CN112786126A - Time sequence analysis method and device of clinical test data, electronic equipment and medium - Google Patents
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Abstract
The disclosure provides a time sequence analysis method and device of clinical test data, electronic equipment and a computer readable storage medium, and relates to the field of data analysis. The time sequence analysis method of the clinical test data comprises the following steps: 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 test data; determining a medical stage to be bound matched with the target case report table based on different analysis dimensions; and binding the medical data dictionary and the target case report table based on the medical stage to be bound, and configuring a binding result into a multi-dimensional information table based on time sequence. Through the technical scheme disclosed by the invention, the auxiliary judgment efficiency of the safety and the effectiveness of clinical test data on a clinical treatment scheme is favorably improved.
Description
Technical Field
The present disclosure relates to the field of data analysis, and in particular, to a method and an apparatus for time sequence analysis of clinical trial data, an electronic device, and a computer-readable storage medium.
Background
In clinical trials, clinical trial data is collected in the Form of CRF (Case Report Form), each CRF represents certain dimension information such as inspection and medication, and statistical analysis is further performed on each dimension by using tools such as SQL (Structured Query Language) or excel, or multidimensional analysis is performed on multiple dimensions.
In the related art, in a clinical test, many data values related to the effectiveness and safety of a clinical treatment scheme are hidden in the test process, and taking AE (Adverse Event) as an example, SQL or python scripts are written in each treatment phase respectively to realize multidimensional analysis of clinical test data, but the scheme has the following defects at present:
since scripts applied to one stage of analysis are difficult to reuse in another stage, clinical trial data aids physicians in making less efficient decisions about the safety and effectiveness of clinical treatment protocols.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The present disclosure is directed to a method, an apparatus, an electronic device, and a computer-readable storage medium for time-series analysis of clinical trial data, which at least to some extent overcome the problem of low efficiency in auxiliary determination of safety and effectiveness of clinical treatment protocols in the related art.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to one aspect of the present disclosure, there is provided a method of time series analysis of clinical trial data, comprising: 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 test data; determining a medical stage to be bound matched with the target case report table based on different analysis dimensions; and binding the medical data dictionary and the target case report table based on the medical stage to be bound, and configuring a binding result into a multi-dimensional information table based on time sequence.
In one embodiment, the extracting phase information of different medical phases in the case report table, and the constructing the medical data dictionary based on the phase information comprises: extracting the stage information of different medical stages from the case report table based on a preset configuration format; and executing dictionary formatting processing on the stage information to construct the medical data dictionary.
In one embodiment, the configuration format includes at least one of a table name, a phase name, a time series name, and an analysis model, and the extracting the phase information of different medical phases from the case report table based on a preset configuration format includes: extracting a name of the case report table based on the table name; extracting diagnosis and treatment names in the case report table based on the stage names; extracting diagnosis and treatment time sequences in the case report table based on the time sequence names; determining a generation model of the diagnosis and treatment name and the diagnosis and treatment time sequence based on an analysis model; generating the stage information based on at least one of a name of the case report table, the diagnosis name, the diagnosis event, and the generated model.
In one embodiment, the performing dictionary formatting on the stage information to construct the medical data dictionary comprises: generating a name sequence based on the phase name in the phase information; generating a stage array corresponding to the name sequence one by one based on at least one of the diagnosis name, the diagnosis event and the generation model; constructing the medical data dictionary based on patient identification, the sequence of names, and the set of phases.
In one embodiment, the determining the medical stage to be bound that matches the target case report form based on different analysis dimensions comprises: when the target case report table and the medical stage are detected to describe the same analysis dimension, determining the medical stage except for describing the same analysis dimension as the medical stage to be bound; determining all medical phases as the to-be-bound medical phases upon detecting that the target case report table is not used to describe the analysis dimension.
In one embodiment, said binding said medical data dictionary to said target case report table based on said to-be-bound medical stage comprises: and executing the binding operation between the medical data dictionary and the target case report table based on the matching relationship between the diagnosis and treatment time sequence recorded in the medical data dictionary and the medical stage to be bound in the medical data dictionary and the case time recorded in the target case report table.
