CN110442732A - A kind of intelligent medical guide method, system and storage medium - Google Patents
A kind of intelligent medical guide method, system and storage medium Download PDFInfo
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Abstract
The present invention provides a kind of intelligent medical guide method, system and storage mediums, comprising: according to the log-on message and authorization message of user, obtains user in the health account data of regional health platform；According to the health account data of the user, the patient health portrait of the user is obtained；It draws a portrait in conjunction with the patient health of the user, by the means of man-machine of intelligent answer, obtains the current symptomatic description of the user；It is described according to the current symptomatic of the user, obtains the suspected diagnosis of the user；It is drawn a portrait according to each doctor of the suspected diagnosis result of the user and the regional health platform, Xiang Suoshu user recommends suitable medical resource.The present invention can draw a portrait in conjunction with the patient health of user, obtain the current symptomatic description of more fully user, so that more accurate suspected diagnosis is obtained, to be to provide more effective guiding doctor before user is medical to recommend.
Description
Technical field
The present invention relates to medicine and hygiene fields, espespecially a kind of intelligent medical guide method, system and storage medium.
Background technique
Traditional interrogation mode is all that hospital is inquired, this brings many inconvenience to most patient users, from
And directly influence the state of an illness of patient.With the rapid development of internet interrogation industry, the intelligence based on disease knowledge map is led
It examines system for distribution of out-patient department and provides the decision support before going to a doctor to patient to a certain extent, alleviate patient and " know that disease does not know disease, knows disease
Do not know section " the phenomenon that.
Current intelligent hospital guide is mostly to pass through patient to input current symptom, is provided using symptom described in patient and disease doubtful
Like Diseases diagnosis, so according to doctor introduce in disease of being good at recommend suitable department and doctor, to the health status of patient,
History medical treatment habit considers less；But the judgement of the disease condition of patient is to need that present illness history and medical history synthesis is combined to sentence
It is disconnected, only consider that present illness history will cause diagnosis and the inaccuracy to disease risks assessment, it is possible to conditions of patients can be affected adversely；And it is right
The judgement that doctor is good at also only is introduced by doctor, is considered its practical professional ability less.
Summary of the invention
An object of the present invention be it is existing in the prior art at least partly insufficient in order to overcome, a kind of intelligence is provided and is led
Hospital's method, system and storage medium.
Technical solution provided by the invention is as follows:
A kind of intelligent medical guide method, comprising: according to the log-on message and authorization message of user, obtain the user in region
The health account data of hygienic platform；According to the health account data of the user, the patient for obtaining corresponding multiple dimensions is special
Levy label, wherein the patient characteristic label of the multiple dimension includes essential information label, disease information label, allergy information
Label, medication information labels and family's medical history label；Obtain the user's according to the patient characteristic label of the multiple dimension
Patient health portrait；It draws a portrait in conjunction with the patient health of the user, by the means of man-machine of intelligent answer, obtains the use
The current symptomatic at family describes；It is described according to the current symptomatic of the user, obtains the suspected diagnosis of the user；According to the use
Each doctor of the suspected diagnosis result at family and regional health platform portrait, Xiang Suoshu user recommend suitable medical treatment money
Source, the medical resource include hospital, department and doctor.
It is further preferred that the patient health of user described in the combination is drawn a portrait, pass through the human-computer dialogue shape of intelligent answer
Formula obtains the current symptomatic description of the user, comprising: according to the input symptom of the user, in conjunction with the medicine constructed in advance
Targetedly symptom question and answer, the current symptomatic for obtaining the user are retouched for knowledge mapping and the progress of the patient health of user portrait
It states.
It is further preferred that described according to the suspected diagnosis result of the user and each doctor of the regional health platform
Raw portrait, Xiang Suoshu user recommend before suitable medical resource, further includes: according to the electronic health record number of regional health platform
According to, the essential information of each doctor and scientific research statistics data, doctor's feature tag of multiple dimensions of each doctor is obtained, it is described
Doctor's feature tag of multiple dimensions constitutes corresponding doctor's portrait；Wherein, doctor's feature tag of the multiple dimension includes
Essential information label, clinical professional skill label, capacity of scientific research label.
It is further preferred that the patient characteristic label for obtaining corresponding multiple dimensions include: using two-way LSTM with
The combinational algorithm of condition random field handles the health account data, obtains the patient characteristic mark of corresponding multiple dimensions
Label；Doctor's feature tag of the multiple dimensions for obtaining each doctor includes: the group using two-way LSTM and condition random field
Hop algorithm handles the electronic health record data, obtains doctor's feature tag of corresponding multiple dimensions.
It is further preferred that the patient characteristic label of the multiple dimension further includes habit label of seeing a doctor；It is described according to institute
The suspected diagnosis result of user and each doctor portrait of the regional health platform are stated, Xiang Suoshu user recommends suitable medical treatment
Resource includes: according to the suspected diagnosis result of the user, the medical treatment habit label of the user and the regional health platform
Each doctor portrait, Xiang Suoshu user recommends suitable medical resource.
It is further preferred that described according to the suspected diagnosis result of the user and each doctor of the regional health platform
Raw portrait, Xiang Suoshu user recommend suitable medical resource include: when the suspected diagnosis result of the user is common disease, to
The user recommends the medical resource of basic hospital；When the suspected diagnosis result of the user is difficult disease, Xiang Suoshu user
Recommend the strongest medical resource of professional ability.
