ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
Harnessing Biomedical Natural Language Processing Tools to Identify Medicinal Plant Knowledge from Historical Texts.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Natural product drug discovery in the artificial intelligence era.Chemical science · 2022Review
- Extracting knowledge networks from plant scientific literature: potato tuber flesh color as an exemplary trait.BMC plant biology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The growing amount of data describing historical medicinal uses of plants from digitization efforts provides the opportunity to develop systematic approaches for identifying potential plant-based therapies. However, the task of cataloguing plant use information from natural language text is a challenging task for ethnobotanists. To date, there have been only limited adoption of informatics approaches used for supporting the identification of ethnobotanical information associated with medicinal uses. This study explored the feasibility of using biomedical terminologies and natural language processing approaches for extracting relevant plant-associated therapeutic use information from historical biodiversity literature collection available from the Biodiversity Heritage Library. The results from this preliminary study suggest that there is potential utility of informatics methods to identify medicinal plant knowledge from digitized resources as well as highlight opportunities for improvement.
Indexed as
Identifiers
29854223PMC5977595What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.