In one embodiment, further comprising: and converting the multi-dimensional information table into a visualized time sequence analysis chart.
In one embodiment, further comprising: receiving application feedback information of a user on the time sequence analysis chart; updating the medical data dictionary based on the application feedback information to update the time sequence analysis chart based on the updated medical data dictionary.
According to another aspect of the present disclosure, there is provided a time-series analysis apparatus of clinical trial data, including: the construction module is used for extracting stage information of different medical stages in a case report table, constructing a medical data dictionary based on the stage information, and the case report table is used for storing the clinical test data; the determining module is used for determining a medical stage to be bound matched with the target case report table based on different analysis dimensions; and the configuration module is used for binding the medical data dictionary and the target case report table based on the medical stage to be bound and configuring a binding result into a multi-dimensional information table based on a time sequence.
According to still another aspect of the present disclosure, there is provided an electronic device including: a processor; and a memory for storing executable instructions for the processor; wherein the processor is configured to perform a method of time series analysis of clinical trial data of any of the above via execution of the executable instructions.
According to yet another aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of time series analysis of clinical trial data as any one of the above.
The clinical trial data time sequence analysis scheme provided by the embodiment of the disclosure can realize multiplexing of patient clinical trial data based on the generated medical data dictionary by performing extraction operation based on different medical stages on an acquired case report table and obtaining stage information to construct the medical data dictionary related to a pathology report table based on the stage information, when data analysis needs to be performed on a target case report table, determine medical stages to be bound respectively describing medical information of different dimensions with the target case report table, determine data in the medical data dictionary having a binding relationship with the target case report table based on the medical stages to be bound, generate a multi-dimensional information table by binding, sort the data of different dimensions based on time sequence to realize data analysis oriented to different medical stages, compared with a stage analysis mode in the related technology, can reduce the execution cost and improve the efficiency of clinical trial data to assist medical personnel in discovering more safety and effectiveness problems in the disease progression process.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
FIG. 1 is a schematic diagram illustrating a system architecture for time series analysis of clinical trial data according to an embodiment of the present disclosure;
FIG. 2 is a flow chart illustrating a method for time series analysis of clinical trial data in an embodiment of the present disclosure;
FIG. 3 is a flow chart illustrating another method for time series analysis of clinical trial data in an embodiment of the present disclosure;
FIG. 4 is a flow chart illustrating a method for time series analysis of clinical trial data according to yet another embodiment of the present disclosure;
FIG. 5 is a flow chart illustrating a method for time series analysis of clinical trial data according to yet another embodiment of the present disclosure;
FIG. 6 illustrates a flow chart of yet another method of time series analysis of clinical trial data according to an embodiment of the present disclosure;
fig. 7 illustrates a binding diagram between different stage information in a medical data dictionary and a target case report table according to an embodiment of the disclosure.
FIG. 8 illustrates a flow chart of yet another method of time series analysis of clinical trial data in accordance with an embodiment of the present disclosure;
FIG. 9 is a schematic diagram of a device for time-series analysis of clinical trial data according to an embodiment of the disclosure;
fig. 10 shows a schematic diagram of an electronic device in an embodiment of the disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different 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.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
According to the scheme, when data analysis needs to be carried out on the target case report table, the medical stages to be bound, which respectively describe medical information with different dimensions, in the medical data dictionary are determined based on the medical stages to be bound, the data, which have the binding relation with the target case report table, in the medical data dictionary are determined, the multidimensional information table is generated by binding, and the data with different dimensions are sequenced based on the time sequence, so that the data analysis facing different medical stages is realized.
The scheme provided by the embodiment of the application relates to technologies such as data analysis of time series data, and is specifically explained by the following embodiment.
Fig. 1 shows a schematic structural diagram of a time sequence analysis system for clinical trial data in an embodiment of the present disclosure, which includes a plurality of terminals 120 and a server cluster 140.
