CN112967814A - Novel coronavirus patient action tracking method and device based on deep learning - Google Patents
Novel coronavirus patient action tracking method and device based on deep learning Download PDFInfo
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Abstract
The application discloses a novel coronavirus patient action tracking method and device based on deep learning. The novel coronavirus patient action tracking method based on deep learning comprises the following steps: constructing a new crown semantic model, wherein the new crown semantic model comprises keyword information and incidence relation information; acquiring an event information database of a novel coronavirus patient; extracting patient information of each patient in the event information; acquiring association map information among patients according to the association relation information and the patient information of each patient; and generating a visual map according to the associated map information or the patient information. According to the new coronavirus patient information acquisition method, basic information of each patient is acquired at first, named entity identification, relation extraction and attribute extraction are performed by adopting a machine learning algorithm, and data fusion and data storage are performed on high-quality patient data. Through the construction of the patient information and the action track knowledge graph, the effective integration of the novel coronary patient information is realized.
Description
Technical Field
The invention relates to the technical field of information collection, in particular to a novel coronavirus patient action tracking method based on deep learning and a novel coronavirus patient action tracking device based on deep learning.
Background
After an epidemic outbreak, patient data of a novel coronavirus pneumonia (Coronavirus disease 2019, COVID-19) shows a blowout type outbreak, and the data are dispersed on different public platforms, but due to the limitation of a data model, a plurality of element data related to the epidemic infection influence cannot be associated, and comprehensive analysis cannot be carried out.
Accordingly, it would be desirable to have a solution that overcomes or at least alleviates at least one of the above-mentioned difficulties of the prior art.
Disclosure of Invention
It is an object of the present invention to provide a novel method for coronavirus patient motion tracking based on deep learning, which overcomes or at least alleviates at least one of the above-mentioned drawbacks of the prior art.
In one aspect of the present invention, a method for tracking actions of a novel coronavirus patient based on deep learning is provided, and the method for tracking actions of a novel coronavirus patient based on deep learning comprises:
constructing a new crown semantic model, wherein the new crown semantic model comprises keyword information and incidence relation information;
acquiring an event information database of a novel coronavirus patient, wherein the event information database comprises at least one event information;
extracting patient information of each patient in the event information;
acquiring association map information among patients according to the association relation information and the patient information of each patient;
and generating a visual map according to the associated map information or the patient information.
Optionally, the method for tracking the actions of the novel coronavirus patient based on deep learning further comprises:
generating a knowledge question-answering base according to the associated map information and/or the patient information;
and performing man-machine interaction with the user according to the knowledge question-answering library.
Optionally, the acquiring an event information database of a patient with a novel coronavirus, where the event information database includes at least one patient event information group, and the acquiring includes:
generating keyword information;
and acquiring matched event information in an event information database according to the keyword information.
Optionally, the extracting patient information of each patient in the event information includes:
identifying character information or picture information in the event information;
and acquiring the character information in the event information or the patient information in the picture information according to the keyword information.
Optionally, the patient information comprises one or more of the following information: patient number information, confirmed hospital information, confirmed diagnosis time information, disease information, hospital information, patient name information, address information, information of the number of close contacts, residence information, age information, sex information, and disease information; the patient trip starting point information, the route information, the trip mode information and the trip time information.
Optionally, the obtaining of the association map information according to the association relationship information and the patient information of each patient includes:
judging whether intersection information exists between every two patients according to the incidence relation information, if so,
the two patients are mapped with the intersection information as a mapping relationship.
The application also provides a novel coronavirus patient action tracking device based on deep learning, the novel coronavirus patient action tracking device based on deep learning includes:
the new crown semantic model building module is used for building a new crown semantic model, and the new crown semantic model comprises keyword information and incidence relation information;
the system comprises an event information database acquisition module, a data processing module and a data processing module, wherein the event information database acquisition module is used for acquiring an event information database of a novel coronavirus patient, and the event information database comprises at least one piece of event information;
a patient information extraction module for extracting patient information of each patient in the event information;
the associated map acquisition module is used for acquiring associated map information among patients according to the associated relation information and the patient information of each patient;
and the visual map generation module is used for generating a visual map according to the associated map information or the patient information.
