CN117275656B

CN117275656B - Method and system for automatically generating standardized report of clinical test record

Info

Publication number: CN117275656B
Application number: CN202311558723.9A
Authority: CN
Inventors: 钱志荣; 李斌; 李炜楠; 邓小燕
Original assignee: Guangzhou Beidou Medical Laboratory Co ltd; Beidou Life Sciences Guangzhou Co ltd
Current assignee: Guangzhou Beidou Medical Laboratory Co ltd; Beidou Life Sciences Guangzhou Co ltd
Priority date: 2023-11-22
Filing date: 2023-11-22
Publication date: 2024-04-09
Anticipated expiration: 2043-11-22
Also published as: CN117275656A

Abstract

The invention provides a method for automatically generating a standardized report of a clinical test record, which comprises the following steps: obtaining a table of test records and tumor, gene and variation relations; a weight data table corresponding to the genes corresponding to the matched tumors and having mutation according to the table; acquiring tumor information of an order, and acquiring a weight value when each gene is mutated in an order detection result according to a weight data table of mutation of genes corresponding to the tumor; searching the relation table and the weight table according to the searching priority; sorting according to the searching priority to obtain a sorting list; after the ordered list is obtained, tumors, genes and variations are taken as query conditions and are sequentially put into a list to be output from top to bottom, the record found first is put at the forefront, and the forefront 5 records are selected and output into the report. The invention reduces human errors as much as possible through strict flow control, and ensures the accuracy of experimental results.

Description

Method and system for automatically generating standardized report of clinical test record

Technical Field

The invention belongs to the field of gene detection, and particularly relates to a method and a system for automatically generating a standardized report of clinical test records.

Background

Clinical trial reports are documents that record and summarize the results and data of conducting clinical trials. It is an important basis for assessing the safety, efficacy and feasibility of new drugs, therapies or treatment regimens. Clinical trial reports detail the study design, sample selection, grouping, follow-up period, etc. of the trial to ensure the scientificity and reliability of the trial. And simultaneously, the main results and data of the test are presented, including the number of patients, the measurement result of the main endpoint index, the measurement result of the secondary endpoint index and the like. These results and data are used to evaluate the effectiveness and safety of the trial interventions. Adverse events and side effects occurring in the test will be described and evaluated in more detail. This helps to assess the safety and tolerability of interventions, providing an important basis for drug registration and use. Clinical trial reports will discuss and interpret trial results, including comparisons with other study results, potential limitations, and recommendations for future studies. Finally, based on the test results, conclusions can be drawn to support or negate the effectiveness and feasibility of the test intervention.

Clinical trial reports have important scientific and practical significance, and they are the primary basis for assessing whether a new drug, therapy or treatment regimen is safe and effective. Meanwhile, the clinical trial report also provides the medical community and researchers with the latest progress and evidence related to the field of disease treatment, and promotes the development of medical research and clinical practice. The accuracy of the clinical report determines the direction of subsequent studies, so how to quickly and accurately obtain a clinical laboratory report is of great importance.

Disclosure of Invention

The invention aims to provide a standardized report generation method for automatically generating clinical test records, which can effectively solve the technical problems in the prior art.

To achieve the above object, an embodiment of the present invention provides a method for automatically generating a standardized report of clinical test records, the method including the steps of:

s1, acquiring a table of relation between test records and tumors, genes and variation;

s2, correspondingly matching a weight data table of the mutation of the genes corresponding to the tumors according to the table;

s3, acquiring tumor information of an order, and acquiring a weight value when each gene in an order detection result is mutated according to a weight data table of mutation of the gene corresponding to the tumor;

s4, searching the relation table and the weight table according to the searching priority; the searching priority levels are searching priority level 1, searching priority level 2, searching priority level 3, searching priority level 4, searching priority level 5, searching priority level 6, searching priority level 7 and searching priority level 8;

s5, sorting according to the searching priority in the step S4 to obtain a sorting list;

s6, after the ordered list is obtained, tumor, gene and mutation are taken as query conditions and are sequentially placed on a list to be output from top to bottom, specifically, a corresponding clinical test record is found through a table of the relation between test records and tumor, gene and mutation, the clinical test record is placed on the list to be output, the record found first is placed on the first row, the record found later is arranged downwards row by row, and finally, the record of the first 5 rows is selected and output in the report.

