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Clinical Information Extraction from Unstructured Free-Texts

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자료유형	E-Book
개인저자	Tao, Mingzhe.
단체저자명	State University of New York at Albany. Information Science.
서명/저자사항	Clinical Information Extraction from Unstructured Free-Texts.
발행사항	[S.l.] : State University of New York at Albany., 2018
발행사항	Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항	142 p.
소장본 주기	School code: 0668.
ISBN	9780438255500
일반주기	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: A. Advisers: Ozlem Uzuner
요약	Information extraction (IE) is a fundamental component of natural language processing (NLP) that provides a deeper understanding of the texts. In the clinical domain, documents prepared by medical experts (e.g., discharge summaries, drug labels,
요약	In the past decade, there have been many efforts focused on extraction of clinical information, i.e., clinical IE. In this dissertation, we present novel extensions to IE methods for automatically identifying clinically-relevant information from
요약	(1) Knowledge representations that utilize real-valued word embeddings outperform their categorical counterparts. Categorical embeddings eliminate word-to-word distances in the high-dimensional space when converting words into discrete labels. R
요약	(2) Introducing pseudo-sequences from unannotated data can improve extraction of entity categories that are sparsely represented in the training data. We use a supervised model trained on annotated data to predict pseudo-sequences from unannotat
요약	(3) We can address lack of available annotated data through pseudo-data generation. We experiment with three different methods of pseudo-data generation. The first method is based on professional gazetteers. It replaces entities in the annotated
요약	(4) Sequence labeling approach to relation extraction can benefit this task. Sequence labeling can identify textual excerpts that contain entities and enables subsequent extraction of sequences of related entities from these excerpts.
요약	Cross-validated results across multiple clinical IE tasks show overall significant performance improvement from the knowledge representations, pseudo-sequences, pseudo-data, and relation extraction models we proposed in our study. The generalize
일반주제명	Information science. Computer science. Bioinformatics.
언어	영어
기본자료 저록	Dissertation Abstracts International79-12A(E). Dissertation Abstract International
대출바로가기	http://www.riss.kr/pdu/ddodLink.do?id=T14999857