CN112232673A

CN112232673A - Early warning method, device, equipment and medium for processing genetic resources

Info

Publication number: CN112232673A
Application number: CN202011111768.8A
Authority: CN
Inventors: 胡磊; 张琰; 李婷; 王程; 黄伊; 吴铮; 于希; 张�浩; 朱真樾
Original assignee: Bayer Healthcare LLC
Current assignee: Bayer Healthcare LLC
Priority date: 2020-10-16
Filing date: 2020-10-16
Publication date: 2021-01-15

Abstract

Embodiments of the present disclosure relate to an early warning method, apparatus, device, and computer storage medium for processing of genetic resources. The method proposed herein comprises: determining constraint information for processing of genetic resources (e.g., human genetic resources) based on approval information associated with the genetic resources, the processing comprising collecting a sample of genetic resources from a subject; determining a target early warning condition to be satisfied from among a plurality of early warning conditions having different levels based on the constraint information and the execution information related to the processing of the genetic resource; and providing an alert corresponding to the level of the target advance warning condition to a user related to the target advance warning condition. In this way, the embodiments of the present disclosure set different levels of early warning conditions, so that a warning about processing of genetic resources can be timely provided to a corresponding user, thereby avoiding the occurrence of an illegal action.

Description

Early warning method, device, equipment and medium for processing genetic resources

Technical Field

Embodiments of the present disclosure relate to the field of genetic resource management, and more particularly, to an early warning method, apparatus, device, and computer storage medium for processing of genetic resources.

Background

Human genetic resources are not only the fundamental material for developing human genome biodiversity, understanding the origin and evolution of human, but also the material basis for the study of human genetic diseases and many serious diseases. Due to the importance of human genetic resources, various countries have relevant regulations for the handling of genetic resources in our country.

In general, scientific research institutions, higher schools, medical institutions or enterprises apply for cooperative scientific research using genetic resources (e.g., human genetic resources), and submit declaration documents to genetic resource management institutions (e.g., the human genetic resource management office of the department of scientific technology, china), and only after approval, relevant genetic resource research can be performed. Such approval would define the scope of approved genetic resource processing, e.g., the number of subjects (e.g., patients) allowed to be screened or the number of samples allowed to be collected from subjects, etc. Once the processing of genetic resources by the scientific research institution, higher school, medical institution or enterprise is beyond the approved scope, there may be a risk of violation.

Disclosure of Invention

Embodiments of the present disclosure provide an early warning scheme for the processing of genetic resources.

According to a first aspect of the present disclosure, a pre-warning method for processing of genetic resources is presented. The method comprises the following steps: determining constraint information for processing of the genetic resource based on approval information associated with the genetic resource, the processing comprising collecting a sample of the genetic resource from the subject; determining a target early warning condition to be satisfied from among a plurality of early warning conditions having different levels based on the constraint information and the execution information related to the processing of the genetic resource; and providing an alert corresponding to the level of the target advance warning condition to a user related to the target advance warning condition.

According to a second aspect of the present disclosure, an apparatus for early warning of processing of genetic resources is presented. The device includes: a constraint information determination module configured to determine constraint information for a process of a genetic resource based on approval information associated with the genetic resource, the process comprising acquiring a sample of the genetic resource from a subject; a first condition determination module configured to determine a target advance warning condition to be satisfied from among a plurality of advance warning conditions having different levels based on the constraint information and execution information related to processing of the genetic resource; and an alert providing module configured to provide an alert corresponding to the level of the target advance warning condition to a user related to the target advance warning condition.

According to a third aspect of the present disclosure, there is provided an electronic device comprising: a memory and a processor; wherein the memory is for storing one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method according to the first aspect of the disclosure.

According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon one or more computer instructions, wherein the one or more computer instructions are executed by a processor to implement a method according to the first aspect of the present disclosure.

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the disclosure, nor is it intended to be used to limit the scope of the disclosure.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be apparent from the following more particular descriptions of exemplary embodiments of the disclosure as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the disclosure.

FIG. 1 illustrates a schematic diagram of an example environment in which embodiments of the present disclosure can be implemented;

fig. 2 illustrates a flow chart of a process for early warning of processing of genetic resources in accordance with an embodiment of the present disclosure;

FIG. 3 illustrates a flow chart of an example process of determining execution information in accordance with an embodiment of the present disclosure;

fig. 4 illustrates a flowchart of an example process of determining a target pre-warning condition, in accordance with some embodiments of the present disclosure;

FIG. 5 illustrates a flow diagram of an example process of determining a target advance warning condition according to further embodiments of the present disclosure;

FIG. 6 illustrates a flow diagram of an example process of determining a target advance warning condition in accordance with further embodiments of the present disclosure;

FIG. 7 illustrates a flow diagram of an example process of determining a target advance warning condition in accordance with further embodiments of the present disclosure;

FIG. 8 illustrates a flow diagram of an example process of determining a target advance warning condition in accordance with further embodiments of the present disclosure;

fig. 9 illustrates a schematic block diagram of an apparatus for early warning of processing of genetic resources, in accordance with some embodiments of the present disclosure; and

FIG. 10 illustrates a schematic block diagram of an example device that can be used to implement embodiments of the present disclosure.

Detailed Description

Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.