Further, the condition of the searching priority 1 is selected according to the mutation classification, the evidence grade and the weight, wherein the mutation classification is the most preferred, the evidence grade is the secondary, and the weight is the last;

the mutation classification and the evidence grade are determined by the detection result of the experiment, and if the genetic mutation is detected, the classification and the sorting are carried out according to the mutation classification of the genes, and the classification is carried out from class I to class II, wherein the class I is the front; the evidence rank ordering is a class a to class D ordering, with class a preceding;

the weight is the weight value sequence of the corresponding genes and variation under the query tumor, and the sequence with small numbers is the front, wherein the numbers start from 1.

Further, when the weight data table has no corresponding variation weight information under the tumor, default the lowest priority of the stage; when the letters are the lowest priority, the letters are sorted from small to large according to the names of the genes.

Further, the condition of searching priority 2 is that when the condition that the tumor is confirmed to be taken as the condition and less than 5 pieces of data are taken, the condition that the tumor is replaced by the solid tumor is continuously classified according to the mutation, the secondary is the evidence grade, and finally the rule query of the weight is carried out;

the condition of searching priority 3 is that when priority 2 takes less than 5 pieces of data, the tumor and the detected genes are confirmed to be continuously classified according to the mutation by taking the conditions as the conditions, the secondary is evidence grade, and finally the rule of the weight is searched;

and the condition of searching the priority 4 is that when the priority 3 takes less than 5 pieces of data, the mutation classification is continuously carried out by using the solid tumor and the detected genes as conditions, the secondary is the evidence grade, and finally the rule inquiry of the weight is carried out.

Further, if the condition of searching priority 5 confirms that MSI-H or TMB-H is detected under the tumor, extracting the result of MSI-H or TMB-H related clinical test of the corresponding tumor;

when the value of TMB is greater than or equal to 10 in the detection result, namely TMB-H is detected; MSI is greater than or equal to 10, namely MSI-H is detected;

the search priority 6 condition is that if MSI-H or TMB-H is detected by using solid tumor as query tumor, MSI is in front, TMB is in back form and added into the ordered list.

Further, the searching priority 7 takes the confirmed tumor as a searching tumor and the no-target gene is added into the ordered list; the search priority 8 takes solid tumors as query tumors and the no-target gene to add to the ordered list.

Further, the tumor is a tumor of a certain kind in the detection, and the solid tumor is a specific tumor defined.

In a second aspect of the present invention, there is provided a standardized report apparatus for automated generation of clinical trial records, comprising:

the data acquisition module is used for acquiring a table of the relation between the test record and the tumor, the gene and the variation;

the data matching module is used for correspondingly matching a weight data table of the mutation of the genes corresponding to the tumors according to the table;

the data query module is used for acquiring tumor information of the order and searching the relation table and the weight table according to the priority; the searching priority levels are searching priority level 1, searching priority level 2, searching priority level 3, searching priority level 4, searching priority level 5, searching priority level 6, searching priority level 7 and searching priority level 8;

the data sorting module is used for sorting according to the searching priority to obtain a sorting list;

and the report output module is used for obtaining a ranking table, taking tumors, genes and variations as query conditions according to the sequence from top to bottom, finding out corresponding clinical test records through a table of the relation between the test records and the tumors, genes and variations, placing the clinical test records into a list to be output, placing the clinical test records into the list to be output, placing the record found first in a first row, arranging the record found later downwards to the forefront row by row, and finally selecting the forefront 5 rows of records to be output in the report.

