An integrated information system for clinical trial archives and a data processing method thereof

Abstract

The application relates to the technical field of computers and discloses an integrated information system for clinical trial archives and a data processing method thereof, the method comprising the following steps: constructing a standardized data model covering the whole process; dynamically structuring and inputting multi-source data and checking the integrity; performing multi-dimensional semantic correlation analysis based on a Bayesian network and a correlation coefficient; performing block chain version tracing and difference comparison; performing four-dimensional access control of roles-sensitivity-operations-context; and performing task-driven collaborative sharing and audit logging. The system comprises six modules, namely, unified modeling, data input, semantic analysis, version tracing, access control and collaborative audit. The application solves the problems of data islands, semantic fragmentation, poor dynamic adaptability and weak security protection in the prior art, realizes intelligent management, semantic interconnection, safe controllability and efficient collaboration of the whole life cycle of clinical trial archives, and significantly improves data quality, audit compliance and multi-role collaboration efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a clinical trial archive-oriented integrated information system and a data processing method thereof. BACKGROUND

[0002] With the rapid development of modern medical research and the increasingly stringent regulatory requirements, clinical trials have become the core link of new drug research and development and medical technology evaluation. The clinical trial process involves a large number of heterogeneous data sources, including electronic data capture systems, laboratory information systems, medical image archiving systems, electronic medical record systems, and drug management systems. The massive archive data generated by these systems have the characteristics of diversity, complexity, and timeliness. At the same time, the regulatory compliance requirements of clinical trials require data to have complete traceability, strict access control, and accurate audit records. Therefore, it is of great significance to build a clinical trial archive management system that can effectively integrate multi-source heterogeneous data, ensure data quality and security, and support collaborative operation, in order to improve the efficiency of clinical trials, protect data integrity, and meet regulatory requirements.

[0003] In the prior art, each data source system lacks a unified data model and standardized architecture, resulting in serious data island phenomenon, difficult data integration, and problems such as inconsistent formats and semantic ambiguity. Secondly, the traditional data import process lacks a perfect data quality control mechanism, and the key links such as abnormal value identification, integrity verification, and time sequence logic verification are not handled finely enough, affecting data reliability. Thirdly, the version traceability function of existing systems is relatively simple and cannot provide fine-grained modification history records and difference comparison capabilities, making it difficult to meet the strict requirements of regulatory audits. In addition, the permission control mechanism is usually based on simple role allocation and lacks comprehensive consideration of data sensitivity, operation type, and access context, making it impossible to achieve fine-grained security control. Finally, there is a lack of effective conflict detection and merging mechanism during multi-user collaborative editing, and there is a lack of deep semantic analysis capability for archive data, making it impossible to fully exploit the association relationships and potential value of data.

[0004] Therefore, the present application provides a clinical trial archive-oriented integrated information system and a data processing method thereof, which builds a standardized data model, realizes intelligent data import and quality control, establishes a perfect version traceability mechanism, designs a multi-dimensional permission control strategy, provides collaborative editing and auditing functions, and builds a clinical trial archive knowledge network combined with semantic analysis technology, thereby fully solving the deficiencies of the prior art and providing strong support for the standardized management of clinical trials and data value mining. SUMMARY

[0005] The application aims to provide an integrated information system for clinical trial archives and a data processing method thereof, so as to solve the problems of data islands, semantic fragmentation, poor dynamic adaptability and weak security protection in the prior art in the management of clinical trial archives.

[0006] To solve the above technical problems, the application provides the following technical solutions.

[0007] An integrated information system for clinical trial archives comprises:

[0008] A clinical modeling module is configured to construct a standardized data model for the whole process of clinical trials, define data entities, attribute fields, data types, value constraints, correlation relationships and lifecycle state identifiers, form a unified metadata architecture, define a set of core data entities covering the whole process of clinical trials, including a subject basic information entity, an informed consent form entity, a screening period data entity, an enrollment data entity, a visit record entity, a laboratory test result entity, an adverse event report entity, a combined medication record entity, a researcher evaluation entity, a monitoring report entity, a data cleaning log entity, a statistical analysis plan entity and a final research report entity.

[0009] For each type of data entity, a set of attribute fields, data types, value constraints and foreign key correlation relationships are defined, and a lifecycle state identifier is added to each data entity, the lifecycle state identifier including six states of draft, to-be-audited, confirmed, locked, archived and destroyed, and the legal migration path and trigger condition between the states are defined.

[0010] A data import module is configured to receive raw archive data from an electronic data collection system, a laboratory information system, a medical image archiving system, an electronic medical record system, a drug management system and a manual input terminal, perform field mapping, format conversion, missing value marking, abnormal value identification, data integrity checking and structured packaging according to the standardized data model, generate standardized archive data units and write them into a central archive storage.

[0011] A semantic analysis module is configured to perform deep analysis and correlation mining of the standardized archive data units at the semantic level, construct a subject longitudinal time axis, a causal correlation graph of visit events and laboratory indicators, a correlation network of adverse events and combined medications, a semantic consistency matrix of researcher evaluation and monitoring conclusions, a mapping relationship table of statistical analysis plans and data results, and form a multi-dimensional clinical trial archive knowledge network.

[0012] A process trace module is configured to establish a version evolution chain for each data unit, record a version identifier, a timestamp, an operator identity, a data snapshot before and after modification, a modification reason, and store them in an independent version log database, support multi-dimensional version history backtracking and difference comparison;

[0013] An access control module is configured to implement a four-dimensional permission control mechanism based on roles, data sensitivity, operation types, and access context, dynamically generate access control decisions through a strategy engine, and ensure that high-sensitivity data is legally accessed within the authorized range;

[0014] A shared audit module is configured to provide a task-driven archive sharing workflow engine, a real-time collaborative editing conflict detection and merging mechanism, a complete operation audit log recording and multi-dimensional query function, and support multi-role collaborative operation and full-process behavior traceability.

