The Future of Healthcare Data Governance AI-Enabled ETL Testing Frameworks on Data Warehousing Testing automation using ML, AI and NLP

The healthcare sector enhances the integration of artificial intelligence, natural language processing and machine learning in data governance for developing automation procedures. Data management offered AI-powered ETL testing with the adoption of an automation framework for resolving data governance issues . This insight promotes operational efficiency with assurance of data quality that empowers data security by ML, AI and NLP frameworks. The framework allows healthcare institutions to arrange unstructured data in effective insights into patient care and treatment procedures. Advanced analytics leads to the development of predictions on patient outcomes in treatment procedures. The automated process of ETL improves efficient information collection by NPL with the creation of predictive models in monitoring data management by ML

Authors

Publication Details

Journal
International Journal For Multidisciplinary Research
Published
2024-10-07
DOI
https://doi.org/10.36948/ijfmr.2024.v06i05.28559
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
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article

The Future of Healthcare Data Governance AI-Enabled ETL Testing Frameworks on Data Warehousing Testing automation using ML, AI and NLP

Arun Kumar Ramachandran Sumangala Devi
International Journal For Multidisciplinary Research
Artificial Intelligence in Healthcare
article

The Future of Healthcare Data Governance AI-Enabled ETL Testing Frameworks on Data Warehousing Testing automation using ML, AI and NLP

Arun Kumar Ramachandran Sumangala Devi
article en

Abstract

The healthcare sector enhances the integration of artificial intelligence, natural language processing and machine learning in data governance for developing automation procedures. Data management offered AI-powered ETL testing with the adoption of an automation framework for resolving data governance issues . This insight promotes operational efficiency with assurance of data quality that empowers data security by ML, AI and NLP frameworks. The framework allows healthcare institutions to arrange unstructured data in effective insights into patient care and treatment procedures. Advanced analytics leads to the development of predictions on patient outcomes in treatment procedures. The automated process of ETL improves efficient information collection by NPL with the creation of predictive models in monitoring data management by ML

International Journal For Multidisciplinary ResearchVol. 6(5)
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Openalex Percentile: Top 76%
Artificial Intelligence in Healthcare
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