CN117271903A - Event searching method and device based on clinical big data of hospital - Google Patents
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Abstract
The invention relates to an event searching method and device based on clinical big data of a hospital, wherein the method comprises the following steps: acquiring original clinical data information of a patient, integrating the original clinical data information, and storing the original clinical data information in a data storage library; preprocessing and standardizing original clinical data information stored in a data storage library in sequence to form a standard data set; based on NLP technology or logic conversion technology, obtaining characteristic information of different diseases, and then carrying out matching and mapping treatment on the characteristic information and a standard data set to generate a mapping matching result; based on an event searching algorithm, a preset inquiry condition and a time relation are input for searching, and a searching result meeting the condition is obtained. Based on the event search algorithm, personalized query and analysis can be performed according to the requirements of the user, query conditions and time relations of the requirements of the user are input, and related event information is acquired, so that more accurate and customized results are provided, and user experience and satisfaction are improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to the technical field of medical treatment and medical information processing, and particularly relates to an event searching method and device based on clinical big data of a hospital.
Background
With the digitization of medical information systems and the large-scale accumulation of clinical data, hospital clinical big data becomes a precious data resource. The data contains a large amount of clinical information such as patient medical records, treatment records, medicine use conditions and the like, and has huge research and application potential. However, due to the large and complex volume of data, it becomes a challenge how to efficiently obtain useful information from such data. The analysis method of the clinical big data of the hospital mainly relies on manual retrieval, and the method has the following problems:
(1) Manual searching requires a lot of time and labor, and is inefficient.
(2) Subjective errors are prone to occur in manual retrieval, resulting in inaccuracy in the results.
(3) Due to the complexity of clinical data, there may also be limitations on the breadth and depth of manual retrieval such that some important information is ignored.
In order to solve the above problems and fully exploit the potential of hospital clinical big data, an efficient, accurate event search method is needed. The invention provides an event searching method and device based on hospital clinical big data, wherein the method can automatically extract information related to specific events from massive clinical data by applying a computer technology and a data mining method based on an event searching algorithm of the hospital clinical big data, and can quickly and accurately search out patient groups meeting specific conditions and related clinical data thereof.
Disclosure of Invention
The invention provides an event searching method and device based on clinical big data of a hospital, which aim to solve the problems of efficiency and accuracy of analysis and retrieval of the clinical big data of the hospital; by applying the computer technology and the data mining method, the information related to the specific event can be automatically extracted from massive clinical data, and quick and accurate event searching and analyzing can be realized.
In order to achieve the above object, the present invention provides an event searching method based on clinical big data of a hospital, comprising:
acquiring original clinical data information of a patient, integrating the original clinical data information, and storing the original clinical data information in a data storage library;
preprocessing and standardizing original clinical data information stored in a data storage library in sequence to form a standard data set;
based on an NLP technology or a logic conversion technology, correspondingly carrying out feature extraction or logic conversion on the original clinical data information of different disease types to obtain feature information of different disease types, and then carrying out matching and mapping processing on the feature information and a standard data set to generate a mapping matching result;
based on an event searching algorithm, a preset query condition and a time relation are input in the mapping matching result to search, and a search result meeting the condition is obtained.
Optionally, the patient's raw clinical data information includes patient diagnostic data, surgical data, medication data, detection data, and pathology data.
Optionally, the preprocessing and normalizing the raw clinical data information stored in the data repository in sequence to form a standard data set includes:
preprocessing the original clinical data information stored in the data storage library to obtain preprocessed clinical data information, wherein the preprocessing step comprises a step of removing repeated data processing, a step of processing missing values and abnormal values and a step of processing standardized data formats;
the method comprises the steps of carrying out word segmentation and semantic understanding on item names of various data in the preprocessed clinical data information to obtain standard identification codes corresponding to the item names of the various data, and forming a standard data set, wherein the item names of the various data comprise item names of diagnostic data, item names of operation data, item names of medication data, item names of detection data and item names of pathological data.