As discussed above, for a scientific research institution or an enterprise, the processing of genetic resources needs to meet the scope approved by the genetic resource institution. Once the processing of genetic resources by the scientific research institution/enterprise is beyond the approved scope, there may be a risk of violation.

In conventional approaches, it may be necessary to manually track the processing of different types of genetic resources and determine if there is a risk based on human experience. For example, it is desirable to manually determine that verification begins with change declarations to ensure that the number of samples collected does not exceed an approved upper limit, thereby avoiding the potential for compliance risks. However, in such risk assessment processes, a large amount of data may need to be aggregated. Furthermore, in a research project, a manager may need to track the processing of a large number of different types of genetic resources simultaneously. Therefore, such a human warning method is difficult to meet practical needs.

According to an embodiment of the present disclosure, an early warning scheme for the processing of genetic resources is provided. In this approach, first, constraint information for processing of genetic resources can be determined based on approval information associated with the genetic resources, where the processing of genetic resources includes collecting a sample of genetic resources from a subject. Then, a target advance warning condition to be satisfied may be determined from a plurality of advance warning conditions having different levels based on the constraint information and the execution information related to the processing of the genetic resource, and a warning corresponding to the level of the target advance warning condition may be further provided to the user related to the target advance warning condition. In this way, the embodiment of the present disclosure can automatically track the execution condition of the processing of the genetic resource, and automatically and timely warn the corresponding user according to the early warning conditions of different levels, thereby avoiding the occurrence of the violation.

For convenience of description, the application is described hereinafter with human genetic resources as an example. It is to be understood that processing with respect to other types of genetic resources (e.g., animal genetic resources or plant genetic resources) may also need to be managed or constrained according to different regulations regarding genetic resources for different regions.

Referring initially to FIG. 1, a schematic diagram of an environment 100 is schematically illustrated in which exemplary implementations according to the present disclosure may be used. As shown in FIG. 1, environment 100 includes a computing device 130. The computing device 130 may obtain the approval information 110. In some implementations, approval information 110 may include, for example, a notification obtained from a genetic resource authority approving execution of a project associated with the genetic resource.

Alternatively, approval information 110 may also be generated based on an approval notification (also referred to as a "notice of approval") issued by a genetic resource authority, for example. For example, the approval information 110 may be generated by the computing device 130 or another computing device text-recognizing the scanned approval notification. Alternatively, the approval information 110 may include, for example, one or more items of content included in the declaration document for which the approval notice is directed.

The approval information 110 includes, for example, content regarding at least one of: the time the project was approved, the expiration date of the project, the number of patients allowed to be screened in the project, the number of patients allowed to be randomly grouped in the project for entry into a clinical trial, the total number of specific genetic resource samples allowed to be collected in the project, etc., and the time the genetic resource samples were allowed to be stored, etc.

In some implementations, the computing device 130 can determine constraint information for processing of the genetic resource based on the approval information 110. In some implementations, such constraint information may include a number constraint for objects or samples associated with the treatment, e.g., an upper limit on the number of patients allowed to be screened or the number of genetic samples allowed to be collected, etc.

In still other implementations, such constraint information may include temporal constraints of particular behaviors associated with the processing of the genetic resource, e.g., an upper bound time period for which the sample is allowed to be saved, or a validity time period for an approval notification (lot notification), etc.

Further, as shown in FIG. 1, the computing device 130 may also determine or obtain the execution information 120, for example. It should be understood that although the execution information 120 is shown in fig. 1 as a separate block outside of the computing device 130, the execution information 120 may also be internal data generated by the computing device 130. Examples of the execution information 120 according to various embodiments of the present disclosure will be described in detail below, and will not be described in detail here.

According to implementations of the present disclosure, computing device 130 may generate alert 140 (or alert 150) based on constraint information determined based on approval information 110 and execution information 120. As shown in fig. 1, the computing device 130 may generate different alerts when the constraint information and the execution information 120 satisfy different pre-alert conditions.

In addition, computing device 130 may also provide different alerts to different users or groups of users. As shown in fig. 1, for example, when certain pre-warning conditions are met, the computing device 130 may generate an alert 140 and provide it to a user 145. When another pre-warning condition is satisfied, the computing device 130 may generate and provide another alert 150 to a different user 155 than the user 145.

In this manner, the computing device 130 can automatically provide alerts to the user related to the processing of the genetic resource, thereby enabling automatic forewarning. A process of the early warning for the processing of genetic resources according to an embodiment of the present disclosure will be described below with reference to fig. 2 to 8. Fig. 2 illustrates a flow diagram of a process 200 for early warning of processing of genetic resources, in accordance with some embodiments of the present disclosure. Process 200 may be implemented, for example, by computing device 130 shown in FIG. 1, or by any other suitable electronic device.

As shown in fig. 2, at block 202, the computing device 130 determines constraint information for processing of the genetic resource based on the approval information 110 associated with the genetic resource, wherein processing includes collecting a sample of the genetic resource from the subject.

In some implementations, as discussed with reference to fig. 1, the computing device 130 may obtain various constraint information related to the processing of the genetic resource from the approval information.

In some implementations, the constraint information may include number constraint information associated with the genetic resource samples. For example, the number constraint information may indicate that the total number of genetic resource samples allowed to be collected is 1000.

