WO2022142748A1 - 临床试验数据的时序分析方法、装置、电子设备和介质 - Google Patents

临床试验数据的时序分析方法、装置、电子设备和介质 Download PDF

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WO2022142748A1
WO2022142748A1 PCT/CN2021/129620 CN2021129620W WO2022142748A1 WO 2022142748 A1 WO2022142748 A1 WO 2022142748A1 CN 2021129620 W CN2021129620 W CN 2021129620W WO 2022142748 A1 WO2022142748 A1 WO 2022142748A1
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medical
stage
case report
clinical trial
analysis
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PCT/CN2021/129620
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English (en)
French (fr)
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艾杰
王军涛
梅昀
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天津开心生活科技有限公司
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2474—Sequence data queries, e.g. querying versioned data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • the present disclosure relates to the field of data analysis, and in particular, to a method, apparatus, electronic device and computer-readable storage medium for time series analysis of clinical trial data.
  • each CRF represents a certain dimension information such as inspection, medication, etc., to further use SQL (Structured Query Language, structured query language) ) or excel and other tools to perform statistical analysis on each dimension, or perform multidimensional analysis on multiple dimensions.
  • SQL Structured Query Language, structured query language
  • the purpose of the present disclosure is to provide a time series analysis method, device, electronic device and computer-readable storage medium for clinical trial data, at least to a certain extent, to overcome the efficiency of auxiliary judgment on the safety and effectiveness of clinical treatment plans in the related art lower problem.
  • a method for time series analysis of clinical trial data including: extracting stage information of different medical stages in a case report table, and constructing a medical data dictionary based on the stage information, wherein the case report table is used for storing the clinical trial data; determine the medical stage to be bound that matches the target case report form based on different analysis dimensions; bind the medical data dictionary and the target case report form based on the medical stage to be bound, And configure the binding result into a multi-dimensional information table based on time series.
  • a time series analysis device for clinical trial data comprising: a construction module configured to extract stage information of different medical stages in a case report table, and to construct a medical data dictionary based on the stage information,
  • the case report form is configured to store the clinical trial data;
  • the determination module is configured to determine the medical stage to be bound that matches the target case report form based on different analysis dimensions;
  • the configuration module is configured to be bound based on the to-be-bind
  • the medical data dictionary is bound with the target case report form in the medical treatment stage, and the binding result is configured as a multi-dimensional information table based on the time series.
  • an electronic device comprising: a processor; and a memory configured to store executable instructions of the processor; wherein the processor is configured to execute any of the foregoing by executing the executable instructions Methods for time-series analysis of clinical trial data.
  • a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements any one of the above-mentioned time series analysis methods for clinical trial data.
  • FIG. 1 shows a schematic diagram of the structure of a time series analysis system for clinical trial data in an embodiment of the present disclosure
  • FIG. 2 shows a flowchart of a time series analysis method for clinical trial data in an embodiment of the present disclosure
  • FIG. 3 shows a flowchart of another method for time series analysis of clinical trial data in an embodiment of the present disclosure
  • FIG. 4 shows a flowchart of still another method for time series analysis of clinical trial data in an embodiment of the present disclosure
  • FIG. 5 shows a flowchart of another method for time series analysis of clinical trial data in an embodiment of the present disclosure
  • FIG. 6 shows a flowchart of another method for time series analysis of clinical trial data according to an embodiment of the present disclosure
  • FIG. 7 is a schematic diagram showing the binding between different stage information in the medical data dictionary and the target case report table according to an embodiment of the present disclosure.
  • FIG. 8 shows a flowchart of another method for time series analysis of clinical trial data according to an embodiment of the present disclosure
  • FIG. 9 shows a schematic diagram of a time series analysis device for clinical trial data in an embodiment of the present disclosure.
  • FIG. 10 shows a schematic diagram of an electronic device in an embodiment of the present disclosure.
  • Example embodiments will now be described more fully with reference to the accompanying drawings.
  • Example embodiments can be embodied in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
  • the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
  • the medical data dictionary to be bound is determined based on the medical stage to be bound by determining the medical stages to be bound that describe medical information of different dimensions from the target case report form.
  • a multi-dimensional information table is generated by binding, and the data of different dimensions is sorted based on the time series, so as to realize the data analysis for different medical stages, and the stage analysis method in related technologies. Compared with this, it can reduce the cost of implementation and improve the efficiency of clinical trial data to assist medical personnel to discover more safety and efficacy issues in the process of disease progression.
  • FIG. 1 shows a schematic structural diagram of a time series analysis system for clinical trial data in an embodiment of the present disclosure, including multiple terminals 120 and a server cluster 140 .
  • the terminal 120 may be a mobile phone, a game console, a tablet computer, an e-book reader, smart glasses, an MP4 (Moving Picture Experts Group Audio Layer IV, a moving image expert compression standard audio layer 4) player, a smart home device, an AR (Augmented Reality) player , augmented reality) equipment, VR (Virtual Reality, virtual reality) equipment and other mobile terminals, or, the terminal 120 may also be a personal computer (Personal Computer, PC), such as a laptop portable computer and a desktop computer and the like.
  • PC Personal Computer
  • the terminal 120 may be installed with an application program for time series analysis of the provided clinical trial data.
  • the terminal 120 and the server cluster 140 are connected through a communication network.
  • the communication network is a wired network or a wireless network.
  • the server cluster 140 is a server, or consists of several servers, or a virtualization platform, or a cloud computing service center.
  • the server cluster 140 is used to provide background services for the time series analysis application that provides clinical trial data.
  • the server cluster 140 undertakes the main computing work, and the terminal 120 undertakes the secondary computing work; alternatively, the server cluster 140 undertakes the secondary computing work, and the terminal 120 undertakes the main computing work; or, the terminal 120 and the server cluster 140 adopt distributed distribution Collaborative computing using a computing architecture.
  • the server cluster 140 is used to store time series analysis models of clinical trial data and the like.
  • the clients of the applications installed in different terminals 120 are the same, or the clients of the applications installed on the two terminals 120 are clients of the same type of application on different control system platforms.
  • the specific form of the client of the application program may also be different, for example, the client of the application program may be a mobile phone client, a PC client, or a World Wide Web (Web) client, etc.
  • the number of the above-mentioned terminals 120 may be more or less.
  • the above-mentioned terminal may be only one, or the above-mentioned terminal may be dozens or hundreds, or more.
  • the embodiments of the present disclosure do not limit the number of terminals and device types.
  • the system may further include a management device (not shown in FIG. 1 ), and the management device and the server cluster 140 are connected through a communication network.
  • the communication network is a wired network or a wireless network.
  • the above-mentioned wireless network or wired network uses standard communication technologies and/or protocols.
  • the network is usually the Internet, but can be any network, including but not limited to Local Area Network (LAN), Metropolitan Area Network (MAN), Wide Area Network (WAN), mobile, wired or wireless network, private network, or any combination of virtual private networks).
  • data exchanged over a network is represented using technologies and/or formats including Hyper Text Mark-up Language (HTML), Extensible Markup Language (XML), and the like.
  • HTML Hyper Text Mark-up Language
  • XML Extensible Markup Language
  • you can also use services such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet Protocol Security (IPsec), etc.
  • SSL Secure Socket Layer
  • TLS Transport Layer Security
  • VPN Virtual Private Network
  • IPsec Internet Protocol Security
  • Conventional encryption techniques to encrypt all or some of the links.