Optionally, the apparatus for tracking the actions of the novel coronavirus patient based on deep learning comprises:
the knowledge question-answer base generation module is used for generating a knowledge question-answer base according to the associated map information and/or the patient information;
and the human-computer interaction module is used for carrying out human-computer interaction with the user according to the knowledge question-answering library.
The present application further provides an electronic device comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the method for tracking the actions of the patients with coronavirus based on deep learning as described above.
The present application also provides a computer readable storage medium storing a computer program which, when executed by a processor, enables the novel method for coronavirus patient action tracking based on deep learning as described above.
Advantageous effects
According to the novel coronavirus patient action tracking method based on deep learning, basic information of each patient is firstly obtained, named entity identification, relation extraction and attribute extraction are carried out by adopting a machine learning algorithm, and data fusion and data storage are carried out on high-quality patient data. Through the construction of the patient information and action track knowledge graph, the effective integration of the novel coronary patient information is realized, and the visual display and intelligent question and answer of the action track of the novel coronavirus patient are realized on the basis of the construction of the knowledge graph.
Drawings
FIG. 1 is a flowchart illustrating a method for tracking the actions of a patient with coronavirus based on deep learning according to a first embodiment of the present invention.
Fig. 2 is a schematic diagram of patient information in the novel coronavirus patient action tracking method based on deep learning shown in fig. 1.
Fig. 3 is a visualization diagram formed by the novel coronavirus patient action tracking method based on deep learning shown in fig. 1.
FIG. 4 is another visualization formed by the novel method for tracking the actions of patients with coronavirus based on deep learning shown in FIG. 1.
FIG. 5 is another visualization formed by the novel method for tracking the actions of patients with coronavirus based on deep learning shown in FIG. 1.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are a subset of the embodiments in the present application and not all embodiments in the present application. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application. Embodiments of the present application will be described in detail below with reference to the accompanying drawings.
In the description of the present application, it is to be understood that the terms "central," "longitudinal," "lateral," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in the orientation or positional relationship indicated in the drawings for convenience in describing the present application and for simplicity in description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated in a particular manner and are not to be considered limiting of the scope of the present application.
FIG. 1 is a flowchart illustrating a method for tracking the actions of a patient with coronavirus based on deep learning according to a first embodiment of the present invention.
The novel coronavirus patient action tracking method based on deep learning shown in fig. 1 comprises the following steps:
step 1: constructing a new crown semantic model, wherein the new crown semantic model comprises keyword information and incidence relation information;
step 2: acquiring an event information database of a novel coronavirus patient, wherein the event information database comprises at least one piece of event information;
and step 3: extracting patient information of each patient in the event information;
and 4, step 4: acquiring association map information among patients according to the association relation information and the patient information of each patient;
and 5: and generating a visual map according to the associated map information or the patient information.
In this embodiment, the method for tracking the actions of the novel coronavirus patient based on deep learning further comprises:
step 6: generating a knowledge question-answering base according to the associated map information and/or the patient information;
and 7: and performing man-machine interaction with the user according to the knowledge question-answering library.
According to the novel coronavirus patient action tracking method based on deep learning, basic information of each patient is firstly obtained, named entity identification, relation extraction and attribute extraction are carried out by adopting a machine learning algorithm, and data fusion and data storage are carried out on high-quality patient data. Through the construction of the patient information and action track knowledge graph, the effective integration of the novel coronary patient information is realized, and the visual display and intelligent question and answer of the action track of the novel coronavirus patient are realized on the basis of the construction of the knowledge graph.
In this embodiment, obtaining an event information database of a patient with a novel coronavirus, where the event information database includes at least one patient event information group including:
generating keyword information;
and acquiring matched event information in an event information database according to the keyword information.
In this embodiment, extracting the patient information of each patient in the event information includes:
identifying character information or picture information in the event information;
and acquiring the character information in the event information or the patient information in the picture information according to the keyword information.