In some implementations, the constraint information may include temporal constraint information associated with the acquisition of the genetic resource sample. For example, the time constraint information may indicate that the time allowed for collecting the genetic resource sample is from 11/month 1/2020 to 10/month 31/2025.

In some implementations, the constraint information may include temporal constraint information associated with preservation of the genetic resource sample. For example, the time constraint information may indicate that a particular genetic resource sample is allowed to be stored for a maximum of 1 year.

In some implementations, the constraint information may include number constraint information associated with the filter object. For example, the number constraint information may indicate that the maximum number of objects (e.g., patients) allowed to be screened is 1000 persons.

In some implementations, the constraint information may include number constraint information associated with the randomly grouped objects. For example, the number constraint information may indicate that the maximum number of subjects (e.g., patients) that pass the screening and are randomly grouped into a clinical trial is 800.

At block 204, the computing device 130 determines a target forewarning condition to be met from a plurality of forewarning conditions having different levels based on the constraint information and the execution information 120 related to the processing of the genetic resource.

Different implementations of block 204 will be discussed below for different types of constraint information.

Early warning of sample number

For examples where the constraint information includes number constraint information associated with genetic resource samples, the execution information 120 may indicate the number of genetic resource samples that have been collected. In some implementations, the computing device 130 may obtain the number of genetic resource samples that have been collected directly from a sample collection facility (e.g., a laboratory) and determine it as the execution information 120. Alternatively, the computing device 130 may determine the performance information 120 from interview-related data and collection-related information populated by the sample collection facility in other systems.

In other implementations, some sample collection institutions (e.g., hospitals) may not be able to accurately provide the number of specific genetic resource samples collected in real-time, but rather may be able to provide information and the number of subjects (e.g., patients) being asked for. Thus, the computing device 130 may need to predict the number of genetic resource samples that have been collected. Fig. 3 shows a flowchart of an example process 300 of determining execution information according to an embodiment of the present disclosure.

As shown in fig. 3, at block 302, computing device 130 obtains information from a sample acquisition facility about objects that have acquired a sample of genetic resource. As discussed above, some hospitals may not be able to provide the number of specific genetic resource samples that have been collected in real-time, but are able to provide information on the completion of study-related visits by subjects.

In some implementations, the computing device 130 may, for example, periodically receive information from the sample collection facility about objects that completed a study-related visit within a predetermined time period (e.g., within a day) to the facility. It should be understood that the subject herein refers to a patient from whom a sample of genetic resources is allowed to be taken based on approval notice from a genetic resource regulatory agency.

At block 304, the computing device 130 may determine a predicted number of genetic resource samples that have been acquired based on the acquired information of the subject and the acquisition plan associated with the genetic resource samples.

In some implementations, generally, the sample acquisition mechanism should perform sample acquisition on the subject according to an acquisition plan associated with the genetic resource sample. For example, according to the collection plan, a patient should be collected 5 serum samples on the first completion visit and 1 urine sample on the second completion visit.

Further, the computing device 130 may determine the acquisition phase the subject is in based on information provided by the acquisition mechanism, for example. For example, the computing device 130 may determine that patient a is the first completion visit and patient B is the second completion visit.

Accordingly, the computing device 130 may determine the number of samples that each subject should be acquired from the acquired information of the subject and the corresponding acquisition plan, thereby determining the predicted number of genetic resource samples that have been acquired. For example, when the computing device 130 determines that the sample collection facility collected genetic samples of 100 patients who completed the visit for the first time and collected genetic samples of 50 patients who completed the visit for the second time on the same day, the computing device 130 may determine that the sample collection facility collected 500 serum samples and 50 urine samples on the same day.

In some implementations, the computing device 130 may also adjust the number of predictions calculated above upward to avoid failing to provide timely warning due to additional collection activities, in view of the extra collection activities that may be generated by the sample collection facility outside of the collection plan in the sample collection facility. Continuing with the previous example, computing device 130 may adjust the calculated predicted number up by 10% as the final predicted number, e.g., computing device 130 may determine that the predicted number of serum samples is 550 and the predicted number of urine samples is 55.

At block 306, the computing device 130 may determine execution information 120 related to the processing of the genetic resource based on the predicted number. In some implementations, the predicted number determined by the computing device 130 may indicate the total number of samples that have been collected since the approval time, which the computing device 130 may determine as the execution information 120.

In other implementations, the predicted number determined by the computing device 130 may indicate the number of samples that have been collected within a predetermined period of time in the past, and the computing device 130 may accumulate the predicted number to a previously determined number of samples that have been collected to determine the total number of samples that have been collected since the approval time. Accordingly, the execution information 120 is the total number of samples that have been collected since the time of approval.

After determining the constraint information and the execution information 120, the computing device 130 may determine a target pre-warning condition based on the constraint information and the execution information 120. The computing device 130 may determine the target pre-alarm condition from a plurality of pre-alarm conditions, for example, based on a ratio of the total number of samples that have been collected since the approval time to an upper limit of samples allowed to be collected. For example, the computing device 130 may determine that the ratio is 90%, and thus determine that the target pre-warning condition "greater than or equal to 90%" is satisfied.

In some implementations, the computing device 130 may also determine the remaining number of samples allowed to be acquired based on, for example, constraint information (e.g., an upper limit of samples allowed to be acquired) and the execution information 120 (e.g., the total number of samples that have been acquired since the approval time).