  • custom and/or dedicated data communication techniques may also be used in place of or in addition to the data communication techniques
  • FIG. 2 shows a flowchart of a method for time series analysis of clinical trial data in an embodiment of the present disclosure.
  • the methods provided in the embodiments of the present disclosure may be executed by any electronic device with computing processing capability, for example, the terminal 120 and/or the server cluster 140 in FIG. 1 .
  • the terminal 120 is used as the execution subject for illustration.
  • the terminal 120 executes the time series analysis method of clinical trial data, including the following steps:
  • Step S202 extracting stage information of different medical stages in the case report table, constructing a medical data dictionary based on the stage information, and the case report table is used for storing clinical trial data.
  • the medical stage includes but is not limited to the diagnosis stage, the medication stage, and the operation stage.
  • Stage information refers to the field name and time, etc., within a certain medical stage, and can perform stage analysis.
  • the case report table includes multiple cases, and the case report table includes but is not limited to the medication information table, the surgery information table, and the AE (Adverse Event, adverse event) information table. Therefore, the case report table usually describes only one dimension of clinical trial data. .
  • the medical data dictionary refers to the definition and description of the data items, data structure, data flow, data storage, processing logic, etc. of clinical trial data.
  • Step S204 determining the medical stage to be bound matched with the target case report form based on different analysis dimensions.
  • the target case report form may be at least one of a medication information form, an operation information form, and an AE information form for any patient.
  • Different diagnosis stages and/or diagnosis angles correspond to different dimensions.
  • the angle described in the target case report form is used as a dimension to determine the matching medical stage, that is, to determine the matching medical stage that can achieve matching and is consistent with the above.
  • the dimensions of are in medical stages of different dimensions, so as to further perform binding operations of information of different dimensions based on the medical stages.
  • Step S206 Bind the medical data dictionary and the target case report table based on the medical stage to be bound, and configure the binding result as a multi-dimensional information table based on the time sequence.
  • the data of the dimension corresponding to the matching medical stage in the medical data dictionary is extracted, and then bound with the dimensional data in the target case report table to obtain multi-dimensional data. Further, a multi-dimensional information table is generated based on the time series arrangement to display the dynamic cooperation between multiple medical stages based on the time series, thereby realizing the time series analysis.
  • an extraction operation based on different medical stages is performed on the collected case report form, and stage information is obtained, and a medical data dictionary related to the pathology report form is constructed based on the stage information, so that the generated medical data can be generated based on the medical data dictionary.
  • the dictionary realizes the reuse of patient clinical trial data; when data analysis is required on the target case report form, the medical stage to be bound that describes the medical information of different dimensions from the target case report form is determined, so as to determine the medical treatment stage based on the medical treatment stage to be bound
  • the data in the data dictionary that has a binding relationship with the target case report table is bound to generate a multi-dimensional information table, and the data of different dimensions is sorted based on time series to realize data analysis for different medical stages.
  • the method in the present disclosure can reduce the execution cost, and improve the efficiency of clinical trial data assisting medical personnel to discover more safety problems and efficacy problems in the process of disease progression.
  • step S202 extracting stage information of different medical stages in the case report table, and constructing a medical data dictionary based on stage information.
  • a specific implementation method includes:
  • Step S302 extracting stage information of different medical stages from the case report table based on a preset configuration format.
  • the preset configuration format includes at least one of a table name, a stage name, a time series name, and an analysis model.
  • Step S304 performing dictionary formatting processing on the stage information to construct a medical data dictionary.
  • the dictionary formatting process includes serializing the stage name, arraying the stage information, and so on.
  • the adapted stage is extracted from the medical data dictionary
  • the information is bound to realize the dynamic selection of different medical stages and the reuse of stage information in the medical data dictionary.
  • step S302 a specific implementation of extracting stage information of different medical stages from the case report table based on a preset configuration format, including:
  • Step S402 extract the name of the case report form based on the form name.
  • the name of the case report form is the medication information form, the surgery information form, and the AE information form.
  • Step S404 extracting the diagnosis and treatment name in the case report table based on the stage name.
  • Step S406 extracting the diagnosis and treatment sequence in the case report table based on the sequence name.
  • Step S408 Determine the configuration model of the diagnosis and treatment name and diagnosis and treatment sequence based on the analysis model.
  • Step S410 generating stage information based on at least one of the name of the case report form, the name of the diagnosis and treatment, the sequence of diagnosis and treatment, and a configuration model.
  • the stage information of different medical stages is extracted from the case report table, including: table name: medication information table; stage name: medication name; time series name: medication time; analysis function: form the stage name and Sequence time handler.
  • the stage information is extracted based on the table name, stage name, time sequence name, etc., so as to ensure the reliability of the extracted stage information for stage analysis.
  • step S304 a dictionary formatting process is performed on the stage information to construct a specific implementation manner of the medical data dictionary, including:
  • Step S502 generating a name sequence based on the stage name in the stage information.
  • Step S504 generating a stage array corresponding to the name sequence one by one based on at least one of the diagnosis and treatment name, the diagnosis and treatment sequence, and the configuration model.
  • Step S506 construct a medical data dictionary based on the patient identifier, the name sequence and the stage array.
  • a medical data dictionary related to the medication information table is formed.
  • One format of the data dictionary is as follows:
  • a formatted medical data dictionary is formed by constructing a medical data dictionary based on a patient identifier, a name sequence of stage names, and a stage array structure corresponding to the name sequence.
  • a medical data dictionary based on a patient identifier, a name sequence of stage names, and a stage array structure corresponding to the name sequence.
  • step S204 a specific implementation of determining a medical stage to be bound that matches the target case report form based on different analysis dimensions includes:
  • Step S602 when it is detected that the target case report form and the medical stage are used to describe the same analysis dimension, determine the medical stage that describes the same analysis dimension as the medical stage to be bound.
  • Step S604 when it is detected that the target case report table is not used to describe the analysis dimension, all medical stages are determined as the medical stages to be bound.
  • the medical stage includes a medication stage, a diagnosis stage and an operation stage
  • the medication stage corresponds to the stage information 702 of the medication stage
  • the diagnosis stage corresponds to the stage of the diagnosis stage
  • the operation stage corresponds to the stage information 706 of the operation stage
  • the target case report table may be at least one of the medication information table 708 , the operation information table 710 and the AE information table 712 .
  • the AE information table 712 when the AE information table 712 is bound to the medical data dictionary 70, since the adverse events do not belong to the same analysis latitude as the medication stage, the operation stage, and the diagnosis stage, the AE information table 712 is bound to Three stages of stage information 702 of the medication stage, stage information 704 of the diagnosis stage, and stage information 706 of the operation stage are determined, which means that the AE information can be easily analyzed and processed in the three stages in subsequent analysis.
  • the meta-information of the AE information table after binding includes: patient identification, AE name, AE time, AE level, medication stage, medication sequence, diagnosis stage, diagnosis sequence, surgery stage, and surgery sequence.
  • the content of medication phase is the phase name in the medication data dictionary in the first step
  • the medication sequence is the sequence name in the medication data dictionary.
  • the medical data dictionary is bound to the target case report form based on the medical stage to be bound, which specifically includes: based on the diagnosis and treatment sequence recorded in the medical stage to be bound in the medical data dictionary, and the target case report
  • the matching relationship between the case times recorded in the table performs the binding operation between the medical data dictionary and the target case report table.
