Oscar Ferrandez-Escamez

Oscar Ferrandez-Escamez
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Main area of research
Natural language & AI

Oscar Ferrandez Escamez is a Senior NLP Research Engineer in the Clinical Language Understanding group with research interests in the areas of Clinical Information Extraction, Machine learning and Statistical methods in the area of Natural Language Processing, Question Answering, Information Retrieval, and Named Entity Recognition, as well as expertise in different Natural Language Processing tools and frameworks. Prior to joining Nuance, Oscar worked as a post-doctoral researcher in the Biomedical Informatics department at the University of Utah. His research was focused on methods for automatic de-identification and Clinical Information Extraction. Oscar obtained his MSc (2004) and PhD (2009) in Computer Science at the University of Alicante (Spain), and work as a research assistant in 2010 at Alicante University. During his PhD his research involved the development of Question Answering and Textual Entailment Systems applied to different domains.

Selected articles

Text de-identification for privacy protection: a study of its impact on clinical text information content

As more and more electronic clinical information is becoming easier to access for secondary uses such as clinical research, approaches that enable faster and more

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How much does automatic text de-identification impact clinical problems, tests, and treatments?

Clinical text de-identification can potentially overlap with clinical information such as medical problems or treatments, therefore causing this information to be lost. In this study,

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BoB, a best-of-breed automated text de-identification system for VHA clinical documents

Objective De-identification allows faster and more collaborative clinical research while protecting patient confidentiality. Clinical narrative de-identification is a tedious process that can be alleviated by

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Generalizability and comparison of automatic clinical text de-identification methods and resources

In this paper, we present an evaluation of the hybrid best-of-breed automated VHA (Veteran’s Health Administration) clinical text de-identification system, nicknamed BoB, developed within the

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Evaluating current automatic de-identification methods with Veteran’s health administration clinical documents

Background: The increased use and adoption of Electronic Health Records (EHR) causes a tremendous growth in digital information useful for clinicians, researchers and many other

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