In some implementations, the computing device 130 may compare the remaining number to pre-alarm conditions having different levels to determine the pre-alarm condition that is satisfied. The warning conditions with different levels may for example indicate different values.

In still other implementations, the computing device 130 may also determine a ratio of the remaining number to an upper limit of allowable samples to be collected and compare the ratio to different levels of pre-alarm conditions to determine the pre-alarm condition that is met.

In some implementations, the computing device 130 may also determine the time required to complete the acquisition of samples based on the remaining number of samples allowed to be acquired. For example, the computing device 130 may determine the length of time still required to complete the acquisition of the remaining number of samples based on the average acquisition speed of the sample acquisition mechanism.

Alternatively, the computing device 130 may also utilize a machine learning method, for example, to build an association between the number of remaining samples and the length of time required to complete the acquisition. For example, the computing device 130 may train the machine learning model based on, for example, the attribute information of the sample acquisition mechanism, the number of remaining samples, and the corresponding actual acquisition time length as training data. Subsequently, the computing device 130 may utilize the trained machine learning model to estimate the length of time required to complete the acquisition of the remaining number of samples.

Subsequently, the computing device 130 may compare the length of time to the remaining length of time for which the sample may be collected to determine a target pre-alarm condition. For example, the computing device 130 may determine that it takes 30 days to collect the remaining number of samples, allowing the remaining length of time to collect samples to be 60 days. Subsequently, the calculation device 130 may calculate, for example, a ratio of 0.5 between them, and determine that the target warning condition "ratio less than 0.6" is satisfied. Accordingly, the computing device 130 may provide a corresponding alert to indicate that the number of days remaining may not be sufficient to complete the collection of the remaining number of samples.

In still other implementations, the computing device 130 may also determine the pre-warning condition by comparing the time required to collect the remaining samples to the time required to complete the change declaration. Illustratively, the computing device 130 may determine that a certain number of additional sample acquisitions have occurred due to unexpected circumstances of the subject, such that a change declaration should be completed to the genetic resource authority, otherwise the sample acquisition activity should be terminated when an approval limit is reached. Therefore, in order to ensure smooth collection of the calculation samples, the scientific research institution should submit a change declaration request to the genetic resource management institution before a certain time.

For example, the computing device 130 may determine the first time to reach the upper limit of the number of currently approved sample acquisitions, e.g., 3 months, based on the acquisition speed based on 100 samples already acquired exceeding 20% of the acquisition sample plan. On the other hand, the computing device 130 may also determine a second time, e.g., 2 months, required to complete the change declaration. For example, the computing device 130 may determine a second time required to complete the change declaration based on a historical time to complete declaring the change.

Additionally, the computing device 130 may determine a target early warning condition from a plurality of early warning conditions based on the first time and the second time. In some implementations, the computing device 130 may determine the target pre-warning condition from a plurality of pre-warning conditions based on a difference between the first time and the second time. For example, the computing device 130 may compare the difference (e.g., 1 month) to a predetermined plurality of pre-alert conditions to determine a target pre-alert condition (e.g., less than 45 days).

In some implementations, there may be a set of pre-warning conditions in the plurality of pre-warning conditions that are simultaneously satisfied. The computing device 130 may further determine a target pre-warning condition from the set of pre-warning conditions. The process 400 of determining the target advance warning condition will be described below with reference to fig. 4.

At block 402, the computing device 130 may determine a set of pre-alert conditions from a plurality of pre-alert conditions that are satisfied. Taking the remaining number of samples allowed to be collected as 200 as an example, the plurality of pre-warning conditions may include, for example, "less than 500", "less than 300", and "less than 100", etc. The computing device 130 may, for example, determine whether the pre-warning conditions "less than 500" and "less than 300" are simultaneously satisfied.

At block 404, the computing device 130 may determine a target early warning condition from a set of early warning conditions, where the target early warning condition has a highest rank among the set of early warning conditions. For example, the plurality of pre-warning conditions "less than 500", "less than 300" and "less than 100" have different levels, wherein the pre-warning condition "less than 100" may be assigned, for example, the highest level to indicate the most stringent matching condition. Accordingly, the computing device 130 may determine that the target advance warning condition is "less than 300" from among the satisfied advance warning conditions "less than 500" and "less than 300", for example.

Upon determining that the target pre-warning condition is satisfied, the computing device 130 may provide a corresponding alert. The manner in which the pre-warning is provided will be described in detail below with reference to block 206.

Early warning of approval expiration

In some implementations, the constraint information may include temporal constraint information associated with acquisition of the genetic resource sample, and the execution information 120 may indicate a length of time between a start time at which the genetic resource sample is allowed to be acquired and a current time. Fig. 5 shows a flowchart of an example process 500 of determining a target pre-warning condition according to yet another embodiment of the present disclosure.

As shown in fig. 5, at block 502, computing device 130 may determine a first progress indication associated with collecting the genetic resource sample based on the time constraint information and the execution information. For example, when the time constraint information indicates that the validity period of the approval is 5 years, and the execution information 120 indicates that 3 years have passed since the date of approval initiation, the computing device 130 may determine, for example, that the length of time available for collecting the sample of genetic resources is 2 years, and determine that the length of time is 2 years as the first progress indication.