The present invention also provides a kind of intelligent medical guide systems, comprising: patient data obtains module, for the login according to user
Information and authorization message obtain the user in the health account data of regional health platform；Patient's portrait generation module, is used for
According to the health account data of the user, the patient characteristic label of corresponding multiple dimensions is obtained, wherein the multiple dimension
Patient characteristic label include essential information label, disease information label, allergy information label, medication information labels and h disease
History label；The patient health portrait of the user is obtained according to the patient characteristic label of the multiple dimension；Suspected diagnosis module,
For the patient health portrait in conjunction with the user, by the means of man-machine of intelligent answer, the current of the user is obtained
Symptom description；It is described according to the current symptomatic of the user, obtains the suspected diagnosis of the user；Medical resource recommending module,
For being drawn a portrait according to the suspected diagnosis result of the user and each doctor of the regional health platform, Xiang Suoshu user recommends
Suitable medical resource, the medical resource include hospital, department and doctor.
It is further preferred that the suspected diagnosis module, is further used for the input symptom according to the user, in conjunction with pre-
The patient health portrait of the medical knowledge map and the user that first construct carries out targetedly symptom question and answer, obtains the user
Current symptomatic description.
It is further preferred that further include: doctor's portrait generation module, for the electronic health record number according to regional health platform
According to, the essential information of each doctor and scientific research statistics data, doctor's feature tag of multiple dimensions of each doctor is obtained, it is described
Doctor's feature tag of multiple dimensions constitutes corresponding doctor's portrait；Wherein, doctor's feature tag of the multiple dimension includes
Essential information label, clinical professional skill label, capacity of scientific research label.
The present invention also provides a kind of computer readable storage mediums, are stored thereon with computer program, the computer journey
Intelligent medical guide method described in aforementioned any one is realized when sequence is executed by processor.
A kind of intelligent medical guide method, system and the storage medium provided through the invention, can bring it is following the utility model has the advantages that
1, the present invention is based on the big datas of regional health platform to obtain patient health portrait, is retouched according to the current symptomatic of user
It states and obtains more accurate suspected diagnosis with patient health portrait, recommend to provide more effective guiding doctor before going to a doctor for user.
2, the present invention combine patient medical history, habit of seeing a doctor, disease severity, medical institutions and doctor draw a portrait to patient into
Row, which is seen a doctor, to be recommended, and more effective guiding doctor is provided before going to a doctor for user and is recommended, and has accomplished that " common disease pushes away base, difficulty nearby
Disease precisely recommends suitable hospital doctor ", the big problem of Grade A hospital doctor's operating pressure is alleviated to a certain extent.
Detailed description of the invention
Below by clearly understandable mode, preferred embodiment is described with reference to the drawings, to a kind of intelligent medical guide method, is
Above-mentioned characteristic, technical characteristic, advantage and its implementation of system and storage medium are further described.
Fig. 1 is a kind of flow chart of one embodiment of intelligent medical guide method of the invention；
Fig. 2 is a kind of flow chart of another embodiment of intelligent medical guide method of the invention；
Fig. 3 is a kind of structural schematic diagram of one embodiment of intelligent medical guide system of the invention；
Fig. 4 is a kind of structural schematic diagram of another embodiment of intelligent medical guide system of the invention.
Drawing reference numeral explanation:
100. patient data obtains module, 200. patients portrait generation module, 300. suspected diagnosis modules, 400. medical treatment moneys
Source recommending module, 500. doctors portrait generation module.
Specific embodiment
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, Detailed description of the invention will be compareed below
A specific embodiment of the invention.It should be evident that drawings in the following description are only some embodiments of the invention, for
For those of ordinary skill in the art, without creative efforts, it can also be obtained according to these attached drawings other
Attached drawing, and obtain other embodiments.
To make simplified form, part related to the present invention is only schematically shown in each figure, they are not represented
Its practical structures as product.In addition, there is identical structure or function in some figures so that simplified form is easy to understand
Component only symbolically depicts one of those, or has only marked one of those.Herein, "one" is not only indicated
" only this ", can also indicate the situation of " more than one ".
In one embodiment of the invention, as shown in Figure 1, a kind of intelligent medical guide method, comprising:
Step S100 obtains the user in the health of regional health platform according to the log-on message and authorization message of user
File data.
Step S200 obtains the patient characteristic label of corresponding multiple dimensions according to the health account data of the user,
Wherein, the patient characteristic label of the multiple dimension includes essential information label, disease information label, allergy information label, uses
Medicine information labels and family's medical history label.
Step S300 draws a portrait according to the patient health that the patient characteristic label of the multiple dimension obtains the user.
Step S400 draws a portrait in conjunction with the patient health of the user, by the means of man-machine of intelligent answer, obtains institute
State the current symptomatic description of user.
Step S500 is described according to the current symptomatic of the user, obtains the suspected diagnosis of the user.
Step S600 draws a portrait according to the suspected diagnosis result of the user and each doctor of the regional health platform, to
The user recommends suitable medical resource, and the medical resource includes hospital, department and doctor.
Specifically, user logs in this system first.It is logged in if it is new user, then requires first to register to log in again, when registration
It is required that input user's real name, the information of identity user unique identities, such as identity card, medical insurance card card number, cell-phone number etc..So can
To find corresponding registration information according to user login information, to obtain subscriber identity information.
After user logs in, user's health account data for authorizing this system to call the user in regional health platform are prompted.
After the authorization for obtaining user, the health account data of this system just available user, for example, the essential information of user, going through
History diagnosis information, physical examination information etc., understand the health status of user.It is authorized, can be increased to user information by setting user
Privacy protection, avoid user information by irrational utilization.
