CN111145847A

CN111145847A - Clinical test data entry method and device, medium and electronic equipment

Info

Publication number: CN111145847A
Application number: CN201911410091.5A
Authority: CN
Inventors: 薛健
Original assignee: Tianjin Happy Life Technology Co ltd; Tianjin Xinkaixin Life Technology Co ltd
Current assignee: Beijing Yiyiyun Technology Co ltd
Priority date: 2019-12-31
Filing date: 2019-12-31
Publication date: 2020-05-12

Abstract

The disclosure provides a method and a device for inputting clinical test data, a medium and electronic equipment, and relates to the technical field of data processing. The method comprises the following steps: acquiring clinical trial data of multiple patients; performing disassembly processing according to time nodes of a clinical test scheme in the clinical test data to obtain clinical test data corresponding to the time nodes; carrying out structuralization processing on the time nodes, and carrying out normalization processing on the clinical test data of the time nodes to obtain the clinical test data after data mining processing; transmitting the data-mined clinical trial data corresponding to the target patient to the target system based on the patient identification of the target patient. Compared with a manual input mode, the technical scheme has the technical effects of high efficiency and low error rate. Therefore, the technical scheme is beneficial to rapidly and accurately counting and researching the clinical data, and further shortens the research period of related research objects.

Description

Clinical test data entry method and device, medium and electronic equipment

Technical Field

The disclosure relates to the technical field of data processing, and in particular relates to a method and a device for inputting clinical test data, a computer readable medium and an electronic device for implementing the method for inputting clinical test data.

Background

In the medical field, a large amount of clinical data is continuously generated, and the clinical data can reflect the symptoms of the drugs used by the patients more truly, so the clinical data can be used for drug research, pathological research and the like. For example: the method adopts a clinical test mode to obtain detailed and accurate clinical data, and further proves or reveals the action, adverse reaction and/or absorption, distribution, metabolism and excretion states of the test medicament so as to determine the effectiveness and safety of the test medicament. Specifically, the clinical data is derived from a patient's medical history, an analysis of the patient's case, a treatment regimen for the patient's disease, and the like.

In order to study the treatment of the test drugs or disease types based on the clinical data, the clinical test data collection, exchange, submission, analysis and other links can be completed according to the standards provided by the clinical data exchange standards association, so as to finally realize the study on the treatment of the test drugs or disease types.

In the related art, clinical data are entered into a computer system in a manual entry mode, so that the entered clinical data are analyzed by the computer system, and finally, research on a research object is realized according to an analysis result. However, the manual entry mode depends on the expertise and working attitude of the entry personnel, and the problems of low efficiency and high error rate exist.

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

An object of the embodiments of the present disclosure is to provide a method and a device for inputting clinical test data, and a computer readable medium and an electronic device for implementing the method, so as to improve the processing efficiency of medical data at least to a certain extent, thereby facilitating the improvement of scientific research efficiency.

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 a first aspect of embodiments of the present disclosure, there is provided a method for entry of clinical trial data, the method comprising:

acquiring clinical trial data of multiple patients;

performing disassembly processing according to time nodes of the clinical test scheme in the clinical test data to obtain clinical test data corresponding to the time nodes;

carrying out structuralization processing on the time nodes, and carrying out normalization processing on the clinical test data of the time nodes to obtain the clinical test data after data mining processing;

transmitting the data-mined clinical trial data corresponding to a target patient to a target system according to a patient identification of the target patient.

In an exemplary embodiment, based on the foregoing, performing a disassembly process according to time nodes of clinical trial scenarios in the clinical trial data includes:

defining clinical trial data about the clinical trial protocol according to the time nodes, and splitting the defined clinical trial data to obtain clinical trial data corresponding to the time nodes.

In an exemplary embodiment, based on the foregoing scheme, the structuring the time node includes:

performing natural language processing in clinical test data of the free text to extract occurrence time of the key event;

wherein the key event comprises one or more of the following information: signing an informed consent, failing to screen, distributing a random number, taking medicine for the first time, and grouping.