In a third aspect of the invention, there is provided an electronic device comprising a processor, a memory, wherein the memory is for storing a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform any of the above-described methods of automated generation of standardized reporting of clinical trial records.

In a fourth aspect of the invention, there is provided a computer readable storage medium storing a computer program comprising program instructions which, when executed by a processor, cause the processor to perform any of the above-described methods of automating the generation of standardized reports of clinical trial records.

Compared with the prior art, the experiment management system for gene detection provided by the embodiment of the invention has the following technical effects: according to the invention, through the whole process management of the samples, the state, the flow direction and the experimental process of each sample are strictly recorded, and the samples can be conveniently checked. And then, by strict flow control, human errors are reduced as much as possible, and the accuracy of experimental results is ensured.

Drawings

In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic diagram of an experiment management system for gene detection according to an embodiment of the present invention.

FIG. 2a is a schematic representation of clinical trial data according to an embodiment of the present invention.

FIG. 2b is a schematic diagram of the output of clinical trial data according to an embodiment of the present invention.

FIG. 3a is a graph showing the intent of the weight data of the genetic variation corresponding to tumor in the embodiment of the present invention.

FIG. 3b is a graph showing the matching output of the weight data table of the genetic variation corresponding to the tumor in the embodiment of the present invention.

Fig. 4 is a weight schematic diagram of a tumor according to example Colorectal Cancer of the present invention.

FIG. 5 is a schematic diagram of the gene sequencing according to the embodiment of the invention.

FIG. 6 is a schematic diagram showing the matching of EGFR gene in an embodiment of the present invention.

FIG. 7 is a schematic diagram showing the sequence of the rectal cancer variant genes according to an embodiment of the present invention.

FIG. 8 is a diagram of an embodiment of the output report.

Fig. 9 is a block diagram of a device for automatically generating and standardizing clinical test records according to an embodiment of the present invention.

Fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Fig. 1 is a method for automatically generating a standardized report for clinical test records according to an embodiment of the present application, where the method includes the steps of:

wherein the weight value is set by the user by refining the results in guidelines issued by the chinese clinical oncology society (CSCO) and the national integrated cancer network (NCCN).

s5, sorting according to the searching priority to obtain a sorting list;

Specifically, the matching rule of the weight data table corresponding to the corresponding gene mutation of the matched tumor according to the table is that the matching is carried out through the names of the tumors, and the same tumor names can be matched; a relational database is used. The relational database (Relational Database) is a relational model-based database management system (DBMS). In a relational database, data is organized into tables (or relationships), each table containing a set of related data items. The tables may be related to each other by a predefined relationship (or key).

If the tumor is the solid tumor, the weight data table corresponding to the tumor is the solid tumor can be found in the table.

The table of the relation between the test record and the tumor, the gene and the variation can be established according to the requirements of customers, and the relation between the test record and the tumor, the gene and the variation is recorded. The data is derived from the user's input. The categories of the table include association ID, experiment record ID, tumor ID, gene ID, and variant ID. Clinical trial data are shown in fig. 2a and 2 b.

The weight data table of the mutation of the genes corresponding to the tumors is shown in FIG. 3a and FIG. 3b,

specifically, the relation table and the weight table are searched according to the search priority, wherein the search priority 1, the search priority 2, the search priority 3, the search priority 4, the search priority 5, the search priority 6, the search priority 7 and the search priority 8 are respectively expressed as follows:

1. priority of best lookup 1: and acquiring order confirmation tumor information, firstly matching according to somatic mutation (including mutation and amplification) of a detection sample, extracting relevant clinical test information if relevant target gene mutation of class I or class II exists in the somatic mutation, and carrying out preferential selection according to mutation classification, evidence grade and weight (the weight information is configured in a clinical test table). The most preferred is variant classification, secondary evidence grade, and finally weight.

