CN112863630A - Personalized accurate medical question-answering system based on data and knowledge - Google Patents
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Abstract
The invention belongs to the field of medical health and artificial intelligence, and particularly relates to a personalized accurate medical question-answering system based on data and knowledge, aiming at solving the problems that the prior art has no pertinence and real-time property, has low data utilization rate, cannot efficiently and accurately understand user problems, and cannot realize real-time deep accurate medical question-answering aiming at individuals. The invention comprises the following steps: the personal health data module acquires personal health data of a user; the user problem understanding module is used for preprocessing the user problem and then analyzing words, syntax and semantic levels in sequence; the efficient knowledge map module performs knowledge extraction, knowledge fusion, knowledge storage and knowledge calculation on a knowledge base to construct an efficient knowledge map; and the intelligent medical service module is used for managing user health data, monitoring health, conducting on-line diagnosis guide and auxiliary decision on problem information and generating response information through semantic matching and knowledge reasoning. The invention has pertinence and real-time performance and high data utilization rate and accuracy.
Description
Technical Field
The invention belongs to the field of medical health and artificial intelligence, and particularly relates to a personalized accurate medical question-answering system based on data and knowledge.
Background
With the improvement of living standard and the change of mental level, people pay more and more attention to health problems, and the phenomenon that ' small diseases are endured and big diseases are dragged ' is less and less in the past, but instead of the phenomenon that people hope to quickly hear the doctor's suggestion to recuperate own body no matter the big diseases and the small diseases. For the health problem, the traditional solution is to seek the help of professional medical care personnel and medical institutions, so that patients prefer to go to a large hospital regardless of large diseases and small diseases, the waiting time is increased, and medical resources are wasted. Therefore, auxiliary medical services are urgently needed to help medical institutions relieve the pressure of visiting patients, provide free consultation for patients anytime and anywhere, help patients to effectively position own illness states, and realize patient health monitoring, online pre-diagnosis, medical knowledge question-answering, medical guidance and the like.
In recent years, with the development of science and technology, portable medical equipment is sold in the market, wherein intelligent wearable equipment is the most popular, the traditional detection mode for human health indexes is changed, and meanwhile, individuals can check the health indexes anytime and anywhere, so that the medical level of China is greatly improved, and the current medical condition of China is improved. However, the massive medical data of the internet of things generated by the medical devices are not effectively value mined and utilized. The precise medical treatment is a novel medical concept and a medical mode, and the emphasis is on giving different targeted treatment modes to different patients, and the emphasis is on the precision. Through technologies such as artificial intelligence, big data analysis, fully excavate intelligent wearing equipment and thing networking medical equipment's thing networking medical data to combine automatic medical treatment to ask the answer technology, can realize individualized accurate medical treatment to ask the answer. At present, the existing automatic question-answering system only utilizes a corpus or a knowledge base [1] [2] [3] which is constructed historically, has no pertinence and real-time performance, and cannot achieve deep and accurate medical question-answering aiming at individuals, such as health monitoring, online pre-diagnosis, auxiliary decision-making, medical guidance and the like. Therefore, there is a pressing need in the art for a system that can provide deep personalized accurate medical questions and answers to patients.
The following documents are background information related to the present invention:
[1] wantianyi, Sun Zhengya, Zhangheng, spoken medical question-answering method and system, 20190426, CN109684445A.
[2] Wangxianli, wuqingfeng, linkunjiao and luomang, a medical question-answering method and system based on context language model and knowledge embedding, 20200811 and CN111524593A.
[3] The medical question and answer retrieval method, system and device based on multi-modal knowledge perception are 20200302 and CN 110895561A.