In other embodiments, the computing device 130 may also be operable to determine a ratio of the length of time that the sample of genetic resources is collected to the validity period of the approval as the first progress indication. Alternatively, the computing device 130 may also use the ratio of the length of time between the start time when the genetic resource sample is allowed to be collected and the current time to the validity period of the approval as the first progress indicator.

In still other embodiments of the present invention, the substrate is,

at block 504, the computing device 130 may determine a target early warning condition that is met from a plurality of early warning conditions based on the first progress indication. Taking the length of time available for collecting a sample of genetic resources as 2 years as an example of a first progress indicator, computing device 130 may determine that the pre-alert conditions of "less than 5 years" and "less than 3 years" are simultaneously satisfied, for example, from a plurality of pre-alert conditions of "less than 5 years", "less than 3 years" and "less than 1 year".

Similar to the process discussed with reference to fig. 3, the computing device 130 may determine the pre-warning condition having the highest level of "less than 3 years" from the pre-warning conditions of "less than 5 years" and "less than 3 years" as the target pre-warning condition.

In still other embodiments, the computing device 130 may also determine the number of samples expected to be collected during the length of time based on the length of time available for collecting the genetic resource samples. Similar to that discussed with respect to the early warning of the number of genetic resource samples, the computing device 130 may also predict the number of samples collected over the length of time based on, for example, the average collection speed of the sample collection mechanism or utilizing a machine learning model. Subsequently, the computing device 130 may also compare the number and the number of samples allowed to be collected to determine a target pre-alarm condition from a plurality of pre-alarm conditions.

Early warning of sample expiration

In some implementations, the constraint information includes temporal constraint information associated with preservation of the genetic resource sample, and the execution information 120 may indicate a length of time that the genetic resource sample has been preserved. Fig. 6 shows a flowchart of an example process 600 of determining a target advance warning condition according to yet another embodiment of the present disclosure.

As shown in fig. 6, at block 602, computing device 130 may determine a second progress indication associated with saving a genetic resource sample based on the time constraint information and the execution information. For example, when the time constraint information indicates that the sample has been stored for a maximum period of 100 days, and the execution information 120 indicates that the sample has been stored for 80 days, then the computing device 130 may determine, for example, that the length of time available to store the sample of genetic resource is 20 days, and determine that the length of time is 20 days as the second progress indication.

In other embodiments, the computing device 130 may also determine a ratio of the length of time available to store the sample of genetic resource to the maximum duration of time the sample is stored as the second progress indication. Alternatively, the computing device 130 may determine a ratio of the length of time that the sample of genetic resource has been stored to the maximum duration of time that the sample has been stored as the second progress indication.

At block 604, the computing device 130 may determine a target advance condition from the plurality of advance condition based on the second progress indication that is met. Taking the length of time for saving the genetic resource sample as 20 days as an example of the second progress indication, the computing device 130 may determine that the early warning conditions "less than 50 days" and "less than 30 days" are simultaneously satisfied, for example, from among a plurality of early warning conditions "less than 50 days", "less than 30 days", and "less than 10 days".

Similar to the process discussed with reference to fig. 3, the computing device 130 may determine the warning condition "less than 30 days" with the highest level from the warning conditions "less than 50 days" and "less than 30 days" as the target warning condition.

Pre-warning of screening objects

In some implementations, the constraint information includes number constraint information associated with the screened objects, and the execution information 120 may indicate the number of objects that have been screened. Fig. 7 shows a flowchart of an example process 700 of determining a target advance warning condition according to yet another embodiment of the present disclosure.

At block 702, the computing device 130 may determine a third progress indication associated with the screening object based on the number constraint information and the execution information. For example, when the number constraint information indicates that the maximum number of objects (e.g., patients) allowed to be filtered is 1000 people and the execution information 120 indicates that the objects already filtered are 800 people, then the computing device 130 may determine, for example, that the remaining number of objects allowed to be filtered is 200 people and determine the remaining number as a third progress indication.

In other embodiments, the computing device 130 may also determine a ratio of the remaining number of objects allowed to be filtered to the maximum number of objects allowed to be filtered as the third progress indication. Alternatively, the computing device 130 may also determine a ratio of the number of objects that have been filtered to the maximum number of objects that are allowed to be filtered as the third progress indication.

At block 704, the computing device 130 may determine a target advance condition from a plurality of advance conditions that is met based on the third advance indication. Taking the remaining number of objects allowed for screening as an example of the third degree indication, the computing device 130 may determine that the alert conditions "less than 500 people" and "less than 300 people" are simultaneously satisfied from among the plurality of alert conditions "less than 500 people", "less than 300 people", and "less than 100 people", for example.

Similar to the process discussed with reference to fig. 3, the computing device 130 may determine the warning condition "less than 300 persons" with the highest level from the warning conditions "less than 500 persons" and "less than 300 persons" as the target warning condition.

Pre-warning of randomly grouped objects

In some implementations, the constraint information includes number constraint information associated with randomly grouped objects, and the execution information 120 may indicate the number of objects that have been randomly grouped. Randomly grouping subjects refers to the process of excluding non-qualified subjects from the screened subjects and randomly grouping the remaining subjects into a clinical trial. Fig. 8 shows a flowchart of an example process 800 of determining a target pre-warning condition according to yet another embodiment of the present disclosure.