(1) The mutation classification and the evidence grade are determined by the detection result, and if the detection result has the genetic mutation classification (the detection has the genetic mutation, the mutation classification is certain);

(2) When the genes are detected, sorting according to the mutation classification of the genes, sorting from class I to class II, wherein the class I is the front; sorting according to the mutation classification, sorting according to the evidence level, sorting from the A level to the D level, and leading the A level. Examples: such as EGFR L858R (class I class A) > KRAS G12C (class I class A) > EGFR G719C (class I class B) > KRAS G12D (class I class B) > EGFR L747P (class II).

(3) And then sorting according to the weight values of the corresponding genes and variation under the inquired tumor, and leading the number to be small. The data are obtained from a weight data table of the genetic variation corresponding to the tumor. When the weight table has no corresponding variation weight information under the tumor, defaulting to the lowest priority of the stage; when the letters are the lowest priority, the letters are sorted from small to large according to the names of the genes. Examples: colorectal Cancer tumors are selected in the weight configuration table, and the weight information of genes and variants under the tumors can be seen through database matching, as shown in fig. 4.

Specifically, the letters of the gene names are sorted from small to large to represent English letters of the whole names, when the first letters are the same, the second letters are compared, and the like, such as EGFR/AKF/ABBA, and the sorting is changed into ABBA, AKF, EGFR.

(4) After final ordering, the data will be presented in the form of the following table, as shown in FIG. 5.

For example, as shown in FIG. 6, amino acid 858 of EGFR gene is replaced with arginine (R) by leucine (L). Evidence grade a, variance classified as I.

2. Search priority 2: when the tumor is confirmed to be taken as a condition and less than 5 pieces of data are taken, the tumor is replaced by 'solid tumor' as a condition to be continuously classified according to the mutation preferentially, the secondary is the evidence grade, and finally the rule query of the weight is carried out.

3. Search priority 3: when the priority 2 takes less than 5 pieces of data, the mutation classification is continued by taking the confirmed tumor and the detected genes as conditions, the secondary is the evidence grade, and finally the rule query of the weight is performed.

4. Search priority 4: when the priority 3 takes less than 5 pieces of data, the mutation classification is continued by using the solid tumor and the detected genes as conditions, the secondary is the evidence grade, and finally the rule query of the weight is performed.

Note that: the tumor is identified as a specific tumor (such as non-small cell lung cancer, colorectal cancer, etc.) identified in the test, and the solid tumor is also defined as a specific tumor, and the tumor query is only used for data query.

5. Search priority 5: MSI-H or TMB-H (data in report is required to meet the condition, MSI > =10 and TMB > =10, MSI is screened first, data is not enough, TMB is screened), and if the variation type exists under the tumor, MSI-H or TMB-H related clinical test of the corresponding tumor is extracted. Such as tumor type: colorectal cancer; type of variation: MSI-H.

Specifically, when the value of TMB is greater than or equal to 10 in the detection result, the detection result is TMB-H; MSI is greater than or equal to 10, which is the MSI-H detected.

Specifically, MSI-H was measured;

in the field of gene detection (genomics), TMB (Tumor Mutational Burden) and MSI (Microsatellite Instability) are two important biomarkers for assessing genetic variation and instability of tumors. They provide information about tumor biology and patient, helping to guide treatment selection and prognosis evaluation.

Tmb (Tumor Mutational Burden): TMB is an indicator used to measure the number of mutations in a tumor. It represents the total number of mutations that occur in tumor cells, typically expressed in units of millions of nucleotides (Mb). High TMB means more genetic mutations in the tumor. These mutations may lead to tumors that are more vulnerable to the immune system, as the immune system can recognize these mutations as foreign. Thus, high TMB tumors may be more responsive to immunotherapy (e.g., immune checkpoint inhibitors).

Msi (Microsatellite Instability): MSI refers to the instability of microsatellite loci in tumor cells, which are DNA sequences consisting of short repeat sequences. Under normal conditions, the length of these microsatellite loci will remain stable during cell division. MSI tumors, however, exhibit irregular amplification or shrinkage of these microsatellite loci, which is caused by defects in the DNA repair system. MSI is often associated with some hereditary tumor syndromes, such as Lynch syndrome. Detecting MSI may help identify these syndromes and tumors associated therewith.