Disclosure of Invention
In order to solve the problems in the prior art, namely, the existing medical question-answering system has no pertinence and instantaneity, has low data utilization rate, cannot efficiently and accurately understand the user problems, and cannot realize real-time deep and accurate medical question-answering aiming at individuals, the invention provides a personalized accurate medical question-answering system based on data and knowledge, which comprises the following modules:
the personal health data module is used for acquiring user background information, historical health data and real-time Internet of things data as user personal health data, acquiring lacking information of a user in a query mode, analyzing information answered by the user, extracting key information and then using the information as perfection information of the user personal health data; the personal health data of the user is input in one or more of voice, characters, videos and images and in the form of data uploading of the Internet of things;
the user problem understanding module is used for preprocessing the word segmentation, part of speech tagging and syntactic type analysis of the problem information input by the user, and analyzing the preprocessed information at a word level, a syntactic level and a semantic level in sequence to obtain a knowledge map query statement;
the efficient knowledge map module is used for acquiring structured, semi-structured and unstructured knowledge bases, and performing knowledge extraction, knowledge fusion, knowledge storage and knowledge calculation in sequence to construct an efficient knowledge map; the system is also used for searching in the high-efficiency knowledge graph by combining the personal health data of the user and knowledge graph query sentences to obtain answer knowledge corresponding to the question information;
and the intelligent medical service module is used for managing and monitoring user health data, conducting on-line diagnosis guide and auxiliary decision on the problem information of the user and generating response information of the problem information through semantic matching and knowledge reasoning on the basis of the medical knowledge map constructed by the high-efficiency knowledge map module and the personal health data of the user.
In some preferred embodiments, the system further comprises a speech recognition module and a speech synthesis module;
the voice recognition module is used for recognizing the voice input by the user when the user selects the voice input to obtain corresponding text problem information;
and the voice synthesis module is used for carrying out voice synthesis on the response information when the user selects voice output to obtain corresponding voice response information.
In some preferred embodiments, the user context information includes user name, gender, age, contact number, home address, education level.
In some preferred embodiments, the historical health data includes disease history, historical medical history, and historical internet of things data.
In some preferred embodiments, the real-time internet of things data includes real-time heart rate, blood oxygen, blood pressure, blood lipid, electrocardiogram, and electroencephalogram of the user.
In some preferred embodiments, the user question understanding module comprises the following elements:
the user question input unit is used for acquiring voice and/or character question information input by a user, converting the voice question information into character question information and acquiring character question information which can be analyzed and processed by the preprocessing unit;
the preprocessing unit is used for preprocessing the word segmentation, part of speech tagging and syntactic type analysis of the character problem information output by the user problem input unit;
the word level analysis unit is used for carrying out entity recognition, coreference resolution and entity disambiguation on the preprocessed information;
the syntactic layer analyzing unit is used for analyzing the syntactic structure, the syntactic relation between words and the syntactic relation between phrases of the information output by the word layer analyzing unit;
and the semantic level analysis unit is used for carrying out sentence structuring and sentence logicalization on the information output by the syntax level analysis unit to obtain the knowledge map query statement.
In some preferred embodiments, the efficient knowledge-graph module comprises the following elements:
the knowledge base selection unit is used for selecting the structured, semi-structured and unstructured knowledge bases and inputting the knowledge bases into the knowledge extraction unit; the structured knowledge base comprises clinical cases, medical literature and an authoritative knowledge base; the semi-structured knowledge base comprises a biomedical database; the unstructured knowledge base comprises an internet database;
the knowledge extraction unit is used for performing attribute extraction, event extraction and entity and associated knowledge joint extraction on the structured, semi-structured and unstructured knowledge bases;
the knowledge fusion unit is used for carrying out knowledge conversion on the information output by the knowledge extraction unit, extracting knowledge factors and then carrying out knowledge fusion according to a fusion method and rules;
and the knowledge storage unit is used for storing the fused knowledge in a mode of an attribute graph, a knowledge description frame and a triple hypergraph to obtain the high-efficiency knowledge map.
In some preferred embodiments, the efficient knowledge-graph module further comprises a knowledge calculation unit;
and the knowledge calculation unit is used for updating the high-efficiency knowledge map after carrying out knowledge statistics, knowledge map mining and knowledge reasoning on the information of the high-efficiency knowledge map so as to obtain a new high-efficiency knowledge map.
In some preferred embodiments, the "entity and associated knowledge joint extraction" is performed by:
and establishing a combined extraction model based on a neural network by sharing parameters of named entity extraction and associated knowledge extraction, and performing entity and associated knowledge combined extraction based on the model.
