Method and system for automating clinical data standards

A clinical data standards automated system for automating clinical data standards and generating study data tabulation model (SDTM) artifacts required for a regulatory submission process using a machine learning model and a natural language processing engine, the system comprising

• a non-transitory, computer readable storage medium configured to store computer program instructions defined by modules of the clinical data standards automated system;
• and
• at least one processor communicatively coupled to the non-transitory computer readable storage media, the at least one processor configured to execute the computer program instructions defined by the modules of the clinical data standards automated system, the modules comprising:
• a metadata extractor for extracting metadata from raw datasets automatically using natural language processing and feeding the extracted metadata into the machine learning model;
• a case report form extractor for pre-filling the extracted metadata into a blank case report form (CRF);
• an annotator for automatically predicting and annotating contents in the blank CRF and the extracted metadata, using the machine learning model, based on annotated metadata information available in the clinical data standards automated system and information acquired from previous learnings, and recording new learnings into the clinical data standards automated system with the minimal user intervention, wherein the previous learnings are learnings recorded by the clinical data standards automated system based on the annotated metadata information manually acquired from a user into the blank case report form (CRF) at beginning of a clinical trial process, wherein the predicted CRF annotations and corresponding text and attributes are sent to an application in the clinical data standards automated system, and wherein the application inserts CRF annotations into the blank CRF pre-filled with the extracted metadata, based on prediction from the machine learning model;
• a smart mapper for mapping the raw datasets against a target SDTM variable based on the extracted metadata available in the clinical data standards automated system and information acquired from the previous learnings;
• a plurality of SDTM generators for generating an SDTM SAS code based on the mapping, the prediction, and derivations, an SDTM specification, and SDTM datasets based on the mapping, wherein the clinical data standards automated system predicts derivation logic from a derivation repository based on mapping combinations, study design, and the extracted metadata, and wherein the generated SDTM SAS code is executable outside the clinical data standards automated system in a SAS application;
• a define package generator for generating a define package based on annotated metadata information and the mapping available in the clinical data standards automated system, wherein the define package is a Clinical Data Interchange Standards Consortium (CDISC) standard “define.xml” file and a reviewer's guide, and wherein the “define.xml” file is an extensible markup language (XML) document that describes structure and content of data collected during the clinical trial process;
• and
• a validator for validating the generated define package and the SDTM artifacts generated during a development and review cycle, and generating validation reports in real time, wherein the SDTM artifacts comprise the generated define.xml files, stylesheets, the SDTM datasets, an annotated case report form (CRF), the reviewers guide, and supporting documents, and wherein the validations are based on the Clinical Data Interchange Standards Consortium (CDISC) or United States Food and Drug Administration (FDA)-published validation rules.

A non-transitory, computer readable storage medium having embodied thereon, computer program codes comprising instructions executable by at least one processor for automating clinical data standards and generating study data tabulation model (SDTM) artifacts required for a regulatory submission process using a machine learning model and a natural language processing engine, the computer program codes comprising:

• a first computer program code for extracting metadata from raw datasets automatically using natural language processing and feeding the extracted metadata into the machine learning model;
• a second computer program code for pre-filling the extracted metadata into a blank case report form (CRF);
• a third computer program code for predicting automatic case report form (CRF) annotations on the extracted metadata using the machine learning model, based on annotated metadata information available in the clinical data standards automated system and information acquired from previous learnings, and for recording new learnings into the clinical data standards automated system with the minimal user intervention, wherein the previous learnings are learnings recorded by the clinical data standards automated system based on the annotated metadata information manually acquired from a user into the blank case report form (CRF) at beginning of a clinical trial process, wherein the predicted CRF annotations and corresponding text and attributes are sent to an application in a clinical data standards automated system, and wherein the application inserts CRF annotations into the blank CRF pre-filled with the extracted metadata, based on prediction from the machine learning model;
• a fourth computer program code for mapping the raw datasets against a target SDTM variable based on the extracted metadata available in the clinical data standards automated system and information acquired from the previous learnings;
• a fifth computer program code for generating an SDTM statistical analysis system (SAS) code based on the mapping, the prediction, and derivations, an SDTM specification, and SDTM datasets based on the mapping, wherein the clinical data standards automated system predicts derivation logic from a derivation repository based on mapping combinations, study design, and the extracted metadata, and wherein the generated SDTM SAS code is executable outside the clinical data standards automated system in a SAS application;
• a sixth computer program code for generating a define package based on annotated metadata information and the mapping available in the clinical data standards automated system, wherein the define package is a Clinical Data Interchange Standards Consortium (CDISC) standard “define.xml” file and a reviewer's guide, and wherein the “define.xml” file is an extensible markup language (XML) document that describes structure and content of data collected during the clinical trial process;
• and
• a seventh computer program code for validating the generated define package and the SDTM artifacts generated during a development and review cycle, and for generating validation reports in real time, wherein the SDTM artifacts comprise the generated define.xml files, stylesheets, the SDTM datasets, an annotated case report form (CRF), the reviewers guide, and supporting documents, and wherein the validations are based on the Clinical Data Interchange Standards Consortium (CDISC) or United States Food and Drug Administration (FDA)-published validation rules.