A survey of current work in biomedical text mining

By Aaron M. Cohen, William R. Hersh. 2005

841 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “mining in cancer research, [24] is a survey for biomedical”
Caipirini: using gene sets to rank literature (2012, background): “As a result, there is an increasing need for methods that can efficiently search this literature [1], and to this end a wide range of tools and services are now available [2,3].”
An adaptive annotation approach for biomedical entity and relation recognition (2016, background): “Recent years have seen a surge on biomedical text processing (see [23] for a survey), most of which rely on the GENIA corpus [24], which is a collection of biomedical abstracts.”
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].”
Simple tricks for improving pattern-based information extraction from the biomedical literature (2010, background): “Introduction As the amount of biomedical knowledge recorded in texts is steadily increasing, it is more and more important to develop efficient techniques to automatically discover new, hidden, or unsuspected knowledge in large text collections [1].”
A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques (2017, background): “Information extraction is typically considered as a preprocessing step in other biomedical text mining applications such as question answering [10], knowledge extraction [124, 136], hypothesis generation[30, 91] and summarization [65].”
Deep learning in bioinformatics (2016, background): “Analysis of dynamic CT and MRI [143, 144] consisting multiple sequential images are one of the areas, and even though we have not focused in this review, biomedical text analysis [145] such as electronic medical records and research papers will be able to make major progress with RNN.”
Evaluating and understanding text-based stock price prediction models (2014, methodology): “We chose the Support Vector Machine as the main driver for model generation since it is well established to be successful for text mining (Cohen & Hersh, 2005; Tang et al., 2009) and more specifically for stock prediction (as can be seen from Table 1).”
CollaboNet: collaboration of deep neural networks for biomedical named entity recognition (2018, methodology): “Text mining can be used to turn the time-consuming task into a fully automated job [2–7].”
AI in Health: State of the Art, Challenges, and Future Directions (2019, background): “Many studies focus on biomedical literature mining; for an early survey, refer to Cohen and Hersh [97].”
A review of feature selection techniques in bioinformatics (2007, background): “Text and literature mining is emerging as a promising area for data mining in biology [17, 54].”
Biomedical language processing: what's beyond PubMed? (2006, background): “A growing body of recent work explores the concept-based approaches to language processing extrinsically, i.e., by quantifying the contributions of concept recognition to other tasks, such as information retrieval and text classification ( Cohen and Hersh, 2005; Caporaso et al., 2005).”
Social big data: Recent achievements and new challenges (2015, methodology): “Text mining techniques have been applied to biomedical text corpora for named entity recognition, text classification, terminology extraction, and relationship extraction [107].”
DISCOVERY AND EXPLANATION OF DRUG-DRUG INTERACTIONS VIA TEXT MINING (2011, methodology): “13 (4) Represent sentences as dependency graphs using the Stanford Parser.”