In some preferred embodiments, the intelligent medical services module comprises the following units:
the personal health data management unit is used for managing the personal health data of the user acquired by the personal health data module;
the health monitoring unit is used for setting a user health threshold value based on user background information and historical health data in the user personal health data and giving an alarm when real-time internet of things data in the user personal health data exceeds the set health threshold value;
the online diagnosis guiding unit is used for carrying out knowledge reasoning on the acquired disease condition information of the user by using the high-efficiency knowledge map, retrieving a corresponding hospital and generating a diagnosis guiding suggestion by combining hospital information, department information and doctor information in a hospital database;
the auxiliary decision unit is used for diagnosing diseases of the user based on the constructed high-efficiency medical knowledge map in combination with personal health data of the user and generating the relationship among medicines, symptoms and diseases and the relationship among medicine contraindications and adaptive symptoms as auxiliary decision information;
the medical knowledge question-answering unit performs data fusion on the personal health data of the user through one or more methods of an algebraic method, wavelet transformation, Bayesian estimation, a D-S reasoning method and a neural network, extracts disease features by utilizing big data analysis, and generates disease pre-diagnosis and early warning information by combining knowledge reasoning; the personal health data is converted into structured data through entity extraction, relationship extraction, attribute extraction and event extraction, and the voice and/or character response information of the question information is generated through establishing a relationship between machine learning and a medical knowledge map.
The invention has the beneficial effects that:
(1) according to the personalized accurate medical question-answering system based on data and knowledge, the medical data of the Internet of things generated by the intelligent wearable device and the medical device of the Internet of things are combined with the medical question-answering system, so that the medical questions of the user can be answered more accurately, functions of health monitoring, online diagnosis guiding, decision making assisting and the like can be realized, and personalized accurate medical knowledge question-answering is realized.
(2) According to the personalized accurate medical question-answering system based on data and knowledge, data fusion and big data analysis are carried out on medical data of the Internet of things generated by intelligent wearable equipment and medical equipment of the Internet of things, a relation between disease characteristics and the data of the Internet of things is established, and the possible diseases of a patient are further determined by combining with the big data analysis of clinical expression intelligent inquiry results of the possible diseases of the patient and personal health data such as electronic medical records and physical examination reports, so that the problem that the traditional intelligent wearable equipment, medical equipment of the Internet of things and the like can only warn abnormal indexes of health indexes and cannot realize online diagnosis and health monitoring of the diseases is solved.
(3) The personalized accurate medical question-answering system based on data and knowledge realizes online disease diagnosis according to personal health data of patients, facilitates the patients to know the own illness state, and assists users to seek medical treatment timely and accurately. In addition, the medical record data is subjected to big data analysis, and the relationship among medicines, symptoms, new curative effects, side effects and the like is excavated, so that reference is provided for doctors to develop the medicines; meanwhile, a knowledge reasoning model based on information characteristics is constructed by combining health data, electronic medical records, inquiry information and the like of the Internet of things of the patient with a medical knowledge map, health assessment and disease intelligent diagnosis are carried out, and knowledge recommendation, real-time reminding and decision reference are given to medical staff in the diagnosis and treatment process of the patient by a doctor, so that a common doctor can provide high-quality diagnosis and treatment service for the patient like a qualified doctor.
(4) The invention is based on the individualized accurate medical question-answering system of data and knowledge, collect information such as medical history and physical symptom description of the patient, utilize technologies such as neural network, natural language processing, knowledge map, etc., construct the online guide model. The patient is helped to quickly find the corresponding doctor to see a doctor by combining the personal health data, the medical knowledge question-answer and the medical knowledge map of the patient, more detailed diagnosis information is provided for the doctor, the diagnosis time is saved, the diagnosis accuracy is improved, the patient seeing a doctor efficiency is improved, and the hospital manpower resources are saved.
(5) The invention relates to a personalized accurate medical question-answering system based on data and knowledge. The user can store the personal health data in the cloud in the forms of voice, text, video, image input and Internet of things uploading, and the personal health data is inquired and visually displayed. If the user information in the user personal health database is incomplete, the user is asked about the lack information in the form of inquiry, the answer result of the user is analyzed, the key information is extracted, and the user personal health database is perfected.
(6) According to the personalized accurate medical question-answering system based on data and knowledge, the existing medical question-answering system mainly inputs questions through texts, and answers of corresponding questions are stored in a knowledge base mainly in the form of texts. However, some people in real life cannot input questions in text form and answers to the questions in text form due to cultural degree, health condition, age, and the like, and therefore, a voice recognition module and a voice synthesis module are designed to convert voice into text and convert the text into voice respectively, and interaction between a user and a question-answering system is facilitated.
