CN112700832A - Personalized electronic case generation method and system - Google Patents
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Abstract
The invention provides a personalized electronic case generation method, and belongs to the field of medical big data. The method comprises the following steps: constructing a knowledge graph according to the existing medical data; acquiring existing case information, and structuring the existing case information according to the knowledge graph to obtain structured data; performing deep simulation training according to the structured data and the existing case information to obtain a case generation model; and generating an individualized case by adopting a case generation model corresponding to the diagnosis information. The electronic medical record generation method provided by the invention does not need manual participation of doctors in the whole process, is efficient and flexible in electronic medical record generation, and can generate personalized medical records according to the preference of different hospitals, departments or doctors. Not only the intelligence of the electronic medical record is improved, but also the individuation degree of the electronic medical record is improved.
Description
Technical Field
The invention relates to the field of medical big data, in particular to a personalized electronic case generation method and a personalized electronic case generation system.
Background
The use of the electronic medical record system saves a large amount of precious time for medical staff to write medical records, and the medical staff is relieved from heavy writing of various records, so that the medical staff has more time to observe the change of the state of an illness and can better contact and communicate with a patient, so that the patient can obtain more care and more complete treatment, and good medical and patient relationship can be established; meanwhile, more time is provided for scientific research activities, and the medical technical level is further improved. The use of the electronic medical record system also greatly improves the medical record quality of hospitals, so that the written medical records are more standard and have more research and utilization values. The management level of the hospital is a new step, and management departments can monitor and examine the work of each department, so that a management means is added for the management and examination of the hospital, such as the maximum or minimum medical records written by the departments, the ward round condition of superior doctors and the like. The use of the electronic medical record can accelerate the circulation of the patient information, so that the patient information can be obtained anywhere at any time, and the service which can not be provided by the paper medical record can be provided. The electronic medical record system is used, so that the medical record is paperless, the hospital expenditure is saved, the operation cost is reduced, and the economic benefit is improved.
However, the existing generation method of the electronic medical record mainly depends on the template, and different templates need to be configured for different departments or diseases, so that the method is not only complicated, but also not flexible enough, and cannot well support complex clinical scenes. In order to solve the problems of complexity and low personalization degree of the conventional electronic medical record generation method, a new electronic medical record generation method needs to be created.
Disclosure of Invention
The invention aims to provide a personalized electronic case generation method and a personalized electronic case generation system, so as to at least solve the problems of complexity and low personalization degree of the conventional electronic case generation method.
In order to achieve the above object, a first aspect of the present invention provides a personalized electronic case generation method, including: constructing a knowledge graph according to the existing medical data; acquiring existing case information, and structuring the existing case information according to the knowledge graph to obtain structured data; performing deep simulation training according to the structured data and the existing case information to obtain a case generation model; and generating an individualized case by adopting a case generation model corresponding to the diagnosis information.
Optionally, the knowledge-graph at least comprises: one or more of chief complaints, current medical history, past history, personal history, family history and menstruation, marriage and childbirth history.
Optionally, the acquiring information of existing cases includes: the method comprises the steps of collecting stored case information of different hospitals, stored case information of different departments of the same hospital and stored case information of different doctors of the same department respectively.
Optionally, the structuring the existing case information according to the knowledge graph to obtain structured data includes: respectively constructing knowledge graphs of different hospitals, knowledge graphs of different departments of the same hospital and knowledge graphs of different doctors of the same department according to the existing case information; and correspondingly structuring the case texts contained in the existing case information into the structured data of different hospitals, the structured data of different departments of the same hospital and the structured data of different doctors of the same department respectively through the knowledge maps of different hospitals, the knowledge maps of different departments of the same hospital and the knowledge maps of different doctors of the same department.
Optionally, the performing deep simulation training according to the structured data and the existing case information to obtain a case generation model includes: and taking the structured data of different hospitals, the structured data of different departments of the same hospital and the structured data of different doctors in the same department as input parameters respectively, correspondingly taking the stored case information of different hospitals, the stored case information of different departments of the same hospital and the stored case information of different doctors in the same department as output results, and performing deep simulation training to obtain case generation models of different hospitals, case generation models of different departments of the same hospital and case generation models of different doctors in the same department.
Optionally, the personalized case generation by using the case generation model corresponding to the diagnosis information includes: acquiring doctor-patient conversation and diagnosis information of a patient needing to generate a case; structuring the doctor-patient conversation and the diagnosis information according to the knowledge graph to obtain diagnosis information structured data; and generating a corresponding personalized case by adopting a case generation model corresponding to the diagnosis information structured data.
