Applying Natural Language Processing to ClinicalTrials.gov: mRNA cancer vaccine case study
Abstract Recently, biotechnology and pharmaceutical industries have made strides to adopt and implement Natural Language Processing (NLP) to address challenges faced when extracting and synthesizing high volumes of information found in unstructured and semistructured text. Here we present, and provi...
| Published in: | Clinical and Translational Science |
|---|---|
| Main Authors: | , , , , , |
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-12-01
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| Online Access: | https://doi.org/10.1111/cts.13648 |
| _version_ | 1852640206749958144 |
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| author | Bianca Vora Denison Kuruvilla Chloe Kim Michael Wu Colby S. Shemesh Gillie A. Roth |
| author_facet | Bianca Vora Denison Kuruvilla Chloe Kim Michael Wu Colby S. Shemesh Gillie A. Roth |
| author_sort | Bianca Vora |
| collection | DOAJ |
| container_title | Clinical and Translational Science |
| description | Abstract Recently, biotechnology and pharmaceutical industries have made strides to adopt and implement Natural Language Processing (NLP) to address challenges faced when extracting and synthesizing high volumes of information found in unstructured and semistructured text. Here we present, and provide a summary of the findings from, a use case where NLP and text mining methodologies were used to extract clinical trial data from ClinicalTrials.gov for mRNA cancer vaccines. |
| format | Article |
| id | doaj-art-7ef953fbeeb640889dffb00922151dc4 |
| institution | Directory of Open Access Journals |
| issn | 1752-8054 1752-8062 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| spelling | doaj-art-7ef953fbeeb640889dffb00922151dc42025-08-19T21:45:11ZengWileyClinical and Translational Science1752-80541752-80622023-12-0116122417242010.1111/cts.13648Applying Natural Language Processing to ClinicalTrials.gov: mRNA cancer vaccine case studyBianca Vora0Denison Kuruvilla1Chloe Kim2Michael Wu3Colby S. Shemesh4Gillie A. Roth5Clinical Pharmacology Genentech, Inc. South San Francisco California USAClinical Pharmacology Genentech, Inc. South San Francisco California USAComputational Sciences Genentech, Inc. South San Francisco California USAComputational Sciences Genentech, Inc. South San Francisco California USAClinical Pharmacology Genentech, Inc. South San Francisco California USAPreclinical and Translational PKPD Genentech, Inc. South San Francisco California USAAbstract Recently, biotechnology and pharmaceutical industries have made strides to adopt and implement Natural Language Processing (NLP) to address challenges faced when extracting and synthesizing high volumes of information found in unstructured and semistructured text. Here we present, and provide a summary of the findings from, a use case where NLP and text mining methodologies were used to extract clinical trial data from ClinicalTrials.gov for mRNA cancer vaccines.https://doi.org/10.1111/cts.13648 |
| spellingShingle | Bianca Vora Denison Kuruvilla Chloe Kim Michael Wu Colby S. Shemesh Gillie A. Roth Applying Natural Language Processing to ClinicalTrials.gov: mRNA cancer vaccine case study |
| title | Applying Natural Language Processing to ClinicalTrials.gov: mRNA cancer vaccine case study |
| title_full | Applying Natural Language Processing to ClinicalTrials.gov: mRNA cancer vaccine case study |
| title_fullStr | Applying Natural Language Processing to ClinicalTrials.gov: mRNA cancer vaccine case study |
| title_full_unstemmed | Applying Natural Language Processing to ClinicalTrials.gov: mRNA cancer vaccine case study |
| title_short | Applying Natural Language Processing to ClinicalTrials.gov: mRNA cancer vaccine case study |
| title_sort | applying natural language processing to clinicaltrials gov mrna cancer vaccine case study |
| url | https://doi.org/10.1111/cts.13648 |
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