In an exemplary embodiment, based on the foregoing scheme, the normalizing process is performed on the clinical trial data of the time node, and includes:

mapping the clinical test data into corresponding standard words based on a preset mapping dictionary; or the like, or, alternatively,

and converting the first clinical test data into second clinical test data based on the preset conversion relation.

In an exemplary embodiment, based on the foregoing protocol, after acquiring clinical trial data for multiple patients, the method further comprises:

and translating the clinical test data of the first language into the clinical test data of the second language according to a preset language conversion relation.

In an exemplary embodiment, based on the foregoing scheme, after the normalizing the clinical trial data of the time node, the method further includes:

the time, age, low frequency words in the clinical trial data after the normalization process were desensitized.

In an exemplary embodiment, based on the foregoing, before transmitting the data-mining processed clinical trial data corresponding to the target patient to a target system, the method further comprises:

and normalizing the data format of the clinical trial data after the data mining processing into a target format.

According to a second aspect of embodiments of the present disclosure, there is provided an apparatus for entry of clinical trial data, the apparatus comprising: the device comprises a data acquisition module, a data disassembly module, a data mining module and a data transmission module.

Wherein the data acquisition module is configured to: acquiring clinical trial data of multiple patients;

the data disassembling module is configured to: performing disassembly processing according to time nodes of the clinical test scheme in the clinical test data to obtain clinical test data corresponding to the time nodes;

the data mining module is configured to: carrying out structuralization processing on the time nodes, and carrying out normalization processing on the clinical test data of the time nodes to obtain the clinical test data after data mining processing; and the number of the first and second groups,

the data transmission module is configured to: transmitting the data-mined clinical trial data corresponding to a target patient to a target system according to a patient identification of the target patient.

According to a third aspect of the embodiments of the present disclosure, there is provided a computer readable medium, on which a computer program is stored, the program, when being executed by a processor, implements the method for entering clinical trial data according to any one of the technical solutions of the first aspect of the embodiments.

According to a fourth aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a method of clinical trial data entry as described in any one of the aspects of the first aspect of the embodiments above.

The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:

in some embodiments of the present disclosure, after acquiring real-world clinical trial data, performing disassembly processing on the real-world clinical trial data according to time nodes of clinical trial scenarios in the clinical trial data to obtain clinical trial data corresponding to the time nodes; furthermore, the time nodes are subjected to structuring processing, and the clinical test data of the time nodes are subjected to normalization processing, so that data mining is realized. Further, the data-mined clinical trial data corresponding to each target patient is transmitted to the target system based on the patient identification of the target patient. According to the technical scheme, the effect of automatically inputting the data into the target system can be achieved through the disassembly processing, the structuralization processing and the normalization processing of the clinical test data. Compared with a manual input mode, the technical scheme has the technical effects of high efficiency and low error rate. Therefore, the technical scheme is beneficial to rapidly and accurately counting and researching the clinical data, and further shortens the research period of related research objects (such as target drugs or target disease species).

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. In the drawings:

FIG. 1 is a schematic diagram of a system architecture for implementing a method and apparatus for clinical trial data entry in an exemplary embodiment of the disclosure;

FIG. 2 shows a flow diagram of a method of entry of clinical trial data according to an embodiment of the present disclosure;

FIG. 3 shows a flow diagram of a method of entry of clinical trial data according to another embodiment of the present disclosure;

FIG. 4 illustrates a data disassembly diagram according to an embodiment of the present disclosure;

FIG. 5 shows a schematic structural diagram of a clinical trial data entry device according to an embodiment of the present disclosure;

FIG. 6 shows a schematic diagram of a structure of a computer storage medium in an exemplary embodiment of the disclosure; and the number of the first and second groups,

fig. 7 shows a schematic structural diagram of an electronic device in an exemplary embodiment of the present 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.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.

The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. 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 means and/or microcontroller means.

The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

The present exemplary embodiment first provides a system architecture for implementing a method for entering clinical trial data, which can be applied to various data processing scenarios. Referring to fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send request instructions or the like. The

terminal devices

101, 102, 103 may have various communication client applications installed thereon, such as a photo processing application, a shopping application, a web browser application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 105 may be a server that provides various services, for example, the server 105 acquires clinical trial data of multiple patients input using the

terminal devices

101, 102, 103. The server 105 performs a disassembly process according to the time node of the clinical trial scenario in the clinical trial data to obtain the clinical trial data corresponding to the time node (for example only). The server 105 may perform a structuring process on the time nodes and perform a normalization process on the clinical trial data of the time nodes, so as to obtain data mining processed clinical trial data (for example only). The server 105 transmits the data-mining processed clinical trial data corresponding to the target patient to a target system according to the patient identification of the target patient.