These two indicators are becoming increasingly popular for use in cancer therapy. High TMB and MSI tumors will generally be considered for use with immunotherapy because they may be more responsive to immunotherapy. In addition, the indexes are also used for prognosis evaluation, so that doctors can know biological characteristics of tumors, and treatment strategies can be better formulated.

6. Search priority 6: MSI-H or TMB-H (data to be in report meets the conditions: MSI > =10; TMB > =10) is detected, solid tumor is used as query tumor, MSI is in front, TMB is in back form and added into the ordered list.

7. Search priority 7: taking the confirmed tumor as the query tumor and adding the no-target gene into the ordered list.

8. Search priority 8: taking the solid tumor as the query tumor and adding the no-target gene into the ordered list.

Note that the no-target gene is an algorithm-defined gene that is not specifically intended as a supplemental query condition.

For example, in the detection result, it is confirmed that the tumor is: the presence of colorectal cancer,

the somatic variations detected were: AA mutation occurs in the A gene, and the mutation is classified into class II and class B; BB mutation occurs in the B gene, and the mutation is classified into class II and class A; CC mutation occurs in the C gene, and the mutation is classified into class I and class A; DD mutation occurs in the D gene, and the mutation is classified into class I and class A;

according to a weight data table of the mutation of the corresponding genes of the tumor, the weight of the mutation of the A gene and the AA is 1, the weight of the mutation of the B gene and the BB is 5, the weight of the mutation of the C gene and the CC is 6,D, and the weight of the mutation of the DD is 2;

MSI is detected as 21, TMB is detected as 22;

using the above assumed data, sorting according to the above priority, and finally obtaining a sorting list as shown in fig. 7.

After the ordered list is obtained, taking tumors, genes and variations as query conditions according to the sequence from top to bottom, finding out corresponding clinical test records through a table of the relation between test records and the tumors, genes and variations, putting the clinical test records into a list to be output, and putting the record found first at the front. For example, the clinical test records a and b are found by colorectal cancer, D gene and DD mutation, and the clinical test records C, D and e are found by colorectal cancer, C gene and CC mutation, and the list to be output is sorted according to a, b, C, D, e.

The last 5 outputs are selected within the report as shown in fig. 8.

A specific implementation manner of the standardized report device for automatically generating clinical test records in this embodiment may refer to a description of the standardized report method for automatically generating clinical test records in the foregoing embodiment, which is not described herein.

As shown in fig. 9, an embodiment of the present invention provides a standardized report generating apparatus for automatically generating clinical test records, including:

the data acquisition module 21 is used for acquiring a table of the relation between the test record and the tumor, the gene and the variation;

the data matching module 22 is used for correspondingly matching a weight data table of the mutation of the genes corresponding to the tumors according to the table;

the data query module 23 is configured to obtain tumor information of an order, and search the relationship table and the weight table according to the priority; the searching priority levels are searching priority level 1, searching priority level 2, searching priority level 3, searching priority level 4, searching priority level 5, searching priority level 6, searching priority level 7 and searching priority level 8;

the data sorting module 24 is configured to sort according to the search priority, so as to obtain a sorted list;

the report output module 25 is configured to obtain the ranking table, then, in order from top to bottom, take the tumor, the gene, and the mutation as query conditions, find a corresponding clinical test record through a table of the relation between the test record and the tumor, the gene, and the mutation, put the clinical test record in the to-be-output list, put the first found record in the first row, and then, the found record is arranged down to the front row by row, and finally, select the first 5 rows of records to be output in the report.

As shown in fig. 10, an embodiment of the present invention provides an electronic device 300, including a memory 310 and a processor 320, where the memory 310 is configured to store one or more computer instructions, and the processor 320 is configured to invoke and execute the one or more computer instructions, thereby implementing any of the above-mentioned methods for automatically generating standardized reports of clinical trial records.

