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Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis
Analysis of health data typically requires development of queries using structured query language (SQL) by a data-analyst. As the SQL queries are manually created, they are prone to errors. In addition, accurate implementation of the queries depends on effective communication with clinical experts,...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9306316/ https://www.ncbi.nlm.nih.gov/pubmed/35874460 http://dx.doi.org/10.1016/j.imu.2022.100996 |
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author | Wagholikar, Kavishwar B. Zelle, David Ainsworth, Layne Chaney, Kira Blood, Alexander J. Miller, Angela Chulyadyo, Rupendra Oates, Michael Gordon, William J. Aronson, Samuel J. Scirica, Benjamin M. Murphy, Shawn N. |
author_facet | Wagholikar, Kavishwar B. Zelle, David Ainsworth, Layne Chaney, Kira Blood, Alexander J. Miller, Angela Chulyadyo, Rupendra Oates, Michael Gordon, William J. Aronson, Samuel J. Scirica, Benjamin M. Murphy, Shawn N. |
author_sort | Wagholikar, Kavishwar B. |
collection | PubMed |
description | Analysis of health data typically requires development of queries using structured query language (SQL) by a data-analyst. As the SQL queries are manually created, they are prone to errors. In addition, accurate implementation of the queries depends on effective communication with clinical experts, that further makes the analysis error prone. As a potential resolution, we explore an alternative approach wherein a graphical interface that automatically generates the SQL queries is used to perform the analysis. The latter allows clinical experts to directly perform complex queries on the data, despite their unfamiliarity with SQL syntax. The interface provides an intuitive understanding of the query logic which makes the analysis transparent and comprehensible to the clinical study-staff, thereby enhancing the transparency and validity of the analysis. This study demonstrates the feasibility of using a user-friendly interface that automatically generate SQL for analysis of health data. It outlines challenges that will be useful for designing user-friendly tools to improve transparency and reproducibility of data analysis. |
format | Online Article Text |
id | pubmed-9306316 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-93063162022-07-22 Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis Wagholikar, Kavishwar B. Zelle, David Ainsworth, Layne Chaney, Kira Blood, Alexander J. Miller, Angela Chulyadyo, Rupendra Oates, Michael Gordon, William J. Aronson, Samuel J. Scirica, Benjamin M. Murphy, Shawn N. Inform Med Unlocked Article Analysis of health data typically requires development of queries using structured query language (SQL) by a data-analyst. As the SQL queries are manually created, they are prone to errors. In addition, accurate implementation of the queries depends on effective communication with clinical experts, that further makes the analysis error prone. As a potential resolution, we explore an alternative approach wherein a graphical interface that automatically generates the SQL queries is used to perform the analysis. The latter allows clinical experts to directly perform complex queries on the data, despite their unfamiliarity with SQL syntax. The interface provides an intuitive understanding of the query logic which makes the analysis transparent and comprehensible to the clinical study-staff, thereby enhancing the transparency and validity of the analysis. This study demonstrates the feasibility of using a user-friendly interface that automatically generate SQL for analysis of health data. It outlines challenges that will be useful for designing user-friendly tools to improve transparency and reproducibility of data analysis. 2022 2022-06-25 /pmc/articles/PMC9306316/ /pubmed/35874460 http://dx.doi.org/10.1016/j.imu.2022.100996 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Wagholikar, Kavishwar B. Zelle, David Ainsworth, Layne Chaney, Kira Blood, Alexander J. Miller, Angela Chulyadyo, Rupendra Oates, Michael Gordon, William J. Aronson, Samuel J. Scirica, Benjamin M. Murphy, Shawn N. Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis |
title | Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis |
title_full | Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis |
title_fullStr | Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis |
title_full_unstemmed | Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis |
title_short | Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis |
title_sort | use of automatic sql generation interface to enhance transparency and validity of health-data analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9306316/ https://www.ncbi.nlm.nih.gov/pubmed/35874460 http://dx.doi.org/10.1016/j.imu.2022.100996 |
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