The technical scheme provides a method and a device for inputting clinical test data, a computer storage medium and electronic equipment. The method for recording clinical trial data is explained as follows:

fig. 2 shows a flow diagram of a method of entry of clinical trial data according to an embodiment of the present disclosure. The method for inputting clinical trial data provided by the embodiment overcomes the above problems in the prior art at least to some extent.

The execution subject of the method for entering clinical test data provided by this embodiment may be a device having a computing processing function, such as a server.

Referring to fig. 2, the method for entering clinical trial data provided by the present embodiment includes:

step S210, acquiring clinical trial data of multiple patients;

step S220, performing disassembly processing according to time nodes of the clinical test scheme in the clinical test data to obtain clinical test data corresponding to the time nodes;

step S230, carrying out structural processing on the time nodes, and carrying out normalization processing on the clinical test data of the time nodes to obtain the clinical test data after data mining processing; and the number of the first and second groups,

step S240, according to the patient identification of the target patient, transmitting the clinical trial data after the data mining processing corresponding to the target patient to a target system.

In the technical solution provided in the embodiment shown in fig. 2, after the real-world clinical trial data is acquired, the clinical trial data is disassembled according to the time node of the clinical trial scenario in the clinical trial data, so as to obtain the clinical trial data corresponding to the time node; furthermore, the time nodes are subjected to structuring processing, and the clinical test data of the time nodes are subjected to normalization processing, so that data mining is realized. Further, the data-mined clinical trial data corresponding to each target patient is transmitted to the target system based on the patient identification of the target patient. According to the technical scheme, the effect of automatically inputting the data into the target system can be achieved through the disassembly processing, the structuralization processing and the normalization processing of the clinical test data. Compared with a manual input mode, the technical scheme has the technical effects of high efficiency and low error rate. Therefore, the technical scheme is beneficial to rapidly and accurately counting and researching the clinical data, and further shortens the research period of related research objects (such as target drugs or target disease species).

In an exemplary embodiment, fig. 3 shows a flow diagram of a method of entry of clinical trial data according to another embodiment of the present disclosure. Details of the implementation of the steps of the solution shown in fig. 2 are described in detail below with reference to the embodiment shown in fig. 3.

In step S210, clinical trial data for multiple patients is acquired.

In an exemplary embodiment, referring to FIG. 3, clinical trial data 320 for a plurality (e.g., thousands) of patients is acquired in a data source system 310. The data source system 310 may be, among other things, an electronic information system of a hospital. Illustratively, the data source System 310 is a Hospital Information System (HIS for short), which specifically provides capabilities of collecting, storing, processing, extracting and data exchanging of patient diagnosis and treatment Information and administrative management Information for each department to which a Hospital belongs by using an electronic computer and communication equipment, and meets the functional requirements of all authorized users; the data source system 310 may also be: laboratory Information Management System (LIS). The LIS is a set of information management systems designed specifically for hospital clinical laboratories. Accordingly, real-world clinical trial data can be obtained through the data source System 310, and by way of example, all clinical trial-related data information of the patient from the signing of the informed consent form and thereafter is extracted from the hospital Electronic information System to be automatically imported into a target System 340, such as an Electronic data capture System (EDC) suitable for clinical trial data acquisition and transmission, after data processing at 330.

In an exemplary embodiment, clinical trial data 320 for a plurality (e.g., thousands) of patients may be acquired from the data source system 310 in a data-stream. For example, the data flow may set the time granularity of T + N for data update according to the data update time period requirement of the clinical trial of the drug. Illustratively, the clinical trial data includes a Case Report Form (CRF).

In an exemplary embodiment, referring to FIG. 3, the data processing system 330 acquires clinical trial data 320 for a plurality (e.g., thousands) of patients. And subjecting it to various processing steps, in particular:

in step S220, performing disassembly processing according to the time node of the clinical trial scenario in the clinical trial data to obtain clinical trial data corresponding to the time node.

