Natural Language Processing in Health Care and Biomedicine

Friedman, Carol; Elhadad, Noémie

doi:10.1007/978-1-4471-4474-8_8

Cited by 39 publications

(24 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Common subtasks and a representative pipeline are illustrated in Figure 2. A brief description of these tasks is provided in Table 1, and a comprehensive review thereof is provided by Friedman[20] and Nadkarni[21] et al While the exact set of components included in a text mining pipeline is application specific, the key ingredients relevant to biomedical text mining appear to be named entity recognition (NER) and relation detection (defined in Table 1). …”

Section: Text Mining Overviewmentioning

confidence: 99%

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

et al. 2014

View full text Add to dashboard Cite

Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. Text mining is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources—such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs—that are amenable to text-mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance.

show abstract

Section: Text Mining Overviewmentioning

confidence: 99%

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

et al. 2014

View full text Add to dashboard Cite

show abstract

“…The two most widely used general architectures are GATE 7 GATE, written in Java, was originally developed at the University of Sheffield in 1995 and is widely used in the NLP community. It includes basic NLP tools for low-level processing (such as tokenizers, sentence splitters, part-of-speech taggers) packaged in a wrapper called CREOLE, and a high-level processor for named entity recognition packaged in an information extraction system called ANNIE.…”

Section: Nlp Tools and Integrated Frameworkmentioning

confidence: 99%

“…Spyns provided an overview of pre-1996 biomedical NLP systems [3], while Demner-Fushman et al more recently reviewed and summarized NLP methods and systems for clinical decision support [4]. The use of NLP in medicine has been comprehensively reviewed by Friedman [5], Nadkami et al [6], and more recently by Friedman and Elhadad [7]. The review in this chapter differs from previous work in that it emphasizes the historical development of landmark clinical NLP systems, and presents each system in light of a unified system architecture.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Natural Language Processing in Biomedicine: A Unified System Architecture Overview

Doan

Conway

Phương³

et al. 2014

Methods in Molecular Biology

View full text Add to dashboard Cite

In modern electronic medical records (EMR) much of the clinically important data -signs and symptoms, symptom severity, disease status, etc. -are not provided in structured data fields, but rather are encoded in clinician generated narrative text. Natural language processing (NLP) provides a means of "unlocking" this important data source for applications in clinical decision support, quality assurance, and public health. This chapter provides an overview of representative NLP systems in biomedicine based on a unified architectural view. A general architecture in an NLP system consists of two main components: background knowledge that includes biomedical knowledge resources and a framework that integrates NLP tools to process text. Systems differ in both components, which we will review briefly. Additionally, challenges facing current research efforts in biomedical NLP include the paucity of large, publicly available annotated corpora, although initiatives that facilitate data sharing, system evaluation, and collaborative work between researchers in clinical NLP are starting to emerge. IntroductionIn modern electronic medical records (EMR) most of the clinically important data -signs and symptoms, symptom severity, disease status, etc. -is not provided in structured data fields, but are rather encoded in clinician-generated narrative text. Natural language processing (NLP) provides a means of "unlocking" this important data source, converting unstructured text to structured, actionable data for use in applications for clinical decision support, quality assurance, and public health surveillance. There are currently many NLP systems that have been 2 successfully applied to biomedical text. It is not our goal to review all of them in this chapter, but rather to provide an overview of how the field evolved from producing monolithic software built on platforms that were available at the time they were developed to contemporary component-based systems built on top of general frameworks. More importantly, the performance of these systems is tightly associated with their "ingredients" (i.e., modules that are used to form its background knowledge), and how these modules are combined on top of the general framework. We highlight certain systems based on their landmark status as well as on the diversity of components and frameworks they are based on. [7]. The review in this chapter differs from previous work in that it emphasizes the historical development of landmark clinical NLP systems, and presents each system in light of a unified system architecture.We consider that each NLP system in biomedicine contains two main components: biomedical background knowledge and a framework that integrates NLP tools. In the rest of this paper, we will first outline our model architecture for NLP systems in biomedicine, before going on to review and summarize representative NLP systems, starting with an early NLP system, LSP-MLP, and closing our discussion with the presentation of a more recent system, cTAKES. Finally, we will discuss...

show abstract

“…Two major subfields of big data in biomedicine, natural language processing (NLP) and biomedical image processing, are not covered in this review. We refer the reader to the excellent overviews by Meystre et al [1], Friedman and Elhadad [2], Deserno [3], and Rubin et al [4] for an in-depth discussion of these subfields.…”

Section: Introductionmentioning

confidence: 99%

Technical Challenges for Big Data in Biomedicine and Health: Data Sources, Infrastructure, and Analytics

Peek¹,

Holmes²,

Sun³

2014

Yearb Med Inform

View full text Add to dashboard Cite

SummaryObjectives: To review technical and methodological challenges for big data research in biomedicine and health. Methods: We discuss sources of big datasets, survey infrastructures for big data storage and big data processing, and describe the main challenges that arise when analyzing big data. Results: The life and biomedical sciences are massively contributing to the big data revolution through secondary use of data that were collected during routine care and through new data sources such as social media. Efficient processing of big datasets is typically achieved by distributing computation over a cluster of computers. Data analysts should be aware of pitfalls related to big data such as bias in routine care data and the risk of false-positive findings in high-dimensional datasets. Conclusions: The major challenge for the near future is to transform analytical methods that are used in the biomedical and health domain, to fit the distributed storage and processing model that is required to handle big data, while ensuring confidentiality of the data being analyzed.

show abstract

Natural Language Processing in Health Care and Biomedicine

Cited by 39 publications

References 48 publications

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

Natural Language Processing in Biomedicine: A Unified System Architecture Overview

Technical Challenges for Big Data in Biomedicine and Health: Data Sources, Infrastructure, and Analytics

Contact Info

Product

Resources

About