That is, the electronic device 300 includes: a processor 320 and a memory 310, wherein computer program instructions are stored in the memory 310, wherein the computer program instructions, when executed by the processor, cause the processor 320 to perform any of the above-described methods of automatically generating standardized reports of clinical trial records.

Further, as shown in fig. 10, the electronic device 300 further includes a network interface 330, an input device 340, a hard disk 350, and a display device 360.

The interfaces and devices described above may be interconnected by a bus architecture. The bus architecture may be a bus and bridge that may include any number of interconnects. One or more Central Processing Units (CPUs), represented in particular by processor 320, and various circuits of one or more memories, represented by memory 310, are connected together. The bus architecture may also connect various other circuits together, such as peripheral devices, voltage regulators, and power management circuits. It is understood that a bus architecture is used to enable connected communications between these components. The bus architecture includes, in addition to a data bus, a power bus, a control bus, and a status signal bus, all of which are well known in the art and therefore will not be described in detail herein.

The network interface 330 may be connected to a network (e.g., the internet, a local area network, etc.), and may obtain relevant data from the network and store the relevant data in the hard disk 350.

The input device 340 may receive various instructions from an operator and transmit the instructions to the processor 320 for execution. The input device 340 may include a keyboard or pointing device (e.g., a mouse, a trackball, a touch pad, or a touch screen, among others).

The display device 360 may display results obtained by the processor 320 executing instructions.

The memory 310 is used for storing programs and data necessary for the operation of the operating system, and data such as intermediate results in the calculation process of the processor 320.

It will be appreciated that memory 310 in embodiments of the invention may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be Read Only Memory (ROM), programmable Read Only Memory (PROM), erasable Programmable Read Only Memory (EPROM), electrically Erasable Programmable Read Only Memory (EEPROM), or flash memory, among others. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. The memory 310 of the apparatus and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.

In some implementations, the memory 310 stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system 311 and applications 312.

The operating system 311 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application programs 312 include various application programs such as a Browser (Browser) and the like for implementing various application services. A program implementing the method of the embodiment of the present invention may be included in the application program 312.

The processor 320 acquires a table of test records related to tumor, gene, and mutation when calling and executing the application program and data stored in the memory 310, specifically, the program or instruction stored in the application program 312; a weight data table corresponding to the genes corresponding to the matched tumors and having mutation according to the table; acquiring tumor information of an order, and acquiring a weight value when each gene is mutated in an order detection result according to a weight data table of mutation of genes corresponding to the tumor; searching the relation table and the weight table according to the searching priority; the searching priority levels are searching priority level 1, searching priority level 2, searching priority level 3, searching priority level 4, searching priority level 5, searching priority level 6, searching priority level 7 and searching priority level 8; sorting according to the searching priority to obtain a sorting list; after the ordered list is obtained, tumor, gene and variation are sequentially placed in a list to be output from top to bottom as query conditions, specifically, a corresponding clinical test record is found through a table of the relation between test records and tumor, gene and variation, the list to be output is placed, the record found first is placed in the first row, the record found later is placed at the forefront row by row in a descending manner, and finally, the forefront 5 rows of records are selected and output in a report.

The method for automatically generating standardized reports on clinical test records disclosed in the above embodiment of the present invention can be applied to the processor 320 or implemented by the processor 320. Processor 320 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware or instructions in software in processor 320. The processor 320 may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, or discrete hardware components, which may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present invention. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 310 and the processor 320 reads the information in the memory 310 and in combination with its hardware performs the steps of the method described above.

It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof.

For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.

Specifically, the processor 320 is further configured to read the computer program and perform any one of the above methods for automatically generating a standardized report of clinical trial records.

The present application also provides a computer readable storage medium storing a computer program comprising program instructions that, when executed by a processor, cause the processor to perform the above method, such as the method performed by the above electronic device, which is not described herein in detail.