According to the health account data of user, key message extraction process is carried out to data, is believed according to the key extracted
Breath obtains the patient characteristic label of multiple dimensions.The patient characteristic label of multiple dimensions includes essential information label, disease information
Label, allergy information label, medication information labels and family's medical history label.Essential information label includes the gender of patient, age
It include having been suffered from the disease in the recent period (including treated and chronic disease etc.), recent operation information, in the recent period going to a doctor Deng, disease information label
Number etc. reflects the passing disease event of patient.Allergy information label includes whether allergy, anaphylactogen etc..Medication information includes
With the presence or absence of drug allergy, whether Long-term taking medicine, Long-term taking medicine drug name etc..Family's medical history label includes hereditary disease, is reflected
Potential disease risks.It can be obtained by patient health portrait according to the patient characteristic label of these multiple dimensions.Preferably, sharp
With the deep learning algorithm of BiLSTM+CRF (two-way shot and long term memory network is combined with condition random field) to health account number
According to progress key message extraction process.BiLSTM+CRF be a kind of name Entity recognition (Named Entity Recognition,
Abbreviation NER) algorithm, required entity word (key message required for reacting) can be extracted from one big section of text.Than
Such as, " hypertension " illness term is extracted from the history diagnosis records of a hypertensive patient.According to each patient characteristic mark
Label need to extract corresponding key message, for example the letter such as illness term, operation names term is extracted in disease information labeling requirement
The information such as anaphylactogen are extracted in breath, allergy information labeling requirement；Medication information labels need to extract the information such as nomenclature of drug.
After logging in system by user, draw a portrait in conjunction with the patient health of the user, by the means of man-machine of intelligent answer,
Obtain the current symptomatic description of user.For example, user can input description symptom by text, system carries out the text of input
Semantic understanding tentatively judges symptom.If judging that user is the trouble of a various aspects health according to the patient health of user portrait
When person, as long as then continuing symptom guidance and consulting by common normal users.If sentenced according to the patient health of user portrait
When disconnected user is a patient for having certain disease, then need to consider the relationship of current symptomatic Yu existing disease；It is mutual when existing
When mutually influencing, then further inquiry is needed to have the performance symptom of disease, in this way, obtaining a more comprehensive state of an illness information.Than
Such as, have a fever user for common cold, can from body temperature, whether cough, whether runny nose, whether the conventional direction such as cold into
Row inquiry；If it is the fever of a hypertension user, then also need to carry out deeper into inquiry, such as blood pressure it is whether normal,
Whether dizziness etc..System can also provide voice input mode, facilitate user speech to describe symptom, the present embodiment is with no restrictions.
Preferably, according to the input symptom of the user, in conjunction with the medical knowledge map and the user constructed in advance
Patient health portrait carries out targetedly symptom question and answer, obtains the current symptomatic description of the user.Utilize medical literature, medical treatment
Website, electronic health record data carry out medical knowledge extraction, and the knowledge extracted are understood and processed, and form medical knowledge
Map is simultaneously applied to intelligent answer, including carrying out between corresponding, reasoning symptom and disease medical speciality term with patient's language
Corresponding relationship and question answer dialog logic etc..Since medical knowledge map is equivalent to an expert system, so it is defeated to be able to guide user
Enter more comprehensively, more targeted symptom description.
It is described according to the current symptomatic of user, suspected diagnosis is carried out to user.Preferentially, it is retouched according to the current symptomatic of user
It states, is drawn a portrait using the patient health of the medical knowledge map and the user constructed in advance, suspected diagnosis is carried out to user.
According to suspected diagnosis result (i.e. suspected disease), then each doctor of matching area health platform is gone to draw a portrait.Matching
Meaning refer to that the business of doctor wants the counterpart suspected disease.A medical resource is selected from doctor's resource of counterpart in business
Recommend user.Preferably, it is accustomed to according to the medical treatment of user, selects a user often to go from doctor's resource of counterpart in business
Hospital, business counterpart department and the department in user feedback favorable comment or doctor that professional ability is strong.Further preferentially,
According to the suspected diagnosis of user as a result, judging whether it is common disease；If it is common disease, then to user's preferential recommendation basic hospital
Medical resource；If not common disease, then further determine whether as difficult disease；It is if it is difficult disease, then preferential to user
Recommend the strongest medical resource of professional ability, for example professional ability is strongest in the business counterpart department and department in Grade A hospital
Doctor, such as director grade doctor.Thus it is possible, on the one hand, can accomplish that " minor issue pushes away base, the big suitable hospital of question recommending nearby
Doctor " carries out medical preceding shunting to the patient of Grade A hospital, the operating pressure of Grade A hospital doctor has been effectively relieved；Another party
Face, the medical treatment experience that the medical cost of user can also be reduced, improve user.
The present embodiment is drawn a portrait in conjunction with the patient health of user, obtains the current symptomatic description of more fully user, thus
To more accurate suspected diagnosis, recommend to provide more effective guiding doctor before going to a doctor for user.
In another embodiment of the present invention, as shown in Fig. 2, a kind of intelligent medical guide method, comprising:
Step S000 is according to the electronic health record data of regional health platform, the essential information of each doctor and scientific research statistics number
According to obtaining doctor's feature tag of multiple dimensions of each doctor, doctor's feature tag of the multiple dimension constitutes corresponding
Doctor's portrait；
Wherein, doctor's feature tag of the multiple dimension includes essential information label, clinical professional skill label, scientific research
Ability label.
