CN112116978A - Method, system and device for recommending rheumatism immunity medicine - Google Patents
Method, system and device for recommending rheumatism immunity medicine Download PDFInfo
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Abstract
The invention provides a method, a system and a device for recommending rheumatism immunity drugs, wherein the recommending method comprises the following steps: acquiring information about the rheumatic immune drugs and constructing a medicine knowledge base; acquiring keywords of various information in the patient medical record based on the current medical record information, judging by using fuzzy rules according to the fuzzy rule base and the keywords, and outputting the symptom type and the severity; matching by combining a medicine knowledge base according to the symptom type and the severity degree, and outputting a primary medicine recommendation result; on the basis of considering the side effect of the medicine, the interaction among the medicines and the individual characteristic factors of the patient, screening the medicine to obtain an alternative medicine list; the personalized medicine recommendation scheme and the medication basis of the patient are output, the medicine knowledge base is constructed by utilizing the information stored in the electronic medical record, the reasonable utilization of medical data resources is realized, the disease condition judgment is carried out by using the fuzzy rule, the diagnosis accuracy rate is improved, the medication scheme is intelligently given in an auxiliary mode, and the medical service quality is improved.
Description
Technical Field
The invention belongs to the technical field of medical science, information science and computer application fusion, and particularly relates to a method, a system and a device for recommending rheumatism immunity drugs.
Background
The rheumatism immune disease is a systemic disease in internal science, has various clinical manifestations, and is easy to be similar to other diseases such as skin diseases, ophthalmia, oral ulcer and the like. In order to smoothly complete a complicated diagnosis and treatment process and avoid misdiagnosis, comprehensive judgment needs to be carried out according to the medical history, symptoms, clinical manifestations, auxiliary examination and the like of a patient, so that symptomatic treatment medicines are provided.
Disclosure of Invention
The invention provides a method, a system and a device for recommending rheumatism immunity drugs, which are used for mining information stored in medical treatment of patients by means of data in an electronic medical record and making full use of the information, so that diagnosis and treatment of doctors are assisted, the hidden danger of drug administration is reduced, the quality of medical care service is improved, a personalized drug recommendation scheme is provided, and safe and effective drug administration is enhanced.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a recommendation method for an immune drug for rheumatism comprises the following steps:
step 1, acquiring information about rheumatic immune drugs and constructing a medicine knowledge base;
step 2, acquiring keywords of various information in the patient medical record based on the current medical record information as input of a symptom matching stage;
step 3, judging by using the fuzzy rule according to the fuzzy rule base and the keywords obtained in the step 2, and outputting the symptom type and the severity;
the fuzzy rule is based on the existing medical record and the professional guidance of a medical doctor in the department of rheumatology, so that a corresponding rule of diseases, the severity and symptoms of the diseases is established;
step 4, matching is carried out according to the symptom type and the severity degree given in the step 3 by combining the medical knowledge base in the step 1, and a primary medicine recommendation result is output; on the basis of considering the side effect of the medicine, the interaction among the medicines and the individual characteristic factors of the patient, screening the medicine to obtain an alternative medicine list; and outputting the personalized medicine recommendation scheme and the medication basis of the patient.
Extracting medicine information from a medicine specification and a past electronic medical record of a hospital, and constructing a medicine knowledge base under the professional guidance of a rheumatism immunology department doctor; the medicine information comprises the medicine curative effect, adverse reaction, usage and dosage and the caution items in the medicine specification; the disease types, the disease severity and the medication suggestions given by doctors are extracted from the medical records of the past of the hospital.
And (3) regularly acquiring the latest electronic medical record data from the hospital electronic database by the medical knowledge base constructed in the step (1).
In step 2, the medical record comprises various information aiming at clinical symptoms and routine examinations and auxiliary information representing individual characteristics of a patient, wherein the auxiliary information at least comprises an allergy history and a family genetic disease history.
The fuzzy rule established in the step 3 is a matching rule of diseases and symptoms.