(7) According to the personalized accurate medical question-answering system based on data and knowledge, when a user asks and answers medical knowledge, the question-answering system firstly needs to accurately understand the questions asked by the user. The traditional knowledge base question-answering method is used for carrying out semantic analysis on a limited-scale knowledge map in a single field and small in scale of multiple involved entities, concepts and relations, and generally adopts a template or a small-scale machine learning algorithm. However, when facing large-scale, multi-domain knowledge bases, the complexity of semantic analysis algorithms also increases exponentially as the scale of entities, concepts, relationships increases. In order to accurately understand the user problem, the deep neural network is utilized to analyze the user problem into a form of implicitly expressed distributed numerical vectors, wherein the implied key semantics of the user question are associated with the knowledge graph in the distributed expression process to reflect the implied key semantics of entities, relations and the like, so that the accurate understanding of the user problem is realized.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a schematic diagram of a data and knowledge based personalized precision medical question-answering system according to the present invention;
FIG. 2 is a schematic diagram of a user question understanding module of an embodiment of the data and knowledge based personalized precision medical question and answer system of the present invention;
FIG. 3 is a schematic diagram of the structure of the high-efficiency knowledge graph module of one embodiment of the data and knowledge based personalized precision medical question-answering system of the present invention;
FIG. 4 is a schematic diagram of an intelligent medical services module according to an embodiment of the present invention.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
According to the difference of target data sources, the existing medical question-answering systems can be divided into three main categories: the system comprises a retrieval type medical question-answering system, a community medical question-answering system and a knowledge base medical question-answering system. The search-type medical question-answering system firstly analyzes the question asked by the user, matches the subject words in the question with the keywords in the database, searches the text segments possibly containing correct answers in the database, and the database resources mainly come from the world wide web, medical books, medical documents, clinical cases and the question-answering peers of online medical websites. The search-type medical question-answering system is only limited to keyword matching, the quality of the matched answers is not ideal, and the health requirements of users cannot be met due to the specificity of the medical field and no guaranteed answer quality. The core problem of the community medical question-answer is to find out the historical medical question with similar semanteme to the question of the user from the large-scale historical question-answer pair data and return the answer to the questioning user. Due to the large scale of medical information, the characteristic of multiple and heterogeneous data sources and the particularity of medical specialties, in the question and answer process, answers of all medical questions can not be obtained by searching or inquiring in a database. The main reason is the limited coverage of the existing medical databases themselves. Although the retrieval type medical question-answering system and the community medical question-answering system are applied to some specific fields or business fields, the core of the retrieval type medical question-answering system is a keyword matching and shallow semantic analysis technology, deep logical reasoning of knowledge is difficult to realize, and the high-level goal of artificial intelligence cannot be achieved. The medical question-answering in the knowledge base is given to natural language questions, semantic understanding and analysis are carried out on the questions, a knowledge base is further utilized to construct a knowledge graph, and inquiry and knowledge reasoning are carried out to obtain implicit question answers. Therefore, medical questions and answers are currently mainly based on knowledge maps, such as the medical brain of Kongfu, Baidu doctor, Watson Health, and the like. The existing medical question-answering system can only answer simple medical knowledge (such as symptoms, pathology, cautions and the like) of a user, cannot achieve deep and accurate medical question-answering aiming at individuals, such as assessment and early warning of physical health conditions of patients, telemedicine, assistance of doctor decision-making and the like, and is very limited in fully utilizing medical resources and reducing burden of doctors.
The invention relates to a personalized accurate medical question-answering system based on data and knowledge, which comprises the following modules:
the personal health data module is used for acquiring user background information, historical health data and real-time Internet of things data as user personal health data, acquiring lacking information of a user in a query mode, analyzing information answered by the user, extracting key information and then using the information as perfection information of the user personal health data; the personal health data of the user is input in one or more of voice, characters, videos and images and in the form of data uploading of the Internet of things;
the user problem understanding module is used for preprocessing the word segmentation, part of speech tagging and syntactic type analysis of the problem information input by the user, and analyzing the preprocessed information at a word level, a syntactic level and a semantic level in sequence to obtain a knowledge map query statement;
the efficient knowledge map module is used for acquiring structured, semi-structured and unstructured knowledge bases, and performing knowledge extraction, knowledge fusion, knowledge storage and knowledge calculation in sequence to construct an efficient knowledge map; the system is also used for searching in the high-efficiency knowledge graph by combining the personal health data of the user and knowledge graph query sentences to obtain answer knowledge corresponding to the question information;
and the intelligent medical service module is used for managing and monitoring user health data, conducting on-line diagnosis guide and auxiliary decision on the problem information of the user and generating response information of the problem information through semantic matching and knowledge reasoning on the basis of the medical knowledge map sent by the high-efficiency knowledge map module and the personal health data of the user.