Optionally, based on the NLP technology, extracting features of the original clinical data information of different disease types to obtain feature information of different disease types, including:
based on NLP technology, text processing and semantic understanding are carried out on the original clinical data information of different disease types, relevant feature points are identified and extracted, then text data contained in the feature points are divided into different application fields, keywords and field terms are extracted, and the feature information of different disease types is obtained.
Optionally, based on the logic conversion technology, performing logic conversion on the original clinical data information of different disease types to obtain characteristic information of different disease types, including:
according to the medical rules, analyzing and extracting the original clinical data information of different disease types to obtain a plurality of matched entities and relation information thereof, carrying out logic conversion on the matched entities and the relation information thereof based on the logic conversion rules, and mapping the matched entities and the relation information thereof into corresponding application fields to obtain the characteristic information of different disease types.
Optionally, the event search algorithm includes retrieving logical operators, time associations, time comparisons of other events with baseline events, and inclusion and exclusion criteria rules.
Optionally, the method further comprises: after the eligible search results are obtained, the search results are presented in a visual form.
The invention also provides an event searching device based on the clinical big data of the hospital, which comprises:
the data acquisition module is used for acquiring original clinical data information of a patient;
the data integration module is used for integrating the original clinical data information and storing the data information in the data storage library;
the data processing module is used for sequentially preprocessing and standardizing the original clinical data information stored in the data storage library to form a standard data set;
the feature recognition module is used for correspondingly carrying out feature extraction or logic conversion on the original clinical data information of different disease types based on an NLP technology or a logic conversion technology to obtain feature information of different disease types, and then carrying out matching and mapping processing on the feature information and a standard data set to generate a mapping matching result;
the event searching module is used for inputting preset query conditions and time relations in the mapping matching results to search based on an event searching algorithm, and obtaining the search results meeting the conditions.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method when executing the computer program.
The invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements the steps of the method.
The invention has the advantages that:
(1) The event searching method and device based on the hospital clinical big data, provided by the invention, can quickly and accurately search related events from the hospital clinical big data, and has the advantages of high event searching speed and high working efficiency compared with the traditional manual searching method which needs to consume a large amount of time and manpower resources, and can effectively improve the event searching and inquiring efficiency.
(2) According to the event searching method and device based on the hospital clinical big data, the potential relevance and trend among the events can be deeply mined based on the event searching algorithm by analyzing the events in the hospital clinical big data, so that the problems in aspects of medical risks, disease modes, drug reactions and the like which possibly exist can be found, and more comprehensive and accurate information support is provided for medical decision.
(3) According to the event searching method and device based on the clinical big data of the hospital, the event searching algorithm provided by the method can perform personalized inquiry and analysis according to the requirements of the user, input the inquiry conditions and the time relation of the requirements of the user, acquire the event information related to the user, so that more accurate and customized results are provided, and further user experience and satisfaction are improved.
Drawings
FIG. 1 is a flow chart of an event searching method based on clinical big data of a hospital in an embodiment of the invention;
FIG. 2 is a schematic diagram of an event search device based on clinical big data of a hospital according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail by the following detailed description with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
Referring to fig. 1, fig. 1 is a flow chart of an event searching method based on clinical big data of a hospital according to the present embodiment, and the method mainly includes the following steps:
s101, acquiring original clinical data information of a patient, integrating the original clinical data information and storing the same in a data storage library.
In this embodiment, the patient's raw clinical data information is first acquired by connection to a medical information system, and then integrated and integrated into a unified data repository.
In this embodiment, the raw clinical data information of the patient includes, but is not limited to, diagnostic data, surgical data, medication data, detection data, pathology data, and the like of the patient.
S102, preprocessing and normalizing the original clinical data information stored in the data storage library in sequence to form a standard data set.
In this embodiment, after the original clinical data information is acquired, the original clinical data information needs to be preprocessed and standardized to ensure the quality and consistency of the data.
In this embodiment, preprocessing and normalization are sequentially performed on the original clinical data information stored in the data repository to form a standard data set, which specifically includes:
preprocessing the original clinical data information stored in the data storage library to obtain preprocessed clinical data information, wherein the preprocessing step comprises a step of removing repeated data processing, a step of processing missing values and abnormal values and a step of processing standardized data formats;
the method comprises the steps of carrying out word segmentation and semantic understanding on item names of various data in the preprocessed clinical data information to obtain standard identification codes corresponding to the item names of the various data, and forming a standard data set, wherein the item names of the various data comprise item names of diagnostic data, item names of operation data, item names of medication data, item names of detection data and item names of pathological data.