[0015] A data processing method for clinical trial archives, comprising:

[0016] Step S1, a standardized data model for the whole process of clinical trials is constructed, data entities, attribute fields, data types, value constraints, association relationships, and lifecycle state identifiers are defined, and a unified metadata architecture is formed;

[0017] The data entities are defined, including: subject basic information entity, informed consent form entity, screening period data entity, enrollment data entity, visit record entity, laboratory test result entity, adverse event report entity, combined medication record entity, researcher evaluation entity, monitoring report entity, data cleaning log entity, statistical analysis plan entity, and final research report entity;

[0018] For each type of data entity, a set of attribute fields, data types, value constraints, and foreign key association relationships are defined. A lifecycle state identifier is added to each data entity, which includes six states: draft, pending review, confirmed, locked, archived, and destroyed, and the legal migration path and trigger conditions between states are defined.

[0019] Step S2, raw archive data from electronic data collection systems, laboratory information systems, medical image archiving systems, electronic medical record systems, drug management systems, and manual input terminals is received, field mapping, format conversion, missing value marking, abnormal value identification, data integrity verification, and structured packaging are performed according to the standardized data model, standardized archive data units are generated and written into the central archive repository;

[0020] A multi-source data access interface is established, and data transmission protocols, authentication methods, and data format templates are configured;

[0021] performing field mapping operation to map original data field names to corresponding attribute fields in the standardized data model;

[0022] performing format conversion operation to convert date format, value format, and encoding format in the original data into system internal standard format;

[0023] performing missing value marking operation to insert special marker "MISSING" and record missing reason code for fields without provided values;

[0024] performing outlier identification operation to perform preset outlier detection rules for numerical value fields, and insert special marker "OUTLIER" and record outlier type code, expected value range, and actual value if an outlier is detected;

[0025] performing data integrity check operation, including six types of rules of field non-empty check, value range check, enumeration value legality check, foreign key association consistency check, time sequence logic check, and signature integrity check;

[0026] generating structured error report and preventing data unit from being written if any check fails, and encapsulating as standardized archive data unit and writing into central archive repository by transaction mechanism only when all checks pass.

[0027] time sequence logic check in the data integrity check includes checking that the visit date cannot be earlier than the screening date, and the laboratory test date cannot be more than seven days later than the corresponding visit date; and signature integrity check includes checking whether the data unit requiring digital signature contains valid and tamper-proof digital signature.

[0028] In step S3, the standardized archive data unit is subjected to deep analysis and correlation mining at the semantic level to construct a subject longitudinal time axis, a causal correlation graph of visit events and laboratory indicators, a relevance network of adverse events and combined medications, a semantic consistency matrix of researcher evaluation and monitoring conclusions, a mapping relationship table of statistical analysis plan and data results, and a multi-dimensional clinical trial archive knowledge network is formed;

[0029] a longitudinal time axis is constructed with subject number as the unique identifier, and all visit records, laboratory test records, adverse event records, combined medication records, and researcher evaluation records of the subject are arranged in chronological order;

[0030] a Bayesian network inference algorithm is used to construct a causal correlation graph of visit events and laboratory indicators, with visit events as nodes, laboratory indicator changes as observation variables, and potential pathological mechanisms as hidden variables, and network parameters are learned by maximum likelihood estimation;

[0031] A correlation network between adverse events and concomitant medications is constructed by fusing Pearson correlation coefficient and Spearman rank correlation coefficient, and a comprehensive correlation score between each pair of adverse events and concomitant medications is calculated, and significant correlation pairs with a score higher than a preset threshold are retained;

[0032] A semantic consistency matrix between investigator assessments and monitoring conclusions is constructed by using a text matching algorithm based on cosine similarity of word vectors, and the text is segmented, word vectors are averaged and pooled, cosine similarity is calculated, and inconsistent items with a similarity lower than a preset threshold are marked;

[0033] A mapping table between statistical analysis plans and data results is constructed to record the actual values, missing rates, distribution characteristics, and deviation degrees from planned expectations of each analysis variable in the data results.

[0034] The actual values, missing rates, distribution characteristics, and deviation degrees from planned expectations of each analysis variable in the data results are integrated to form a multi-dimensional clinical trial archive knowledge network supporting intelligent retrieval, trend prediction, and risk warning.

[0035] Step S4, a version evolution chain is established for each data unit, and the version identifier, timestamp, operator identity, data snapshots before and after modification, and modification reason explanation of each modification operation are recorded and stored in an independent version log database, supporting multi-dimensional version history backtracking and difference comparison.

[0036] A version identifier using a hierarchical coding structure is assigned to the data unit, the first layer is the project number, the second layer is the subject number, the third layer is the data entity type code, the fourth layer is the data entity instance number, the fifth layer is the version sequence number, and the sixth layer is the timestamp hash value.

[0037] A version record is generated, including the new version identifier, operation timestamp, operator identity, operation type code, modification reason text, data snapshot before modification, and data snapshot after modification.

[0038] The version record is stored in an independent version log database using a block chain-like chain storage structure, and each version record includes the previous version hash value, current version data hash value, and operation metadata hash value.

[0039] Version history backtracking is supported according to subject number, visit number, data type, and operation time interval, field-level difference comparison and highlighting between any two version records are supported, and version rollback operation is supported and a new version record is generated.

[0040] Step S5, a four-dimensional permission control mechanism based on role, data sensitivity, operation type, and access context is implemented, and access control decisions are dynamically generated by a strategy engine to ensure that high-sensitive data is legally accessed within the authorized range.