A second aspect of the invention provides a personalized electronic case generation system, the system comprising: the acquisition unit is used for acquiring the existing medical data and the existing case information; the processing unit is used for constructing a knowledge graph according to the existing medical data, and carrying out information structuralization on the existing case according to the knowledge graph to obtain structured data; the system is also used for carrying out deep simulation training according to the structured data and the existing case information to obtain a case generation model; a storage unit for storing the knowledge-graph and the case generation model; the acquisition unit is also used for acquiring diagnosis information; and a case generation unit for generating an individual case by using a case generation model corresponding to the diagnosis information.
Optionally, the acquisition unit is constructed by an existing medical knowledge base, and the existing medical knowledge base includes: public network medical database, hospital private network medical database, department private network medical database and doctor personal medical database.
Optionally, the case generating unit further includes: the display module is used for displaying the generated personalized case; and the pushing module is used for pushing the generated personalized case to a user side or a printing side.
In another aspect, the present invention provides a computer-readable storage medium having instructions stored thereon, which when executed on a computer, cause the computer to perform the personalized electronic case generation method described above.
By the technical scheme, the case text and the structured data are utilized to construct training data, the structured data is used as input, the original case text is used as output, and a case generation model is trained. And inputting the structured data into the model to obtain the personalized case. The whole process does not need manual participation of doctors, the electronic medical record generation is efficient and flexible, and personalized cases can be generated according to the preferences of different hospitals, departments or doctors. Not only the intelligence of the electronic medical record is improved, but also the individuation degree of the electronic medical record is improved.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
fig. 1 is a flowchart illustrating steps of a personalized electronic case generation method according to an embodiment of the present invention;
FIG. 2 is a system block diagram of a personalized electronic case generation system provided in one embodiment of the present invention;
fig. 3 is a block diagram of a case generation unit in the personalized electronic case generation system according to an embodiment of the present invention.
Description of the reference numerals
10-an acquisition unit; 20-a processing unit; 30-a storage unit; 40-a case generation unit;
401-a display module; 402-push module.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
Fig. 2 is a system configuration diagram of a personalized electronic case generation system according to an embodiment of the present invention. As shown in fig. 2, an embodiment of the present invention provides a personalized electronic case generation system, including: the acquisition unit 10 is used for acquiring the existing medical data and the existing case information; the processing unit 20 is configured to construct a knowledge graph according to the existing medical data, and perform information structuring on the existing case according to the knowledge graph to obtain structured data; the system is also used for carrying out deep simulation training according to the structured data and the existing case information to obtain a case generation model; a storage unit 30 for storing the knowledge-graph and the case generation model; a case generation unit 40, configured to perform personalized case generation according to the case generation model and diagnosis information of a patient needing to generate a case; the collecting unit 10 is also used for collecting the diagnosis information of the patient needing to generate the case.
Preferably, the acquisition unit 10 is constructed by an existing medical knowledge base; wherein, the existing medical knowledge base comprises: public network medical database, hospital private network medical database, department private network medical database and doctor personal medical database.
In the embodiment of the invention, the differentiated case generation characteristic training needs huge training sample data, and the data comprises known medical knowledge data, hospital existing case information, department existing case information and doctor personal existing case information. And finding out a personalized case generation basis through feature recognition. This also requires interfacing with public network medical databases, hospital private network medical databases, department private network medical databases, and doctor personal medical databases. Preferably, the database access is carried out by acquiring information access keys of departments and doctors of the hospital, and relevant data acquisition can be carried out only through the acquisition units 10 which are legal for records of the departments and the doctors of the hospital, so that information leakage of the doctors and patients is avoided.
Preferably, as shown in fig. 3, the case generating unit 40 further includes: a display module 401, configured to display the generated personalized case; a pushing module 402, configured to push the generated personalized case to a user side or a printing side.
In the embodiment of the invention, the finally generated personalized case is an electronic case, and the specific case pushing and displaying are carried out according to the case generation request initiating end, so that a doctor or a patient can check the case. Meanwhile, the pushing module 402 is connected with a printing end for conveniently and timely printing cases and increasing the use convenience of the system.
Fig. 1 is a flowchart of a method for generating a personalized electronic case according to an embodiment of the present invention. As shown in fig. 1, an embodiment of the present invention provides a personalized electronic case generation method, including: constructing a knowledge graph according to the existing medical data; acquiring existing case information, and structuring the existing case information according to the knowledge graph to obtain structured data; performing deep simulation training according to the structured data and the existing case information to obtain a case generation model; and generating personalized cases according to the case generation model.
