This paper from Hongfang Liu's group at Mayo Clinic
demonstrates how text embeddings trained on biomedical or clinical text can, but don't always, perform better on biomedical natural language processing tasks. That being said, pre-trained embeddings may be appropriate for your needs, especially as training domain-specific…
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- Biomedical Information Extraction
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- 2026-10-10
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BioBERT: a pre-trained biomedical language representation model for biomedical text mining academic.oup.com
Bioinformatics, 2020. [All Versions]. Answering medical questions, identifying relevant clinical trials, and diagnosing diseases based on symptoms, making medical information more accessible to the general public.
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Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text ncbi.nlm.nih.gov
A 2017 review of natural language processing methods applied to information extraction in health records and social media text. An important note from this review: "One of the main challenges in the field is the availability of data that can be shared and which can be used by…
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BioWordVec figshare.com
paper - code - Word embeddings derived from biomedical text (>27 million PubMed titles and abstracts), including subword embedding model based on MeSH.
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Getting Started in Text Mining journals.plos.org
A brief introduction to bio-text mining from Cohen and Hunter. More than ten years old but still quite relevant. See also an earlier paper by the same authors.
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Mayo Clinic's clinical natural language processing program mayo.edu
Several groups at Mayo Clinic have made major contributions to BioIE (for example, the Apache cTAKES platform) over the past 20 years.
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Biomedical Literature Mining link.springer.com
A (non-free) volume of Methods in Molecular Biology from 2014. Chapters covers introductory principles in text mining, applications in the biological sciences, and potential for use in clinical or medical safety scenarios.
Biomedical Information Extraction › Techniques and Models > Text Embeddings: “demonstrates how text embeddings trained on biomedical or clinical text can, but don't always, perform better on biomedical natural language processing tasks. That being said, pre-trained embeddings may be appropriate for your needs, especially as training domain-specific…”