The terminal 120 may be a mobile terminal such as a mobile phone, a game console, a tablet Computer, an e-book reader, smart glasses, an MP4(Moving Picture Experts Group Audio Layer IV) player, an intelligent home device, an AR (Augmented Reality) device, a VR (Virtual Reality) device, or a Personal Computer (PC), such as a laptop Computer and a desktop Computer.
Among them, the terminal 120 may have installed therein an application program for time-series analysis of the provided clinical trial data.
The terminals 120 are connected to the server cluster 140 through a communication network. Optionally, the communication network is a wired network or a wireless network.
The server cluster 140 is a server, or is composed of a plurality of servers, or is a virtualization platform, or is a cloud computing service center. The server cluster 140 is used to provide background services for the timing analysis application that provides clinical trial data. Optionally, the server cluster 140 undertakes primary computational work and the terminal 120 undertakes secondary computational work; alternatively, the server cluster 140 undertakes secondary computing work and the terminal 120 undertakes primary computing work; alternatively, the terminal 120 and the server cluster 140 perform cooperative computing by using a distributed computing architecture.
In some alternative embodiments, the server cluster 140 is used to store a time series analysis model of clinical trial data, and the like.
Alternatively, the clients of the applications installed in different terminals 120 are the same, or the clients of the applications installed on two terminals 120 are clients of the same type of application of different control system platforms. Based on different terminal 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.
Those skilled in the art will appreciate that the number of terminals 120 described above may be greater or fewer. For example, the number of the terminals may be only one, or several tens or hundreds of the terminals, or more. The number of terminals and the type of the device are not limited in the embodiments of the present application.
Optionally, the system may further include a management device (not shown in fig. 1), and the management device is connected to the server cluster 140 through a communication network. Optionally, the communication network is a wired network or a wireless network.
Optionally, the wireless network or wired network described above uses standard communication techniques and/or protocols. The Network is typically the Internet, but may be any Network including, but not limited to, a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a mobile, wireline or wireless Network, a private Network, or any combination of virtual private networks. In some embodiments, data exchanged over a network is represented using techniques and/or formats including Hypertext Mark-up Language (HTML), Extensible markup Language (XML), and the like. All or some of the links may also be encrypted using conventional encryption techniques such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet protocol Security (IPsec). In other embodiments, custom and/or dedicated data communication techniques may also be used in place of, or in addition to, the data communication techniques described above.
Hereinafter, each step in the method for time-series analysis of clinical trial data according to the exemplary embodiment will be described in more detail with reference to the drawings and examples.
FIG. 2 is a flow chart illustrating a method for time-series analysis of clinical trial data according to an embodiment of the disclosure. The method provided by the embodiment of the present disclosure may be performed by any electronic device with computing processing capability, for example, the terminal 120 and/or the server cluster 140 in fig. 1. In the following description, the terminal 120 is taken as an execution subject for illustration.
As shown in fig. 2, the terminal 120 performs a method for time-series analysis of clinical trial data, including the following steps:
step S202, 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 clinical test data.
The medical stage includes, but is not limited to, a diagnosis stage, a medication stage, an operation stage, and the like.
The phase information refers to a field name, time, and the like within a certain medical phase and capable of performing phase analysis.
Preferably, the case report table includes a plurality of case report tables, including but not limited to a medication information table, a surgery information table, and an AE (Adverse Event) information table, so the case report table generally describes only one dimension of clinical trial data.
The medical data dictionary refers to the definition and description of data items, data structures, data flows, data stores, processing logic, etc. of clinical trial data, and is intended to provide a detailed description of the various elements in the data flow diagram.
And step S204, determining the medical stage to be bound matched with the target case report table based on different analysis dimensions.
The target case report table may be at least one of a medication information table, a surgical information table, and an AE information table of any patient.
And after the target case report table is determined, determining a matched medical stage by taking the angle described by the target case report table as a dimension, namely determining the medical stage which can realize matching and is in different dimensions with the dimension, so as to further perform binding operation of different dimension information based on the medical stage.
And step S206, binding the medical data dictionary and the target case report table based on the medical stage to be bound, and configuring the binding result into a multi-dimensional information table based on time sequence.