  • time-series-based matching information of different latitudes can be queried in one time-series area in the multi-dimensional information table.
  • the analysis of multi-dimensional clinical trial data is realized, and on the other hand, it is also convenient for different stages. conversion analysis between.
  • the method further includes: converting the multi-dimensional information table into a visual time series analysis diagram.
  • multi-dimensional analysis, time series analysis, and phase analysis are performed based on tables dynamically bound by stages, and more intuitive analysis charts such as pie charts, bar charts, and bar charts are dynamically generated through automated programs, so as to provide more intuitive Demonstrate the value of data.
  • time series information table is converted into a time series analysis diagram based on python and/or EXCEL programs.
  • the method further includes: receiving application feedback information on the time series analysis diagram from the user; and updating the medical data dictionary based on the application feedback information, so as to update the time series analysis diagram based on the updated medical data dictionary.
  • this stage is mainly to collect the feedback of medical personnel on the analysis chart, to precipitate the stage analysis that medical personnel think is valuable in the form of stage data dictionary in the knowledge base, and to analyze the new stage analysis that medical personnel want to see. Sort out, re-execute the entire process, and iterate continuously to reach a final satisfactory conclusion. At the same time, the constantly updated knowledge base will provide valuable empirical knowledge for subsequent disease analysis.
  • the time series analysis method of clinical trial data includes:
  • Step S802 based on the medication stage, the operation stage and the diagnosis stage, stage information such as field names and time for performing stage analysis are extracted from the case report table.
  • Step S804 performing dictionary formatting processing on the stage information to construct a medical data dictionary.
  • Step S806 Determine the medical stage to be bound that matches the target case report form based on different analysis dimensions.
  • Step S808 Dynamically bind the stage information configured in the medical data dictionary with the target case report table based on the medical stage to be bound to obtain a multi-dimensional information table.
  • Step S810 converting the multi-dimensional information table into a visualized time sequence analysis diagram.
  • Step S812 generating an analysis result based on the timing analysis diagram.
  • aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention can be embodied in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, which may be collectively referred to herein as implementations "circuit", “module” or "system”.
  • the time series analysis apparatus 900 of clinical trial data will be described below with reference to FIG. 9 .
  • the apparatus 900 for analyzing the time series of clinical trial data shown in FIG. 9 is only an example, and should not impose any limitations on the functions and scope of use of the embodiments of the present invention.
  • the time series analysis apparatus 900 of clinical trial data is represented in the form of a hardware module.
  • the components of the clinical trial data time series analysis apparatus 900 may include, but are not limited to: a construction module 902, configured to extract stage information of different medical stages in the case report table, and construct a medical data dictionary based on the stage information, the case report table. is configured to store the clinical trial data; the determination module 904 is configured to determine, based on different analysis dimensions, the medical stages to be bound that match the target case report form; the configuration module 906 is configured to be based on the medical treatment to be bound The stage binds the medical data dictionary with the target case report table, and configures the binding result into a multi-dimensional information table based on time series.
  • the construction module 902 is further configured to: extract the stage information of the different medical stages from the case report table based on a preset configuration format; perform dictionary formatting processing on the stage information, to construct the medical data dictionary.
  • the configuration format includes at least one of a table name, a stage name, a time series name, and an analysis model
  • the construction module 902 is further configured to: extract the name of the case report table based on the table name; Extract the diagnosis and treatment name in the case report table based on the stage name; extract the diagnosis and treatment sequence in the case report table based on the time series name; determine the configuration model of the diagnosis and treatment name and the diagnosis and treatment sequence based on the analysis model;
  • the stage information is generated by at least one of the name of the case report form, the diagnosis and treatment name, the diagnosis and treatment sequence, and the configuration model.
  • the construction module 902 is further configured to: generate a name sequence based on the stage name in the stage information; generate a name sequence based on at least one of the diagnosis and treatment name, the diagnosis and treatment sequence, and the configuration model an array of stages corresponding to the sequence of names one by one; the medical data dictionary is constructed based on the patient identification, the sequence of names and the array of stages.
  • the determining module 904 is further configured to: upon detecting that the target case report form and the medical stage are configured to describe the same analysis dimension, describe the medical treatment outside the same analysis dimension The stage is determined as the medical stage to be bound; when it is detected that the target case report form is not configured to describe the analysis dimension, all medical stages are determined as the medical stage to be bound.
  • the configuration module 906 is further configured to: based on the diagnosis and treatment sequence recorded in the medical data dictionary to be bound in the medical data dictionary in the medical data dictionary, and the case time recorded in the target case report table The matching relationship between the medical data dictionary and the target case report form is executed.
  • it further includes: a conversion module 908, configured to convert the multi-dimensional information table into a visualized time series analysis diagram.
  • it further includes: an optimization module 910, configured to receive user application feedback information on the time sequence analysis diagram; update the medical data dictionary based on the application feedback information, so as to update the medical data dictionary based on the updated medical
  • the data dictionary updates the timing analysis graph.
  • the electronic device 1000 according to this embodiment of the present invention is described below with reference to FIG. 10 .
  • the electronic device 1000 shown in FIG. 10 is only an example, and should not impose any limitations on the function and scope of use of the embodiments of the present invention.
  • electronic device 1000 takes the form of a general-purpose computing device.
  • Components of the electronic device 1000 may include, but are not limited to, the above-mentioned at least one processing unit 1010 , the above-mentioned at least one storage unit 1020 , and a bus 1030 connecting different system components (including the storage unit 1020 and the processing unit 1010 ).
  • the storage unit stores program codes, which can be executed by the processing unit 1010, so that the processing unit 1010 performs the steps according to various exemplary embodiments of the present invention described in the above-mentioned "Exemplary Methods" section of this specification.
  • the processing unit 1010 may perform steps S202 , S204 and S206 as shown in FIG. 2 , as well as other steps defined in the method for time series analysis of clinical trial data of the present disclosure.
  • the storage unit 1020 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 10201 and/or a cache storage unit 10202 , and may further include a read only storage unit (ROM) 10203 .
  • RAM random access storage unit
  • ROM read only storage unit
  • the storage unit 1020 may also include a program/utility 10204 having a set (at least one) of program modules 10205 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, An implementation of a network environment may be included in each or some combination of these examples.
  • the bus 1030 may be representative of one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local area using any of a variety of bus structures bus.
  • the electronic device 1000 may also communicate with one or more external devices 1060 (eg, keyboards, pointing devices, Bluetooth devices, etc.), with one or more devices that enable a user to interact with the electronic device, and/or with The electronic device 1000 can communicate with any device (eg, router, modem, etc.) that communicates with one or more other computing devices. Such communication may occur through input/output (I/O) interface 1050 . Also, the electronic device 1000 may also communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN), and/or a public network such as the Internet) through a network adapter 1050 . As shown, network adapter 1050 communicates with other modules of electronic device 1000 via bus 1030 .
  • I/O input/output
  • the electronic device 1000 may also communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN), and/or a public network such as the Internet) through a network adapter 1050 . As shown, network adapter
  • the exemplary embodiments described herein may be implemented by software, or may be implemented by software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure may be embodied in the form of software products, and the software products may be stored in a non-volatile storage medium (which may be CD-ROM, U disk, mobile hard disk, etc.) or on the network , including several instructions to cause a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to an embodiment of the present disclosure.
  • a computing device which may be a personal computer, a server, a terminal device, or a network device, etc.