Specifically, using the electronic health record data of regional health platform, the Disease Distribution, pre- that doctor sees the patient examined is obtained
About amount, outpatient service and inpatient's quantity, the hospitalization average cost for seeing the people that diagnoses a disease, inpatient's average stay, healing
The statistical informations such as improvement rate according to these statistical informations and are good at disease brief introduction and obtain the clinical professional skill label of doctor.Root
According to the essential information of doctor, such as age, educational background, academic title's rank, place hospital and department, essential information label is obtained.According to
Publish thesis number, patented invention number etc., obtains the capacity of scientific research label of doctor.In summary doctor's feature mark of multiple dimensions
Label obtain corresponding doctor's portrait.
Preferably, the deep learning of BiLSTM+CRF (two-way shot and long term memory network is combined with condition random field) is utilized
Algorithm carries out the keys such as patient's illnesses, accurate visit, interrogation doctor, admission time, discharge time to electronic health record data
The extraction process of information counts these key messages, is analyzed, to obtain the disease point that each doctor sees the patient examined
Cloth, reservation amount, outpatient service and inpatient's quantity, cure the statistical informations such as improvement rate at inpatient's average stay, are cured
Raw clinical professional skill label.
Step S100 obtains the user in the health of regional health platform according to the log-on message and authorization message of user
File data.
Step S210 obtains the patient characteristic label of corresponding multiple dimensions according to the health account data of the user,
Wherein, the patient characteristic label of the multiple dimension includes essential information label, disease information label, allergy information label, uses
Medicine information labels, see a doctor habit label and family's medical history label.
Specifically, according to the health account data of user, it can be seen that the hospital or certain class disease that user often goes often went
Hospital and department, or often register the department of Chinese medicine or doctor trained in Western medicine, to obtain the medical treatment habit label of user.
Step S300 draws a portrait according to the patient health that the patient characteristic label of the multiple dimension obtains the user.
Step S410 is according to the input symptom of the user, in conjunction with the medical knowledge map and the user constructed in advance
Patient health portrait carries out targetedly symptom question and answer, obtains the current symptomatic description of the user.
Step S500 is described according to the current symptomatic of the user, obtains the suspected diagnosis of the user.
Specifically, carrying out medical knowledge extraction using medical literature, medical web site, electronic health record data, and to being extracted
Knowledge understood and processed, formed medical knowledge map simultaneously be applied to intelligent answer, including by medical speciality term and suffer from
Person's language corresponds to, the corresponding relationship between reasoning symptom and disease and question answer dialog logic etc..Due to medical knowledge map phase
When in an expert system, thus be able to guide user's input more comprehensively, the description of more targeted symptom.
It is described according to the current symptomatic of user, suspected diagnosis is carried out to user.Preferentially, it is retouched according to the current symptomatic of user
It states, is drawn a portrait using the patient health of the medical knowledge map and the user constructed in advance, suspected diagnosis is carried out to user.
Step S610 is defended according to the suspected diagnosis result of the user, the medical treatment habit label of the user and the region
Each doctor of life platform draws a portrait, and Xiang Suoshu user recommends suitable medical resource, the medical resource include hospital, department and
Doctor.
Specifically, be accustomed to according to the medical treatment of user, the hospital that preferred, users are often gone from doctor's resource of counterpart in business,
Selected in the hospital often gone business counterpart department and user feedback favorable comment in the department or doctor that professional ability is strong.
Further preferentially, according to the suspected diagnosis of user as a result, judging whether it is common disease；If it is common disease, then
To the medical resource of user's preferential recommendation basic hospital；If not common disease, then further determine whether as difficult disease；If
It is difficult disease, then to the strongest medical resource of user's preferential recommendation professional ability.Hospital, department and doctor constitute one group of medical treatment money
Source.The professional ability of medical resource can be assessed with the weighted sum of doctor's professional ability, department's professional ability and hospital's ability,
One group of medical resource of highest scoring is the strongest medical resource of professional ability.In this way, can accomplish that " minor issue pushes away base nearby
Layer, the big suitable hospital doctor of question recommending " carries out medical preceding shunting to the patient of Grade A hospital, Grade A hospital has been effectively relieved
The operating pressure of doctor.
The present embodiment, in conjunction with patient medical history, habit of seeing a doctor, disease severity, medical institutions and doctor's portrait to patient
Medical treatment recommendation is carried out, more effective guiding doctor is provided before going to a doctor for user and is recommended, and has accomplished that " common disease pushes away base nearby, doubts
Difficult disease precisely recommends suitable hospital doctor ", the big problem of Grade A hospital doctor's operating pressure is alleviated to a certain extent.
In one embodiment of the invention, as shown in figure 3, a kind of intelligent medical guide system, comprising:
Patient data obtains module 100 and obtains the user in area for the log-on message and authorization message according to user
The health account data of domain health platform.
Patient's portrait generation module 200 obtains corresponding multiple dimensions for the health account data according to the user
Patient characteristic label, wherein the patient characteristic label of the multiple dimension include essential information label, disease information label,
Allergy information label, medication information labels and family's medical history label；Institute is obtained according to the patient characteristic label of the multiple dimension
State the patient health portrait of user.
Suspected diagnosis module 300 passes through the human-computer dialogue of intelligent answer for the patient health portrait in conjunction with the user
Form obtains the current symptomatic description of the user；It is described according to the current symptomatic of the user, obtains the doubtful of the user
Diagnosis.
Medical resource recommending module 400, for according to the user suspected diagnosis result and the regional health platform
Each doctor portrait, Xiang Suoshu user recommends suitable medical resource, and the medical resource includes hospital, department and doctor.