[0041] Define a set of clinical trial participant roles, including sponsor project manager, research center investigator, independent data monitoring committee member, statistical analyst, auditor, ethics committee member;

[0042] Define data sensitivity levels, including public, restricted, confidential, top secret, public data includes project number, research center number, visit schedule, restricted data includes subject number, visit date, laboratory test item name, confidential data includes laboratory test specific value, adverse event description text, combined medication name and dose, top secret data includes subject real identity information, informed consent form scan, gene sequencing raw data;

[0043] Define operation type permissions, including read-only, edit, export, delete, version rollback;

[0044] Define access context constraints, including limited to specified IP address segment, limited to specified device fingerprint, limited to specified time period access, device fingerprint is generated by combining six parameters: device model, operating system version, browser type, screen resolution, font list, time zone setting;

[0045] Through the policy engine, dynamically combine the four-dimensional parameters of role, data sensitivity, operation type, and access context, query the preset policy rule library to generate access control decisions, and only when all dimension constraints are met, allow access operation execution.

[0046] Step S6, provide task-driven archive sharing workflow engine, real-time collaborative editing conflict detection and merging mechanism, complete operation audit log recording and multi-dimensional query function, support multi-role collaborative operation and full-process behavior traceability.

[0047] Define sharing trigger conditions and sharing target roles, automatically generate sharing tasks and grant preset access permissions to target roles when conditions are met;

[0048] Use operation conversion algorithm to solve multi-user concurrent editing conflicts, decompose editing operations into atomic operation sequences and exchange sort to ensure final data state consistency;

[0049] Record the performer, execution time, operation object, operation content, and operation result status of each data access, modification, export, sharing, and deletion operation, operation object is recorded as data unit version identifier, operation content is recorded as specific modified field and value;

[0050] Store audit logs in write-once read-many storage media and set read-only access permissions, support audit query and report generation by time range, operation type, user identity, and data entity dimensions.

[0051] Compared with the prior art, the present application has the beneficial effects that:

[0052] The present application solves the integration difficulty problem caused by data structure heterogeneity of the existing system by constructing a standardized data model covering the whole process of clinical trials, realizes seamless fusion and unified management of multi-source data; through the dynamic structured data intake mechanism and integrity checking rules, the high-quality conversion of original data to standardized archives is ensured, and data pollution and semantic distortion are avoided.

[0053] The present application overcomes the functional limitations of the existing system which only provides static storage and simple retrieval by using a multi-dimensional semantic correlation analysis engine to deeply mine the potential correlation between archive data and support intelligent risk warning and decision assistance; the present application realizes complete recording and non-tamperable of data modification history through the whole process version tracing mechanism and block chain type log storage, which meets the stringent requirements of clinical trial data audit and supervision.

[0054] The present application realizes precise permission management of high-sensitive data through a four-dimensional fine-grained access control model, effectively preventing data leakage and unauthorized access risks; the present application improves the multi-role collaboration efficiency through the collaborative sharing workflow and real-time conflict resolution mechanism, and avoids data version confusion and operation conflicts.

[0055] The overall system architecture of the present application has high scalability and adaptability, and can flexibly cope with different scales and types of clinical trial project requirements, providing a full life cycle, full element coverage and full chain controllable technical solution for clinical trial archive management. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 is a kind of integrated information system architecture schematic diagram for clinical trial archives proposed by the present application;

[0057] Figure 2 is a kind of data processing method flow schematic diagram for clinical trial archives proposed by the present application. DETAILED DESCRIPTION

[0058] The features and exemplary embodiments of various aspects of the present application will be described in detail below, in order to make the purpose, technical scheme and advantages of the present application more clear and clear, the present application will be further described in detail below in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are intended to explain the present application, not to limit the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0059] It is to be understood that the terminology used herein such as first and second, and the like, is only used to distinguish one entity or action from another entity or action, and does not necessarily require or imply any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0060] In the embodiments of the present application, the same reference numerals are used to represent the same components, and for the sake of brevity, detailed description of the same components is omitted in different embodiments. It should be understood that the thickness, length, width, and other dimensions of various components in the embodiments of the present application shown in the drawings, and the overall thickness, length, width, and other dimensions of the integrated device are only exemplary and should not constitute any limitation on the present application.

[0061] An integrated information system for clinical trial archives comprises:

[0062] A clinical modeling module is configured to build a standardized data model for the whole process of a clinical trial, define data entities, attribute fields, data types, value constraints, correlation relationships, and lifecycle state identifiers, and form a unified metadata architecture.

[0063] A set of core data entities covering the whole process of a clinical trial is defined, including a subject basic information entity, an informed consent form entity, a screening period data entity, an enrollment data entity, a visit record entity, a laboratory test result entity, an adverse event report entity, a combined medication record entity, a researcher assessment entity, an inspection report entity, a data cleaning log entity, a statistical analysis plan entity, and a final research report entity.

[0064] A set of attribute fields, data types, value constraints, and foreign key correlation relationships is defined for each type of data entity. A lifecycle state identifier is added to each data entity, and the lifecycle state identifier includes six states, namely, a draft state, a pending review state, a confirmed state, a locked state, an archived state, and a destroyed state, and the legal migration paths and triggering conditions between the states are defined.

[0065] A data intake module is configured to receive raw archival data from an electronic data capture system, a laboratory information system, a picture archiving and communication system, an electronic medical record system, a drug management system, and a manual input terminal, perform field mapping, format conversion, missing value marking, outlier identification, data integrity checking, and structured packaging according to a standardized data model, generate standardized archival data units, and write the standardized archival data units into a central archival repository.

[0066] A semantic analysis module is configured to perform deep analysis and correlation mining of the standardized archival data units at a semantic level, construct a longitudinal time axis of a subject, a causal correlation graph of a visit event and a laboratory index, a correlation network of adverse events and concomitant medications, a semantic consistency matrix of researcher evaluation and monitoring conclusions, a mapping relationship table of a statistical analysis plan and data results, and form a multi-dimensional clinical trial archival knowledge network.

[0067] A process traceability module is configured to establish a version evolution chain for each data unit, record a version identifier, a timestamp, an operator identity, a data snapshot before and after modification, and a modification reason for each modification operation, and store the data in an independent version log database, to support multi-dimensional version history backtracking and difference comparison.