  • a computer-readable storage medium on which a program product capable of implementing the above-described method of the present specification is stored.
  • various aspects of the present invention can also be implemented in the form of a program product, which includes program code, when the program product runs on a terminal device, the program code is used to cause the terminal device to execute the above-mentioned description in this specification.
  • the steps according to various exemplary embodiments of the present invention are described in the "Example Methods" section.
  • a program product for implementing the above method according to an embodiment of the present invention may adopt a portable compact disc read only memory (CD-ROM) and include program codes, and may run on a terminal device, such as a personal computer.
  • CD-ROM compact disc read only memory
  • the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • a computer readable signal medium may include a propagated data signal in baseband or as part of a carrier wave with readable program code embodied thereon. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a readable signal medium can also be any readable medium, other than a readable storage medium, that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
  • Program code embodied on a readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
  • Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages—such as Java, C++, etc., as well as conventional procedural Programming Language - such as the "C" language or similar programming language.
  • the program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server execute on.
  • the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (eg, using an Internet service provider business via an Internet connection).
  • LAN local area network
  • WAN wide area network
  • an external computing device eg, using an Internet service provider business via an Internet connection
  • modules or units of the apparatus for action performance are mentioned in the above detailed description, this division is not mandatory. Indeed, according to embodiments of the present disclosure, the features and functions of two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above may be further divided into multiple modules or units to be embodied.
  • the technical solutions according to the embodiments of the present disclosure may be embodied in the form of software products, and the software products may be stored in a non-volatile storage medium (which may be CD-ROM, U disk, mobile hard disk, etc.) or on the network , including several instructions to cause a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to an embodiment of the present disclosure.
  • a computing device which may be a personal computer, a server, a mobile terminal, or a network device, etc.

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Abstract

一种临床试验数据的时序分析方法、装置、电子设备和计算机可读存储介质，涉及数据分析领域。其中，临床试验数据的时序分析方法包括：提取病例报告表中不同医疗阶段的阶段信息，基于所述阶段信息构造医疗数据字典，所述病例报告表用于存储所述临床试验数据（S202）；基于不同的分析维度确定与目标病例报告表匹配的待绑定医疗阶段（S204）；基于所述待绑定医疗阶段将所述医疗数据字典与所述目标病例报告表进行绑定，并基于时序将绑定结果配置为多维信息表（S206）。通过上述方法，有利于提高临床试验数据对临床治疗方案的安全性和有效性的辅助判断效率。

Description

临床试验数据的时序分析方法、装置、电子设备和介质
本公开要求于2020年12月31日提交的申请号为202011633309.6、名称为“临床试验数据的时序分析方法、装置、电子设备和介质”的中国专利申请的优先权，该中国专利申请的全部内容通过引用全部并入本文。
技术领域
本公开涉及数据分析领域，尤其涉及一种临床试验数据的时序分析方法、装置、电子设备和计算机可读存储介质。
背景技术
在临床试验中，临床试验数据以CRF(Case Report Form，病例报告表)的形式进行收集，每一个CRF代表检验、用药等某个维度信息，以进一步采用SQL(Structured Query Language，结构化查询语言)或excel等工具对每个维度做统计分析，或者对多个维度进行多维分析。
相关技术中，在临床试验中，很多和临床治疗方案有效性和安全性相关的数据价值都潜藏在试验过程之中，以AE(Adverse Event，不良事件)为例，通过在每个治疗阶段分别编写SQL或者python脚本，实现临床试验数据的多维分析，但是该方案目前存在以下缺陷：