In order to more clearly describe the personalized precise medical question-answering system based on data and knowledge, the following describes each module in the embodiment of the present invention in detail with reference to fig. 1.
The individualized accurate medical question-answering system based on data and knowledge in the first embodiment of the invention comprises a personal health data module, a user question understanding module, a high-efficiency knowledge map module and an intelligent medical service module, wherein the modules are described in detail as follows:
the personal health data module is used for acquiring user background information, historical health data and real-time Internet of things data as user personal health data, acquiring lacking information of a user in a query mode, analyzing information answered by the user, extracting key information and then using the information as perfection information of the user personal health data; the personal health data of the user is input in one or more of voice, text, video and image, and is input in the form of data uploading of the Internet of things.
The user background information comprises user name, gender, age, contact telephone, home address and education level; the historical health data comprises disease history, historical medical history and historical internet of things data; the real-time internet of things data comprises real-time heart rate, blood oxygen, blood pressure, blood fat, electrocardiogram and electroencephalogram of the user.
The personal health data module is mainly used for managing personal health data of users and providing data support for personalized accurate medical question answering. The user can store the personal health data in the cloud in a voice, text, video and image input and internet of things data real-time uploading mode, and the personal health data is inquired and visually displayed. If the user information in the user personal health database is incomplete, the system can ask relevant information-lacking questions to the user in an inquiry mode, analyze the answer results of the user, extract key information and perfect the user personal health database.
The personalized accurate medical question-answering system based on data and knowledge further comprises a voice recognition module and a voice synthesis module: the voice recognition module is used for recognizing the voice input by the user when the user selects the voice input to obtain corresponding text problem information; and the voice synthesis module is used for carrying out voice synthesis on the response information when the user selects voice output to obtain corresponding voice response information.
The voice recognition module and the voice synthesis module support the voice input and text input modes of the user questions and the system to answer the user questions in the voice and text modes, and the user can freely select and switch.
And the user problem understanding module is used for preprocessing the word segmentation, part of speech tagging and syntactic type analysis of the voice and/or character problem information input by the user, and analyzing the preprocessed information in a word level, a syntactic level and a semantic level in sequence to obtain a knowledge map query statement.
As shown in fig. 2, which is a schematic structural diagram of a user question understanding module according to an embodiment of the system for personalized and accurate medical question answering based on data and knowledge of the present invention, the user question understanding module includes the following units:
the user question input unit is used for acquiring voice and/or character question information input by a user, converting the voice question information into character question information and acquiring character question information which can be analyzed and processed by the preprocessing unit; the user question is input mainly through two modes of voice and text, and the mode of the system for answering the user question mainly through text and voice. The voice recognition module converts the questions in the form of user voice into characters, and the voice synthesis module converts the result response of the system into voice, so that the user can conveniently interact with the system;
the preprocessing unit is used for preprocessing the word segmentation, part of speech tagging and syntactic type analysis of the character problem information output by the user problem input unit;
in order to accurately understand the problems of the user, the system realizes the sentence understanding of the user from three layers of words, syntax and semantics:
the word level analysis unit is used for carrying out entity recognition, coreference resolution and entity disambiguation on the preprocessed information; on the word level, named entity recognition is carried out through a statistical machine learning-based method, information such as words, dictionaries, rules, contexts and the like is used as characteristics, and an entity extraction model is trained by using a machine learning supervision algorithm (a support vector machine, a hidden Markov model, a conditional random field and the like); the statistical coreference resolution method based on clustering is characterized in that candidate noun phrases are expressed into feature vectors, the possibility of coreference of the candidate noun phrases is measured by using features, and the candidate noun phrases are clustered based on the coreference possibility among the candidate noun phrases; an intra-attention layer and an inter-attention layer are introduced into a deep neural network based on an entity disambiguation model of a multi-direction attention mechanism, so that the robustness of the model in a complex context environment and the disambiguation capability of the model for high ambiguity designation are improved;
the syntactic layer analyzing unit is used for analyzing the syntactic structure, the syntactic relation between words and the syntactic relation between phrases of the information output by the word layer analyzing unit; on a syntactic level, analyzing the relation of sentences through deep learning, expressing the sentences into a matrix by word vectors, obtaining the vector expression of the sentences by utilizing a convolutional neural network and maxporoling, and classifying the vectors by using a softmax classifier to obtain the relation category of the sentences;
the semantic level analysis unit is used for carrying out sentence structuring and sentence logicalization on the information output by the syntax level analysis unit to obtain a knowledge map query statement; at semantic level, analyzing the natural language question into a computable and structuralized logic expression form according to the analysis results of word level and syntax level; structuring the sentences into knowledge map query sentences, searching in a knowledge base, matching out entities and entities with high relation and problem semantic relevance, and simultaneously sequencing the relevant entities according to importance and returning the relevant entities to the user.