Alternatively, a storage medium, such as a computer readable storage medium, to which the present application relates may be nonvolatile or may be volatile.

Alternatively, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like. The blockchain referred to in the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the described order of action, as some steps may take other order or be performed simultaneously according to the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.

In the several embodiments provided in this application, it should be understood that the disclosed methods and apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may be physically included separately, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.

The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform part of the steps of the transceiving method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing disclosure is only illustrative of the preferred embodiments of the present invention and is not to be construed as limiting the scope of the invention, as it is understood by those skilled in the art that all or part of the above-described embodiments may be practiced without resorting to the equivalent thereof, which is intended to fall within the scope of the invention as defined by the appended claims.

Claims

1. A method for automatically generating a standardized report of a clinical trial record, the method comprising the steps of:

s6, after a sorting table is obtained, taking tumors, genes and variations as query conditions, sequentially placing the query conditions into a list to be output, specifically, finding out corresponding clinical test records through a table of relation between test records and the tumors, genes and variations, placing the clinical test records into the list to be output, placing the first found records in a first row, arranging the later found records in a row by row and downwards to the front, and finally selecting the first 5 rows of records to be output in a report;

the condition of the searching priority 1 is selected according to the mutation classification, the evidence grade and the weight, wherein the most preferred is the mutation classification, the secondary is the evidence grade and the last is the weight;

the condition of searching priority 2 is that when the condition of confirming the tumor takes less than 5 pieces of data as a condition, the condition of replacing the tumor with the solid tumor is continuously classified according to the variation preferentially, the secondary is the evidence grade, and finally the rule inquiry of the weight is carried out;

the condition of searching the priority 4 is that when the priority 3 takes less than 5 pieces of data, the entity tumor and the detected genes are used as conditions to be continuously classified according to the mutation preferentially, the secondary is evidence grade, and finally the rule query of the weight is carried out;

if the condition of searching priority 5 confirms that MSI-H or TMB-H is detected under the tumor, extracting the result of MSI-H or TMB-H related clinical test of the corresponding tumor;

2. The automated standardized report generating method of claim 1 wherein the variance classification and evidence level are determined by the test results, and if genetic variance is detected, the classification is ordered by variance classification of the genes, from class I to class II, wherein class I is preceded; the evidence rank ordering is a class a to class D ordering, with class a preceding; the weight is the weight value sequence of the corresponding genes and variation under the query tumor, and the sequence with small numbers is the front, wherein the numbers start from 1.

3. The method for automatically generating a standardized report of clinical trial record according to claim 2, wherein when there is no variation weight information corresponding to the current tumor in the weight data table, the lowest priority of the stage; when the letters are the lowest priority, the letters are sorted from small to large according to the names of the genes.

4. The automated clinical trial record generation standardized report method of claim 1 wherein the find priority 7 condition takes a confirmed tumor as a query tumor and a no-target gene is added to the ordered list; the search priority 8 takes solid tumors as query tumors and the no-target gene to add to the ordered list.

5. The method for automatically generating a standardized report of clinical trial recordings according to claim 1, wherein the tumor is a tumor of a certain type in the examination and the solid tumor is a defined specific tumor.

6. A standardized report device for automatically generating clinical test records, comprising:

the report output module is used for obtaining a ranking table, taking tumors, genes and variations as query conditions according to the sequence from top to bottom, finding out corresponding clinical test records through a table of the relation between test records and the tumors, genes and variations, putting the clinical test records into a list to be output, putting the first found records in a first row, arranging the later found records in a line-by-line and downwards to the forefront, and finally selecting the forefront 5 rows of records to be output in the report;

7. An electronic device comprising a processor, a memory, wherein the memory is configured to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform a method of automated clinical trial record generation standardized reporting of any of claims 1-5.

8. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to perform a method of automating the generation of standardized reports of a clinical trial record of any of claims 1-5.