由于应用于一个阶段分析的脚本难以在另一个阶段复用，导致临床试验数据辅助医生完成对临床治疗方案的安全性和有效性判断的效率较低。
需要说明的是，在上述背景技术部分公开的信息仅用于加强对本公开的背景的理解，因此可以包括不构成对本领域普通技术人员已知的现有技术的信息。
发明内容
本公开的目的在于提供一种临床试验数据的时序分析方法、装置、电子设备和计算机可读存储介质，至少在一定程度上克服相关技术中对临床治疗方案的安全性和有效性的辅助判断效率较低的问题。
本公开的其他特性和优点将通过下面的详细描述变得显然，或部分地通过本公开的实践而习得。
根据本公开的一个方面，提供一种临床试验数据的时序分析方法，包括：提取病例报告表中不同医疗阶段的阶段信息，基于所述阶段信息构造医疗数据字典，所述病例报告表用于存储所述临床试验数据；基于不同的分析维度确定与目标病例报告表匹配的待绑定医疗阶段；基于所述待绑定医疗阶段将所述医疗数据字典与所述目标病例报告表进行绑定，并基于时序将绑定结果配置为多维信息表。
根据本公开的另一个方面，提供一种临床试验数据的时序分析装置，包括：构造模块，被配置为提取病例报告表中不同医疗阶段的阶段信息，基于所述阶段信息构造医疗数据字典，所述病例报告表被配置为存储所述临床试验数据；确定模块，被配置为基于不同的分析维度确定与目标病例报告表匹配的待绑定医疗阶段；配置模块，被配置为基于所述待绑定医疗阶段将所述医疗数据字典与所述目标病例报告表进行绑定，并基于时序将绑定结果配置为多维信息表。
根据本公开的再一个方面，提供一种电子设备，包括：处理器；以及存储器，被配置为存储处理器的可执行指令；其中，处理器配置为经由执行可执行指令来执行上述任意一项的临床试验数据的时序分析方法。
根据本公开的又一个方面，提供一种计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现上述任意一项的临床试验数据的时序分析方法。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，并不能限制本公开。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本公开的实施例，并与说明书一起用于解释本公开的原理。显而易见地，下面描述中的附图仅仅是本公开的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1示出本公开实施例中一种临床试验数据的时序分析系统结构的示意图；
图2示出本公开实施例中一种临床试验数据的时序分析方法的流程图；
图3示出本公开实施例中另一种临床试验数据的时序分析方法的流程图；
图4示出本公开实施例中再一种临床试验数据的时序分析方法的流程图；
图5示出本公开实施例中又一种临床试验数据的时序分析方法的流程图；
图6示出本公开实施例的又一种临床试验数据的时序分析方法的流程图；
图7示出本公开实施例医疗数据字典中不同的阶段信息与目标病例报告表之间的绑定示意图。
图8示出本公开实施例的又一种临床试验数据的时序分析方法的流程图；
图9示出本公开实施例中一种临床试验数据的时序分析装置的示意图；
图10示出本公开实施例中一种电子设备的示意图。
具体实施方式
现在将参考附图更全面地描述示例实施方式。然而，示例实施方式能够以多种形式实 施，且不应被理解为限于在此阐述的范例；相反，提供这些实施方式使得本公开将更加全面和完整，并将示例实施方式的构思全面地传达给本领域的技术人员。所描述的特征、结构或特性可以以任何合适的方式结合在一个或更多实施方式中。
此外，附图仅为本公开的示意性图解，并非一定是按比例绘制。图中相同的附图标记表示相同或类似的部分，因而将省略对它们的重复描述。附图中所示的一些方框图是功能实体，不一定必须与物理或逻辑上独立的实体相对应。可以采用软件形式来实现这些功能实体，或在一个或多个硬件模块或集成电路中实现这些功能实体，或在不同网络和/或处理器装置和/或微控制器装置中实现这些功能实体。
本公开提供的方案，在需要对目标病例报告表进行数据分析时，通过确定与目标病例报告表分别描述不同维度的医疗信息的待绑定医疗阶段，以基于待绑定医疗阶段确定医疗数据字典中与目标病例报告表具有绑定关系的数据，通过进行绑定生成多维信息表，并基于时序对不同维度的数据进行排序，以实现面向不同医疗阶段的数据分析，与相关技术中阶段分析方式相比，能够降低执行成本，并提高了临床试验数据辅助医学人员发现更多疾病进展过程中的安全性问题和有效性问题的效率。
本公开实施例提供的方案涉及时序数据的数据分析等技术，具体通过如下实施例进行说明。
图1示出本公开实施例中一种临床试验数据的时序分析系统的结构示意图，包括多个终端120和服务器集群140。
终端120可以是手机、游戏主机、平板电脑、电子书阅读器、智能眼镜、MP4(Moving Picture Experts Group Audio Layer IV，动态影像专家压缩标准音频层面4)播放器、智能家居设备、AR(Augmented Reality，增强现实)设备、VR(Virtual Reality，虚拟现实)设备等移动终端，或者，终端120也可以是个人计算机(Personal Computer，PC)，比如膝上型便携计算机和台式计算机等等。
其中，终端120中可以安装有用于提供的临床试验数据的时序分析的应用程序。
终端120与服务器集群140之间通过通信网络相连。可选的，通信网络是有线网络或无线网络。
服务器集群140是一台服务器，或者由若干台服务器组成，或者是一个虚拟化平台，或者是一个云计算服务中心。服务器集群140用于为提供临床试验数据的时序分析应用程序提供后台服务。可选地，服务器集群140承担主要计算工作，终端120承担次要计算工作；或者，服务器集群140承担次要计算工作，终端120承担主要计算工作；或者，终端120和服务器集群140之间采用分布式计算架构进行协同计算。
在一些可选的实施例中，服务器集群140用于存储临床试验数据的时序分析模型等。
可选地，不同的终端120中安装的应用程序的客户端是相同的，或两个终端120上安装的应用程序的客户端是不同控制系统平台的同一类型应用程序的客户端。基于终端平台的不同，该应用程序的客户端的具体形态也可以不同，比如，该应用程序客户端可以是 手机客户端、PC客户端或者全球广域网(World Wide Web，Web)客户端等。
本领域技术人员可以知晓，上述终端120的数量可以更多或更少。比如上述终端可以仅为一个，或者上述终端为几十个或几百个，或者更多数量。本公开实施例对终端的数量和设备类型不加以限定。
可选的，该系统还可以包括管理设备(图1未示出)，该管理设备与服务器集群140之间通过通信网络相连。可选的，通信网络是有线网络或无线网络。
可选的，上述的无线网络或有线网络使用标准通信技术和/或协议。网络通常为因特网、但也可以是任何网络，包括但不限于局域网(Local Area Network，LAN)、城域网(Metropolitan Area Network，MAN)、广域网(Wide Area Network，WAN)、移动、有线或者无线网络、专用网络或者虚拟专用网络的任何组合)。在一些实施例中，使用包括超文本标记语言(Hyper Text Mark-up Language，HTML)、可扩展标记语言(Extensible MarkupLanguage，XML)等的技术和/或格式来代表通过网络交换的数据。此外还可以使用诸如安全套接字层(Secure Socket Layer，SSL)、传输层安全(Transport Layer Security，TLS)、虚拟专用网络(Virtual Private Network，VPN)、网际协议安全(Internet ProtocolSecurity，IPsec)等常规加密技术来加密所有或者一些链路。在另一些实施例中，还可以使用定制和/或专用数据通信技术取代或者补充上述数据通信技术。
下面，将结合附图及实施例对本示例实施方式中的临床试验数据的时序分析方法中的各个步骤进行更详细的说明。
图2示出本公开实施例中一种临床试验数据的时序分析方法流程图。本公开实施例提供的方法可以由任意具备计算处理能力的电子设备执行，例如如图1中的终端120和/或服务器集群140。在下面的举例说明中，以终端120为执行主体进行示例说明。
如图2所示，终端120执行临床试验数据的时序分析方法，包括以下步骤：
步骤S202，提取病例报告表中不同医疗阶段的阶段信息，基于阶段信息构造医疗数据字典，病例报告表用于存储临床试验数据。
其中，医疗阶段包括但不限于诊断阶段、用药阶段与手术阶段等。
阶段信息指某个医疗阶段内的，并能够执行阶段分析的字段名称和时间等。
