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 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..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:16 |
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| Enthalten in: |
Clinical and Translational Science - 16(2023), 12, Seite 2417-2420 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Bianca Vora [VerfasserIn] |
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| Links: |
doi.org [kostenfrei] |
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| Themen: |
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| doi: |
10.1111/cts.13648 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
DOAJ099278073 |
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| 520 | |a 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. | ||
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