[0068] An access control module is configured to implement a four-dimensional permission control mechanism based on roles, data sensitivity, operation types, and access context, dynamically generate access control decisions through a strategy engine, and ensure that high-sensitivity data is legally accessed within an authorized range.

[0069] A shared audit module is configured to provide a task-driven archival sharing workflow engine, a real-time collaborative editing conflict detection and merging mechanism, a complete operation audit log recording and multi-dimensional query function, and support multi-role collaborative operation and full-process behavior traceability.

[0070] A data processing method for clinical trial archives, comprising:

[0071] Step S1, a standardized data model for the whole process of a clinical trial is constructed, data entities, attribute fields, data types, value constraints, correlation relationships, and lifecycle state identifiers are defined, and a unified metadata architecture is formed.

[0072] First, a set of core data entities covering the whole process of a clinical trial is defined, including a subject basic information entity, an informed consent form entity, a screening period data entity, an enrollment data entity, a visit record entity, a laboratory test result entity, an adverse event report entity, a concomitant medication record entity, a researcher evaluation entity, a monitoring report entity, a data cleaning log entity, a statistical analysis plan entity, and a final research report entity.

[0073] Each type of data entity needs to be clearly defined its attribute field set, such as the subject basic information entity contains subject number, gender, birth date, height, weight, race, medical history, allergy history and other fields; the informed consent form entity contains the signing date, the identity of the signer, the version number, the ethics approval number, the storage path of the scanned copy and other fields; the visit record entity contains visit number, visit date, research center number, researcher number, visit type, visit status and other fields.

[0074] All attribute fields need to specify their data types, such as string, integer, float, boolean, date, binary large object, etc., and set their value constraint conditions, for example, the gender field only allows values of "male" or "female", the birth date field must be earlier than the current system date, the height field must be between 100 cm and 250 cm, and the weight field must be between 30 kg and 300 kg.

[0075] In addition, the association between each data entity needs to be defined, such as the visit record entity is associated with the subject basic information entity through the subject number field, the adverse event report entity is associated with the visit record entity through the visit number field, and the laboratory test result entity is associated with the visit record entity through the sample number field; all data entities need to be attached with a lifecycle state identifier, which includes six states: draft, pending review, confirmed, locked, archived, and destroyed.

[0076] The state migration path needs to be strictly defined, the data unit needs to be migrated from the draft state to the pending review state by the submitter submitting a review request, and the system automatically records the submission timestamp and operator identity; from the pending review state to the confirmed state, the specified review role needs to perform the confirmation operation, and the system automatically records the review timestamp, reviewer identity, and review opinion text; from the confirmed state to the locked state, it needs to meet the preset data freezing conditions, the current date is later than the project preset locking date or the current visit cycle has completed more than 90%; from the locked state to the archived state, it needs to complete all visit cycles and be approved by the project leader, and the system automatically records the archiving timestamp and approver identity; from the archived state to the destroyed state, it needs to meet the statutory retention period and be authorized by the ethics committee in writing, and the system automatically records the destruction timestamp, authorization document number, and executor identity; all state migration operations need to trigger system event log recording and synchronize the version evolution chain of the data entity.

[0077] Step S2, receiving raw data files from electronic data acquisition system, laboratory information system, medical image archive system, electronic medical record system, drug management system and manual input terminal, performing field mapping, format conversion, missing value marking, outlier identification, data integrity checking and structured packaging according to the standardized data model, generating standardized data files and writing them into the central data repository.

[0078] A multi-source data access interface is established, each interface corresponds to a data source system, and the data transmission protocol, authentication method and data format template are configured; when the raw data arrives at the system entrance, the system first performs field mapping operation to map the field name in the raw data to the corresponding attribute field in the standardized data model, such as mapping the "SUBJID" field in the electronic data acquisition system to the "subject number" field in the subject basic information entity, and mapping the "TEST_DATE" field in the laboratory information system to the "test date" field in the laboratory test result entity.

[0079] Then perform format conversion operation to convert the date format, numerical format and encoding format in the raw data to the system internal standard format, convert the "MM / DD / YYYY" format date to "YYYY-MM-DD" format, convert the "1.23E+03" format scientific notation value to "1230.0" format floating point number, and convert the "UTF-8" encoded text to "GBK" encoding.

[0080] After format conversion, the system performs missing value marking operation to explicitly mark all fields without value, the marking method is to insert a special marker "MISSING" in the field value position, and record the missing reason code, such as "raw data not collected", "lost in transmission process", "field mapping failed"; then perform outlier identification operation, perform preset outlier detection rules on all numerical fields, detect whether the height field exceeds 100-250 cm, whether the weight field exceeds 30-300 kg, and whether the laboratory test result field exceeds the medical reference range of the test item.

[0081] If an outlier is detected, the system inserts a special marker "OUTLIER" at the value position of the field, and records the type code of the outlier, the expected value range, and the actual value. After the missing value marker and outlier identification are completed, the system performs a data integrity check operation, which includes six types of rules: field non-empty check, checking whether all mandatory fields have non-empty values; numerical range check, checking whether all numerical fields are within their preset ranges; enumeration value legality check, checking whether all enumeration fields are within their preset enumeration value set; foreign key association consistency check, checking whether the primary keys referenced by all foreign key fields exist in the target data entity; time sequence logic check, checking whether all time fields meet the preset time sequence logic, such as the visit date cannot be earlier than the screening date, and the laboratory test date cannot be more than seven days later than the visit date; signature integrity check, checking whether all data units that require digital signatures contain valid digital signatures and have not been tampered with.

[0082] If any of the check rules fails, the system generates a structured error report, which includes the error type, error field, error value, expected value, and error position identifier, and prevents the data unit from entering the central archive repository, while sending an error notification to the data submitter, requiring them to correct the data and resubmit. Only when all checks pass, the system encapsulates the original data as a standardized archive data unit, which includes the data entity identifier, attribute field value set, data source identifier, data collection timestamp, data submitter identity identifier, and data check pass timestamp, and writes it into the central archive repository. The write operation adopts a transaction mechanism to ensure the atomicity and consistency of data writing. If any exception occurs during the writing process, the system automatically rolls back the entire transaction and records the rollback reason.