In this embodiment, the patient information includes one or more of the following: patient number information, confirmed hospital information, confirmed diagnosis time information, disease information, hospital information, patient name information, address information, information of the number of close contacts, residence information, age information, sex information, and disease information; the patient trip starting point information, the route information, the trip mode information and the trip time information.
In this embodiment, the obtaining of the association map information between the patients according to the association relationship information and the patient information of each patient includes:
judging whether intersection information exists between every two patients according to the incidence relation information, if so,
the two patients are mapped with the intersection information as a mapping relationship.
Referring to fig. 2, in this embodiment, we propose a semantic relationship construction method of information coronavirus patient information in combination with an ontology semantic construction method.
Secondly, the patient information tracking knowledge graph constructed by the method takes an event occurrence entity as a center, and data extraction is performed by considering the time, the place, the patient information and the travel information of an event, so that the event basic information needs to be acquired on a public website to obtain the detailed information of the patient and a corresponding URL list, then the URL list is sequentially accessed, and the patient information, the patient travel information and the hospital information confirmed by the patient related to the event information are obtained by analyzing page information.
The method comprises the steps of extracting event information of a novel coronavirus, storing obtained original event data, storing event data based on a neo4j graph database, identifying event entities of patient data of the novel coronavirus due to the particularity of the event information, dividing text data by a text and Chinese processing packet, obtaining the part of speech, dependency, grammar and the like of related elements after division of the words by a word dividing system, dividing the definitions of all words into 5 types according to event elements, namely time, place, trigger words, participants and objects, wherein the trigger words are important characteristics for identifying event types, different events have different trigger words, such as ' diagnosis confirmed event ' and ' travel out event ' with ' riding Trigger words such as "self-driving", "walking", and the like; the identification of the event type is determined by the event trigger words and the event elements, the event trigger words are compared with the trigger words marked in the corpus after word segmentation, and if the event trigger words are the same, the event type is marked as the trigger words. The factors such as participants, objects, time, places and the like are also set into categories in the mode, corresponding labels are identified, and finally the labels are saved as csv files and are imported into a graph database.
The relationship design of the novel coronavirus patient is mainly because the novel coronavirus information has time, space and semantic correlation relationships in the relationship establishment process, and the specific pattern layer correlation relationships comprise infection relationships, social relationships, case event relationships and time sequence relationships. The social relationship belongs to semantic relationships, including relatives, co-workers, and the like; the infection relation is related to the contact between cases, namely related to case activities, and has rich semantic and spatiotemporal relations. The purpose of relationship extraction is to extract triples of two entities and relationships.
Case activity events include aggregate events (e.g., dinner event), simple events (e.g., contact event, household isolation event, hospitalization event), trip events (e.g., travel event, shopping event), phenomenological events (e.g., fever event). In addition, the semantic relationship of the event comprises composition, cause and effect, following and concurrency relationship, mainly exists in a non-hierarchical relationship, and can depict the infection relationship between cases. The time sequence relation can describe the time characteristics of the infectious disease activity event through the time points and the time periods, and can also extract the time sequence relation of the event on the basis of the time sequence relation, thereby discovering the mutual dependence relation and the action mechanism of the event in the adjacent time domain. The semantic relationships of different infectious events include composition relationships, causal relationships, follow-up relationships, concurrency relationships, and the like.
On the basis of the relation extraction, the Bi-LSTM-CRF algorithm is used in the patent.
A clustering model is established for patient information, experts are clustered by using a trained embedded carrier to verify the effectiveness of the expert, 500 events in an embedded space are clustered by using Kmeans, and then an embedded vector is projected to a two-dimensional space to realize the visualization of event clustering. As shown in fig. 4, events are grouped into different clusters, and events with close similarity are grouped, which proves that the learning graph embedding vector can use event semantic representation. As shown in FIG. 3, four typical clusters are circled and labeled using the classification of the novel coronavirus event.
