Frontiers of biomedical text mining: current progress

By Pierre Zweigenbaum, Dina Demner-Fushman, Hong Yu, K. Cohen. Briefings Bioinform., 2007

315 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3ef14b5d72a802267a35beefc2e6f67b0d8a0805

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Citations in the corpus

Simple tricks for improving pattern-based information extraction from the biomedical literature (2010, background): “Many text mining (TM) methods have been developed in order to achieve this goal [2].”
Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research (2008, methodology): “Research on information extraction from the biomedical literature is not discussed in this paper, but is well described in reviews by Cohen et al. [2] and by Zweigenbaum et al. [3].”
Concept annotation in the CRAFT corpus (2012, background): “Substantial progress in biomedical natural-language processing (NLP), particularly in the tasks of information retrieval, concept recognition, and information extraction [1-5] raises the possibility of creating formal representations for the entire biomedical literature.”
Is searching full text more effective than searching abstracts? (2009, background): “[2,3] identified analysis of full-text articles as one of the frontiers in biomedical text mining.”
DUTIR in BioNLP-ST 2016: Utilizing Convolutional Network and Distributed Representation to Extract Complicate Relations (2016, background): “With the demands of rapidly and accurately locating key issues about life and biology increasing, bio-IE appears timely and has attracted more and more researchers to address this question (Krallinger et al., 2005; Zweigenbaum et al., 2007).”
Detection of drug‐drug interactions through data mining studies using clinical sources, scientific literature and social media (2018, unknown): “Moreover, these automatic tools are essential in the construction of more complete drug knowledge databases [86, 87].”
Biomedical text mining and its applications in cancer research (2013, unknown): “Several review articles for biomedical text mining have been published in past years [2–8], in this review, we pay much attention to the application of text mining in cancer research.”
Deploying an interactive machine learning system in an evidence-based practice center: abstrackr (2012, background): “Conducting such reviews requires reviewers (usually physicians) to search literature repositories (e.g., PubMed) to retrieve all potentially eligible citations (comprising titles and abstracts).”
Semi-automated screening of biomedical citations for systematic reviews (2010, background): “(In any case, the choice of classification algorithm seems to matter less than the choice of features when working with biomedical texts [22].”
Large language models are few-shot clinical information extractors (2022, background): “Clinical text contains a large amount of valuable information that is not captured by the structured data fields in electronic health records (Zweigenbaum et al., 2007; Wang et al., 2018).”