[0083] Step S3, the standardized archive data unit is subjected to deep analysis and correlation mining at the semantic level, to construct a subject longitudinal time axis, a causal correlation graph of visit events and laboratory indicators, a correlation network of adverse events and combined medications, a semantic consistency matrix of researcher assessments and monitoring conclusions, and a mapping relationship table of statistical analysis plans and data results, to form a multi-dimensional clinical trial archive knowledge network;

[0084] A subject longitudinal time axis is constructed, which takes the subject number as the unique identifier, and arranges all visit records, laboratory test records, adverse event records, combined medication records, and researcher assessment records of the subject in chronological order. Each event on the time axis is labeled with its occurrence time, event type, event description, and associated data entity identifier. Subsequently, the system constructs a causal correlation graph of visit events and laboratory indicators, which uses Bayesian network inference algorithm, takes visit events as nodes, takes laboratory indicator changes as observation variables, and takes potential pathological mechanisms as hidden variables.

[0085] Firstly, the system extracts the joint distribution samples of visit events and laboratory indicators from historical data, and then learns the structure parameters and conditional probability parameters of the Bayesian network through maximum likelihood estimation. After learning, the system can calculate the conditional probability distribution of the change of laboratory indicators under the condition of any visit event, thereby quantifying the causal dependence strength between events and indicators. The system can calculate the probability of serum creatinine level rising after giving a certain drug or the probability of white blood cell count decreasing after a certain adverse event occurs. Subsequently, the system constructs a correlation network of adverse events and concomitant medications, which uses a fusion method of Pearson correlation coefficient and Spearman rank correlation coefficient.

[0086] Firstly, the system extracts the frequency sequence of adverse events and the dose sequence of concomitant medications for all subjects, then calculates the Pearson correlation coefficient and the Spearman rank correlation coefficient between each pair of adverse events and concomitant medications, and finally obtains the comprehensive correlation score by weighted average of the two coefficients. The system sets a correlation score threshold, only retains significant correlation pairs with scores higher than the threshold, and constructs a correlation network graph, in which nodes are adverse events or concomitant medications, and edges are correlation scores. Subsequently, the system constructs a semantic consistency matrix of investigator assessments and monitoring conclusions, which uses a text matching algorithm based on word vector cosine similarity. The system first performs word segmentation on the investigator assessment text and the monitoring conclusion text, then converts each word into a high-dimensional vector using a pre-trained clinical medicine field word vector model, and then performs average pooling on all word vectors of each text to obtain the semantic vector representation of the text. Finally, the system calculates the cosine similarity between the semantic vectors of each pair of investigator assessment text and monitoring conclusion text as their semantic consistency score.

[0087] The system sets a similarity threshold, marks all inconsistent items with scores below the threshold, and generates an inconsistency report containing inconsistent text pairs, similarity scores, and keyword difference analysis. Subsequently, the system constructs a mapping table of statistical analysis plans and data results, which takes the analysis variables in the statistical analysis plan as rows and the corresponding variables in the actual data results as columns, records the actual value, missing rate, distribution characteristics, and deviation degree from the planned expectation of each analysis variable in the data results. All the above analysis results are integrated into a multi-dimensional clinical trial archive knowledge network, which supports semantic-based intelligent retrieval, such as querying "all adverse events significantly related to drug A", the system will return all adverse event nodes connected to drug A in the correlation network; supports trend prediction, such as querying "the probability of subject B having liver function abnormalities in future visits", the system will return the conditional probability value based on Bayesian network inference; supports risk warning, such as when the change pattern of a subject's laboratory indicators and the conditional probability of high-risk pathological mechanisms exceed the preset threshold, the system automatically triggers a risk warning notification.

[0088] Step S4, for each data unit, establish a version evolution chain, record the version identifier, timestamp, operator identity, data snapshot before and after modification, modification reason explanation of each modification operation, and store in an independent version log database, support multi-dimensional version history backtracking and difference comparison;

[0089] The system assigns a unique version identifier to each data unit in the central archive repository, which adopts a hierarchical coding structure. The first layer is the project number, encoded with four digits. The second layer is the subject number, encoded with six digits. The third layer is the data entity type code, encoded with two letters, for example, "SB" represents subject basic information, "VS" represents visit record, and "LB" represents laboratory test result. The fourth layer is the data entity instance number, encoded with eight digits. The fifth layer is the version sequence number, encoded with three digits, starting from 001 and increasing. The sixth layer is the timestamp hash value, which is calculated by hashing the operation timestamp using the SHA256 algorithm and taking the first eight hexadecimal characters. This hierarchical coding structure ensures the uniqueness and traceability of the version identifier worldwide.

[0090] Whenever a data unit undergoes any modification operation, the system automatically generates a new version record, which contains the new version identifier, operation timestamp, operator identity, operation type code, modification reason text, pre-modification data snapshot, and post-modification data snapshot. The pre-modification data snapshot and post-modification data snapshot are both stored in JSON format, which records all field values of the data unit before and after modification. All version records are stored in an independent version log database, which adopts a blockchain-like chain storage structure. Each version record contains the previous version hash value, current version data hash value, and operation metadata hash value.

[0091] The previous version hash value is the hash value of the globally unique identifier of the previous version record. The current version data hash value is the hash value of the post-modification data snapshot in the current version record. The operation metadata hash value is the hash value of all fields in the current version record except the data snapshot. Through this chain structure, any tampering with historical version records will cause the hash values of all subsequent version records to mismatch, thereby achieving the tamper-proof nature of the version evidence chain.