The step 4 is as follows:
step 41, matching the medicine knowledge base in the step 1 with the division and classification results in the step 3, and acquiring all medicine recommendation lists which accord with the disease types and the disease severity of the patients;
step 42, comprehensively considering the medical knowledge base in the step 1, the medical record information in the step 2 and the classification results divided in the step 3, screening the medicine recommendation results in the step 41, and setting a specified value for judging the severity of the disease condition of the patient according to the actual situation, wherein if the severity of the disease condition obtained in the step 3 is less than or equal to the specified value, the disease condition of the patient is mild; if the severity of the disease condition obtained in the step 3 is greater than the specified value, the disease condition of the patient is more serious;
and 43, forming a personalized medicine recommendation scheme aiming at the patient based on the alternative medicine list, and giving a medication basis.
In the step 4, the severity of the illness state and the treatment effect of the medicine are comprehensively considered in the screening of the medicine; when the patient has a light illness, the screening of the medicines is put at the head, namely the medicines which can cause negative effects are removed, and then the rest medicines are sorted according to the medicine effect to generate an alternative medicine list; when the illness state of a patient is serious, the screening of the medicines is put at the end, namely, the medicines are firstly sorted according to the medicine effect, the medicines with good medicine effect and negative effect in the safe use range are kept on the basis of the medicine sorting result, the medicines which do not meet the requirement are removed, and an alternative medicine list is generated.
A rheumatism immunity medicine recommending system comprises the following modules:
the system comprises a text mining module, a symptom matching module and a medicine information acquisition module, wherein the text mining module is used for acquiring required medicine information and medical record information, extracting information from a medicine specification and an electronic medical record system by using a named entity identification method, completing construction of a medicine knowledge base and acquiring input data of the symptom matching module;
the disease condition division and classification module is used for matching symptoms by using a fuzzy rule according to the information such as patient symptoms and the like given by the text mining module, and outputting the disease type and the disease severity as input data of the medicine recommendation module by comparing the corresponding relation between the symptoms and the diseases in the system;
the medicine list acquisition module is used for matching according to the illness state information given by the illness state division and classification module and combining a medicine knowledge base of the text mining module and outputting a primary medicine recommendation result; then on the basis of considering factors such as side effects of the drugs, interaction among the drugs, individual characteristics of patients and the like, screening the drugs to obtain an alternative drug list; finally, outputting the personalized medicine recommendation scheme of the patient and giving the medication basis.
A computer device comprises one or more processors and at least one memory, wherein the memory is used for storing computer executable programs, the processors read part or all of the computer executable programs from the memory and execute the computer executable programs, and when the processors execute part or all of the computer executable programs, the rheumatism immune drug recommendation method can be realized.
A computer-readable storage medium, in which a computer program is stored, which, when executed by a processor, is capable of implementing the method for recommending an immune drug for rheumatism according to the present invention.
The invention has the following beneficial effects: the method of the invention not only can give a medicine recommendation list, but also can indicate the medication basis to medical care personnel and patients, thereby reducing the confusion of users and improving the satisfaction degree of users; the invention constructs the medicine knowledge base by using the information stored in the electronic medical record, realizes the reasonable utilization of medical data resources, lays a foundation for medicine recommendation and ensures the accuracy of a recommendation result; the invention uses the fuzzy rule to judge the illness state, not only can clearly describe the illness type and the severity, but also can simulate the thinking and the decision of a human, and improve the diagnosis accuracy, thereby realizing the intelligent auxiliary administration scheme, reducing the decision burden of a doctor and improving the medical service quality; the invention fully considers the action effect of the medicine, not only considers the positive treatment effect, but also considers the negative effects of the medicine, namely side effects, adverse reactions among the medicines and the like, and provides a personalized medicine recommendation scheme in a targeted manner by combining the illness state of a patient, thereby realizing reasonable medicine administration and ensuring the medicine administration safety.
Drawings
Fig. 1 is a flow chart of a recommendation method of an immune drug against rheumatism provided by the invention.