The efficient knowledge map module is used for acquiring structured, semi-structured and unstructured knowledge bases, and performing knowledge extraction, knowledge fusion, knowledge storage and knowledge calculation in sequence to construct an efficient knowledge map; and the system is also used for searching in the high-efficiency knowledge graph by combining the personal health data of the user and the knowledge graph query statement to obtain answer knowledge corresponding to the question information.
As shown in fig. 3, which is a schematic structural diagram of an efficient knowledge graph module according to an embodiment of the present invention, the efficient knowledge graph module includes the following units:
the knowledge base selection unit is used for selecting the structured, semi-structured and unstructured knowledge bases and inputting the knowledge bases into the knowledge extraction unit; the structured knowledge base comprises clinical cases, medical literature and an authoritative knowledge base; the semi-structured knowledge base comprises a biomedical database; the unstructured knowledge base comprises an internet database;
the knowledge extraction unit is used for performing attribute extraction, event extraction and entity and associated knowledge joint extraction on the structured, semi-structured and unstructured knowledge bases; aiming at the problem that the extraction result of the associated knowledge is limited by the extraction result of the entity because the extraction of the entity and the extraction of the associated knowledge are respectively carried out in the traditional serial knowledge extraction mode, the entity and associated knowledge combined method is provided, and a combined extraction model based on a neural network is constructed by sharing the parameters of the extraction of the named entity and the extraction of the associated knowledge, so that the combined extraction of the entity and the associated knowledge is realized;
the knowledge fusion unit is used for carrying out knowledge conversion on the information output by the knowledge extraction unit, extracting knowledge factors and then carrying out knowledge fusion according to a fusion method and rules; establishing a knowledge fusion rule aiming at multi-source heterogeneous data, converting the extracted knowledge to obtain a knowledge factor hidden in a data source, and realizing knowledge fusion according to a fusion algorithm and the fusion rule;
the knowledge storage unit is used for storing the fused knowledge in a mode of an attribute graph, a knowledge description frame and a triple hypergraph to obtain a high-efficiency knowledge map; in order to improve the management and calculation efficiency of the knowledge graph, three graph models of an attribute graph, a knowledge description framework and a triple hypergraph are mainly utilized through a knowledge graph storage mode based on a graph structure.
The high-efficiency knowledge map module also comprises a knowledge calculation unit, and after carrying out knowledge statistics, knowledge map mining and knowledge reasoning on the information of the high-efficiency knowledge map, the high-efficiency knowledge map module realizes quick knowledge query, knowledge prediction, relation and information reasoning and the like, updates the high-efficiency knowledge map and obtains a new high-efficiency knowledge map.
And the intelligent medical service module is used for managing and monitoring user health data, conducting on-line diagnosis guide and auxiliary decision on the problem information of the user and generating voice and/or character response information of the problem information through semantic matching and knowledge reasoning on the basis of the medical knowledge map constructed by the high-efficiency knowledge map module and the personal health data of the user.