[0092] The system supports version history backtracking according to subject number, visit number, data type, operation time interval, and other multi-dimensional conditions. Users can specify any query conditions, and the system returns a list of all version records that meet the conditions. Users can also select any two version records for difference comparison, and the system automatically calculates the field-level differences between the two version data snapshots and displays the difference fields, original values, and new values in highlighted mode.

[0093] In addition, the system supports version rollback operation, users can choose to roll back data units to any historical version, the system will automatically perform reverse operation, restore data to the specified version state, and generate a new version record, recording the timestamp, operator identity, and rollback target version identifier of the rollback operation.

[0094] Step S5, implement a four-dimensional permission control mechanism based on role, data sensitivity, operation type, and access context, dynamically generate access control decisions through a policy engine, and ensure that high-sensitivity data is legally accessed within the authorized scope.

[0095] First, define a set of roles for clinical trial participants, including sponsor project manager, research center researcher, independent data monitoring committee member, statistical analyst, inspector, and ethics committee member. Each role is assigned a set of basic permissions, such as the sponsor project manager having read and write permissions for project-level metadata, the research center researcher having read and write permissions for the data of the research center he is responsible for, the independent data monitoring committee member having read-only permissions for safety data, the statistical analyst having read and export permissions for analysis data sets, the inspector having read-only permissions for operation logs, and the ethics committee member having read-only permissions for informed consent forms and ethics approval documents.

[0096] Subsequently, the system defines data sensitivity levels, divided into four levels: public, restricted, confidential, and top secret. Public-level data includes project number, research center number, and visit schedule; restricted-level data includes subject number, visit date, and laboratory test item name; confidential-level data includes laboratory test specific values, adverse event description text, and combined medication name and dosage; and top-secret-level data includes subject real identity information, informed consent form scans, and genetic sequencing raw data. Each type of data entity is labeled with its default sensitivity level in the standardized data model, and the system supports dynamic adjustment of the sensitivity level of specific data instances at runtime.

[0097] Subsequently, the system defines operation type permissions, including read-only, edit, export, delete, and version rollback, read-only permission allows users to view data content but not modify; edit permission allows users to modify data content and submit new versions; export permission allows users to export data into external file formats; delete permission allows users to mark data as logically deleted; version rollback permission allows users to roll back data to historical versions. Subsequently, the system defines access context constraints, including only specified IP address segments, only specified device fingerprints, and only specified time period access constraints. Device fingerprints are generated by combining six parameters: device model, operating system version, browser type, screen resolution, font list, and time zone settings, and stored in a trusted device registration library. The system maintains a policy engine that dynamically combines the requester's role, the data sensitivity level accessed, the operation type performed, and the access context in which the request occurs four-dimensional parameters each time a data access request occurs, queries the pre-set policy rule library, and generates the final access control decision. The policy rule library is stored in a triple form, and each triple contains the combined conditions of the role, data sensitivity, and operation type and the allowed or denied decision results.

[0098] The rule "sponsor project manager + confidential level data + edit → allow" indicates that the sponsor project manager can edit confidential level data; the rule "statistical analyst + top secret level data + export → deny" indicates that the statistical analyst cannot export top secret level data. If the access context contains IP address segment constraints, the system additionally checks whether the request source IP is in the allowed list; if it contains device fingerprint constraints, the system additionally checks whether the request device fingerprint is in the trusted device registration library; if it contains time period constraints, the system additionally checks whether the current system time is within the allowed time period. Only when all dimensional constraints are met, the system allows access operation to be performed. All access control decisions are recorded in the audit log, including decision timestamp, requester identity, request data identification, request operation type, decision result, and decision basis rule identification.

[0099] Step S6, provide a task-driven based archive sharing workflow engine, real-time collaborative editing conflict detection and merging mechanism, complete operation audit log recording and multi-dimensional query function, support multi-role collaborative operation and full-process behavior traceability.

[0100] The system provides a task-driven archive sharing workflow engine that supports administrators to define sharing trigger conditions and sharing target roles. For example, "when a subject completes the last visit, automatically share the subject's complete data to the statistician role"; or "when a study center completes the enrollment of all subjects, automatically share the center's enrollment data summary table to the sponsor project manager role". Sharing trigger conditions can be defined based on data state changes, time conditions, event triggers, and other methods. When the trigger condition is met, the system automatically generates a sharing task, sends a notification to the target role, and grants it the pre-set access rights. The validity period of the rights can be set to a fixed length of time or until the task is completed. The system provides real-time collaborative editing conflict detection and merging mechanisms. When multiple users edit the same data unit at the same time, the system uses an operation conversion algorithm to resolve conflicts.

[0101] Specifically, the system decomposes each editing operation into a sequence of atomic operations, such as "insert character X in field A" and "modify the value of field B from Y to Z". When concurrent operations are detected, the system performs exchange sorting on these atomic operation sequences to ensure that the final execution order satisfies the exchange law of operations, thereby guaranteeing the consistency of the final data state. For example, if user A performs "modify field B from Y to Z" and user B performs "modify field B from Y to W" at the same time, the system will detect the conflict and prompt the user to choose which modification to keep or automatically merge into "modify field B from Y to Z+W". All conflict detection and merging operations are recorded in the operation log.

[0102] The system provides complete operation audit log recording functions, recording the performer, execution time, operation object, operation content, and operation result status of each data access, modification, export, sharing, and deletion operation. The operation object is recorded as the version identifier of the data unit, the operation content is recorded as the specific modified field and value, and the operation result status is recorded as success or failure and failure reason code. The audit log is stored in a write-once read-many storage medium and is set to read-only access rights to prevent the log from being tampered with or deleted. The system supports audit queries by time range, operation type, user identity, data entity, and other dimensions. Users can specify any query conditions, and the system returns a list of all audit records that meet the conditions and supports generating audit reports. The report includes operation statistical summary, abnormal operation marker, and permission change history.