Wherein, based on the matched medical stage, the data of the dimension corresponding to the matched medical stage in the medical data dictionary is extracted to be bound with the dimension data in the target case report table to obtain multidimensional data,
based on the time sequence arrangement, a multi-dimensional information table is generated to display dynamic cooperation among a plurality of medical stages based on the time sequence, thereby realizing time sequence analysis.
In the embodiment, the collected case report table is subjected to extraction operation based on different medical stages, the stage information is obtained, the medical data dictionary related to the pathological report table is constructed based on the stage information, the multiplexing of the clinical test data of the patient can be realized based on the generated medical data dictionary, when the data analysis needs to be carried out on the target case report table, the medical stages to be bound, which respectively describe the medical information with different dimensions, in the medical data dictionary are determined based on the medical stages to be bound, the multidimensional information table is generated by binding, and the data with different dimensions are sequenced based on time sequence, so that the data analysis oriented to different medical stages is realized, and compared with the stage analysis mode in the related technology, the execution cost can be reduced, and improves the efficiency of clinical trial data to assist medical personnel in finding more safety and effectiveness problems in the process of disease progression.
In one embodiment, step S202, extracting phase information of different medical phases in the case report table, and constructing a specific implementation of the medical data dictionary based on the phase information includes:
step S302, phase information of different medical phases is extracted from a case report table based on a preset configuration format.
The preset configuration format comprises at least one of a table name, a phase name, a time sequence name and an analysis model.
Step S304, dictionary formatting processing is carried out on the stage information to construct a medical data dictionary.
The dictionary formatting process includes performing serialization operation on the phase names, performing digitization process on the phase information, and the like.
In the embodiment, the stage information of different medical stages is extracted from the case report table, the medical data dictionary is generated based on the stage information, and when the target case report table needs to be analyzed, the adapted stage information is extracted from the medical data dictionary and bound, so that the dynamic selection of different medical stages and the multiplexing of the stage information in the medical data dictionary are realized.
In one embodiment, step S302, a specific implementation of extracting stage information of different medical stages from a case report table based on a preset configuration format includes:
in step S402, the name of the case report table is extracted based on the table name.
For example, the names of the case report tables include a medication information table, a surgical information table, and an AE information table.
In step S404, the diagnosis name in the case report table is extracted based on the phase name.
In step S406, the diagnosis and treatment time sequence in the case report table is extracted based on the time sequence name.
Step S408, determining a diagnosis and treatment name and a generation model of the diagnosis and treatment time sequence based on the analysis model.
Step S410, generating stage information based on at least one of the name, the diagnosis event and the generation model of the case report table.
Specifically, taking the medication information table as an example, the extracting of the phase information of different medical phases from the case report table includes:
table name: a medication information table.
The phase name: name of medication.
Time sequence name: the time of administration.
Analysis function: a processing function of the phase name and the time of the sequence is formed.
In this embodiment, the phase information is extracted based on the table name, the phase name, the time sequence name, and the like, so as to ensure the reliability of the extracted phase information for the phase analysis.
In one embodiment, step S304, performing dictionary formatting on the stage information to construct a specific implementation of the medical data dictionary, includes:
in step S502, a name sequence is generated based on the phase name in the phase information.
Step S504, generating a stage array which corresponds to the name sequence one by one based on at least one of the diagnosis name, the diagnosis event and the generation model.
Step S506, constructing a medical data dictionary based on the patient identification, the name sequence and the phase group.
Still taking the medication information table as an example, after being processed by the configuration format, a medical data dictionary related to the medication information table is formed, and one format of the data dictionary is as follows: {
Patient identification: {
Sequence name: phase 1 name-phase 2 name- … -phase n name
Stage array: [{
The phase name: name of specific drug
Stage time: specific administration time
}，…{}
]
}，
…
}
In this embodiment, the medical data dictionary is constructed based on the patient identifier, the name sequence of the phase name, and the phase array structure corresponding to the name sequence to form a formatted medical data dictionary, which facilitates performing subsequent binding operations and ensures reusability of phase data in the medical data dictionary.