According to the application, a visual analysis system of COVID-19 patient information and action tracking knowledge base is constructed by utilizing visualization tools ECharts and Django and combining the characteristics of abundant information of novel coronavirus and interactive visual analysis of a visual analysis system. The system interface is shown in fig. 5, and the current epidemic situation can be fully mastered by using a multi-view collaborative visual analysis method.
Because the novel coronavirus data are data which are paid attention to everyday, the data are necessary to be combed to form a knowledge question and answer base, real-time state control is carried out on relevant medical workers according to the requirements of the knowledge question and answer base, an interface capable of carrying out man-machine interaction is built according to the needs of users, and the question and answer through natural language is a communication mode which people are accustomed to, so that the knowledge question and answer base of the novel coronavirus information based on the knowledge graph is built by applying a machine learning technology.
The method comprises the steps of performing word vector processing by using natural language question answering, firstly converting natural language into a vector sequence through word vectors, performing attribute linkage through an entity alignment method based on a traditional probability model, finally obtaining an expert knowledge corpus, converting natural language questions into Cypher query language of a Neo4j graphic database based on a corpus matching mode, completing knowledge query in a novel coronavirus expert knowledge atlas, and returning visual query results to a user. And the information retrieval and result display of the novel coronavirus are realized. By the method and the system, knowledge reasoning of the novel coronavirus information is improved, and man-machine interaction service is provided for the user.
In summary, the method establishes a construction process for constructing the novel coronavirus patient information map from a large amount of scattered network data, and verifies the validity of using the embedded vector as entity semantic representation through a construction field clustering algorithm of the novel coronavirus patient information map. The application method is combined with a naive Bayes method, the propagation of the novel coronavirus is deduced, and the propagation track of the novel coronavirus is recommended and predicted. The patent can also be applied to search engines for determining diagnosis of different cities and regions of the novel coronavirus. In addition, the vector is embedded into the neural network to realize the transfer of knowledge, and the application of the neural network in the knowledge map is realized.
The present application is described in detail below by way of examples, and it should be understood that the examples are not to be construed as limiting the present application in any way.
For example, a news report reports the following:
at the first time point, a new coronavirus nucleic acid positive was detected in a forest of a febrile patient admitted to the department of the city A. The close contact person and the suspected infection source are checked and then deployed.
The investigator knows that a forest played mahjong in the E-board room at a second time point with a leaf from place B. The husband of the forest has a certain A in the side view.
At night, the staff examined the leaf certain, wife of the leaf certain and Zhangqi A. Through the examination of hospital H, the leaf and plum have no obvious discomfort and fever, and the lung CT indicates that the lung has a little infection. Zhangzhi A self-states that cough and expectoration symptoms occur at the third time point, no fever occurs, no other physical discomfort occurs, and the attention is not paid all the time.
The detection result shows that the three persons are positive to the novel coronavirus nucleic acid.
Through investigation, workers know that the leaf and plum have business in the camp clothing of the place B for a long time and live in the large included street of the place B. At a second point in time, both return home from place B to place C. Subsequently, the staff examined and paid close attention again to the close contacts of the four persons, and no new infected persons were found.
But the clues of infection are virtually uninterrupted.
At the fourth time, one hospital in A received two patients with fever Zhang-somewhat B and wife-Yang-somewhat. At the sixth time point, both people were positive for the novel coronavirus nucleic acid by assay.
Zhang-A and Yang-A have passed business at C and returned to home at the fifth time point. No history of going out of the field B, no farmer market and no history of wild animal exposure. These two people, although from the same village as the four people previously diagnosed, are not life-wise intersected.
As the survey progresses, the investigators end up finding an important clue: chess and card room in village. One B has arrived at the A chess and card room to play the poker. Through repeated inquiry, Zhanga B remembers that the playing card was played in the chess and card room A and the forest which is the first to get ill.
The method for acquiring the information of the new coronavirus patient specifically comprises the following steps:
step 1: constructing a new crown semantic model, wherein the new crown semantic model comprises keyword information and incidence relation information; the keyword information includes names (patient, patient name, etc.), place names (place a, place B, etc.), hospitals, symptom keywords (fever, cough, lung, new crown, positive, negative, etc.), travel, and the like.