In an exemplary embodiment, referring to fig. 3, data disassembly is performed in step S31. Specifically, clinical trial data about the clinical trial plan are defined according to time nodes, and the defined clinical trial data are split, so that the clinical trial data corresponding to the time nodes are obtained.

In an exemplary embodiment, as shown in fig. 4, the specific implementation of step S31 includes parsing and defining each time node (study schedule) in the clinical trial protocol of the drug, and finally forming a specific time node mapping rule.

Referring to fig. 4, the time node mapping rule includes: the visit cycle (the time length of one treatment course) is 28 days, wherein the drug administration is continuously carried out on the 1 st to 21 st days, and then the drug administration is stopped for 7 days; study schedules focused on days 1, 14, 21 of visit cycles 1 and 2, and day 1 of the remaining visit cycles (with error ranges less than ± 2 days).

In an exemplary embodiment, the specific implementation of step S31 further includes mapping the CRF to specific fields in the data set of the data processing system 330 to refine the data mining rules according to the specific scope of the plan of the research objective. Illustratively, the data mining rule includes: (1) the CRF is resolved, for example, in "laboratory studies" the "blood biochemistry" in CRF includes the sub-items "aspartate Aminotransferase (AST)/glutamate pyruvate transaminase (ALT)", "alkaline phosphatase", "sodium", "potassium", "magnesium", "total calcium", "total bilirubin", "urea nitrogen", "serum creatinine", and "albumin". (2) The relationship between the required research syndrome item in the scheme and the actual syndrome item in the clinical trial process is converted, for example, the required research syndrome item is designated as 'urea nitrogen' in the scheme, and the actual syndrome item in the clinical trial is 'urea'. At this time, a conversion rule between urea and urea nitrogen needs to be established through professional medical knowledge, and the specific mapping rule is as follows: the nitrogen content of 1mmol/L urea (NH2-CO-NH2) is 2mmol/L urea nitrogen (N).

In an exemplary embodiment, with continued reference to FIG. 3, data mining is performed in step S32. Specifically, the data mining process may include performing a structuring process on the time nodes and performing a normalization process on the clinical test data of the time nodes in step S230 to obtain the clinical test data after the data mining process.

In an exemplary embodiment, structuring the time node comprises: performing natural language processing in clinical test data of the free text to extract occurrence time of the key event; wherein the event comprises one or more of the following information: signing an informed consent, failing to screen, distributing a random number, taking medicine for the first time, and grouping.

Illustratively, the functions required to be implemented for the structuring of the time node of the critical event are: simple Natural Language Processing (NLP) is performed in free text to extract the time of occurrence of the key event. Taking the time of signing the informed consent as an example, the detailed implementation rules are as follows: according to the past data mining experience, the event of signing the informed consent form is mainly stored in free texts such as superior ward round and admission record, and the texts comprise keywords such as the item number, signing and informed consent of the clinical test. And matching the keywords and taking the time of successful first matching as the occurrence time of the event of signing the informed consent form. Examples of free text containing keywords are as follows:

". attending physician's ward round: considering that the patient initially met the requirements for study enrollment, the attending physicians communicated with them and fully presented the study content 2015-9-25, the patients indicated an informed consent to participate in the study after understanding the question, the attending physicians taught this informed consent for the patient and the neutral witness because they had no reading ability, signed an informed consent by their daughter for the study a (version date: 2015 3, 17), and kept a three-party informed consent with a uniform signature and date to the subjects while the investigator remained in a folder. "

Through data mining on free text and natural language processing on key events of the free text by a structured algorithm, a patient key event and time node mapping relation table shown below is obtained. Specifically, the results are shown in Table 1.

TABLE 1

Patient ID	Time node	Key event
			372c…e053	2015/9/25	Sign an informed consent
372c…e053	2015/10/15	Randomization
			372c…e053	2015/10/15	First application
372c…e053	2015/11/16	Go out of group

In an exemplary embodiment, the normalizing the clinical trial data for the time node includes: mapping the clinical test data into corresponding standard words based on a preset mapping dictionary; or, converting the first clinical trial data into the second clinical trial data based on the preset conversion relation.