The word segmentation method is mainly used for segmenting the names of the items to be standardized according to a certain rule, and aims to segment the names of the items to be standardized into basic semantic units, so that subsequent processing and understanding are facilitated.
The semantic understanding method is mainly used for understanding semantic features of the item names to be standardized on the basis of word segmentation and named entity recognition by using an NLP technology, and the process can be realized by converting words into vector representations and calculating semantic similarity among the words.
In this embodiment, the specific steps of the pretreatment are as follows: by de-duplicating the unique identifier of the data, deleting data values that are partially empty and should not exist under the preset rules, converting different time formats into a uniform time format and converting different unit representations into a uniform unit.
In this embodiment, the present invention firstly normalizes five major classes of data to obtain a standard data set, so that feature information obtained by feature extraction or logic conversion subsequently matches and maps with the standard data set to obtain standard identification codes corresponding to item names of the five major classes of data, and the item names and the standard identification codes strictly follow relevant specifications and standards of the medical field (for example, encoding by using CCS or ICD encoding technology) in the preparation process so as to ensure the unification and standardization of the data.
Among the five major classes of data referred to above are diagnostic data, surgical data, medication data, detection data, and pathology data, which are used to describe and characterize specific events.
S103, based on an NLP technology or a logic conversion technology, correspondingly performing feature extraction or logic conversion on the original clinical data information of different disease types to obtain feature information of different disease types, and then performing matching and mapping processing on the feature information and a standard data set to generate a mapping matching result.
In this embodiment, based on the NLP technique, feature extraction is performed on the original clinical data information of different disease types to obtain feature information of different disease types, including:
based on NLP technology, text processing and semantic understanding are carried out on the original clinical data information of different disease types, relevant feature points are identified and extracted, then text data contained in the feature points are divided into different application fields, keywords and field terms are extracted, and the feature information of different disease types is obtained.
In this embodiment, based on the NLP technology, specific steps of feature extraction are performed on the original clinical data information of different disease types, for example: the method comprises the steps of performing text processing and semantic understanding on original clinical data information by utilizing an NLP technology, automatically identifying and extracting important feature points, dividing text data into different application fields by utilizing a text classification algorithm in the NLP technology, and identifying keywords and domain terms by utilizing an entity identification algorithm in the NLP technology, so as to extract relevant feature information.
In this embodiment, based on the logic conversion technique, logic conversion is performed on the original clinical data information of different disease types to obtain the characteristic information of different disease types, including:
according to the medical rules, analyzing and extracting the original clinical data information of different disease types to obtain a plurality of matched entities and relation information thereof, carrying out logic conversion on the matched entities and the relation information thereof based on the logic conversion rules, and mapping the matched entities and the relation information thereof into corresponding application fields to obtain the characteristic information of different disease types.
In this embodiment, the logic conversion technology related to the present invention also plays a role in the data conversion and normalization process, and through defining and applying logic rules, data in the original clinical data information can be mapped into corresponding application fields after conversion. The specific steps are as follows: analyzing and extracting original clinical data information of different diseases according to medical rules to obtain a plurality of entities and relation information thereof, matching the analyzed and extracted entities and relation information thereof with predefined medical rules, wherein the medical rules can be formulated based on professional knowledge and experience and are used for judging and deducing relations or attributes among different entities, and on the basis of matching the medical rules, the matched entities and relation information thereof are subjected to logic conversion based on logic conversion rules and mapped into corresponding application fields to obtain characteristic information of different diseases.
In this embodiment, based on the logic conversion technique, specific steps of performing logic conversion on the original clinical data information of different disease types are as follows: for a specific disease, certain clinical indexes can be associated with corresponding fields according to specific disease standards, or new fields are generated through logic judgment to represent specific diagnosis and treatment processes so as to obtain characteristic information of the specific disease.