As shown in fig. 4, which is a schematic structural diagram of an intelligent medical service module according to an embodiment of the personalized precise medical question-answering system based on data and knowledge of the present invention, the intelligent medical service module includes the following units:
the personal health data management unit is used for managing the personal health data of the user acquired by the personal health data module;
the health monitoring unit is used for setting a user health threshold value based on user background information and historical health data in the user personal health data and giving an alarm when real-time internet of things data in the user personal health data exceeds the set health threshold value;
the method comprises the steps of performing data fusion and big data analysis on Internet of things medical data generated by intelligent wearable equipment and Internet of things medical equipment, establishing a relation between disease characteristics and the Internet of things data, determining possible diseases of a patient by combining big data analysis on clinical expression intelligent inquiry results of the possible diseases of the patient and personal health data such as electronic medical records and physical examination reports, and solving the problems that the traditional intelligent wearable equipment and the Internet of things medical equipment can only perform abnormal index warning and cannot realize online disease diagnosis and health monitoring;
the online diagnosis guiding unit is used for carrying out knowledge reasoning on the acquired disease condition information of the user by using the high-efficiency knowledge map, retrieving a corresponding hospital and generating a diagnosis guiding suggestion by combining hospital information, department information and doctor information in a hospital database;
the online diagnosis guide combines the online disease diagnosis, the health monitoring result, the subjective self-feeling description and the medical knowledge map to determine a department for treating the user disease, and guides the user to the corresponding department of the hospital to treat the disease online, so that the problem that the user cannot accurately register due to lack of medical knowledge is avoided, and the burden of medical staff in the hospital is reduced;
the auxiliary decision unit is used for diagnosing diseases of the user based on the constructed high-efficiency medical knowledge map in combination with personal health data of the user and generating the relationship among medicines, symptoms and diseases and the relationship among medicine contraindications and adaptive symptoms as auxiliary decision information;
the medical knowledge question-answering unit performs data fusion on the personal health data of the user through one or more methods of an algebraic method, wavelet transformation, Bayesian estimation, a D-S reasoning method and a neural network, extracts disease features by utilizing big data analysis, and generates disease pre-diagnosis and early warning information by combining knowledge reasoning; converting personal health data into structured data through entity extraction, relationship extraction, attribute extraction and event extraction, and generating voice and/or character response information of question information through establishing a relationship between machine learning and a medical knowledge map;
the medical knowledge question-answering combines user background information, historical health data, internet-of-things medical data generated by intelligent wearable equipment and internet-of-things medical equipment with a medical question-answering system, a medical knowledge map is used for constructing a health evaluation model, the health condition of a user is monitored in real time, the user possibly suffering from diseases is guided to visit a corresponding department for medical treatment according to the health evaluation result, knowledge recommendation, real-time reminding, decision reference and the like can be provided for doctors, not only can the conventional medical knowledge question-answering be realized, but also deep and accurate medical question-answering aiming at individuals, such as health monitoring, online pre-diagnosis, decision assistance, medical guidance and the like, can be realized.
The invention combines the traditional question-answering system based on historical data and the real-time data of the Internet of things, expands the accurate question-answering aiming at individuals, and further extends some application scenes, such as assistant decision, health diagnosis, disease early warning and the like.
It should be noted that, the system and method for personalized and accurate medical question answering based on data and knowledge provided by the above embodiments are only exemplified by the division of the above functional modules, and in practical applications, the above functions may be allocated to different functional modules according to needs, that is, the modules or steps in the embodiments of the present invention are further decomposed or combined, for example, the modules in the embodiments may be combined into one module, or may be further split into a plurality of sub-modules, so as to complete all or part of the above described functions. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not to be construed as unduly limiting the present invention.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
Claims (10)
1. A personalized accurate medical question-answering system based on data and knowledge is characterized by comprising the following modules:
the personal health data module is used for acquiring user background information, historical health data and real-time Internet of things data as user personal health data, acquiring lacking information of a user in a query mode, analyzing information answered by the user, extracting key information and then using the information as perfection information of the user personal health data; the personal health data of the user is input in one or more of voice, characters, videos and images and in the form of data uploading of the Internet of things;
the user problem understanding module is used for preprocessing the word segmentation, part of speech tagging and syntactic type analysis of the problem information input by the user, and analyzing the preprocessed information at a word level, a syntactic level and a semantic level in sequence to obtain a knowledge map query statement;
the efficient knowledge map module is used for acquiring structured, semi-structured and unstructured knowledge bases, and performing knowledge extraction, knowledge fusion, knowledge storage and knowledge calculation in sequence to construct an efficient knowledge map; the system is also used for searching in the high-efficiency knowledge graph by combining the personal health data of the user and knowledge graph query sentences to obtain answer knowledge corresponding to the question information;
and the intelligent medical service module is used for managing and monitoring user health data, conducting on-line diagnosis guide and auxiliary decision on the problem information of the user and generating response information of the problem information through semantic matching and knowledge reasoning on the basis of the medical knowledge map constructed by the high-efficiency knowledge map module and the personal health data of the user.
2. The data and knowledge based personalized precision medical question answering system according to claim 1, further comprising a speech recognition module and a speech synthesis module;
the voice recognition module is used for recognizing the voice input by the user when the user selects the voice input to obtain corresponding text problem information;
and the voice synthesis module is used for carrying out voice synthesis on the response information when the user selects voice output to obtain corresponding voice response information.