Illustratively, the same thing is expressed differently due to the reasons of non-uniformity of internal systems of the hospital, numerous descriptions of medical names, difference of the writing habits of doctors and the like. Normalizing clinical trial data refers to the process of mapping different representations of the same thing onto a standard word. The conversion process not only includes the mapping relation from different expressions to dictionaries of standard words, but also includes the conversion relation between standard words. The normalization processes that are common in pharmaceutical clinical trials include: laboratory test information, drug information, and the like.

Taking laboratory test information in clinical tests as an example, the different expressions "absolute leukocyte value", "leukocyte count" and "total leukocyte" in the test sub-item are actually descriptions of the same test sub-item name, and the "absolute leukocyte value" and "total leukocyte" are finally normalized to "leukocyte count" through a normalization algorithm. There is a switching relationship between "percent neutrophil" and "neutrophil count": the sub-term "percent neutrophil" can be converted from "neutrophil count" to "white blood cell count".

The data mining process of step S32 is completed by the above embodiment, and further, during the processing of the clinical trial data, the method further includes: step S33-step S35.

In step S33, the language conversion is performed. That is, the clinical trial data in the first language is translated into the clinical trial data in the second language according to the predetermined language conversion relationship.

In an exemplary embodiment, the language transposition of the clinical trial data includes: medical noun translation. Illustratively, conversion between Chinese and other languages is performed for compatibility with international collaboration projects or projects having specific language requirements. The method specifically comprises the following steps: converting Chinese name of the test and medicine in clinical test into English name, etc. Taking drug translation as an example, the preset language conversion relationship includes World Health Organization (WHODrug), and the specific language conversion process is as follows:

(1) according to the Chinese information (such as name, specification, manufacturer and the like) originally recorded in the clinical test, inquiring on a WHO Drug Chinese edition to obtain a specific Drug identifier (Drug code);

(2) searching a corresponding English name on a WHO Drug English edition according to the corresponding Drug code;

(3) establishing a Chinese-English mapping dictionary and importing the Chinese-English mapping dictionary into a translation engine;

(4) and the translation engine finishes Chinese and English translation according to the dictionary and a specific data processing algorithm.

With continued reference to fig. 3, encryption/desensitization processing is performed in step S34.

In an exemplary embodiment, the present solution includes two desensitization processes: the first time is as follows: the underlying criteria for desensitization according to medical data is to desensitize its prescribed basic fields following the Health Insurance Portability and accountability Act (HIPAA for short). In addition, in order to further avoid the possibility of patient information leakage. A second desensitization of fields outside the HIPAA range is required for a particular item.

For example, in a clinical trial of a drug, common secondary desensitization fields include: various types of IDs, various types of time, age, other low frequency words (e.g., rare diseases), etc. The second desensitization step is as follows:

(1) for the time field: taking a certain time point as reference time, wherein a time field needing desensitization is the difference value between field time and the reference time;

(2) for the ID field: performing secondary randomization;

(3) for the age field: classifying according to specific numerical values, and displaying the categories;

(4) for the low frequency field: directly emptying.

With continued reference to fig. 3, the format conversion process is performed in step S35. That is, the data format of the clinical trial data after the data mining process is normalized to the target format.

In an exemplary embodiment, the data format conversion is mainly to solve the problem that the data format is inconsistent with the EDC system data format during data processing. Generally, for data mining in the medical field, a commonly used data format is more prone to select a JS Object Notation (JSON) format. If the data format of the target, such as the system, is an Extensible Markup Language (XML) format. The specific data format conversion engine automatically completes the data format conversion mainly through the field mapping configuration and the mapping algorithm. The method mainly comprises the following steps:

(1) and performing secondary mapping according to the data after the language conversion processing, wherein for example, a common name (comm _ name) for medication is mapped to an output field 'sctCM 001_01.0. cmtrt', wherein sctCM001_01 is a preset table name.

(2) The specific mapping relation is arranged into a mapping dictionary with a specified format and is led into a format conversion engine;

(3) and the format conversion engine completes the conversion result according to the algorithm.