At block 802, the computing device 130 may determine a fourth progress indication associated with the random grouping object based on the number constraint information and the execution information. For example, when the number constraint information indicates that the maximum number of objects (e.g., patients) that are allowed to be randomly grouped is 800 people, and the execution information 120 indicates that the objects that have been randomly grouped are 600 people, the computing device 130 may determine, for example, that the remaining number of objects that are allowed to be randomly grouped is 200 people, and determine the remaining number as the fourth progress indication.

In still other embodiments, the computing device 130 may also determine a ratio of the remaining number of objects allowed to be randomly grouped to the maximum number of objects allowed to be randomly grouped as the fourth progress indication. Alternatively, the computing device 130 may also determine a ratio of the number of objects that have been randomly grouped to the maximum number of objects that are allowed to be randomly grouped as the fourth progress indication.

At block 804, the computing device 140 may determine a target early warning condition from a plurality of early warning conditions that is met based on the fourth progress indication. Taking the remaining number of objects allowed to be randomly grouped as 200 persons as an example of the fourth progress indication, the computing device 130 may determine that the warning conditions "less than 500 persons" and "less than 300 persons" are simultaneously satisfied from among the plurality of warning conditions "less than 500 persons", "less than 300 persons", and "less than 100 persons", for example.

The determination process regarding the target pre-warning condition is described above in connection with various embodiments, and it should be understood that the specific numerical values referred to in the above embodiments are only illustrative and not intended to be limiting on the present disclosure.

With continued reference to fig. 2, at block 206, the computing device 130 provides an alert corresponding to the level of the target advance warning condition to the user related to the target advance warning condition.

In some implementations, different users associated with different levels of pre-alarm conditions may have different permissions with respect to processes that manage genetic resources. For example, the computing device 130 may, for example, send an alert to a user with lower administrative privileges when lower level pre-alarm conditions are satisfied. Conversely, when a higher level of pre-warning conditions are met, the computing device 130 may, for example, send a warning to a user with higher administrative privileges.

In addition, the alerts corresponding to different levels may also be different to indicate different severity levels. For example, when a lower level of alert conditions are satisfied, the computing device 130 may present a text-type alert to the corresponding user, such as by pop-up window.

Conversely, when a higher-level pre-alert condition is satisfied, the computing device 130 may, for example, call the corresponding user to announce a voice message. Alternatively, the computing device 130 may utilize the same form of warning when a higher level of warning conditions are met, but may emphasize that the warning has a higher severity by extending the time of the warning, or the like. For example, the computing device 130 may provide a pop-up window that presents a size larger than the pop-up windows provided by other alerts. Additionally or alternatively, the time device 130 may also provide a combination of multiple alerts, such as a pop-up alert, a mail alert, a call alert, and the like, simultaneously, when a higher level of pre-alert conditions are met.

Based on the above-described early warning method for processing of genetic resources, embodiments of the present disclosure can automatically track the execution of the processing of genetic resources and automatically warn the corresponding user in time according to early warning conditions of different levels, thereby avoiding the occurrence of an illegal action.

In some implementations, the constraint information and execution information collected in the above process may also be used for the generation of compliance reports, further reducing the labor and time costs that need to be expended.

Embodiments of the present disclosure also provide corresponding apparatus of the methods and/or processes discussed with reference to fig. 2-8. Fig. 9 illustrates a schematic block diagram of an apparatus 900 for declaration about genetic resources, in accordance with some embodiments of the present disclosure.

As shown in fig. 9, the apparatus 900 may include: a constraint information determination module 910 configured to determine constraint information for a process of the genetic resource based on approval information associated with the genetic resource, the process comprising acquiring a sample of the genetic resource from the subject. The apparatus 900 further comprises a first condition determining module 920 configured to determine a target forewarning condition to be met from a plurality of forewarning conditions having different levels based on the constraint information and the execution information related to the processing of the genetic resource. Further, the apparatus 900 further comprises an alert providing module 930 configured to provide an alert corresponding to the level of the target advance warning condition to a user related to the target advance warning condition.

In some implementations, the constraint information includes number constraint information associated with the genetic resource samples, the apparatus 900 further comprising: a number acquisition module configured to acquire information of an object, from which a genetic resource sample has been acquired, from a sample acquisition mechanism; a number prediction module configured to determine a predicted number of genetic resource samples that have been collected based on the acquired information of the subject and a collection plan associated with the genetic resource samples; and an execution information determination module configured to determine execution information related to processing of the genetic resource based on the predicted number.

In some implementations, the constraint information includes temporal constraint information associated with acquisition of the genetic resource sample, and wherein the first condition determining module 920 includes: a first progress indication determination module configured to determine a first progress indication associated with collecting the genetic resource sample based on the time constraint information and execution information, the execution information indicating a length of time between a start time at which the genetic resource sample is allowed to be collected and a current time; and a second condition determination module configured to determine a target early warning condition that is met from the plurality of early warning conditions based on the first progress indication.

In some implementations, the constraint information includes temporal constraint information associated with preservation of the genetic resource sample, and wherein the first condition determining module 920 includes: a second progress indication determination module configured to determine a second progress indication associated with saving the genetic resource sample based on the time constraint information and execution information, the execution information indicating a length of time the genetic resource sample has been saved; and a third condition determining module configured to determine a target early warning condition that is satisfied from the plurality of early warning conditions based on the second progress indication.