Specifically, the generation of the current case mainly depends on a template, different templates need to be configured for different departments or diseases, and the method is not only complicated, but also not flexible enough, and cannot well support complex clinical scenes. And the habits of patients and doctors in different hospitals, different departments of the same hospital and different doctors in the same department are different, and if the unified case template specification is carried out, the unified case template cannot be guaranteed to be suitable for all disease condition lists on one hand, and on the other hand, the personalized requirements of each hospital, department and doctor cannot be met. Therefore, in order to perform personalized case generation without hospitals, different departments, different doctors and different diseases, the rules of cases without hospitals, different departments, different doctors and different diseases need to be known respectively, and differential case generation is performed according to the rules, so that the information listing efficiency of cases is improved, and the personalized case requirements without hospitals, different departments, different doctors and different diseases are met. Specifically, the method comprises the following steps:
step S10: and constructing a knowledge graph according to the existing medical data.
Specifically, when acquiring the case rule without using a hospital, different departments, different doctors and different diseases, enough data samples need to be acquired first, and the larger the data samples are, the more the summarized rule is fit to the reality. In order to obtain more complete medical data, the acquisition unit 10 is constructed from various databases. It is necessary to acquire regular data of case information, so that it is necessary to acquire a large amount of known case information and medical knowledge information. For example, through comparison and judgment of a large amount of case information, the main contents of conventional case information include main complaints, current medical history, past history, personal history, family history, menstrual marriage and childbirth history, and the structural map lists relatively wide contents and completely records the state information of patients. And (4) performing medical knowledge retrieval aiming at the summarized conventional case structure map, and filling known illness state knowledge corresponding to each structural unit. For example, the current medical history includes various symptoms, signs and medication conditions known, and the family history includes various genetic diseases and family infringements. The information has complete clinical experience and the structural units are mutually connected. For example, in the case of fever and cold, symptoms such as fever, cough, thin nasal discharge and the like are often accompanied, the relationship interconnection of the structural units is performed, the relationship interconnection of the content of each unit is performed, after doctor diagnosis information is obtained at a later stage, the corresponding current medical history and past medical history can be automatically generated or associated, and through association input, the workload of doctors is reduced, and the case generation efficiency is improved.
On the other hand, although the existing case structure map is complete in recording the patient condition information, the structure map is not suitable for all departments and all doctors. For example, when a common bone fracture patient is treated by a common orthopedic outpatient service, only known common bone fractures need to be diagnosed and administered, other contents such as family history and menstruation, marriage and childbirth history do not need to be filled and confirmed except current medical history and past medical history, but the template vacancy information reduces the user experience, so that template customization needs to be performed according to the actual diagnosis and treatment conditions of each department, and the flexibility of case generation is reduced. Therefore, in the second aspect, it is necessary to determine the contents of the normal records generated by the medical record generation performed by each department and doctor by interfacing the medical database of each department with the medical database of the doctor. For example, the doctor is judged to be a common fever clinic doctor by sorting all case information recorded by the doctor, and the doctor has concise records for patient chief complaints and only introduces the most core symptoms and duration; while another physician has a thorough record of patient complaints, including all symptoms and duration of all symptoms. Aiming at the personal preferences of the two doctors, in order to make the generated case information follow the personal habits of the doctors, the personal knowledge graph of the corresponding doctors needs to be generated, so that the case generation training which is adapted to the personal habits of the doctors is performed in a targeted manner when the case generation training is performed in the later period, and the training trial-and-error rate is reduced. According to the above rules, different hospitals have different habits of generating cases, such as general clinics, orthopedic hospitals, gynecological hospitals and general hospitals, and the same hospital has different departments, so the habits of writing cases for different hospitals, different departments and different doctors are preferably generated correspondingly on the basis of the known public network medical data and the conventional case structure map, and the knowledge map is the training basic data of the corresponding hospital, department or doctor. Through the sorted differentiated big data, targeted case rule statistics is carried out, so that case high-frequency units of hospitals, departments or doctors can be obtained, and corresponding case history content display can be carried out according to personal preference.
Step S20: and acquiring the information of the existing case, and structuring the information of the existing case according to the knowledge graph to obtain structured data.