In the exemplary embodiment, with continued reference to FIG. 3, the data generated at step S35 is transmitted to target entry system 340 through an API interface at step S36.

In an exemplary embodiment, in step S240, the data-mining processed clinical trial data corresponding to the target patient is transmitted to a target system according to a patient identification of the target patient. Therefore, in the input target system, the clinical test data processed by the data processing system 330 is classified and stored according to the patient identification, so that the change condition of the illness state of the target patient can be observed conveniently, the clinical data can be counted and researched quickly and accurately, and the research period of related research objects can be shortened.

Those skilled in the art will appreciate that all or part of the steps for implementing the above embodiments are implemented as computer programs executed by processors, including Central Processing Units (CPUs) and Graphics Processing Units (GPUs). When the computer program is executed by a CPU or a GPU, the above-described functions defined by the above-described methods provided by the present disclosure are performed. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.

Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the methods according to exemplary embodiments of the present disclosure, 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.

The following describes embodiments of the clinical trial data entry device of the present disclosure, which can be used to execute the clinical trial data entry method provided by the above embodiments of the present disclosure.

Fig. 5 shows a schematic structural diagram of a clinical test data entry device according to an embodiment of the present disclosure, and referring to fig. 5, the present embodiment provides a clinical test data entry device 500, including: the system comprises a data acquisition module 501, a data disassembly module 502, a data mining module 503 and a data transmission module 504.

The data obtaining module 501 is configured to: acquiring clinical trial data of multiple patients;

the data unpacking module 502 is configured to: performing disassembly processing according to time nodes of the clinical test scheme in the clinical test data to obtain clinical test data corresponding to the time nodes;

the data mining module 503 is configured to: carrying out structuralization processing on the time nodes, and carrying out normalization processing on the clinical test data of the time nodes to obtain the clinical test data after data mining processing; and the number of the first and second groups,

the data transmission module 504 is configured to: transmitting the data-mined clinical trial data corresponding to a target patient to a target system according to a patient identification of the target patient.

In an embodiment of the present disclosure, based on the foregoing scheme, the data disassembling module 502 is specifically configured to:

In an embodiment of the present disclosure, based on the foregoing solution, the data mining module 503 includes: a structuring sub-module. Wherein:

the above-mentioned structural sub-module configured to: performing natural language processing in clinical test data of the free text to extract occurrence time of the key event;

In an embodiment of the present disclosure, based on the foregoing solution, the data mining module 503 further includes: and (5) normalizing the submodule. Wherein:

the normalization sub-module configured to:

mapping the clinical test data into corresponding standard words based on a preset mapping dictionary; or, converting the first clinical trial data into the second clinical trial data based on the preset conversion relation.

In an embodiment of the present disclosure, based on the foregoing scheme, the apparatus 500 for entering clinical test data further includes: and a language conversion module. Wherein:

the language conversion module is configured to: after the data acquiring module 501 acquires clinical trial data of multiple patients, the clinical trial data of the first language is translated into clinical trial data of the second language according to a preset language conversion relationship.

In an embodiment of the present disclosure, based on the foregoing scheme, the apparatus 500 for entering clinical test data further includes: a desensitization module. Wherein:

the desensitization module described above, configured to: after the normalization sub-module normalizes the clinical test data of the time node, desensitization processing is carried out on time, age and low-frequency words in the clinical test data after normalization processing.

In an embodiment of the present disclosure, based on the foregoing scheme, the apparatus 500 for entering clinical test data further includes: and a format conversion module. Wherein:

the format conversion module is configured to: normalizing the data format of the data-mined clinical trial data to a target format before the data transmission module 504 transmits the data-mined clinical trial data corresponding to the target patient to a target system.

For details which are not disclosed in the embodiment of the device for entering clinical test data of the present disclosure, please refer to the embodiment of the method for entering clinical test data of the present disclosure for details which are not disclosed in the embodiment of the device for entering clinical test data of the present disclosure.

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.

In an exemplary embodiment of the present disclosure, there is also provided a computer storage medium capable of implementing the above method. On which a program product capable of implementing the above-described method of the present specification is stored. In some possible embodiments, various aspects of the present disclosure may also be implemented in the form of a program product including program code for causing a terminal device to perform the steps according to various exemplary embodiments of the present disclosure described in the "exemplary methods" section above of this specification when the program product is run on the terminal device.