In this embodiment, through the application of NLP technology or logic conversion technology, the original clinical data information can be converted into a structured, directly retrievable, derived and calculated field with a unified naming convention and format, making the data easier to manage, query and analyze.
S104, based on an event searching algorithm, inputting a preset query condition and a time relation in the mapping matching result to search, and obtaining a search result meeting the condition.
In this embodiment, the event search algorithm includes retrieving logical operators, time associations, time comparisons of other events with baseline events, and inclusion criteria and exclusion criteria rules.
The search logic operator comprises an OR operator and an AND operator, and the OR operator is characterized in that: independent inquiry is carried out aiming at each condition to obtain a diagnosis set meeting each condition, and each inquiry result is combined to obtain a final diagnosis set; for the "and" operator, the rules are: and carrying out independent query on each condition to obtain a diagnosis set meeting each condition, and taking intersection of each query result to obtain a final diagnosis set.
The time association relation related by the invention has the rule that: each event variable is located in a database table with a corresponding unique time variable, for example: drug administration time, test sampling time, test time, etc. At the time of the query, different events may be associated by specifying a time range or a specific point in time.
The time comparison relation between other events and the baseline event is related to by the invention, and the rule is as follows: expanding all visits to the patient in a time dimension and focusing on the time of admission and discharge of the baseline event; the patient's time axis is then divided into three segments: before baseline admission, from baseline admission to discharge, after baseline discharge; for other events, the occurrence time point is compared with the admission time point or discharge time point of the baseline event, and the time period to which the event belongs is judged. I.e. according to all visit records of the patient, spread out in time dimension and segment the time axis of the patient according to the time of admission and discharge of the baseline event (hospitalization), resulting in three time periods (before baseline admission, baseline admission to discharge, after baseline discharge).
The invention relates to an inclusion standard and an exclusion standard rule, wherein the rule is as follows: and independently inquiring each admission condition and the exclusion standard to obtain a candidate set meeting each condition, and removing candidates meeting the exclusion conditions from the admission set to obtain a final admission set.
S105, presenting the search result in a visual form.
In this embodiment, according to the query condition and the time relationship specified by the user, the patient's treatment data can be accurately retrieved, and the set operation and the time comparison are performed according to the given logic, so as to obtain a result set meeting the condition; and then presenting and analyzing the searched results, and displaying the searched results in a visual mode, wherein the search results comprise a patient list, a data chart and the like. Meanwhile, further statistical analysis and mining can be performed on the search results to acquire deeper information and insight.
The above procedure is illustrated by the following specific examples:
step 1: a corresponding baseline event is set.
The specific process comprises the following steps:
(1) selecting the time (first/last) of event occurrence according to the requirement;
(2) the category (diagnosis/medication/operation/detection/pathology) and specific variable of the occurrence event are selected, and the related event type and variable classification are shown in table 1;
(3) selecting a logical relationship between the variable and the keyword;
(4) and inputting keywords to be searched or defined query conditions and time relations.
TABLE 1 related event type and variable classification
Step 2: other events are set.
The specific process comprises the following steps:
(1) confirming occurrence frequency of other events 1;
(2) selecting specific variables of other event 1 (including but not limited to variables of baseline event, but all structured fields presented in the entire scientific research-specific disease library);
(3) selecting a logical relationship between the variable and the keyword;
(4) inputting keywords to be searched or defined query conditions and time relations;
(5) setting or not setting the occurrence time of other events 1;
(6) and selecting whether other events 2/3/4 … … are set or not, and selecting the logic relation among the events to finish the editing of the condition group 1.
Step 3: and selecting whether to set 2/3/4 … … of the condition groups or not, and selecting the logic relation among the condition groups to finish the editing of the whole 'search condition'.
Step 4: an event search is performed.
Step 5: visual presentation of search results.
For example: in the event searching process, the user needs to search for primary liver cancer patients who use specific PD-1/PD-L1 drugs for anti-tumor treatment; setting "primary liver cancer (concept set) for the first time" as a baseline event, and within 1 year after the baseline, the search condition of the patient treated with the specific PD-1/PD-L1 drug is shown in FIG. 2, and the search result of the patient treated with the specific PD-1/PD-L1 drug is shown in FIG. 3.