3. The data and knowledge based personalized precise medical question answering system according to claim 1, wherein the user context information comprises user name, gender, age, contact phone, home address, education level.
4. The data and knowledge based personalized accurate medical question answering system according to claim 1, wherein the historical health data comprises disease history, historical medical history, historical internet of things data.
5. The data and knowledge based personalized accurate medical question answering system according to claim 1, wherein the real-time internet of things data comprises real-time user heart rate, blood oxygen, blood pressure, blood lipid, electrocardiogram, electroencephalogram.
6. The data and knowledge based personalized precision medical question answering system according to claim 1, wherein the user question understanding module comprises the following elements:
the user question input unit is used for acquiring voice and/or character question information input by a user, converting the voice question information into character question information and acquiring character question information which can be analyzed and processed by the preprocessing unit;
the preprocessing unit is used for preprocessing the word segmentation, part of speech tagging and syntactic type analysis of the character problem information output by the user problem input unit;
the word level analysis unit is used for carrying out entity recognition, coreference resolution and entity disambiguation on the preprocessed information;
the syntactic layer analyzing unit is used for analyzing the syntactic structure, the syntactic relation between words and the syntactic relation between phrases of the information output by the word layer analyzing unit;
and the semantic level analysis unit is used for carrying out sentence structuring and sentence logicalization on the information output by the syntax level analysis unit to obtain the knowledge map query statement.
7. The system of claim 1, wherein the high-efficiency knowledge graph module comprises the following elements:
the knowledge base selection unit is used for selecting the structured, semi-structured and unstructured knowledge bases and inputting the knowledge bases into the knowledge extraction unit; the structured knowledge base comprises clinical cases, medical literature and an authoritative knowledge base; the semi-structured knowledge base comprises a biomedical database; the unstructured knowledge base comprises an internet database;
the knowledge extraction unit is used for performing attribute extraction, event extraction and entity and associated knowledge joint extraction on the structured, semi-structured and unstructured knowledge bases;
the knowledge fusion unit is used for carrying out knowledge conversion on the information output by the knowledge extraction unit, extracting knowledge factors and then carrying out knowledge fusion according to a fusion method and rules;
and the knowledge storage unit is used for storing the fused knowledge in a mode of an attribute graph, a knowledge description frame and a triple hypergraph to obtain the high-efficiency knowledge map.
8. The data and knowledge based personalized accurate medical question answering system according to claim 7, wherein the high efficiency knowledge graph module further comprises a knowledge calculation unit;
and the knowledge calculation unit is used for updating the high-efficiency knowledge map after carrying out knowledge statistics, knowledge map mining and knowledge reasoning on the information of the high-efficiency knowledge map so as to obtain a new high-efficiency knowledge map.
9. The system according to claim 7, wherein the "entity and associated knowledge joint extraction" is performed by:
and establishing a combined extraction model based on a neural network by sharing parameters of named entity extraction and associated knowledge extraction, and performing entity and associated knowledge combined extraction based on the model.
10. The system of claim 1, wherein the intelligent medical services module comprises the following elements:
the personal health data management unit is used for managing the personal health data of the user acquired by the personal health data module;
the health monitoring unit is used for setting a user health threshold value based on user background information and historical health data in the user personal health data and giving an alarm when real-time internet of things data in the user personal health data exceeds the set health threshold value;
the online diagnosis guiding unit is used for carrying out knowledge reasoning on the acquired disease condition information of the user by using the high-efficiency knowledge map, retrieving a corresponding hospital and generating a diagnosis guiding suggestion by combining hospital information, department information and doctor information in a hospital database;
the auxiliary decision unit is used for diagnosing diseases of the user based on the constructed high-efficiency medical knowledge map in combination with personal health data of the user and generating the relationship among medicines, symptoms and diseases and the relationship among medicine contraindications and adaptive symptoms as auxiliary decision information;
the medical knowledge question-answering unit performs data fusion on the personal health data of the user through one or more methods of an algebraic method, wavelet transformation, Bayesian estimation, a D-S reasoning method and a neural network, extracts disease features by utilizing big data analysis, and generates disease pre-diagnosis and early warning information by combining knowledge reasoning; the personal health data is converted into structured data through entity extraction, relationship extraction, attribute extraction and event extraction, and the voice and/or character response information of the question information is generated through establishing a relationship between machine learning and a medical knowledge map.
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