Specifically, user logs in this system first.It is logged in if it is new user, then requires first to register to log in again, when registration
It is required that input user's real name, the information of identity user unique identities, such as identity card, medical insurance card card number, cell-phone number etc..So can
To find corresponding registration information according to user login information, to obtain subscriber identity information.
After user logs in, user's health account data for authorizing this system to call the user in regional health platform are prompted.
After the authorization for obtaining user, the health account data of this system just available user, for example, the essential information of user, going through
History diagnosis information, physical examination information etc., understand the health status of user.It is authorized, can be increased to user information by setting user
Privacy protection, avoid user information by irrational utilization.
According to the health account data of user, key message extraction process is carried out to data, is believed according to the key extracted
Breath obtains the patient characteristic label of multiple dimensions.The patient characteristic label of multiple dimensions includes essential information label, disease information
Label, allergy information label, medication information labels and family's medical history label.Essential information label includes the gender of patient, age
It include having been suffered from the disease in the recent period (including treated and chronic disease etc.), recent operation information, in the recent period going to a doctor Deng, disease information label
Number etc. reflects the passing disease event of patient.Allergy information label includes whether allergy, anaphylactogen etc..Medication information includes
With the presence or absence of drug allergy, whether Long-term taking medicine, Long-term taking medicine drug name etc..Family's medical history label includes hereditary disease, is reflected
Potential disease risks.It can be obtained by patient health portrait according to the patient characteristic label of these multiple dimensions.Preferably, sharp
With the deep learning algorithm of BiLSTM+CRF (two-way shot and long term memory network is combined with condition random field) to health account number
According to progress key message extraction process.BiLSTM+CRF be a kind of name Entity recognition (Named Entity Recognition,
Abbreviation NER) algorithm, required entity word (key message required for reacting) can be extracted from one big section of text.Than
Such as, " hypertension " illness term is extracted from the history diagnosis records of a hypertensive patient.According to each patient characteristic mark
Label need to extract corresponding key message, for example the letter such as illness term, operation names term is extracted in disease information labeling requirement
The information such as anaphylactogen are extracted in breath, allergy information labeling requirement；Medication information labels need to extract the information such as nomenclature of drug.
After logging in system by user, draw a portrait in conjunction with the patient health of the user, by the means of man-machine of intelligent answer,
Obtain the current symptomatic description of user.For example, user can input description symptom by text, system carries out the text of input
Semantic understanding tentatively judges symptom.If judging that user is the trouble of a various aspects health according to the patient health of user portrait
When person, as long as then continuing symptom guidance and consulting by common normal users.If sentenced according to the patient health of user portrait
When disconnected user is a patient for having certain disease, then need to consider the relationship of current symptomatic Yu existing disease；It is mutual when existing
When mutually influencing, then further inquiry is needed to have the performance symptom of disease, in this way, obtaining a more comprehensive state of an illness information.Than
Such as, have a fever user for common cold, can from body temperature, whether cough, whether runny nose, whether the conventional direction such as cold into
Row inquiry；If it is the fever of a hypertension user, then also need to carry out deeper into inquiry, such as blood pressure it is whether normal,
Whether dizziness etc..System can also provide voice input mode, facilitate user speech to describe symptom, the present embodiment is with no restrictions.
Preferably, according to the input symptom of the user, in conjunction with the medical knowledge map and the user constructed in advance
Patient health portrait carries out targetedly symptom question and answer, obtains the current symptomatic description of the user.Utilize medical literature, medical treatment
Website, electronic health record data carry out medical knowledge extraction, and the knowledge extracted are understood and processed, and form medical knowledge
Map is simultaneously applied to intelligent answer, including carrying out between corresponding, reasoning symptom and disease medical speciality term with patient's language
Corresponding relationship and question answer dialog logic etc..Since medical knowledge map is equivalent to an expert system, so it is defeated to be able to guide user
Enter more comprehensively, more targeted symptom description.
It is described according to the current symptomatic of user, suspected diagnosis is carried out to user.Preferentially, it is retouched according to the current symptomatic of user
It states, is drawn a portrait using the patient health of the medical knowledge map and the user constructed in advance, suspected diagnosis is carried out to user.
According to suspected diagnosis result (i.e. suspected disease), then each doctor of matching area health platform is gone to draw a portrait.Matching
Meaning refer to that the business of doctor wants the counterpart suspected disease.A medical resource is selected from doctor's resource of counterpart in business
Recommend user.Preferably, it is accustomed to according to the medical treatment of user, selects a user often to go from doctor's resource of counterpart in business
Hospital, business counterpart department and the department in user feedback favorable comment or doctor that professional ability is strong.Further preferentially,
According to the suspected diagnosis of user as a result, judging whether it is common disease；If it is common disease, then to user's preferential recommendation basic hospital
Medical resource；If not common disease, then further determine whether as difficult disease；It is if it is difficult disease, then preferential to user
Recommend the strongest medical resource of professional ability, for example professional ability is strongest in the business counterpart department and department in Grade A hospital
Doctor, such as director grade doctor.Thus it is possible, on the one hand, can accomplish that " minor issue pushes away base, the big suitable hospital of question recommending nearby
Doctor " carries out medical preceding shunting to the patient of Grade A hospital, the operating pressure of Grade A hospital doctor has been effectively relieved；Another party
Face, the medical treatment experience that the medical cost of user can also be reduced, improve user.
The present embodiment is drawn a portrait in conjunction with the patient health of user, obtains the current symptomatic description of more fully user, thus
To more accurate suspected diagnosis, recommend to provide more effective guiding doctor before going to a doctor for user.