The event searching method based on the clinical big data of the hospital provided by the embodiment comprises the following steps: acquiring original clinical data information of a patient, integrating the original clinical data information, and storing the original clinical data information in a data storage library; preprocessing and standardizing original clinical data information stored in a data storage library in sequence to form a standard data set; based on an NLP technology or a logic conversion technology, correspondingly carrying out feature extraction or logic conversion on the original clinical data information of different disease types to obtain feature information of different disease types, and then carrying out matching and mapping processing on the feature information and a standard data set to generate a mapping matching result; based on an event searching algorithm, inputting a preset query condition and a time relation in the mapping matching result to search, and obtaining a search result meeting the condition; the implementation method can automatically extract information related to specific events from massive clinical data by applying a computer technology and a data mining method, realizes rapid and accurate event search and analysis, integrates key links such as data acquisition, preprocessing, feature extraction, search algorithm design and result presentation, and the like, and aims to realize efficient and accurate event search and analysis based on clinical big data of hospitals. By the method, medical decision-making and researchers can conveniently acquire required information from massive clinical data, and work such as medical decision-making, disease research and medical quality improvement is supported.
Example two
The present embodiment provides, based on the first embodiment, an event searching device 200 based on clinical big data of a hospital, please refer to fig. 2, for implementing the steps of the event searching method based on clinical big data of a hospital described in the first embodiment, the device 200 mainly includes: a data acquisition module 210, a data integration module 220, a data processing module 230, a feature identification module 240, and an event search module 250, wherein,
a data acquisition module 210 for acquiring raw clinical data information of a patient;
a data integration module 220 for integrating the raw clinical data information and storing in a data repository;
the data processing module 230 is configured to sequentially perform preprocessing and normalization processing on the original clinical data information stored in the data repository to form a standard data set;
the feature recognition module 240 is configured to perform feature extraction or logic conversion on the original clinical data information of different disease types based on the NLP technology or the logic conversion technology, obtain feature information of different disease types, and then perform matching and mapping processing on the feature information and the standard data set to generate a mapping matching result;
the event searching module 250 is configured to input a preset query condition and a time relationship in the mapping matching result to search based on an event searching algorithm, so as to obtain a search result meeting the condition.
Example III
The present embodiment further provides an electronic device based on the first embodiment, please refer to fig. 3, and the electronic device shown in fig. 3 is only an example, and should not bring any limitation to the function and the application scope of the embodiments of the present disclosure.
As shown in fig. 3, the electronic device may include a processing means (e.g., a central processor, a graphics processor, etc.) 301 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
In general, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, a touch panel, a keyboard, a mouse, a camera, etc., output devices 307 including, for example, a Liquid Crystal Display (LCD), a speaker, etc., storage devices 308 including, for example, a magnetic tape, a hard disk, etc., and communication devices 309. The communication means 309 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. While fig. 3 shows an electronic device having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 3 may represent one device or a plurality of devices as needed.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 309, or from storage device 308, or from ROM 302. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing means 301.
Example IV
The present embodiment provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method described above.
It should be noted that, in some embodiments of the present disclosure, the computer readable medium may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In this embodiment, the client, server, etc. may communicate using any currently known or future developed network protocol, such as HTTP (HyperText TransferProtocol ), etc., and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the apparatus or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring training data, and converting the training data to obtain initial data; determining an initial rule base based on the initial data, and performing parameter optimization on the initial rule base to obtain a target rule base; calculating rules in the target rule base according to a preset activation weight calculation formula to obtain activation weights; and determining abnormal information according to the test data and the activation weight.
Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts 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 disclosure. 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 shown 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 units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor includes a data acquisition unit, a rule determination unit weight calculation unit, and an abnormality determination unit. The names of these units do not constitute a limitation on the unit itself in some cases, and for example, the data acquisition unit may also be described as "a unit that acquires training data".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
The foregoing is a further detailed description of the invention in connection with specific embodiments, and is not intended to limit the practice of the invention to such descriptions. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
It will be apparent to those skilled in the art that the various step embodiments of the invention described above may be performed in ways other than those described herein, including but not limited to simulation methods and experimental apparatus described above. The steps of the invention described above may in some cases be performed in a different order than that shown or described above, and may be performed separately. Therefore, the present invention is not limited to any specific combination of hardware and software.