Specifically, after acquiring basic data of each hospital, each department and each doctor, that is, the correspondingly constructed knowledge graph, a targeted case generation simulation can be performed according to the corresponding knowledge graph. The process of case generation is to regularly count disordered patient chief complaint information and doctor diagnosis and treatment information into each knowledge map unit and finally to differentially present the information to the patient and the doctor. Preferably, case generation training is performed using a deep learning algorithm. Deep learning is the final ideal of artificial intelligence development, and the machine has the ability of analyzing and learning like a human by learning the internal rules and the representation levels of sample data. Therefore, the basic condition for deep learning is that a large amount of sample data needs to be prepared, and the final learning result is obtained through the rule that the machine finds the sample data and the trial-and-error training for countless times. In the personalized case generation method, sample data are messy patient chief complaint information and doctor diagnosis and treatment information which are hidden in the existing case information texts, and cases can be fictional or known. The known case information is imported into the processing unit 20, and the processing unit 20 performs corresponding case information structuring according to the generated knowledge maps of hospitals, departments and doctors, such as the above-mentioned common orthopedics clinic, and only the chief complaint, the current medical history and the past history are structured to obtain corresponding structured data. These structured data are sample data for case generation training performed by the processing unit 20, and in order to reduce trial and error rate and increase the probability that the training set results are close to reality, targeted case generation training is performed for different hospitals, different departments and different doctors, and thus, targeted sample data needs to be prepared. And respectively obtaining the corresponding structured data of each hospital, each department and each doctor according to the generated knowledge graph of each hospital, each department and each doctor.
Step S30: and carrying out deep simulation training according to the structured data and the existing case information to obtain a case generation model.
Specifically, structured data of each hospital, each department and each doctor is obtained as a training data sample, and a corresponding case is generated as a training result to adapt to new training. The deep learning algorithm has many types, mainly including three types, namely a neural network system based on convolution operation, a self-coding neural network based on multilayer neurons, and pre-training in a multilayer self-coding neural network manner, the case information is various and complex, and if the unsupervised self-coding neural network based on multilayer neurons and the pre-training algorithm in the multilayer self-coding neural network manner are performed, the amount of training sample data is too large, the calculation requirement on the processing unit 20 is very high, which not only aggravates the working load of the processing unit 20, but also reduces the training efficiency. Therefore, in order to achieve both training efficiency and fitting of training results to reality, it is preferable to select a convolutional neural network algorithm for supervised learning to perform case generation training. The convolutional neural network algorithm has the characteristic learning capacity and can carry out translation invariant classification on input information according to the hierarchical structure of the convolutional neural network algorithm. The preliminary steps of deep learning supervised learning are completed through the constructed structured data of each hospital, each department and each doctor, namely, the data needing to be trained is extracted, the information content of cases which are never used by each hospital, each department or each doctor is deleted, only the sample data needing to be trained is reserved, the training data amount is reduced, and the probability of feature extraction of the processing unit 20 is increased. The processing unit 20 performs case simulation generation through a convolutional neural network algorithm, simulates a specific hospital, a specific department or a specific doctor to perform case generation, simulates each piece of known case information to generate a corresponding personalized case, and obtains a case generation model of each hospital, a case generation model of each department and a case generation model of each doctor through a large amount of feature simulation training. The generated case generation model is stored in the storage unit 30, facilitating the case generation unit 40 to perform model extraction.
Step S40: and generating personalized cases according to the case generation model.
Specifically, the patient condition information and the diagnosis and treatment information of the patient are acquired in real time, and the uploading information of the doctor and the chief complaint information of the patient are recorded. When a patient needs to be generated, if a person is the patient, the case production unit generates personalized cases according to the patient visit hospital, department or doctor. The diagnosis and treatment information of the patient, the diagnosis information uploaded by the doctor and the patient chief complaint information are structured according to the corresponding knowledge map, namely, disordered information is normalized and characterized, and then corresponding personalized case generation is carried out according to the generated case generation models of hospitals, departments and doctors and is sent to the patient end. If the artificial doctor is initiated, personalized case generation is carried out on the structured information of the patient according to the case generation model corresponding to the doctor, and a case meeting personal habits and preferences of the doctor is automatically generated on the premise of not needing handwriting of the doctor, so that the case output efficiency is improved, and the accuracy of the coming case is also improved.
In one possible embodiment, the clinical information of the patient is uploaded to a public medical information base, and only the patient himself or the doctor who is interfacing can access the information by means of the patient personal information key. When a patient goes from one hospital to another hospital for diagnosis and treatment, the personal information key of the patient is used for accessing information such as the past history and family history of the patient, and the amount of information accessed by doctors and the amount of information complained by the patient are reduced. When patient medical record is generated, the patient information can be directly extracted conveniently, so that the generated patient medical record information is more comprehensive and accurate.