优选的，病例报告表包括多个，病例报告表包括但不限于用药信息表、手术信息表与AE(Adverse Event，不良事件)信息表，因此病例报告表通常只描述了一个维度的临床试验数据。
医疗数据字典指对临床试验数据的数据项、数据结构、数据流、数据存储、处理逻辑等进行定义和描述，其目的是对数据流程图中的各个元素做出详细的说明。
步骤S204，基于不同的分析维度确定与目标病例报告表匹配的待绑定医疗阶段。
其中，目标病例报告表可以为任意患者的用药信息表、手术信息表与AE信息表中的至少一种。
不同的诊断阶段和/或诊断角度对应于不同的维度，在确定目标病例报告表后，将目 标病例报告表描述的角度作为一个维度，确定匹配的医疗阶段，即确定能够实现匹配的并与上述的维度处于不同维度的医疗阶段，以进一步基于医疗阶段进行执行不同维度信息的绑定操作。
步骤S206，基于待绑定医疗阶段将医疗数据字典与目标病例报告表进行绑定，并基于时序将绑定结果配置为多维信息表。
其中，基于匹配的医疗阶段，即提取医疗数据字典中与匹配的医疗阶段对应维度的数据，以与目标病例报告表中的维度数据进行绑定，得到多维数据。进一步，基于时序排列生成多维信息表，以基于时间顺序显示多个医疗阶段之间的动态协作，从而实现时序分析。
在该实施例中，通过对已采集的病例报告表执行基于不同的医疗阶段的提取操作，并得到阶段信息，基于阶段信息构造与病理报告表相关的医疗数据字典，从而能够基于生成的医疗数据字典实现患者临床试验数据的复用；在需要对目标病例报告表进行数据分析时，确定与目标病例报告表描述不同维度的医疗信息的待绑定医疗阶段，以基于待绑定医疗阶段确定医疗数据字典中与目标病例报告表具有绑定关系的数据，通过进行绑定生成多维信息表，并基于时序对不同维度的数据进行排序，以实现面向不同医疗阶段的数据分析。与相关技术中阶段分析方式相比，本公开中方法能够降低执行成本，并提高了临床试验数据辅助医学人员发现更多疾病进展过程中的安全性问题和有效性问题的效率。
在一个实施例中，步骤S202，提取病例报告表中不同医疗阶段的阶段信息，基于阶段信息构造医疗数据字典的一种具体实现方式，包括：
步骤S302，基于预设的配置格式从病例报告表中提取不同医疗阶段的阶段信息。
其中，预设的配置格式包括表名、阶段名称、时序名称和分析模型中的至少一种。
步骤S304，对阶段信息执行字典格式化处理，以构造出医疗数据字典。
其中，字典格式化处理包括对阶段名称进行序列化操作，对阶段信息进行数组化处理等。
在该实施例中，通过在病例报告表中提取不同医疗阶段的阶段信息，并基于阶段信息生成医疗数据字典，在需要对目标病例报告表进行分析时，从医疗数据字典中提取适配的阶段信息进行绑定，实现不同医疗阶段的动态选择以及医疗数据字典中阶段信息的复用。
在一个实施例中，步骤S302，基于预设的配置格式从病例报告表中提取不同医疗阶段的阶段信息的一种具体实现方式，包括：
步骤S402，基于表名提取病例报告表的名称。
比如病例报告表的名称为用药信息表、手术信息表以及AE信息表等。
步骤S404，基于阶段名称提取病例报告表中的诊疗名称。
步骤S406，基于时序名称提取病例报告表中的诊疗时序。
步骤S408，基于分析模型确定诊疗名称与诊疗时序的配置模型。
步骤S410，基于病例报告表的名称、诊疗名称、诊疗时序与配置模型中的至少一种，生成阶段信息。
具体地，以用药信息表为例，病例报告表中提取不同医疗阶段的阶段信息，包括：表名：用药信息表；阶段名称：用药名称；时序名称：用药时间；分析函数：形成阶段名称和时序时间的处理函数。
在该实施例中，通过基于表名、阶段名称和时序名称等进行阶段信息的提取，以保证提取出的阶段信息用于阶段分析的可靠性。
在一个实施例中，步骤S304，对阶段信息执行字典格式化处理，以构造出医疗数据字典的一种具体实现方式，包括：
步骤S502，基于阶段信息中的阶段名称生成名称序列。
步骤S504，基于诊疗名称、诊疗时序与配置模型中的至少一种生成与名称序列逐一对应的阶段数组。
步骤S506，基于患者标识、名称序列与阶段数组构造医疗数据字典。
仍以用药信息表为例，经过配置格式进行处理过后，形成了一个与用药信息表相关的医疗数据字典，数据字典的一种格式如下所示：
Figure PCTCN2021129620-appb-000001
Figure PCTCN2021129620-appb-000002
在该实施例中，通过基于患者标识、阶段名称的名称序列、以及与名称序列对应的阶段数组结构构造医疗数据字典，形成格式化的医疗数据字典，一方面，便于执行后续的绑定操作，另一方面，也能够保证数据字典中阶段数据的复用性。
在一个实施例中，步骤S204，基于不同的分析维度确定与目标病例报告表匹配的待绑定医疗阶段的一种具体实现方式，包括：
步骤S602，在检测到目标病例报告表与医疗阶段用于描述同一分析维度时，将描述同一分析维度之外的医疗阶段确定为待绑定医疗阶段。
步骤S604，在检测到目标病例报告表不用于描述分析维度时，将所有医疗阶段确定为待绑定医疗阶段。
如图7所示，在临床试验数据中，在医疗数据字典70中，医疗阶段包括用药阶段、诊断阶段和手术阶段，用药阶段对应于用药阶段的阶段信息702，诊断阶段对应于诊断阶段的阶段信息704，手术阶段对应于手术阶段的阶段信息706，目标病例报告表可以为用 药信息表708、手术信息表710和AE信息表712中的至少一种。
具体地，以AE信息表为例，在将AE信息表712与医疗数据字典70进行绑定时由于不良事件与用药阶段、手术阶段以及诊断阶段等不属于同一分析纬度，因此AE信息表712绑定了用药阶段的阶段信息702、诊断阶段的阶段信息704以及手术阶段的阶段信息706三个阶段，代表AE信息可以在后续分析很方便的进行三种阶段的分析处理。
AE信息表在绑定后的元信息包括：患者标识、AE名称、AE时间、AE等级、用药阶段、用药时序、诊断阶段、诊断时序、手术阶段以及手术时序等。
其中，以用药为例，用药阶段内容为第一步中用药数据字典中的阶段名称，用药时序为用药数据字典中的序列名称，经过动态绑定后，AE信息可以根据不同的阶段灵活分析，得出更为准确的结论。
在一个实施例中，步骤S206，基于待绑定医疗阶段将医疗数据字典与目标病例报告表进行绑定，具体包括：基于医疗数据字典中待绑定医疗阶段记录的诊疗时序，与目标病例报告表中记录的病例时间之间的匹配关系，执行医疗数据字典与目标病例报告表之间的绑定操作。
在该实施例中，通过基于时序的匹配，能够在多维信息表中的一个时序区域内分别查询不同纬度的信息，一方面，实现多维的临床试验数据的分析，另一方面，也便于不同阶段之间的转换分析。
在一个实施例中，还包括：将多维信息表转化为可视化的时序分析图。
在该实例中，基于经过阶段动态绑定的表进行多维分析、时序分析和阶段分析并且通过自动化程序动态生成饼状图、柱状图和条形图等更为直观的分析图表，以更直观的体现数据价值。
具体地，基于python和/或EXCEL程序将时序信息表转化为时序分析图。
在一个实施例中，还包括：接收用户对时序分析图的应用反馈信息；基于应用反馈信息更新医疗数据字典，以基于更新后的医疗数据字典更新时序分析图。
其中，该阶段主要是收集医学相关人员对分析图表的反馈情况，将医学人员认为有价值的阶段分析在知识库中以阶段数据字典的形式进行沉淀，对于医学人员想看的新的阶段分析进行整理，重新执行全部过程，不断迭代得出最终满意的结论。同时不断更新的知识库会为后续疾病的分析给出宝贵的经验知识。
如图8所示，根据本公开的一个实施例的临床试验数据的时序分析方法包括：
步骤S802，基于用药阶段、手术阶段和诊断阶段从病例报告表中提取用于执行阶段分析的字段名称和时间等阶段信息。
步骤S804，对阶段信息进行字典格式化处理，以构造出医疗数据字典。
步骤S806，基于不同的分析维度确定与目标病例报告表匹配的待绑定医疗阶段。
步骤S808，基于待绑定医疗阶段将医疗数据字典中配置后的阶段信息与目标病例报告表进行动态绑定，得到多维信息表。
步骤S810，将所述多维信息表转化为可视化的时序分析图。
步骤S812，基于时序分析图生成分析结果。
需要注意的是，上述附图仅是根据本发明示例性实施例的方法所包括的处理的示意性说明，而不是限制目的。易于理解，上述附图所示的处理并不表明或限制这些处理的时间顺序。另外，也易于理解，这些处理可以是例如在多个模块中同步或异步执行的。
所属技术领域的技术人员能够理解，本发明的各个方面可以实现为系统、方法或程序产品。因此，本发明的各个方面可以具体实现为以下形式，即：完全的硬件实施方式、完全的软件实施方式(包括固件、微代码等)，或硬件和软件方面结合的实施方式，这里可以统称为“电路”、“模块”或“系统”。
下面参照图9来描述根据本发明的这种实施方式的临床试验数据的时序分析装置900。图9所示的临床试验数据的时序分析装置900仅仅是一个示例，不应对本发明实施例的功能和使用范围带来任何限制。