Fig. 2 is a flow chart of a drug recommendation stage of the rheumatism immunity drug recommendation method provided by the invention.
Fig. 3 is a structural diagram of a recommendation system for an immune medicament for rheumatism provided by the invention.
Fig. 4 is a data interaction diagram of the rheumatism immune drug recommendation system provided by the invention.
Detailed Description
FIG. 1 is a flow chart of the recommendation method of the rheumatism immunity drug provided by the invention. The technical scheme of the invention is further explained by combining the attached drawings, and comprises the following steps:
step 1, establishing a medicine knowledge base.
The establishment of a medicine knowledge base and the acquisition of information constituting the knowledge base mainly depend on a Chinese named entity recognition method. Information is extracted from a medicine instruction book and a past medical record of a hospital, a medicine knowledge base is established under the professional guidance of a doctor, and the corresponding relation between medicines and diseases and basic information of the medicines, such as medicine curative effect, adverse reaction, usage amount, attention and the like, are stored.
The invention carries out entity recognition based on a CRF model, after the model is trained, key information can be extracted from unstructured information by using the model, for example, which disease the medicine given in a medicine instruction book is applicable to, which medicine can interact with, diagnosis results given in past electronic medical records of a hospital and corresponding medicine application suggestions are collected, the information is gathered, the corresponding relation between the disease and the medicine is formed under the professional guidance of a rheumatism immunologist, and the corresponding relation is stored in a medicine knowledge base, so that the medicine application recommendations can be generated conveniently later, and the accuracy rate of the medicine application recommendations can be ensured.
And 2, extracting various information keywords of the patient.
And extracting key information from the medical record of the patient by using a named entity identification method to prepare for a disease condition judgment stage. The key information is mainly divided into various information of symptom examination, such as joint swelling, tenderness and the like; various items of information of routine examination, such as "blood sedimentation 32 mm/h", "urine routine is not abnormal", etc.; auxiliary information for drug recommendation, such as 'penicillin allergy', 'pregnant woman' and the like.
And 3, diagnosing the disease and the severity of the patient.
And (3) judging the disease type and the severity of the patient according to various key information extracted in the step (2) to prepare for a medicine recommendation stage. Since the extracted description of the symptoms is sometimes not a very accurate concept, for example: the 'mild degree' in the 'mild joint swelling' is a fuzzy concept and cannot be simply described by an accurate value, so that a fuzzy rule method is introduced, and the degree of swelling of the joint of a patient is represented by a fuzzy set, so that the problem of description of symptoms is solved.
Further, by using the past medical record information acquired in the step 1, under the professional guidance of the medical doctors in the rheumatoid and immune departments, the internal relation between symptoms and diseases and the severity is found, and fuzzy rules are established around various information of symptom examination and routine examination, wherein the form is as follows:
Rn:If X11∩…∩X1n∩X21∩…∩X2n Then Yn
wherein "Ri"represents the established ith rule. "X1i"describes the premise of symptom examination, and the contents are" finger joint slight swelling "," joint flexion and extension activity is limited ", and the like. "X2i"describes the premise of routine examination, and the contents are" rheumatoid factor: 152IU/ml "," C-reactive protein: 47.67mg/L ", etc. "Y" isi"describes the diseased condition, and includes all disease types and severity corresponding to symptoms, such as" mild osteoarthritis "," mild rheumatoid arthritis ", etc.
And (3) comparing each item of keyword information extracted in the step (2) with the existing symptoms in the rule base to obtain matching similarity, and if the similarity reaches an activation threshold, exciting the corresponding rule to obtain the matched diseased category and the severity thereof.
And 4, generating a personalized medicine recommendation scheme.
The flow chart of the drug recommendation phase is shown in fig. 2, and the specific steps are as follows:
step 41, primary medicine recommendation. And (3) matching the medical knowledge base in the step (1) with the diagnosis result in the step (3) to obtain all the medicine recommendation lists which accord with the disease types and the disease severity of the patients.