In another embodiment of the present invention, as shown in figure 4, a kind of intelligent medical guide system, comprising:
Doctor draws a portrait generation module 500, for according to the electronic health record data of regional health platform, each doctor it is basic
Information and scientific research statistics data, obtain doctor's feature tag of multiple dimensions of each doctor, and the doctor of the multiple dimension is special
It levies label and constitutes corresponding doctor's portrait；Wherein, doctor's feature tag of the multiple dimension includes essential information label, clinic
Professional skill label, capacity of scientific research label.
Specifically, using the electronic health record data of regional health platform, the Disease Distribution, pre- that doctor sees the patient examined is obtained
About amount, outpatient service and inpatient's quantity, the hospitalization average cost for seeing the people that diagnoses a disease, inpatient's average stay, healing
The statistical informations such as improvement rate according to these statistical informations and are good at disease brief introduction and obtain the clinical professional skill label of doctor.Root
According to the essential information of doctor, such as age, educational background, academic title's rank, place hospital and department, essential information label is obtained.According to
Publish thesis number, patented invention number etc., obtains the capacity of scientific research label of doctor.In summary doctor's feature mark of multiple dimensions
Label obtain corresponding doctor's portrait.
Preferably, the deep learning of BiLSTM+CRF (two-way shot and long term memory network is combined with condition random field) is utilized
Algorithm carries out the keys such as patient's illnesses, accurate visit, interrogation doctor, admission time, discharge time to electronic health record data
The extraction process of information counts these key messages, is analyzed, to obtain the disease point that each doctor sees the patient examined
Cloth, reservation amount, outpatient service and inpatient's quantity, cure the statistical informations such as improvement rate at inpatient's average stay, are cured
Raw clinical professional skill label.
Patient data obtains module 100 and obtains the user in area for the log-on message and authorization message according to user
The health account data of domain health platform.
Patient's portrait generation module 200 obtains corresponding multiple dimensions for the health account data according to the user
Patient characteristic label, wherein the patient characteristic label of the multiple dimension include essential information label, disease information label,
Allergy information label, medication information labels and family's medical history label；Institute is obtained according to the patient characteristic label of the multiple dimension
State the patient health portrait of user.
Specifically, using the electronic health record data of regional health platform, the Disease Distribution, pre- that doctor sees the patient examined is obtained
About amount, outpatient service and inpatient's quantity, the hospitalization average cost for seeing the people that diagnoses a disease, inpatient's average stay, healing
The statistical informations such as improvement rate according to these statistical informations and are good at disease brief introduction and obtain the clinical professional skill label of doctor.Root
According to the essential information of doctor, such as age, educational background, academic title's rank, place hospital and department, essential information label is obtained.According to
Publish thesis number, patented invention number etc., obtains the capacity of scientific research label of doctor.In summary doctor's feature mark of multiple dimensions
Label obtain corresponding doctor's portrait.
Preferably, the deep learning of BiLSTM+CRF (two-way shot and long term memory network is combined with condition random field) is utilized
Algorithm carries out the keys such as patient's illnesses, accurate visit, interrogation doctor, admission time, discharge time to electronic health record data
The extraction process of information counts these key messages, is analyzed, to obtain the disease point that each doctor sees the patient examined
Cloth, reservation amount, outpatient service and inpatient's quantity, cure the statistical informations such as improvement rate at inpatient's average stay, are cured
Raw clinical professional skill label.
Suspected diagnosis module 300, for the input symptom according to the user, in conjunction with the medical knowledge map constructed in advance
And the patient health portrait of the user carries out targetedly symptom question and answer, obtains the current symptomatic description of the user；According to
The current symptomatic of the user describes, and obtains the suspected diagnosis of the user.
Specifically, carrying out medical knowledge extraction using medical literature, medical web site, electronic health record data, and to being extracted
Knowledge understood and processed, formed medical knowledge map simultaneously be applied to intelligent answer, including by medical speciality term and suffer from
Person's language corresponds to, the corresponding relationship between reasoning symptom and disease and question answer dialog logic etc..Due to medical knowledge map phase
When in an expert system, thus be able to guide user's input more comprehensively, the description of more targeted symptom.
It is described according to the current symptomatic of user, suspected diagnosis is carried out to user.Preferentially, it is retouched according to the current symptomatic of user
It states, is drawn a portrait using the patient health of the medical knowledge map and the user constructed in advance, suspected diagnosis is carried out to user.
Medical resource recommending module 400, for being accustomed to according to the suspected diagnosis result of the user, the medical treatment of the user
Each doctor of label and regional health platform portrait, Xiang Suoshu user recommend suitable medical resource, the medical treatment money
Source includes hospital, department and doctor.
Specifically, be accustomed to according to the medical treatment of user, the hospital that preferred, users are often gone from doctor's resource of counterpart in business,
Selected in the hospital often gone business counterpart department and user feedback favorable comment in the department or doctor that professional ability is strong.
Further preferentially, according to the suspected diagnosis of user as a result, judging whether it is common disease；If it is common disease, then
To the medical resource of user's preferential recommendation basic hospital；If not common disease, then further determine whether as difficult disease；If
It is difficult disease, then to the strongest medical resource of user's preferential recommendation professional ability.Hospital, department and doctor constitute one group of medical treatment money
Source.The professional ability of medical resource can be assessed with the weighted sum of doctor's professional ability, department's professional ability and hospital's ability,
One group of medical resource of highest scoring is the strongest medical resource of professional ability.In this way, can accomplish that " minor issue pushes away base nearby
Layer, the big suitable hospital doctor of question recommending " carries out medical preceding shunting to the patient of Grade A hospital, Grade A hospital has been effectively relieved
The operating pressure of doctor.