[0103] In the embodiment, the clinical modeling module is responsible for performing step S1, which internally comprises a data entity definition submodule, an attribute field constraint submodule, an association relationship configuration submodule, and a lifecycle state management submodule; the data ingestion module is responsible for performing step S2, which internally comprises a multi-source data access submodule, a field mapping engine submodule, a format converter submodule, a missing value and abnormal value marking submodule, an integrity checker submodule, and a structured packager submodule; the semantic analysis module is responsible for performing step S3, which internally comprises a longitudinal time axis construction submodule, a causal association graph reasoning submodule, a correlation network calculation submodule, a semantic consistency matrix generation submodule, a mapping relationship table construction submodule, and a knowledge network integration submodule; the process traceability module is responsible for performing step S4, which internally comprises a version identifier generation submodule, a version record construction submodule, a blockchain storage submodule, a historical backtracking interface submodule, a difference comparison engine submodule, and a version rollback execution submodule; the access control module is responsible for performing step S5, which internally comprises a role permission definition submodule, a data sensitivity annotation submodule, an operation type authorization submodule, an access context verification submodule, a strategy engine decision submodule, and a permission dynamic adjustment submodule; the sharing audit module is responsible for performing step S6, which internally comprises a task-driven workflow engine submodule, a real-time collaborative conflict detection submodule, an operation conversion and merging submodule, an audit log record submodule, a multi-dimensional query interface submodule, and a report generator submodule. All the modules are deployed in a distributed server cluster, and data exchange and event notification are performed through an internal message bus, so as to ensure the high availability and scalability of the whole system.

[0104] The above merely describes a preferred specific embodiment of the present application, and is not intended to limit the protection scope of the present application. Any equivalent changes made to the structure, shape, or principle of the present application shall be encompassed within the protection scope of the present application.

Claims

1. An integrated information system for clinical trial records, characterized in that, include: The clinical modeling module is used to build a standardized data model for the entire clinical trial process, defining data entities, attribute fields, data types, value constraints, relationships, and lifecycle status identifiers to form a unified metadata architecture. The clinical modeling module is used to define a set of core data entities covering the entire clinical trial process, including subject basic information entity, informed consent form entity, screening period data entity, enrollment data entity, visit record entity, laboratory test result entity, adverse event report entity, concomitant medication record entity, investigator evaluation entity, monitoring report entity, data cleaning log entity, statistical analysis plan entity, and final study report entity. Define the attribute field set, data type, value constraints, and foreign key relationships for each type of data entity; attach a lifecycle status identifier to each data entity, which includes six states: draft, pending review, confirmed, locked, archived, and destroyed, and define the legal migration paths and triggering conditions between states; The data intake module is used to receive raw archival data from electronic data acquisition systems, laboratory information systems, medical image archiving systems, electronic medical record systems, drug management systems, and manual data entry terminals. Based on the standardized data model, it performs field mapping, format conversion, missing value marking, outlier identification, data integrity verification, and structured encapsulation to generate standardized archival data units and write them into the central archival repository. The semantic analysis module is used to perform in-depth semantic analysis and correlation mining on the standardized archive data units, constructing a longitudinal timeline of subjects, a causal relationship map of visit events and laboratory indicators, a correlation network of adverse events and concomitant medications, a semantic consistency matrix of investigator assessment and monitoring conclusions, and a mapping relationship table between statistical analysis plans and data results, forming a multi-dimensional clinical trial archive knowledge network. The process traceability module is used to establish a version evolution chain for each data unit, record the version identifier, timestamp, operator identity, data snapshots before and after the modification, and explanation of the reason for the modification for each modification operation, and store them in an independent version log database, supporting multi-dimensional version history backtracking and difference comparison; The access control module is used to implement a four-dimensional permission control mechanism based on roles, data sensitivity, operation type, and access context. It dynamically generates access control decisions through a policy engine to ensure that highly sensitive data is legally accessed within the authorized scope. The shared audit module provides a task-driven file sharing workflow engine, a real-time collaborative editing conflict detection and merging mechanism, complete operation audit log recording and multi-dimensional query functions, and supports multi-role collaborative operation and full-process behavior traceability.

2. A data processing method for clinical trial records, applied to the integrated information system for clinical trial records as described in claim 1, characterized in that, include: Step S1: Construct a standardized data model for the entire clinical trial process, defining data entities, attribute fields, data types, value constraints, relationships, and lifecycle status identifiers to form a unified metadata architecture; In step S1, the defined data entities include: subject basic information entity, informed consent form entity, screening period data entity, enrollment data entity, visit record entity, laboratory test result entity, adverse event report entity, combined medication record entity, investigator evaluation entity, monitoring report entity, data cleaning log entity, statistical analysis plan entity, and final study report entity. Define the attribute field set, data type, value constraints, and foreign key relationships for each type of data entity; attach a lifecycle status identifier to each data entity, which includes six states: draft, pending review, confirmed, locked, archived, and destroyed, and define the legal migration paths and triggering conditions between states; Step S2: Receive raw archive data from electronic data acquisition system, laboratory information system, medical image archiving system, electronic medical record system, drug management system and manual input terminal; perform field mapping, format conversion, missing value marking, outlier identification, data integrity verification and structured encapsulation according to the standardized data model; generate standardized archive data units and write them into the central archive repository. Step S3: Perform deep semantic analysis and correlation mining on the standardized archive data units to construct a longitudinal timeline of subjects, a causal relationship map of visit events and laboratory indicators, a correlation network of adverse events and concomitant medications, a semantic consistency matrix of investigator assessment and monitoring conclusions, and a mapping relationship table of statistical analysis plans and data results, forming a multi-dimensional clinical trial archive knowledge network. Step S4: Establish a version evolution chain for each data unit, record the version identifier, timestamp, operator identity, data snapshots before and after the modification, and explanation of the reason for the modification for each modification operation, and store them in an independent version log database to support multi-dimensional version history backtracking and difference comparison. Step S5: Implement a four-dimensional access control mechanism based on role, data sensitivity, operation type, and access context. The policy engine dynamically generates access control decisions to ensure that highly sensitive data is legally accessed within the authorized scope. Step S6 provides a task-driven file sharing workflow engine, a real-time collaborative editing conflict detection and merging mechanism, complete operation audit log recording and multi-dimensional query function, and supports multi-role collaborative operation and full-process behavior traceability.