And 42, matching drug screening, and screening a drug recommendation result by comprehensively considering all information in the steps 1, 2 and 3 to ensure the drug safety.
In the application of the invention, a specified value is set according to the actual situation for judging the severity of the illness of the patient, and if the severity of the illness obtained in the step 3 is less than or equal to the specified value, the patient is represented to be light; if the severity of the disease obtained in step 3 is greater than the specified value, it means that the disease is more serious.
When the patient has a light illness, all the alternative medicines which can generate negative effects on the patient in the primary medicine recommendation list are removed according to the individual attributes of the patient, such as the allergic history, the pregnant woman and the like, and then the alternative medicine list is formed by sequencing based on the medicine effects of the residual medicines.
When the patient has serious illness, the medicines of the primary medicine recommendation list are ranked based on the medicine effect of the primary medicine recommendation list, then all the alternative medicines with good medicine effect and negative effect within a certain range are selected according to the information of the negative effect of the medicines, the individual attribute of the patient and the like, and other medicines which do not meet the requirement are removed to form an alternative medicine list.
Step 43, final medical recommendation. And forming a personalized medicine recommendation scheme aiming at the patient based on the alternative medicine list, and giving medication basis.
Based on the same inventive concept, as shown in fig. 3, the invention provides a rheumatism immunity medicine recommendation system, which mainly comprises the following modules:
the text mining module is used for acquiring all required text information, and acquiring key medicine information and medical record information by using a Chinese named entity identification method, wherein the key medicine information and the medical record information mainly comprise information such as medicine curative effect, adverse reaction and the like in a medicine specification; the patient's condition in the past case history of the hospital, the medication advice given by the doctor, etc.; the current patient to be treated is subjected to various information keywords such as symptom examination, routine examination and the like. The first two items are used for completing the construction of a medicine knowledge base, and the latter item is used as input data of a disease condition division and classification module.
Specifically, the text mining module extracts text information by using a Chinese named entity identification method, generates medicine information, medical record information and information of a patient at present, and generates a medicine knowledge base based on the medicine information and the medical record information; the disease condition division and classification module is used for forming internal relation between symptoms and diseases and severity by utilizing the past medical record information acquired by the text mining module under the professional guidance of a rheumatism immunologist and describing by using a fuzzy rule; and comparing the current patient symptoms and other information acquired by the text mining module with the existing symptoms in the rule base, so as to realize disease judgment and output the disease types and the disease severity of the current patient.
The medicine list acquisition module is used for dividing the disease condition information given by the classification module according to the disease condition, wherein the disease condition information comprises disease types and disease severity, matching is carried out by combining a medicine knowledge base obtained by the text mining module, outputting a primary medicine recommendation list, and screening medicines on the basis of considering the side effect of the medicines, the interaction among the medicines and the individual characteristics of the patient to obtain an alternative medicine list; finally, outputting the personalized medicine recommendation scheme of the patient, and generating a medication basis; the specific operation of the drug screening stage is the same as that of the above-mentioned recommendation method for the rheumatoid immune drugs, and is not described herein again.
When the recommending system is executed, the steps of the medicine recommending method are realized. The text mining module is used for acquiring information about the rheumatic immune drugs and constructing a medicine knowledge base; keywords of various information in the patient medical record are obtained based on the current medical record information and are used as input of a symptom matching stage;
the disease condition division and classification module judges by using the fuzzy rule according to the fuzzy rule base and the acquired keywords and outputs the symptom type and the severity; the fuzzy rule is based on the existing medical record and the professional guidance of a medical doctor in the department of rheumatology, so that a corresponding rule of diseases, the severity and symptoms of the diseases is established;
the medicine list acquisition module is used for matching by combining a medicine knowledge base acquired by the text mining module according to the symptom type and the severity and outputting a primary medicine recommendation result; on the basis of considering the side effect of the medicine, the interaction among the medicines and the individual characteristic factors of the patient, screening the medicine to obtain an alternative medicine list; and outputting the personalized medicine recommendation scheme and the medication basis of the patient.