Step 2: acquiring an event information database of a novel coronavirus patient, wherein the event information database comprises at least one event information; in this embodiment, the event information database is a network, and the event information is the above-mentioned news report.
And step 3: extracting patient information of each patient in the event information according to the keyword information; specifically, the following information is acquired:
patient 1: when a woodworker is played in a chess and card room E, the novel coronavirus is positive in nucleic acid, a doctor H visits, no obvious discomfort and fever are caused to the patient with symptoms, and the lung is slightly infected by CT (computed tomography);
patient 2: zhangzhi A, a forest man wife and a novel coronavirus are positive, a doctor H visits a doctor, and symptoms such as cough and expectoration are caused, and fever is avoided;
patient 3: some leaves, playing cards in the chess and card room E, having positive novel coronavirus nucleic acid, coming from the place A and the hospital H, having no obvious discomfort and fever in symptoms, and prompting little infection in the lung by CT in the lung;
patient 4: li Yi, leaf Yi and wife, hospital H, new type coronavirus nucleic acid positive, no obvious discomfort, no fever, lung CT suggests a little infection in lung;
patient 5: when a certain B, E chess and card room is played, the novel coronavirus nucleic acid is positive.
Patient 6: yang is certain, Zhang is certain B wife, and the new type coronavirus nucleic acid is positive.
And 4, step 4: acquiring association graph information among patients according to the association relationship information and the patient information of each patient, specifically, the association relationship information includes an association relationship keyword, in this embodiment, the association relationship keyword includes a couple relationship and an action track relationship, in this embodiment, a node link network diagram is used to show relationships such as patient character relationship, patient-location, patient-event, patient-receiving hospital and the like, wherein, the forest, leaf and Zhang B are associated together through the action track relationship, and are associated together, and Zhang A is associated with the forest, the plum is associated with the leaf, the poplar is associated with the Zhang B through the couple relationship, so that the 6 patients are associated together to form an association graph information.
And 5: and generating a visual map according to the associated map information or the patient information. Specifically, in one embodiment, a visualization map, such as a star map, a relationship map, or the like, may be made by mapping software.
The application also provides a novel coronavirus patient action tracking device based on deep learning, the new coronavirus patient information acquisition device comprises a new crown semantic model construction module, an event information database acquisition module, a patient information extraction module, an associated map acquisition module and a visual map generation module,
the new crown semantic model building module is used for building a new crown semantic model, and the new crown semantic model comprises keyword information and incidence relation information;
the event information database acquisition module is used for acquiring an event information database of a novel coronavirus patient, and the event information database comprises at least one piece of event information;
the patient information extraction module is used for extracting patient information of each patient in the event information;
the associated map acquisition module is used for acquiring associated map information among patients according to the associated relation information and the patient information of each patient;
the visual map generation module is used for generating a visual map according to the associated map information or the patient information.
In this embodiment, the apparatus for tracking the actions of the patients with the coronavirus based on deep learning further comprises a knowledge question-answer base generation module and a human-computer interaction module, wherein the knowledge question-answer base generation module is used for generating a knowledge question-answer base according to the associated map information and/or the patient information; and the human-computer interaction module is used for performing human-computer interaction with the user according to the knowledge question-answering base.
It should be noted that the foregoing explanations of the method embodiments also apply to the apparatus of this embodiment, and are not repeated herein.
The application also provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the above deep learning-based novel coronavirus patient action tracking method.
For example, an electronic device includes an input device, an input interface, a central processing unit, a memory, an output interface, and an output device. The input interface, the central processing unit, the memory and the output interface are mutually connected through a bus, and the input equipment and the output equipment are respectively connected with the bus through the input interface and the output interface and further connected with other components of the computing equipment. Specifically, the input device receives input information from the outside and transmits the input information to the central processing unit through the input interface; the central processing unit processes the input information based on the computer executable instructions stored in the memory to generate output information, temporarily or permanently stores the output information in the memory, and then transmits the output information to the output device through the output interface; the output device outputs the output information to an exterior of the computing device for use by a user.
