shown in fig. 3 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
It is to be appreciated that the processor 3011 can be a central processing unit (Central Processing Unit, CPU), and that the processor 3011 can also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein in some embodiments the processor 3011 is configured to run a computer program stored in a memory to implement the steps of:
when a patient consultation request is received, acquiring descriptive information from the patient consultation request;
identifying keywords in the description information, and acquiring a disease information list matched with the keywords;
identifying distinguishing symptom features among the respective disease information when a plurality of disease information exists in the disease information list, so as to generate a first questionnaire according to the distinguishing symptom features, and transmitting the first questionnaire to a target patient;
acquiring a first response result of the target patient to the first questionnaire, so as to select target disease information from the disease information list according to the first response result;
acquiring a second questionnaire corresponding to the target disease information, and sending the second questionnaire to the target patient;
and obtaining a second response result of the target patient to the second questionnaire, so as to judge whether the target patient suffers from the target disease corresponding to the target disease information according to the second response result.
In some embodiments, the processor 3011 is configured to, when identifying keywords in the description information and obtaining a list of disease information matching the keywords, implement:
Performing word segmentation processing on the description information to obtain a first word segmentation set;
filtering useless word segmentation in the first word segmentation set to obtain a second word segmentation set;
identifying a classification label corresponding to a second word in the second word set, and selecting the second word of the symptom label corresponding to the classification label from the second word set as a keyword, wherein the number of the keywords can be one or more;
and screening disease information of which symptoms comprise at least one keyword from a disease database to obtain a disease information list.
In some embodiments, before selecting the second word of the symptom tag corresponding to the category tag from the second word set as a keyword, the processor 3011 is further configured to implement:
judging whether special word segmentation corresponding to the special case label exists in the second word segmentation set or not;
and prompting the target patient to go to a hospital for a doctor when the special word is included in the second word segmentation set.
In some embodiments, the processor 3011 is configured to, when there are a plurality of disease information in the list of disease information, identify a distinguishing symptom feature between each of the disease information to generate a first questionnaire based on the distinguishing symptom feature:
Collecting symptom characteristics from each piece of disease information in the disease information list to obtain a symptom characteristic set;
sequentially selecting candidate symptom characteristics from the symptom characteristic set, and judging whether each piece of disease information in the disease information list contains the candidate symptom characteristics or not;
determining that the candidate symptom feature is a distinguishing symptom feature when at least one piece of disease information does not contain the candidate symptom feature in the disease information list;
and acquiring symptom degree options corresponding to the distinguishing symptom features, and generating a first questionnaire according to the distinguishing symptom features and the symptom degree options.
In some embodiments, the processor 3011 is configured to, when selecting target disease information from the list of disease information according to the first reply result, implement:
determining a target symptom level selected by the target patient for the distinguishing symptom feature according to the first reply result;
and screening the disease information of which the symptom degree of the distinguishing symptom characteristic is matched with the target symptom degree from the disease information list to obtain target disease information.
In some embodiments, the processor 3011 is configured to, when determining whether the target patient has the target disease corresponding to the target disease information according to the second reply result, implement:
Calculating a matching score of the target patient and the target disease corresponding to the target disease information according to the second response result;
when the matching score reaches a preset score, judging that the target patient suffers from the target disease;
generating a diagnosis result according to the second reply result, and acquiring a treatment suggestion according to the diagnosis result;
and generating a diagnosis and treatment report according to the diagnosis result and the treatment suggestion, and sending the diagnosis and treatment report to the target patient.
In some embodiments, the processor 3011 is further configured to, after calculating a matching score for the target patient to a target disease corresponding to the target disease information based on the second answer result, implement:
when the matching score does not reach a preset score, removing the target disease information from the disease information list;
and re-executing the step of selecting target disease information from the disease information list according to the first reply result until the diagnosis and treatment report is generated, and sending the diagnosis and treatment report to the target patient.
It should be noted that, for convenience and brevity of description, the specific working process of the computer device described above may refer to the corresponding process in the foregoing embodiment of the self-help diagnosis method, which is not described herein again.
The embodiment of the application also provides a storage medium, which is a computer readable storage medium, and a computer program is stored on the computer readable storage medium, wherein the computer program comprises program instructions, and the method implemented by the program instructions when being executed can refer to various embodiments of the self-help diagnosis method.
The computer readable storage medium may be an internal storage unit of the computer device according to the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, which are provided on the computer device.