临床试验数据的时序分析装置900以硬件模块的形式表现。临床试验数据的时序分析装置900的组件可以包括但不限于：构造模块902，被配置为提取病例报告表中不同医疗阶段的阶段信息，基于所述阶段信息构造医疗数据字典，所述病例报告表被配置为存储所述临床试验数据；确定模块904，被配置为基于不同的分析维度确定与目标病例报告表匹配的待绑定医疗阶段；配置模块906，被配置为基于所述待绑定医疗阶段将所述医疗数据字典与所述目标病例报告表进行绑定，并基于时序将绑定结果配置为多维信息表。
在一个实施例中，构造模块902还被配置为：基于预设的配置格式从所述病例报告表中提取不同所述医疗阶段的所述阶段信息；对所述阶段信息执行字典格式化处理，以构造出所述医疗数据字典。
在一个实施例中，所述配置格式包括表名、阶段名称、时序名称和分析模型中的至少一种，构造模块902还被配置为：基于所述表名提取所述病例报告表的名称；基于所述阶段名称提取所述病例报告表中的诊疗名称；基于所述时序名称提取所述病例报告表中的诊疗时序；基于分析模型确定所述诊疗名称与所述诊疗时序的配置模型；基于所述病例报告表的名称、所述诊疗名称、所述诊疗时序与所述配置模型中的至少一种，生成所述阶段信息。
在一个实施例中，构造模块902还被配置为：基于所述阶段信息中的所述阶段名称生成名称序列；基于所述诊疗名称、所述诊疗时序与所述配置模型中的至少一种生成与所述名称序列逐一对应的阶段数组；基于患者标识、所述名称序列与所述阶段数组构造所述医疗数据字典。
在一个实施例中，确定模块904还被配置为：在检测到所述目标病例报告表与所述医疗阶段被配置为描述同一分析维度时，将描述所述同一分析维度之外的所述医疗阶段确定为所述待绑定医疗阶段；在检测到所述目标病例报告表不被配置为描述所述分析维度时，将所有医疗阶段确定为所述待绑定医疗阶段。
在一个实施例中，配置模块906还被配置为：基于所述医疗数据字典中医疗数据字典所述待绑定医疗阶段记录的所述诊疗时序，与所述目标病例报告表中记录的病例时间之间的匹配关系，执行所述医疗数据字典与所述目标病例报告表之间的绑定操作。
在一个实施例中，还包括：转化模块908，被配置为将所述多维信息表转化为可视化的时序分析图。
在一个实施例中，还包括：优化模块910，被配置为接收用户对所述时序分析图的应用反馈信息；基于所述应用反馈信息更新所述医疗数据字典，以基于更新后的所述医疗数据字典更新所述时序分析图。
下面参照图10来描述根据本发明的这种实施方式的电子设备1000。图10显示的电子设备1000仅仅是一个示例，不应对本发明实施例的功能和使用范围带来任何限制。
如图10所示，电子设备1000以通用计算设备的形式表现。电子设备1000的组件可以包括但不限于：上述至少一个处理单元1010、上述至少一个存储单元1020、连接不同系统组件(包括存储单元1020和处理单元1010)的总线1030。
其中，存储单元存储有程序代码，程序代码可以被处理单元1010执行，使得处理单元1010执行本说明书上述“示例性方法”部分中描述的根据本发明各种示例性实施方式的步骤。例如，处理单元1010可以执行如图2中所示的步骤S202、S204与S206，以及本公开的临床试验数据的时序分析方法中限定的其他步骤。
存储单元1020可以包括易失性存储单元形式的可读介质，例如随机存取存储单元(RAM)10201和/或高速缓存存储单元10202，还可以进一步包括只读存储单元(ROM)10203。
存储单元1020还可以包括具有一组(至少一个)程序模块10205的程序/实用工具10204，这样的程序模块10205包括但不限于：操作系统、一个或者多个应用程序、其它程序模块以及程序数据，这些示例中的每一个或某种组合中可能包括网络环境的实现。
总线1030可以为表示几类总线结构中的一种或多种，包括存储单元总线或者存储单元控制器、外围总线、图形加速端口、处理单元或者使用多种总线结构中的任意总线结构的局域总线。
电子设备1000也可以与一个或多个外部设备1060(例如键盘、指向设备、蓝牙设备等)通信，还可与一个或者多个使得用户能与该电子设备交互的设备通信，和/或与使得该电子设备1000能与一个或多个其它计算设备进行通信的任何设备(例如路由器、调制解调器等等)通信。这种通信可以通过输入/输出(I/O)接口1050进行。并且，电子设备1000还可以通过网络适配器1050与一个或者多个网络(例如局域网(LAN)，广域网(WAN)和/或公共网络，例如因特网)通信。如图所示，网络适配器1050通过总线1030与电子设备1000的其它模块通信。应当明白，尽管图中未示出，可以结合电子设备使用其它硬件和/或软件模块，包括但不限于：微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、RAID系统、磁带驱动器以及数据备份存储系统等。
通过以上的实施方式的描述，本领域的技术人员易于理解，这里描述的示例实施方式可以通过软件实现，也可以通过软件结合必要的硬件的方式来实现。因此，根据本公开实施方式的技术方案可以以软件产品的形式体现出来，该软件产品可以存储在一个非易失性存储介质(可以是CD-ROM，U盘，移动硬盘等)中或网络上，包括若干指令以使得一台计算设备(可以是个人计算机、服务器、终端装置、或者网络设备等)执行根据本公开实施方式的方法。
在本公开的示例性实施例中，还提供了一种计算机可读存储介质，其上存储有能够实现本说明书上述方法的程序产品。在一些可能的实施方式中，本发明的各个方面还可以实现为一种程序产品的形式，其包括程序代码，当程序产品在终端设备上运行时，程序代码用于使终端设备执行本说明书上述“示例性方法”部分中描述的根据本发明各种示例性实施方式的步骤。
根据本发明的实施方式的用于实现上述方法的程序产品，其可以采用便携式紧凑盘只读存储器(CD-ROM)并包括程序代码，并可以在终端设备，例如个人电脑上运行。然而，本发明的程序产品不限于此，在本文件中，可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。
计算机可读信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了可读程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。可读信号介质还可以是可读存储介质以外的任何可读介质，该可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。
可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于无线、有线、光缆、RF等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言的任意组合来编写用于执行本发明操作的程序代码，所述程序设计语言包括面向对象的程序设计语言—诸如Java、C++等，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算设备上执行、部分地在用户设备上执行、作为一个独立的软件包执行、部分在用户计算设备上部分在远程计算设备上执行、或者完全在远程计算设备或服务器上执行。在涉及远程计算设备的情形中，远程计算设备可以通过任意种类的网络，包括局域网(LAN)或广域网(WAN)，连接到用户计算设备，或者，可以连接到外部计算设备(例如利用因特网服务提供商来通过因特网连接)。
应当注意，尽管在上文详细描述中提及了用于动作执行的设备的若干模块或者单元，但是这种划分并非强制性的。实际上，根据本公开的实施方式，上文描述的两个或更多模块或者单元的特征和功能可以在一个模块或者单元中具体化。反之，上文描述的一个模块或者单元的特征和功能可以进一步划分为由多个模块或者单元来具体化。
此外，尽管在附图中以特定顺序描述了本公开中方法的各个步骤，但是，这并非要求 或者暗示必须按照该特定顺序来执行这些步骤，或是必须执行全部所示的步骤才能实现期望的结果。附加的或备选的，可以省略某些步骤，将多个步骤合并为一个步骤执行，以及/或者将一个步骤分解为多个步骤执行等。通过以上的实施方式的描述，本领域的技术人员易于理解，这里描述的示例实施方式可以通过软件实现，也可以通过软件结合必要的硬件的方式来实现。因此，根据本公开实施方式的技术方案可以以软件产品的形式体现出来，该软件产品可以存储在一个非易失性存储介质(可以是CD-ROM，U盘，移动硬盘等)中或网络上，包括若干指令以使得一台计算设备(可以是个人计算机、服务器、移动终端、或者网络设备等)执行根据本公开实施方式的方法。