Embodiments of the present invention also provide a computer-readable storage medium having instructions stored thereon, which, when executed on a computer, cause the computer to perform the above-mentioned personalized electronic case generating method.
Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiments may be implemented by a program, which is stored in a storage medium and includes several instructions to enable a single chip, a chip, or a processor (processor) to execute all or part of the steps in the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
While the embodiments of the present invention have been described in detail with reference to the accompanying drawings, the embodiments of the present invention are not limited to the details of the above embodiments, and various simple modifications can be made to the technical solution of the embodiments of the present invention within the technical idea of the embodiments of the present invention, and the simple modifications are within the scope of the embodiments of the present invention. It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, the embodiments of the present invention will not be described separately for the various possible combinations.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as disclosed in the embodiments of the present invention as long as it does not depart from the spirit of the embodiments of the present invention.
Claims (10)
1. A method of personalized electronic case generation, the method comprising:
constructing a knowledge graph according to the existing medical data;
acquiring existing case information, and structuring the existing case information according to the knowledge graph to obtain structured data;
performing deep simulation training according to the structured data and the existing case information to obtain a case generation model;
and generating an individualized case by adopting a case generation model corresponding to the diagnosis information.
2. The personalized electronic case generation method of claim 1, wherein the knowledge-graph comprises at least:
one or more of chief complaints, current medical history, past history, personal history, family history and menstruation, marriage and childbirth history.
3. The personalized electronic case generation method of claim 2, wherein the collecting existing case information comprises:
the method comprises the steps of collecting stored case information of different hospitals, stored case information of different departments of the same hospital and stored case information of different doctors of the same department respectively.
4. The method of claim 3, wherein the structuring the existing case information according to the knowledge-graph to obtain structured data comprises:
respectively constructing knowledge graphs of different hospitals, knowledge graphs of different departments of the same hospital and knowledge graphs of different doctors of the same department according to the existing case information;
and correspondingly structuring the case texts contained in the existing case information into the structured data of different hospitals, the structured data of different departments of the same hospital and the structured data of different doctors of the same department respectively through the knowledge maps of different hospitals, the knowledge maps of different departments of the same hospital and the knowledge maps of different doctors of the same department.
5. The method of claim 4, wherein the deep simulation training based on the structured data and the existing case information to obtain a case generation model comprises:
and taking the structured data of different hospitals, the structured data of different departments of the same hospital and the structured data of different doctors in the same department as input parameters respectively, correspondingly taking the stored case information of different hospitals, the stored case information of different departments of the same hospital and the stored case information of different doctors in the same department as output results, and performing deep simulation training to obtain case generation models of different hospitals, case generation models of different departments of the same hospital and case generation models of different doctors in the same department.
6. The personalized electronic case generation method of claim 5, wherein the personalized case generation using the case generation model corresponding to the diagnostic information comprises:
acquiring doctor-patient conversation and diagnosis information of a patient needing to generate a case;
structuring the doctor-patient conversation and the diagnosis information according to the knowledge graph to obtain diagnosis information structured data;
and generating a corresponding personalized case by adopting a case generation model corresponding to the diagnosis information structured data.
7. A personalized electronic case generation system, the system comprising:
the acquisition unit is used for acquiring the existing medical data and the existing case information;
the processing unit is used for constructing a knowledge graph according to the existing medical data, and carrying out information structuralization on the existing case according to the knowledge graph to obtain structured data; the system is also used for carrying out deep simulation training according to the structured data and the existing case information to obtain a case generation model;
a storage unit for storing the knowledge-graph and the case generation model;
the acquisition unit is also used for acquiring diagnosis information;
and a case generation unit for generating an individual case by using a case generation model corresponding to the diagnosis information.
8. The personalized electronic case generation system of claim 7, wherein the acquisition unit is built from an existing medical knowledge base comprising:
public network medical database, hospital private network medical database, department private network medical database and doctor personal medical database.
9. The personalized electronic case generation system of claim 7, wherein the case generation unit further comprises:
the display module is used for displaying the generated personalized case;
and the pushing module is used for pushing the generated personalized case to a user side or a printing side.
10. A computer-readable storage medium having instructions stored thereon, which when executed on a computer, cause the computer to perform the method of personalized electronic case generation of any of claims 1 to 6.
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