The present embodiment, in conjunction with patient medical history, habit of seeing a doctor, disease severity, medical institutions and doctor's portrait to patient
Medical treatment recommendation is carried out, more effective guiding doctor is provided before going to a doctor for user and is recommended, and has accomplished that " common disease pushes away base nearby, doubts
Difficult disease precisely recommends suitable hospital doctor ", the big problem of Grade A hospital doctor's operating pressure is alleviated to a certain extent.
In one embodiment of the invention, a kind of computer readable storage medium is stored thereon with computer program, institute
The intelligent medical guide method recorded such as previous embodiment can be realized by stating when computer program is executed by processor.It that is to say, when aforementioned
Some or all of technical solution that the embodiment of the present invention contributes to the prior art is by way of computer software product
When emerging from, aforementioned computer software product is stored in a computer readable storage medium.It is described computer-readable to deposit
Storage media can be any portable computers program code entity apparatus or equipment.For example, the computer-readable storage medium
Matter can be USB flash disk, mobile disk, magnetic disk, CD, computer storage, read-only memory, random access memory etc..
It should be noted that above-described embodiment can be freely combined as needed.The above is only of the invention preferred
Embodiment, it is noted that for those skilled in the art, in the premise for not departing from the principle of the invention
Under, several improvements and modifications can also be made, these modifications and embellishments should also be considered as the scope of protection of the present invention.
Claims (10)
1. a kind of intelligent medical guide method characterized by comprising
According to the log-on message and authorization message of user, the user is obtained in the health account data of regional health platform；
According to the health account data of the user, the patient characteristic label of corresponding multiple dimensions is obtained, wherein the multiple
The patient characteristic label of dimension includes essential information label, disease information label, allergy information label, medication information labels and family
Race's medical history label；
The patient health portrait of the user is obtained according to the patient characteristic label of the multiple dimension；
It draws a portrait in conjunction with the patient health of the user, by the means of man-machine of intelligent answer, obtains the current of the user
Symptom description；
It is described according to the current symptomatic of the user, obtains the suspected diagnosis of the user；
It is drawn a portrait according to each doctor of the suspected diagnosis result of the user and the regional health platform, Xiang Suoshu user recommends
Suitable medical resource, the medical resource include hospital, department and doctor.
2. a kind of intelligent medical guide method according to claim 1, which is characterized in that the patient of user described in the combination is strong
Health portrait obtains the current symptomatic description of the user by the means of man-machine of intelligent answer, comprising:
According to the input symptom of the user, draw a portrait in conjunction with the patient health of the medical knowledge map and the user constructed in advance
Targetedly symptom question and answer are carried out, the current symptomatic description of the user is obtained.
3. a kind of intelligent medical guide method according to claim 1, which is characterized in that described to be examined according to the doubtful of the user
Each doctor of disconnected result and regional health platform portrait, before Xiang Suoshu user recommends suitable medical resource, is also wrapped
It includes:
According to the electronic health record data of regional health platform, the essential information of each doctor and scientific research statistics data, obtain each
Doctor's feature tag of multiple dimensions of doctor, doctor's feature tag of the multiple dimension constitute corresponding doctor's portrait；
Wherein, doctor's feature tag of the multiple dimension includes essential information label, clinical professional skill label, the capacity of scientific research
Label.
4. a kind of intelligent medical guide method according to claim 3, it is characterised in that:
The patient characteristic label for obtaining corresponding multiple dimensions includes:
The health account data are handled using the combinational algorithm of two-way LSTM and condition random field, are obtained corresponding more
The patient characteristic label of a dimension；
Doctor's feature tag of the multiple dimensions for obtaining each doctor includes:
The electronic health record data are handled using the combinational algorithm of two-way LSTM and condition random field, are obtained corresponding more
Doctor's feature tag of a dimension.
5. a kind of intelligent medical guide method according to claim 1, it is characterised in that:
The patient characteristic label of the multiple dimension further includes habit label of seeing a doctor；
It is described to be drawn a portrait according to the suspected diagnosis result of the user and each doctor of the regional health platform, Xiang Suoshu user
The suitable medical resource is recommended to include:
According to each of the suspected diagnosis result of the user, the medical treatment habit label of the user and the regional health platform
Doctor's portrait, Xiang Suoshu user recommend suitable medical resource.
6. a kind of intelligent medical guide method according to claim 1, which is characterized in that described to be examined according to the doubtful of the user
Each doctor of disconnected result and regional health platform portrait, the suitable medical resource of Xiang Suoshu user's recommendation include:
When the suspected diagnosis result of the user is common disease, Xiang Suoshu user recommends the medical resource of basic hospital；
When the suspected diagnosis result of the user is difficult disease, Xiang Suoshu user recommends the strongest medical resource of professional ability.
7. a kind of intelligent medical guide system characterized by comprising
Patient data obtains module and obtains the user in regional health for the log-on message and authorization message according to user
The health account data of platform；
Patient's portrait generation module obtains the patient of corresponding multiple dimensions for the health account data according to the user
Feature tag, wherein the patient characteristic label of the multiple dimension includes essential information label, disease information label, allergy letter
Cease label, medication information labels and family's medical history label；The user is obtained according to the patient characteristic label of the multiple dimension
Patient health portrait；
Suspected diagnosis module, by the means of man-machine of intelligent answer, is obtained for the patient health portrait in conjunction with the user
The current symptomatic of the user is taken to describe；It is described according to the current symptomatic of the user, obtains the suspected diagnosis of the user；
Medical resource recommending module, for according to the suspected diagnosis result of the user and each doctor of the regional health platform
Raw portrait, Xiang Suoshu user recommend suitable medical resource, and the medical resource includes hospital, department and doctor.