In one embodiment, the step S204 of determining a specific implementation of the medical stage to be bound, which is matched with the target case report form, based on different analysis dimensions includes:
step S602, when the target case report table and the medical stage are detected to describe the same analysis dimension, determining the medical stage except for describing the same analysis dimension as the medical stage to be bound.
In step S604, when it is detected that the target case report table is not used for describing the analysis dimension, all medical phases are determined as medical phases to be bound.
As shown in fig. 7, in the clinical trial data, in the medical data dictionary 70, the medical phases include a medication phase corresponding to phase information 702 of the medication phase, a diagnosis phase corresponding to phase information 704 of the diagnosis phase, and an operation phase corresponding to phase information 706 of the operation phase, and the target case report table may be at least one of a medication information table 708, an operation information table 710, and an AE information table 712.
Specifically, taking the AE information table as an example, when the AE information table 712 is bound to the medical data dictionary 70, since the adverse event does not belong to the same analysis latitude as the medication phase, the operation phase, the diagnosis phase, and the like, the AE information table 712 binds three phases of the phase information 702 of the medication phase, the phase information 704 of the diagnosis phase, and the phase information 706 of the operation phase, and represents that the AE information can be conveniently analyzed and processed in three phases in the subsequent analysis.
The meta information of the AE information table after binding includes:
patient identification, AE name, AE time, AE grade, medication phase, medication timing, diagnostic phase, diagnostic timing, surgical phase, surgical timing, and the like.
Taking medication as an example, the content of the medication stage is the stage name in the medication data dictionary in the first step, the medication timing sequence is the sequence name in the medication data dictionary, and after dynamic binding, the AE information can be flexibly analyzed according to different stages to obtain a more accurate conclusion.
In one embodiment, the step S206, binding the medical data dictionary with the target case report table based on the to-be-bound medical stage includes: and executing the binding operation between the medical data dictionary and the target case report table based on the matching relation between the diagnosis and treatment time sequence recorded in the medical data dictionary at the medical stage to be bound and the case time recorded in the target case report table.
In this embodiment, by time-series-based matching, information of different latitudes can be respectively queried in a time-series region in the multi-dimensional information table, so that on one hand, analysis of multi-dimensional clinical trial data is realized, and on the other hand, conversion analysis between different stages is facilitated.
In one embodiment, further comprising: and converting the multi-dimensional information table into a visualized time sequence analysis chart.
In this example, multi-dimensional analysis, timing analysis, and phase analysis are performed based on the table dynamically bound by the phase, and more intuitive analysis charts such as a pie chart, a bar chart, and the like are dynamically generated by an automation program, so as to more intuitively embody the data value.
Specifically, the timing information table is converted into the timing analysis graph based on python and/or EXCEL programs.
In one embodiment, further comprising: receiving application feedback information of a user on the time sequence analysis chart; updating the medical data dictionary based on the application feedback information to update the timing analysis chart based on the updated medical data dictionary.
The stage mainly collects the feedback condition of medical related personnel on the analysis chart, deposits the stage analysis considered to be valuable by the medical personnel in a knowledge base in the form of a stage data dictionary, sorts the new stage analysis which the medical personnel want to see, re-executes the whole process, and continuously iterates to obtain the final satisfactory conclusion. While the constantly updated knowledge base will give valuable empirical knowledge for subsequent disease analysis.
As shown in fig. 8, a method of time-series analysis of clinical trial data according to one embodiment of the present disclosure includes:
in step S802, phase information such as a field name and time for performing phase analysis is extracted from the case report table based on the medication phase, the surgical phase, and the diagnostic phase.
Step S804, performing dictionary formatting processing on the stage information to construct a medical data dictionary.
Step S806, determining the medical stage to be bound matched with the target case report table based on different analysis dimensions.
And step S808, dynamically binding 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 multidimensional information table into a visualized time sequence analysis chart.
In step S812, an analysis result is generated based on the time-series analysis chart.