Referring to fig. 6, a program product 600 for implementing the above method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this 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.

The program product described above may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an erasable programmable Read-Only Memory (EPROM or flash Memory), an optical fiber, a portable compact disk Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

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 the present disclosure 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 Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure 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.

An electronic device 700 according to this embodiment of the disclosure is described below with reference to fig. 7. The electronic device 700 shown in fig. 7 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 7, electronic device 700 is embodied in the form of a general purpose computing device. The components of the electronic device 700 may include, but are not limited to: the at least one processing unit 710, the at least one memory unit 720, and a bus 730 that couples various system components including the memory unit 720 and the processing unit 710.

Wherein the storage unit stores program codes, which can be executed by the processing unit 710, so that the processing unit 710 executes the steps according to various exemplary embodiments of the present disclosure described in the "exemplary method" section above in this specification. For example, the processing unit 710 described above may perform the following as shown in fig. 2: step S210, acquiring clinical trial data of multiple patients; step S220, performing disassembly processing according to time nodes of the clinical test scheme in the clinical test data to obtain clinical test data corresponding to the time nodes; step S230, carrying out structural processing on the time nodes, and carrying out normalization processing on the clinical test data of the time nodes to obtain the clinical test data after data mining processing; and step S240, transmitting the data mining processed clinical test data corresponding to the target patient to a target system according to the patient identification of the target patient.

Illustratively, the processing unit 710 may also perform a method of entering clinical trial data as shown in fig. 3.

The storage unit 720 may include readable media in the form of volatile storage units, such as: a Random Access Memory (RAM) 7201 and/or a cache Memory 7202, and may further include a Read-Only Memory (ROM) 7203.

The storage unit 720 may also include a program/utility 7204 having a set (at least one) of program modules 7205, such program modules 7205 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.

Bus 730 may be any representation of one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 700 may also communicate with one or more external devices 800 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 700, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 700 to communicate with one or more other computing devices. Such communication may be through an Input/Output (I/O) interface 750. Further, the I/O interface 750 is connected with the display unit 740 to transmit the content to be displayed to the display unit 740 through the I/O interface 750 for viewing by the user.

Also, the electronic device 700 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 760. As shown, the network adapter 760 communicates with the other modules of the electronic device 700 via the bus 730. 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 700, 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.

Furthermore, the above-described figures are merely schematic illustrations of processes included in methods according to exemplary embodiments of the present disclosure, 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.

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

1. A method of clinical trial data entry, the method comprising:

acquiring clinical trial data of multiple patients;

2. The method of claim 1, wherein performing a disassembly process based on time nodes of clinical trial scenarios in the clinical trial data comprises:

3. The method of claim 1, wherein structuring the time node comprises:

4. The method of claim 1, wherein normalizing the clinical trial data for the time nodes comprises:

5. The method of any one of claims 1 to 4, wherein after acquiring clinical trial data for multiple patients, the method further comprises:

6. The method of any one of claims 1 to 4, wherein after normalizing the clinical trial data for the time node, the method further comprises:

7. The method of any one of claims 1 to 4, wherein prior to transmitting the data-mining processed clinical trial data corresponding to the target patient to a target system, the method further comprises:

8. An apparatus for entry of clinical trial data, the apparatus comprising:

a data acquisition module configured to: acquiring clinical trial data of multiple patients;

a data disassembly module configured to: performing disassembly processing according to time nodes of the clinical test scheme in the clinical test data to obtain clinical test data corresponding to the time nodes;

a data mining module configured to: carrying out structuralization processing on the time nodes, and carrying out normalization processing on the clinical test data of the time nodes to obtain the clinical test data after data mining processing;

a data transmission module configured to: transmitting the data-mined clinical trial data corresponding to a target patient to a target system according to a patient identification of the target patient.

9. A computer-readable medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out a method of clinical trial data entry according to any one of claims 1 to 7.

10. An electronic device, comprising:

one or more processors;

a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a method of entry of clinical trial data as claimed in any one of claims 1 to 7.