In some implementations, the constraint information includes number constraint information associated with the filter object, and wherein the first condition determining module 920 includes: a third progressive indication determination module configured to determine a third progressive indication associated with the screened objects based on the number constraint information and the execution information, the execution information indicating a number of objects that have been screened; and a fourth condition determining module configured to determine a target advance warning condition that is met from the plurality of advance warning conditions based on the third progress indication.

In some implementations, the constraint information includes number constraint information associated with the randomly grouped objects, and wherein the first condition determining module 920 includes: a fourth progress indication determination module configured to determine a fourth progress indication associated with randomly grouping the objects based on the number constraint information and execution information, the execution information indicating the number of the objects that have been randomly grouped; and a fifth condition determining module configured to determine a target advance warning condition that is satisfied from the plurality of advance warning conditions based on the fourth progress indication.

In some implementations, the first condition determining module 920 includes: a screening module configured to determine a set of pre-warning conditions from a plurality of pre-warning conditions that are satisfied; and a sixth condition determining module configured to determine a target early warning condition from a set of early warning conditions, the target early warning condition having a highest level among the set of early warning conditions.

In some implementations, different users associated with different levels of pre-alarm conditions have different permissions with respect to processes that manage genetic resources.

Fig. 10 illustrates a block diagram of a computing device/server 1000 in which one or more embodiments of the disclosure may be implemented. It should be understood that the computing device/server 1000 illustrated in fig. 10 is merely exemplary and should not constitute any limitation as to the functionality and scope of the embodiments described herein.

As shown in fig. 10, computing device/server 1000 is in the form of a general purpose computing device. The components of computing device/server 1000 may include, but are not limited to, one or more processors or processing units 1010, memory 1020, storage 1030, one or more communication units 1040, one or more input devices 1050, and one or more output devices 1060. The processing unit 1010 may be a real or virtual processor and can perform various processes according to programs stored in the memory 1020. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of computing device/server 1000.

Computing device/server 1000 typically includes a number of computer storage media. Such media may be any available media that is accessible by computing device/server 1000 and includes, but is not limited to, volatile and non-volatile media, removable and non-removable media. Memory 720 may be volatile memory (e.g., registers, cache, Random Access Memory (RAM)), non-volatile memory (e.g., Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory), or some combination thereof. Storage 730 may be a removable or non-removable medium and may include a machine-readable medium, such as a flash drive, a magnetic disk, or any other medium, which may be capable of being used to store information and/or data (e.g., training data for training) and which may be accessed within computing device/server 1000.

Computing device/server 1000 may further include additional removable/non-removable, volatile/nonvolatile storage media. Although not shown in FIG. 10, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to a bus (not shown) by one or more data media interfaces. Memory 1020 may include a computer program product 1025 having one or more program modules configured to perform the various methods or acts of the various embodiments of the present disclosure.

The communication unit 1040 enables communication with other computing devices over a communication medium. Additionally, the functionality of the components of computing device/server 1000 may be implemented in a single computing cluster or multiple computing machines capable of communicating over a communications connection. Thus, computing device/server 1000 may operate in a networked environment using logical connections to one or more other servers, network Personal Computers (PCs), or another network node.

Input device 1050 may be one or more input devices such as a mouse, keyboard, trackball, or the like. Output device 1060 may be one or more output devices such as a display, speakers, printer, or the like. Computing device/server 1000 may also communicate with one or more external devices (not shown), such as storage devices, display devices, etc., as desired through communication unit 1040, with one or more devices that enable a user to interact with computing device/server 1000, or with any device (e.g., network card, modem, etc.) that enables computing device/server 1000 to communicate with one or more other computing devices. Such communication may be performed via input/output (I/O) interfaces (not shown).

According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which one or more computer instructions are stored, wherein the one or more computer instructions are executed by a processor to implement the above-described method.

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products implemented in accordance with the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various implementations of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The foregoing has described implementations of the present disclosure, and the above description is illustrative, not exhaustive, and not limited to the implementations disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The terminology used herein was chosen in order to best explain the principles of implementations, the practical application, or improvements to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the implementations disclosed herein.

Claims

1. A forewarning method for processing of genetic resources, comprising:

determining constraint information for a process of a genetic resource based on approval information associated with the genetic resource, the process comprising collecting a sample of genetic resource from a subject;

determining a target advance warning condition to be satisfied from among a plurality of advance warning conditions having different levels based on the constraint information and execution information related to processing of the genetic resource; and

providing an alert corresponding to the level of the target advance warning condition to a user related to the target advance warning condition.

2. The method of claim 1, wherein the constraint information comprises number constraint information associated with the genetic resource sample, the method further comprising:

obtaining information of an object from which a genetic resource sample has been collected from a sample collection facility;

determining a predicted number of genetic resource samples that have been collected based on the acquired information of the subject and a collection plan associated with the genetic resource samples; and

determining the execution information related to the processing of the genetic resource based on the predicted number.

3. The method of claim 1, wherein the constraint information comprises temporal constraint information associated with acquisition of the genetic resource sample, and wherein determining the target forewarning condition comprises:

determining a first progress indication associated with collecting the genetic resource sample based on the time constraint information and the execution information, the execution information indicating a length of time between a start time when the genetic resource sample is allowed to be collected and a current time; and

determining the target pre-warning condition from the plurality of pre-warning conditions to be met based on the first progress indication.