It is to be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the method according to an exemplary embodiment of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
A time-series analysis apparatus 900 of clinical trial data according to this embodiment of the invention is described below with reference to fig. 9. The apparatus 900 for analyzing clinical trial data shown in fig. 9 is merely an example, and should not impose any limitation on the function and scope of use of the embodiment of the present invention.
The clinical trial data time-series analysis device 900 is represented in the form of a hardware module. The components of the apparatus 900 for time series analysis of clinical trial data may include, but are not limited to: a construction module 902, configured to extract phase information of different medical phases in a case report table, construct a medical data dictionary based on the phase information, where the case report table is used to store the clinical trial data; a determining module 904, configured to determine, based on different analysis dimensions, a medical stage to be bound that matches the target case report table; a configuration module 906, configured to bind the medical data dictionary with the target case report table based on the to-be-bound medical stage, and configure a binding result into a multidimensional information table based on a time sequence.
In one embodiment, the constructing module 902 is further configured to: extracting the stage information of different medical stages from the case report table based on a preset configuration format; and executing dictionary formatting processing on the stage information to construct the medical data dictionary.
In one embodiment, the configuration format includes at least one of a table name, a phase name, a timing name, and an analysis model, and the constructing module 902 is further configured to: extracting a name of the case report table based on the table name; extracting diagnosis and treatment names in the case report table based on the stage names; extracting diagnosis and treatment time sequences in the case report table based on the time sequence names; determining a generation model of the diagnosis and treatment name and the diagnosis and treatment time sequence based on an analysis model; generating the stage information based on at least one of a name of the case report table, the diagnosis name, the diagnosis event, and the generated model.
In one embodiment, the constructing module 902 is further configured to: generating a name sequence based on the phase name in the phase information; generating a stage array corresponding to the name sequence one by one based on at least one of the diagnosis name, the diagnosis event and the generation model; constructing the medical data dictionary based on patient identification, the sequence of names, and the set of phases.
In one embodiment, the determining module 904 is further configured to: when the target case report table and the medical stage are detected to describe the same analysis dimension, determining the medical stage except for describing the same analysis dimension as the medical stage to be bound; determining all medical phases as the to-be-bound medical phases upon detecting that the target case report table is not used to describe the analysis dimension.
In one embodiment, the configuration module 906 is further to: and executing the binding operation between the medical data dictionary and the target case report table based on the matching relationship between the diagnosis and treatment time sequence recorded in the medical data dictionary and the medical stage to be bound in the medical data dictionary and the case time recorded in the target case report table.
In one embodiment, further comprising: a converting module 908, configured to convert the multidimensional information table into a visualized time sequence analysis chart.
In one embodiment, further comprising: an optimization module 910, configured to receive application feedback information of the user on the timing analysis graph; updating the medical data dictionary based on the application feedback information to update the time sequence analysis chart based on the updated medical data dictionary.
An electronic device 1000 according to this embodiment of the invention is described below with reference to fig. 10. The electronic device 1000 shown in fig. 10 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 10, the electronic device 1000 is embodied in the form of a general purpose computing device. The components of the electronic device 1000 may include, but are not limited to: the at least one processing unit 1010, the at least one memory unit 1020, and a bus 1030 that couples various system components including the memory unit 1020 and the processing unit 1010.
Where the storage unit stores program code that may be executed by the processing unit 1010 to cause the processing unit 1010 to perform the steps according to various exemplary embodiments of the present invention described in the "exemplary methods" section above in this specification. For example, the processing unit 1010 may perform steps S202, S204, and S206 as shown in fig. 2, and other steps defined in the method for time-series analysis of clinical trial data of the present disclosure.
The storage unit 1020 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)10201 and/or a cache memory unit 10202, and may further include a read-only memory unit (ROM) 10203.
The memory unit 1020 may also include a program/utility 10204 having a set (at least one) of program modules 10205, such program modules 10205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 1000 may also communicate with one or more external devices 1060 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 1000 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interfaces 1050. Also, the electronic device 1000 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) via the network adapter 1050. As shown, the network adapter 1050 communicates with the other modules of the electronic device 1000 via a bus 1030. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable 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 the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above-mentioned "exemplary methods" section of the present description, when the program product is run on the terminal device.