本领域技术人员在考虑说明书及实践这里公开的发明后，将容易想到本公开的其它实施方案。本公开旨在涵盖本公开的任何变型、用途或者适应性变化，这些变型、用途或者适应性变化遵循本公开的一般性原理并包括本公开未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的，本公开的真正范围和精神由所附的权利要求指出。

Claims (11)

  1. 一种临床试验数据的时序分析方法，包括：
    提取病例报告表中不同医疗阶段的阶段信息，基于所述阶段信息构造医疗数据字典，所述病例报告表用于存储所述临床试验数据；
    基于不同的分析维度确定与目标病例报告表匹配的待绑定医疗阶段；
    基于所述待绑定医疗阶段将所述医疗数据字典与所述目标病例报告表进行绑定，并基于时序将绑定结果配置为多维信息表。
  2. 根据权利要求1所述的临床试验数据的时序分析方法，其中，所述提取病例报告表中不同医疗阶段的阶段信息，基于所述阶段信息构造医疗数据字典包括：
    基于预设的配置格式从所述病例报告表中提取所述不同医疗阶段的阶段信息；
    对所述阶段信息执行字典格式化处理，以构造出所述医疗数据字典。
  3. 根据权利要求2所述的临床试验数据的时序分析方法，其中，所述配置格式包括表名、阶段名称、时序名称和分析模型中的至少一种，所述基于预设的配置格式从所述病例报告表中提取所述不同医疗阶段的阶段信息包括：
    基于所述表名提取所述病例报告表的名称；
    基于所述阶段名称提取所述病例报告表中的诊疗名称；
    基于所述时序名称提取所述病例报告表中的诊疗时序；
    基于分析模型确定所述诊疗名称与所述诊疗时序的配置模型；
    基于所述病例报告表的名称、所述诊疗名称、所述诊疗时序与所述配置模型中的至少一种，生成所述阶段信息。
  4. 根据权利要求3所述的临床试验数据的时序分析方法，其中，所述对所述阶段信息执行字典格式化处理，以构造出所述医疗数据字典包括：
    基于所述阶段信息中的阶段名称生成名称序列；
    基于所述诊疗名称、所述诊疗时序与所述配置模型中的至少一种生成与所述名称序列逐一对应的阶段数组；
    基于患者标识、所述名称序列与所述阶段数组构造所述医疗数据字典。
  5. 根据权利要求1所述的临床试验数据的时序分析方法，其中，所述基于不同的分析维度确定与目标病例报告表匹配的待绑定医疗阶段包括：
    在检测到所述目标病例报告表与所述医疗阶段用于描述同一分析维度时，将描述所述同一分析维度之外的所述医疗阶段确定为所述待绑定医疗阶段；
    在检测到所述目标病例报告表不用于描述所述分析维度时，将所有医疗阶段确定为所述待绑定医疗阶段。
  6. 根据权利要求3所述的临床试验数据的时序分析方法，其中，所述基于所述待绑定医疗阶段将所述医疗数据字典与所述目标病例报告表进行绑定包括：
    基于所述医疗数据字典中待绑定医疗阶段记录的所述诊疗时序，与所述目标病例报告表中记录的病例时间之间的匹配关系，执行所述医疗数据字典与所述目标病例报告表之间的绑定操作。
  7. 根据权利要求1至6中任一项所述的临床试验数据的时序分析方法，其中，还包括：
    将所述多维信息表转化为可视化的时序分析图。
  8. 根据权利要求7所述的临床试验数据的时序分析方法，其中，还包括：
    接收用户对所述时序分析图的应用反馈信息；
    基于所述应用反馈信息更新所述医疗数据字典，以基于更新后的所述医疗数据字典更新所述时序分析图。
  9. 一种临床试验数据的时序分析装置，包括：
    构造模块，被配置为提取病例报告表中不同医疗阶段的阶段信息，基于所述阶段信息构造医疗数据字典，所述病例报告表被配置为存储所述临床试验数据；
    确定模块，被配置为基于不同的分析维度确定与目标病例报告表匹配的待绑定医疗阶段；
    配置模块，被配置为基于所述待绑定医疗阶段将所述医疗数据字典与所述目标病例报告表进行绑定，并基于时序将绑定结果配置为多维信息表。
  10. 一种电子设备，包括：
    处理器；以及
    存储器，被配置为存储所述处理器的可执行指令；
    其中，所述处理器配置为经由执行所述可执行指令来执行权利要求1～8中任意一项所述的临床试验数据的时序分析方法。
  11. 一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现权利要求1～8中任意一项所述的临床试验数据的时序分析方法。
PCT/CN2021/129620 2020-12-31 2021-11-09 临床试验数据的时序分析方法、装置、电子设备和介质 WO2022142748A1 (zh)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110148440A (zh) * 2019-03-29 2019-08-20 北京汉博信息技术有限公司 一种医疗信息查询方法
CN111081329A (zh) * 2019-12-20 2020-04-28 天津开心生活科技有限公司 临床数据自动录入方法及装置、电子设备、存储介质
CN111145847A (zh) * 2019-12-31 2020-05-12 天津新开心生活科技有限公司 临床试验数据的录入方法及装置、介质和电子设备
US20200357490A1 (en) * 2019-05-07 2020-11-12 International Business Machines Corporation System for creating a virtual clinical trial from electronic medical records
CN112786126A (zh) * 2020-12-31 2021-05-11 天津开心生活科技有限公司 临床试验数据的时序分析方法、装置、电子设备和介质

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120035963A1 (en) * 2009-03-26 2012-02-09 Koninklijke Philips Electronics N.V. System that automatically retrieves report templates based on diagnostic information
CN107993695A (zh) * 2017-12-13 2018-05-04 泰康保险集团股份有限公司 诊疗数据的展示方法、装置、设备和存储介质
CN109902112B (zh) * 2019-01-24 2021-02-02 西安交通大学 一种基于时间轴的电子病历可视化方法及可视化系统
CN109830303A (zh) * 2019-02-01 2019-05-31 上海众恒信息产业股份有限公司 基于互联网一体化医疗平台的临床数据挖掘分析与辅助决策方法
CN111370132B (zh) * 2020-02-28 2022-04-15 平安医疗健康管理股份有限公司 电子文件的解析方法及装置、计算机设备、存储介质
CN111462845A (zh) * 2020-03-17 2020-07-28 平安国际智慧城市科技股份有限公司 动态表单生成方法、装置、计算机设备和存储介质

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110148440A (zh) * 2019-03-29 2019-08-20 北京汉博信息技术有限公司 一种医疗信息查询方法
US20200357490A1 (en) * 2019-05-07 2020-11-12 International Business Machines Corporation System for creating a virtual clinical trial from electronic medical records
CN111081329A (zh) * 2019-12-20 2020-04-28 天津开心生活科技有限公司 临床数据自动录入方法及装置、电子设备、存储介质
CN111145847A (zh) * 2019-12-31 2020-05-12 天津新开心生活科技有限公司 临床试验数据的录入方法及装置、介质和电子设备
CN112786126A (zh) * 2020-12-31 2021-05-11 天津开心生活科技有限公司 临床试验数据的时序分析方法、装置、电子设备和介质

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