The data interaction of the rheumatism immune drug recommendation system provided by the invention is shown in fig. 4.
In the practical application process, medical data come from the practical diagnosis and treatment process, medical staff upload the medical data to an electronic medical database to form electronic medical big data based on text data, the electronic medical big data are converted into structured information through a text mining module of the system, and under the guidance of a doctor in the department of rheumatology, the medical big data are extracted from past medical records, corresponding medication information and medicine information, wherein the medical record information comprises a chief complaint record of a historical patient, routine physical examination and a physical examination result aiming at the department of rheumatology, a medicine knowledge base and a fuzzy rule base of disease types, symptoms and examination results are established for reference of a system disease condition dividing and classifying module and can be matched with the medicine knowledge base according to information keywords of a patient to be diagnosed; in addition, medical personnel can adjust the medicine knowledge base and the fuzzy rule base according to needs, and comprehensiveness of data is guaranteed.
After receiving the information of the current patient, the system extracts information keywords through a text mining module, transmits the information keywords into an illness state division and classification module, and then matches the information keywords with a fuzzy rule base to generate ill information; the medicine list acquisition module receives the ill information, matches the ill information with the medicine knowledge base, generates a personalized medicine recommendation scheme and a medicine use basis on the basis of considering medicine efficacy, negative effects and individual characteristics of patients, and feeds back the personalized medicine recommendation scheme and the medicine use basis to the user through a display page of the system.
Optionally, the present invention further provides a computer device, including but not limited to one or more processors and a memory, where the memory is used to store a computer executable program, the processor reads part or all of the computer executable program from the memory and executes the computer executable program, the processor can implement part or all of the steps of the method for recommending an immune medicament for rheumatism according to the present invention when executing part or all of the computer executable program, and the memory is further used to store information of an immune medicament for rheumatism and an electronic medical record.
A computer-readable storage medium, in which a computer program is stored, which, when executed by a processor, is capable of implementing the method for recommending an immune drug for rheumatism according to the present invention.
The computer device may be a notebook computer, a tablet computer, a desktop computer, a mobile phone or a workstation.
The invention also provides an output device for outputting the prediction result, wherein the output device is connected with the output end of the processor, and the output device is a display or a printer.
The processor may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), or an off-the-shelf programmable gate array (FPGA).
The memory of the invention can be an internal storage unit of a notebook computer, a tablet computer, a desktop computer, a mobile phone or a workstation, such as a memory and a hard disk; external memory units such as removable hard disks, flash memory cards may also be used.
Computer-readable storage media may include computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. The computer-readable storage medium may include: a Read Only Memory (ROM), a Random Access Memory (RAM), a Solid State Drive (SSD), or an optical disc. The Random Access Memory may include a resistive Random Access Memory (ReRAM) and a Dynamic Random Access Memory (DRAM).
Claims (9)
1. A recommendation method for an immune drug for rheumatism is characterized by comprising the following steps:
step 1, acquiring information about rheumatic immune drugs and constructing a medicine knowledge base;
step 2, acquiring keywords of various information in the patient medical record based on the current medical record information as input of a symptom matching stage;
step 3, judging by using the fuzzy rule according to the fuzzy rule base and the keywords obtained in the step 2, and outputting the symptom type and the severity;
the fuzzy rule is based on the existing medical record and the professional guidance of a medical doctor in the department of rheumatology, so that a corresponding rule of diseases, the severity and symptoms of the diseases is established;
step 4, matching is carried out according to the symptom type and the severity degree given in the step 3 by combining the medical knowledge base in the step 1, and a primary medicine recommendation result is output; on the basis of considering the side effect of the medicine, the interaction among the medicines and the individual characteristic factors of the patient, screening the medicine to obtain an alternative medicine list; and outputting the personalized medicine recommendation scheme and the medication basis of the patient.