According to the program product for realizing the method, the portable compact disc read only memory (CD-ROM) can be adopted, the program code is included, and the program product can be operated on terminal equipment, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a 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.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a 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 readable medium may be transmitted using any appropriate 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 for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like 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 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. In the case of a remote computing device, 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 (e.g., through the internet using an internet service provider).
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable 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 the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (11)
1. A method for time series analysis of clinical trial data, comprising:
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 test data;
determining a medical stage to be bound matched with the target case report table based on different analysis dimensions;
and binding the medical data dictionary and the target case report table based on the medical stage to be bound, and configuring a binding result into a multi-dimensional information table based on time sequence.
2. The method for temporal analysis of clinical trial data according to claim 1, wherein the extracting phase information of different medical phases in a case report form, and the constructing a medical data dictionary based on the phase information comprises:
extracting the stage information of different medical stages from the case report table based on a preset configuration format;
and executing dictionary formatting processing on the stage information to construct the medical data dictionary.
3. The method for temporal analysis of clinical trial data according to claim 2, wherein the configuration format includes at least one of a table name, a phase name, a temporal name, and an analysis model, and the extracting the phase information of the different medical phases from the case report table based on a preset configuration format includes:
extracting a name of the case report table based on the table name;
extracting diagnosis and treatment names in the case report table based on the stage names;
extracting diagnosis and treatment time sequences in the case report table based on the time sequence names;
determining a generation model of the diagnosis and treatment name and the diagnosis and treatment time sequence based on an analysis model;
generating the stage information based on at least one of a name of the case report table, the diagnosis name, the diagnosis event, and the generated model.
4. The method for temporal analysis of clinical trial data according to claim 3, wherein the performing dictionary formatting on the stage information to construct the medical data dictionary comprises:
generating a name sequence based on the phase name in the phase information; generating a stage array corresponding to the name sequence one by one based on at least one of the diagnosis name, the diagnosis event and the generation model;
constructing the medical data dictionary based on patient identification, the sequence of names, and the set of phases.
5. The method for temporal analysis of clinical trial data according to claim 1, wherein the determining the medical stage to be bound that matches the target case report form based on different analysis dimensions comprises:
when the target case report table and the medical stage are detected to describe the same analysis dimension, determining the medical stage except for describing the same analysis dimension as the medical stage to be bound;
determining all medical phases as the to-be-bound medical phases upon detecting that the target case report table is not used to describe the analysis dimension.
6. The method for temporal analysis of clinical trial data according to claim 3, wherein said binding the medical data dictionary to the target case report form based on the to-be-bound medical stage comprises:
and executing the binding operation between the medical data dictionary and the target case report table based on the matching relationship between the diagnosis and treatment time sequence recorded in the medical data dictionary and the medical stage to be bound in the medical data dictionary and the case time recorded in the target case report table.
7. The method for temporal analysis of clinical trial data according to any of claims 1 to 6, further comprising:
and converting the multi-dimensional information table into a visualized time sequence analysis chart.
8. The method for temporal analysis of clinical trial data according to claim 7, further comprising:
receiving application feedback information of a user on the time sequence analysis chart;
updating the medical data dictionary based on the application feedback information to update the time sequence analysis chart based on the updated medical data dictionary.
9. An apparatus for time series analysis of clinical trial data, comprising:
the construction module is used for extracting stage information of different medical stages in a case report table, constructing a medical data dictionary based on the stage information, and the case report table is used for storing the clinical test data;
the determining module is used for determining a medical stage to be bound matched with the target case report table based on different analysis dimensions;
and the configuration module is used for binding the medical data dictionary and the target case report table based on the medical stage to be bound and configuring a binding result into a multi-dimensional information table based on a time sequence.
10. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of time series analysis of clinical trial data of any of claims 1 to 8 via execution of the executable instructions.
11. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of time series analysis of clinical trial data according to any one of claims 1 to 8.
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