It is to be understood that the terminology used in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments. While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A self-help consultation method, comprising:
when a patient consultation request is received, acquiring descriptive information from the patient consultation request;
identifying keywords in the description information, and acquiring a disease information list matched with the keywords;
identifying distinguishing symptom features among the respective disease information when a plurality of disease information exists in the disease information list, so as to generate a first questionnaire according to the distinguishing symptom features, and transmitting the first questionnaire to a target patient;
acquiring a first response result of the target patient to the first questionnaire, so as to select target disease information from the disease information list according to the first response result;
acquiring a second questionnaire corresponding to the target disease information, and sending the second questionnaire to the target patient;
and obtaining a second response result of the target patient to the second questionnaire, so as to judge whether the target patient suffers from the target disease corresponding to the target disease information according to the second response result.
2. The method of claim 1, wherein the identifying keywords in the descriptive information and obtaining a list of disease information matching the keywords comprises:
Performing word segmentation processing on the description information to obtain a first word segmentation set;
filtering useless word segmentation in the first word segmentation set to obtain a second word segmentation set;
identifying a classification label corresponding to a second word in the second word set, and selecting the second word of the symptom label corresponding to the classification label from the second word set as a keyword, wherein the number of the keywords can be one or more;
and screening disease information of which symptoms comprise at least one keyword from a disease database to obtain a disease information list.
3. The method according to claim 2, wherein before the selecting, from the second keyword set, the second keyword of the symptom tag corresponding to the category tag, the method further comprises:
judging whether special word segmentation corresponding to the special case label exists in the second word segmentation set or not;
and prompting the target patient to go to a hospital for a doctor when the special word is included in the second word segmentation set.
4. The method of claim 2, wherein when there are a plurality of disease information in the list of disease information, identifying a distinguishing symptom feature between the respective disease information to generate a first questionnaire based on the distinguishing symptom feature, comprises:
Collecting symptom characteristics from each piece of disease information in the disease information list to obtain a symptom characteristic set;
sequentially selecting candidate symptom characteristics from the symptom characteristic set, and judging whether each piece of disease information in the disease information list contains the candidate symptom characteristics or not;
determining that the candidate symptom feature is a distinguishing symptom feature when at least one piece of disease information does not contain the candidate symptom feature in the disease information list;
and acquiring symptom degree options corresponding to the distinguishing symptom features, and generating a first questionnaire according to the distinguishing symptom features and the symptom degree options.
5. The method of claim 4, wherein selecting the target disease information from the list of disease information based on the first reply result comprises:
determining a target symptom level selected by the target patient for the distinguishing symptom feature according to the first reply result;
and screening the disease information of which the symptom degree of the distinguishing symptom characteristic is matched with the target symptom degree from the disease information list to obtain target disease information.
6. The method according to claim 1, wherein the determining whether the target patient has the target disease corresponding to the target disease information according to the second reply result includes:
Calculating a matching score of the target patient and the target disease corresponding to the target disease information according to the second response result;
when the matching score reaches a preset score, judging that the target patient suffers from the target disease;
generating a diagnosis result according to the second reply result, and acquiring a treatment suggestion according to the diagnosis result;
and generating a diagnosis and treatment report according to the diagnosis result and the treatment suggestion, and sending the diagnosis and treatment report to the target patient.
7. The method of claim 6, wherein after calculating a matching score of the target patient to a target disease corresponding to the target disease information based on the second response result, the method further comprises:
when the matching score does not reach a preset score, removing the target disease information from the disease information list;
and re-executing the step of selecting target disease information from the disease information list according to the first reply result until the diagnosis and treatment report is generated, and sending the diagnosis and treatment report to the target patient.
8. A self-help consultation device, comprising:
The consultation processing module is used for acquiring descriptive information from the patient consultation request when the patient consultation request is received;
the keyword recognition module is used for recognizing keywords in the description information and acquiring a disease information list matched with the keywords;
a first questionnaire module for identifying distinguishing symptom features among the respective disease information when there are a plurality of disease information in the disease information list, generating a first questionnaire according to the distinguishing symptom features, and transmitting the first questionnaire to a target patient;
the first diagnosis module is used for obtaining a first answer result of the target patient to the first questionnaire so as to select target disease information from the disease information list according to the first answer result;
a second questionnaire module for acquiring a second questionnaire corresponding to the target disease information and sending the second questionnaire to the target patient;
and the second diagnosis module is used for acquiring a second answer result of the target patient to the second questionnaire so as to judge whether the target patient suffers from the target disease corresponding to the target disease information according to the second answer result.
9. A computer device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program when executed by the processor implements the steps of the self-help consultation method of any of claims 1 to 7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program, wherein the computer program, when executed by a processor, implements the steps of the self-help consultation method according to any of the claims 1 to 7.
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