2. The recommendation method for rheumatic immune drugs according to claim 1, wherein the drug information is extracted from the drug instruction manual and the past electronic medical record of hospital, and under the professional guidance of the doctor of the rheumatic immune department, a medical knowledge base is constructed; the medicine information comprises the medicine curative effect, adverse reaction, usage and dosage and the caution items in the medicine specification; the disease types, the disease severity and the medication suggestions given by doctors are extracted from the medical records of the past of the hospital.
3. The method of claim 1, wherein the medical knowledge base constructed in step 1 periodically obtains the latest electronic medical record data from hospital electronic databases.
4. The recommendation method for rheumatoid immunity drug as claimed in claim 1, wherein in step 2, said medical record comprises various information for clinical symptoms and routine examination and auxiliary information representing individual characteristics of patient, said auxiliary information at least comprises allergy history and family genetic history.
5. The recommendation method for an immune drug against rheumatism according to claim 1, wherein the step 4 is as follows:
step 41, matching the medicine knowledge base in the step 1 with the division and classification results in the step 3, and acquiring all medicine recommendation lists which accord with the disease types and the disease severity of the patients;
step 42, comprehensively considering the medical knowledge base in the step 1, the medical record information in the step 2 and the classification results divided in the step 3, screening the medicine recommendation results in the step 41, and setting a specified value for judging the severity of the disease condition of the patient according to the actual situation, wherein if the severity of the disease condition obtained in the step 3 is less than or equal to the specified value, the disease condition of the patient is mild; if the severity of the disease condition obtained in the step 3 is greater than the specified value, the disease condition of the patient is more serious;
and 43, forming a personalized medicine recommendation scheme aiming at the patient based on the alternative medicine list, and giving a medication basis.
6. The recommendation method for rheumatic immune drugs according to claim 5, wherein in step 4, the severity of disease and the therapeutic effect of the drugs are considered in the screening of the drugs; when the patient has a light illness, the screening of the medicines is put at the head, namely the medicines which can cause negative effects are removed, and then the rest medicines are sorted according to the medicine effect to generate an alternative medicine list; when the illness state of a patient is serious, the screening of the medicines is put at the end, namely, the medicines are firstly sorted according to the medicine effect, the medicines with good medicine effect and negative effect in the safe use range are kept on the basis of the medicine sorting result, the medicines which do not meet the requirement are removed, and an alternative medicine list is generated.
7. A rheumatism immunity medicine recommending system is characterized by comprising the following modules:
the system comprises a text mining module, a symptom matching module and a medicine information acquisition module, wherein the text mining module is used for acquiring required medicine information and medical record information, extracting information from a medicine specification and an electronic medical record system by using a named entity identification method, completing construction of a medicine knowledge base and acquiring input data of the symptom matching module;
the disease condition division and classification module is used for matching symptoms by using a fuzzy rule according to the information such as patient symptoms and the like given by the text mining module, and outputting the disease type and the disease severity as input data of the medicine recommendation module by comparing the corresponding relation between the symptoms and the diseases in the system;
the medicine list acquisition module is used for matching according to the illness state information given by the illness state division and classification module and combining a medicine knowledge base of the text mining module and outputting a primary medicine recommendation result; then on the basis of considering factors such as side effects of the drugs, interaction among the drugs, individual characteristics of patients and the like, screening the drugs to obtain an alternative drug list; finally, outputting the personalized medicine recommendation scheme of the patient and giving the medication basis.
8. A computer device, comprising one or more processors and at least one memory, wherein the memory is used for storing computer executable programs, the processors read part or all of the computer executable programs from the memory and execute the computer executable programs, and the processor can realize the rheumatism immune drug recommendation method according to any one of claims 1-6 when executing part or all of the computer executable programs.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, is adapted to carry out the method of recommendation of an immune pharmaceutical from rheumatism according to any one of claims 1 to 6.
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| CN112712870A (en) * | 2020-12-30 | 2021-04-27 | 北京懿医云科技有限公司 | Internet hospital medication scheme determination method and device |
| CN112951362A (en) * | 2021-02-23 | 2021-06-11 | 上海商汤智能科技有限公司 | Medicine recommendation method, device, equipment and storage medium |
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