The present application also provides a computer-readable storage medium storing a computer program which, when executed by a processor, can implement the above novel method for tracking actions of patients with coronavirus based on deep learning.
Although the present application has been described with reference to the preferred embodiments, it is not intended to limit the present application, and those skilled in the art can make variations and modifications without departing from the spirit and scope of the present application.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media include both non-transitory and non-transitory, removable and non-removable media that implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Furthermore, it will be obvious that the term "comprising" does not exclude other elements or steps. A plurality of units, modules or devices recited in the device claims may also be implemented by one unit or overall device by software or hardware. The terms first, second, etc. are used to identify names, but not any particular order.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks identified in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The Processor in this embodiment may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the apparatus/terminal device by running or executing the computer programs and/or modules stored in the memory, as well as by invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
In this embodiment, the module/unit integrated with the apparatus/terminal device may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer readable medium may contain content that is appropriately increased or decreased as required by legislation and patent practice in the jurisdiction.
Although the invention has been described in detail hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (10)
1. A novel coronavirus patient action tracking method based on deep learning is characterized by comprising the following steps of:
constructing a new crown semantic model, wherein the new crown semantic model comprises keyword information and incidence relation information;
acquiring an event information database of a novel coronavirus patient, wherein the event information database comprises at least one event information;
extracting patient information of each patient in the event information;
acquiring association map information among patients according to the association relation information and the patient information of each patient;
and generating a visual map according to the associated map information or the patient information.
2. The method for tracking the actions of a novel coronavirus patient based on deep learning of claim 1, wherein the method for tracking the actions of a novel coronavirus patient based on deep learning further comprises:
generating a knowledge question-answering base according to the associated map information and/or the patient information;
and performing man-machine interaction with the user according to the knowledge question-answering library.
3. The method of claim 2, wherein the step of obtaining an event information database of the new coronavirus patient comprises:
generating keyword information;
and acquiring matched event information in an event information database according to the keyword information.
4. The method of claim 3, wherein the extracting the patient information of each patient in the event information comprises:
identifying character information or picture information in the event information;
and acquiring the character information in the event information or the patient information in the picture information according to the keyword information.
5. The method of claim 4, wherein the patient information comprises one or more of the following: patient number information, confirmed hospital information, confirmed diagnosis time information, disease information, hospital information, patient name information, address information, information of the number of close contacts, residence information, age information, sex information, and disease information; the patient trip starting point information, the route information, the trip mode information and the trip time information.
6. The method for tracking the actions of the patients with the coronavirus based on deep learning of claim 5, wherein the obtaining of the correlation map information between the patients according to the correlation relationship information and the patient information of each patient comprises:
judging whether intersection information exists between every two patients according to the incidence relation information, if so,
the two patients are mapped with the intersection information as a mapping relationship.
7. A novel coronavirus patient movement tracking device based on deep learning, which is characterized by comprising:
the new crown semantic model building module is used for building a new crown semantic model, and the new crown semantic model comprises keyword information and incidence relation information;
the system comprises an event information database acquisition module, a data processing module and a data processing module, wherein the event information database acquisition module is used for acquiring an event information database of a novel coronavirus patient, and the event information database comprises at least one piece of event information;
a patient information extraction module for extracting patient information of each patient in the event information;
the associated map acquisition module is used for acquiring associated map information among patients according to the associated relation information and the patient information of each patient;
and the visual map generation module is used for generating a visual map according to the associated map information or the patient information.
8. The deep learning-based novel coronavirus patient action tracking device of claim 7, wherein the deep learning-based novel coronavirus patient action tracking device comprises:
the knowledge question-answer base generation module is used for generating a knowledge question-answer base according to the associated map information and/or the patient information;
and the human-computer interaction module is used for carrying out human-computer interaction with the user according to the knowledge question-answering library.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the method for deep learning based novel coronavirus patient action tracking according to any one of claims 1-6.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, is capable of implementing the method for deep learning-based novel coronavirus patient action tracking according to any one of claims 1 to 6.
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