4. The method of claim 1, wherein the constraint information comprises temporal constraint information associated with preservation of the genetic resource sample, and wherein determining the target pre-alarm condition comprises:

determining a second progress indication associated with saving the genetic resource sample based on the time constraint information and the execution information, the execution information indicating a length of time the genetic resource sample has been saved; and

determining the target pre-warning condition from the plurality of pre-warning conditions to be met based on the second progress indication.

5. The method of claim 1, wherein the constraint information comprises number constraint information associated with screening the subject, and wherein determining the target advance warning condition comprises:

determining a third progress indication associated with screening objects based on the number constraint information and the execution information, the execution information indicating a number of objects that have been screened; and

determining the target early warning condition to be met from the plurality of early warning conditions based on a third progress indication.

6. The method of claim 1, wherein the constraint information comprises a number constraint information associated with randomly grouping the objects, and wherein determining the target advance warning condition comprises:

determining a fourth progress indication associated with randomly grouping objects based on the number constraint information and the execution information, the execution information indicating a number of objects that have been randomly grouped; and

determining the target early warning condition to be met from the plurality of early warning conditions based on a fourth progress indication.

7. The method of claim 1, wherein determining that a target advance warning condition is met comprises:

determining a set of pre-warning conditions from the plurality of pre-warning conditions that are met; and

determining the target pre-warning condition from the set of pre-warning conditions, the target pre-warning condition having a highest rank among the set of pre-warning conditions.

8. The method of claim 1, wherein different users associated with different levels of pre-alarm conditions have different rights with respect to managing the processing of the genetic resource.

9. An early warning apparatus for processing of genetic resources, comprising:

a constraint information determination module configured to determine constraint information for a process of a genetic resource based on approval information associated with the genetic resource, the process comprising acquiring a sample of the genetic resource from a subject;

a first condition determination module configured to determine a target advance warning condition to be satisfied from among a plurality of advance warning conditions having different levels based on the constraint information and execution information related to processing of the genetic resource; and

an alert providing module configured to provide an alert corresponding to a level of the target pre-warning condition to a user related to the target pre-warning condition.

10. The apparatus of claim 9, wherein the constraint information comprises a number constraint information associated with the genetic resource sample, the apparatus further comprising:

a number acquisition module configured to acquire information of an object, from which a genetic resource sample has been acquired, from a sample acquisition mechanism;

a number prediction module configured to determine a predicted number of genetic resource samples that have been collected based on the acquired information of the subject and a collection plan associated with the genetic resource samples; and

an execution information determination module configured to determine the execution information related to the processing of the genetic resource based on the predicted number.

11. The apparatus of claim 9, wherein the constraint information comprises temporal constraint information associated with acquisition of the genetic resource sample, and wherein the first condition determining module comprises:

a first progress indication determination module configured to determine a first progress indication associated with collecting the genetic resource sample based on the time constraint information and the execution information, the execution information indicating a length of time between a start time at which the genetic resource sample is allowed to be collected and a current time; and

a second condition determination module configured to determine the target pre-warning condition from the plurality of pre-warning conditions to be met based on the first progress indication.

12. The apparatus of claim 9, wherein the constraint information comprises temporal constraint information associated with preservation of the genetic resource sample, and wherein the first condition determining module comprises:

a second progress indication determination module configured to determine a second progress indication associated with saving the genetic resource sample based on the time constraint information and the execution information, the execution information indicating a length of time the genetic resource sample has been saved; and

a third condition determination module configured to determine the target pre-warning condition from the plurality of pre-warning conditions to be met based on the second progress indication.

13. The apparatus of claim 9, wherein the constraint information comprises a number constraint information associated with filtering the object, and wherein the first condition determination module comprises:

a third progressive indication determination module configured to determine a third progressive indication associated with screening the objects based on the number constraint information and the execution information, the execution information indicating a number of objects that have been screened; and

a fourth condition determination module configured to determine the target advance condition from the plurality of advance conditions to be met based on a third progress indication.

14. The apparatus of claim 9, wherein the constraint information comprises a number constraint information associated with randomly grouping the objects, and wherein the first condition determination module comprises:

a fourth progress indication determination module configured to determine a fourth progress indication associated with randomly grouping objects based on the number constraint information and the execution information, the execution information indicating the number of objects that have been randomly grouped; and

a fifth condition determination module configured to determine the target advance warning condition that is met from the plurality of advance warning conditions based on a fourth progress indication.

15. The apparatus of claim 9, wherein the first condition determining module comprises:

a screening module configured to determine a set of pre-warning conditions from the plurality of pre-warning conditions that are met; and

a sixth condition determining module configured to determine the target advance warning condition from the set of advance warning conditions, the target advance warning condition having a highest level in the set of advance warning conditions.

16. The apparatus of claim 9, wherein different users associated with different levels of pre-alarm conditions have different rights with respect to managing the processing of the genetic resource.

17. An electronic device, comprising:

a memory and a processor;

wherein the memory is to store one or more computer instructions, wherein the one or more computer instructions are to be executed by the processor to implement the method of any one of claims 1 to 8.

18. A computer readable storage medium having one or more computer instructions stored thereon, wherein the one or more computer instructions are executed by a processor to implement the method of any one of claims 1 to 8.