The foregoing is a further detailed description of the invention in connection with specific embodiments, and is not intended to limit the practice of the invention to such descriptions. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
Claims (10)
1. The event searching method based on the clinical big data of the hospital is characterized by comprising the following steps:
acquiring original clinical data information of a patient, integrating the original clinical data information, and storing the original clinical data information in a data storage library;
preprocessing and standardizing original clinical data information stored in a data storage library in sequence to form a standard data set;
based on an NLP technology or a logic conversion technology, correspondingly carrying out feature extraction or logic conversion on the original clinical data information of different disease types to obtain feature information of different disease types, and then carrying out matching and mapping processing on the feature information and a standard data set to generate a mapping matching result;
based on an event searching algorithm, a preset query condition and a time relation are input in the mapping matching result to search, and a search result meeting the condition is obtained.
2. The hospital clinical big data based event searching method according to claim 1, wherein the patient's raw clinical data information includes patient's diagnostic data, surgical data, medication data, detection data, and pathology data.
3. The method for searching for events based on clinical big data of hospital according to claim 2, wherein the preprocessing and normalizing the raw clinical data information stored in the data storage library in sequence to form a standard data set comprises:
preprocessing the original clinical data information stored in the data storage library to obtain preprocessed clinical data information, wherein the preprocessing step comprises a step of removing repeated data processing, a step of processing missing values and abnormal values and a step of processing standardized data formats;
the method comprises the steps of carrying out word segmentation and semantic understanding on item names of various data in the preprocessed clinical data information to obtain standard identification codes corresponding to the item names of the various data, and forming a standard data set, wherein the item names of the various data comprise item names of diagnostic data, item names of operation data, item names of medication data, item names of detection data and item names of pathological data.
4. The method for searching events based on clinical big data of hospital according to claim 1, wherein the feature extraction is performed on the original clinical data information of different disease types based on the NLP technology, so as to obtain the feature information of different disease types, comprising:
based on NLP technology, text processing and semantic understanding are carried out on the original clinical data information of different disease types, relevant feature points are identified and extracted, then text data contained in the feature points are divided into different application fields, keywords and field terms are extracted, and the feature information of different disease types is obtained.
5. The method for searching events based on clinical big data of hospital according to claim 1, wherein the logic conversion technology is used for logic conversion of the original clinical data information of different disease types to obtain the characteristic information of different disease types, comprising:
according to the medical rules, analyzing and extracting the original clinical data information of different disease types to obtain a plurality of matched entities and relation information thereof, carrying out logic conversion on the matched entities and the relation information thereof based on the logic conversion rules, and mapping the matched entities and the relation information thereof into corresponding application fields to obtain the characteristic information of different disease types.
6. The hospital clinical big data based event search method of claim 1, wherein the event search algorithm comprises a search logic operator, a temporal association, a temporal comparison of other events with baseline events, and inclusion and exclusion criteria rules.
7. The hospital clinical big data based event searching method according to claim 1, further comprising: after the eligible search results are obtained, the search results are presented in a visual form.
8. An event search device based on clinical big data of a hospital, comprising:
the data acquisition module is used for acquiring original clinical data information of a patient;
the data integration module is used for integrating the original clinical data information and storing the data information in the data storage library;
the data processing module is used for sequentially preprocessing and standardizing the original clinical data information stored in the data storage library to form a standard data set;
the feature recognition module is used for correspondingly carrying out feature extraction or logic conversion on the original clinical data information of different disease types based on an NLP technology or a logic conversion technology to obtain feature information of different disease types, and then carrying out matching and mapping processing on the feature information and a standard data set to generate a mapping matching result;
the event searching module is used for inputting preset query conditions and time relations in the mapping matching results to search based on an event searching algorithm, and obtaining the search results meeting the conditions.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 7.
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