3. The data processing method for clinical trial archives according to claim 2, characterized in that, Step S2 includes: Establish multi-source data access interfaces and configure data transmission protocols, authentication methods, and data format templates; Perform field mapping operations to map the original data field names to the corresponding attribute fields in the standardized data model; Perform a format conversion operation to convert the date format, numeric format, and encoding format in the original data into the system's internal standard format. Perform a missing value marking operation, inserting the special marker "MISSING" for fields that do not provide values ​​and recording the missing reason code; Perform outlier identification. Apply preset outlier detection rules to numeric fields. If an outlier is detected, insert a special marker "OUTLIER" and record the outlier type code, expected value range, and actual value. Perform data integrity verification operations, including six categories of rules: field non-empty verification, numerical range verification, enumeration value validity verification, foreign key association consistency verification, time series logic verification, and signature integrity verification. If any check fails, a structured error report is generated and the data unit is blocked from being written. Only when all checks pass is the data unit packaged into a standardized archival data unit and written to the central archival repository using a transaction mechanism.

4. The data processing method for clinical trial archives according to claim 2, characterized in that, Step S3 includes: Construct a longitudinal timeline with subject ID as the unique identifier, and arrange all visit records, laboratory test records, adverse event records, concomitant medication records, and investigator evaluation records for that subject in chronological order; A causal relationship graph between visit events and laboratory indicators was constructed using a Bayesian network inference algorithm. Visit events were used as nodes, changes in laboratory indicators were used as observed variables, and potential pathological mechanisms were used as latent variables. The network parameters were learned through maximum likelihood estimation. A correlation network between adverse events and concomitant medications was constructed using a dual-index fusion method of Pearson correlation coefficient and Spearman rank correlation coefficient. The comprehensive correlation score between each pair of adverse events and concomitant medications was calculated, and significant correlation pairs with scores higher than a preset threshold were retained. A semantic consistency matrix for researchers' evaluation and monitoring conclusions is constructed using a text matching algorithm based on word vector cosine similarity. The text is segmented, word vector average pooling is performed, and cosine similarity is calculated. Inconsistent entries with similarity below a preset threshold are marked. Construct a mapping table between the statistical analysis plan and the data results, and record the actual value, missing rate, distribution characteristics, and degree of deviation from the planned expectations for each analysis variable in the data results; The above analysis results are integrated to form a multi-dimensional clinical trial archive knowledge network that supports intelligent retrieval, trend prediction, and risk warning.

5. The data processing method for clinical trial archives according to claim 2, characterized in that, Step S4 includes: The data units are assigned version identifiers with a hierarchical coding structure. The first layer is the project number, the second layer is the subject number, the third layer is the data entity type code, the fourth layer is the data entity instance number, the fifth layer is the version number, and the sixth layer is the timestamp hash value. Generate a version record, including the new version identifier, operation timestamp, operator identity identifier, operation type code, text explaining the reason for modification, data snapshot before modification, and data snapshot after modification; Version records are stored in an independent version log database that uses a blockchain-style chain storage structure. Each version record contains the hash value of the previous version, the hash value of the current version data, and the hash value of the operation metadata. It supports version history tracing by subject number, visit number, data type, and operation time interval; it supports field-level difference comparison and highlighting of any two version records; and it supports version rollback operations and the generation of new version records.

6. The data processing method for clinical trial archives according to claim 2, characterized in that, Step S5 includes: Define the set of roles for clinical trial participants, including sponsor project managers, research center researchers, independent data monitoring committee members, statistical analysts, auditors, and ethics committee members; Define data sensitivity levels, including public, restricted, confidential, and top secret. Public data includes project number, research center number, and visit schedule. Restricted data includes subject number, visit date, and laboratory test item name. Confidential data includes specific laboratory test values, adverse event description text, and names and dosages of concomitant drugs. Top secret data includes the subject's real identity information, scanned copy of informed consent form, and raw gene sequencing data. Define operation type permissions, including read-only, edit, export, delete, and version rollback; Define access context constraints, including access is limited to a specified IP address range, access is limited to a specified device fingerprint, and access is limited to a specified time period. The device fingerprint is generated by a combination of six parameters: device model, operating system version, browser type, screen resolution, font list, and time zone setting. The strategy engine dynamically combines four dimensions of parameters: role, data sensitivity, operation type, and access context. It queries the preset policy rule base to generate access control decisions and allows access operations to be executed only when all dimensional constraints are met.

7. The data processing method for clinical trial archives according to claim 2, characterized in that, Step S6 includes: Define the sharing trigger conditions and the sharing target role. When the conditions are met, automatically generate a sharing task and grant preset access permissions to the target role. An operation transformation algorithm is used to resolve multi-user concurrent editing conflicts. Editing operations are decomposed into atomic operation sequences and swapped and sorted to ensure the consistency of the final data state. Record the executor, execution time, operation object, operation content, and operation result status for each data access, modification, export, sharing, and deletion operation. The operation object is recorded as the version identifier of the data unit, and the operation content is recorded as the specific fields and values ​​that are modified. The audit logs are stored on a write-once, read-many storage medium with read-only access permissions, supporting audit queries and report generation by time range, operation type, user identity, and data entity dimension.

8. The data processing method for clinical trial archives according to claim 3, characterized in that: The time-series logic verification in the data integrity verification includes checking that the visit date is not earlier than the screening date and the laboratory testing date is not more than seven days later than the corresponding visit date; the signature integrity verification includes checking whether the data unit that needs to be digitally signed contains a valid and tamper-proof digital signature.

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