8. a kind of intelligent medical guide system according to claim 7, it is characterised in that:
The suspected diagnosis module is further used for the input symptom according to the user, in conjunction with the medical knowledge constructed in advance
Map and the patient health of user portrait carry out targetedly symptom question and answer, obtain the current symptomatic description of the user.
9. a kind of intelligent medical guide system according to claim 7, which is characterized in that further include:
Doctor draw a portrait generation module, for according to the electronic health record data of regional health platform, the essential information of each doctor and
Scientific research statistics data obtain doctor's feature tag of multiple dimensions of each doctor, doctor's feature tag of the multiple dimension
Constitute corresponding doctor's portrait；Wherein, doctor's feature tag of the multiple dimension includes essential information label, clinical business water
Flat label, capacity of scientific research label.
10. a kind of computer readable storage medium, is stored thereon with computer program, it is characterised in that:
The computer program realizes intelligent medical guide side according to any one of claim 1 to 6 when being executed by processor
Method.
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|---|---|---|---|---|
| CN111564210A (en) * | 2020-04-01 | 2020-08-21 | 心医国际数字医疗系统(大连)有限公司 | Intelligent diagnosis guiding method and device, electronic equipment and storage medium |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106227880A (en) * | 2016-08-01 | 2016-12-14 | 挂号网（杭州）科技有限公司 | Doctor searches for the implementation method of recommendation |
| CN107887025A (en) * | 2017-12-19 | 2018-04-06 | 济南大象信息技术有限公司 | A kind of medical brain system |
| CN108461135A (en) * | 2018-03-21 | 2018-08-28 | 吉林师范大学 | A kind of shared medical data analysis system and method |
| CN108763555A (en) * | 2018-06-01 | 2018-11-06 | 北京奇虎科技有限公司 | Representation data acquisition methods and device based on demand word |
| CN109543863A (en) * | 2018-10-27 | 2019-03-29 | 平安医疗健康管理股份有限公司 | A kind of medical treatment task management method, server and storage medium |
| CN110021391A (en) * | 2017-10-09 | 2019-07-16 | 东莞东阳光科研发有限公司 | One kind point examines method and apparatus |
-
2019
- 2019-07-24 CN CN201910673832.2A patent/CN110442732A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106227880A (en) * | 2016-08-01 | 2016-12-14 | 挂号网（杭州）科技有限公司 | Doctor searches for the implementation method of recommendation |
| CN110021391A (en) * | 2017-10-09 | 2019-07-16 | 东莞东阳光科研发有限公司 | One kind point examines method and apparatus |
| CN107887025A (en) * | 2017-12-19 | 2018-04-06 | 济南大象信息技术有限公司 | A kind of medical brain system |
| CN108461135A (en) * | 2018-03-21 | 2018-08-28 | 吉林师范大学 | A kind of shared medical data analysis system and method |
| CN108763555A (en) * | 2018-06-01 | 2018-11-06 | 北京奇虎科技有限公司 | Representation data acquisition methods and device based on demand word |
| CN109543863A (en) * | 2018-10-27 | 2019-03-29 | 平安医疗健康管理股份有限公司 | A kind of medical treatment task management method, server and storage medium |
Cited By (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111564210A (en) * | 2020-04-01 | 2020-08-21 | 心医国际数字医疗系统(大连)有限公司 | Intelligent diagnosis guiding method and device, electronic equipment and storage medium |
| CN111710385A (en) * | 2020-06-23 | 2020-09-25 | 常州市第三人民医院 | Multi-dimensional personal health portrait system and method |
| CN111710385B (en) * | 2020-06-23 | 2023-07-07 | 常州市第三人民医院 | Multi-dimensional personal health portrait system and method |
| CN111785350A (en) * | 2020-06-30 | 2020-10-16 | 易联众信息技术股份有限公司 | Information extraction method, application, device and medium |
| TWI823015B (en) * | 2020-07-13 | 2023-11-21 | 神經元科技股份有限公司 | Decision support system and method thereof for neurological disorders |
| CN111916213A (en) * | 2020-08-12 | 2020-11-10 | 广州驰兴通用技术研究有限公司 | Medical service method and device based on cloud computing |
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| CN112163129A (en) * | 2020-10-10 | 2021-01-01 | 微医云(杭州)控股有限公司 | Data processing method, device and equipment |
| WO2022141926A1 (en) * | 2020-12-31 | 2022-07-07 | 上海明品医学数据科技有限公司 | Gastrointestinal perforation diagnosis and intervention device, and diagnosis and intervention system |
| CN113140323A (en) * | 2020-12-31 | 2021-07-20 | 上海明品医学数据科技有限公司 | Health portrait generation method, system, medium and server |
| CN112598090A (en) * | 2021-03-08 | 2021-04-02 | 北京冠新医卫软件科技有限公司 | Method, device, equipment and system for health portrait |
| CN112598090B (en) * | 2021-03-08 | 2021-05-18 | 北京冠新医卫软件科技有限公司 | Method, device, equipment and system for health portrait |
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| CN113488126A (en) * | 2021-07-27 | 2021-10-08 | 心医国际数字医疗系统(大连)有限公司 | Information processing method, information processing device, electronic equipment and storage medium |
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| CN114628012B (en) * | 2022-03-21 | 2023-09-05 | 中国人民解放军西部战区总医院 | Emergency department's preliminary examination sorting system |
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