| LDR | | 03252nam u200493 4500 |
| 001 | | 000000418486 |
| 005 | | 20190215162913 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438255500 |
| 035 | |
▼a (MiAaPQ)AAI10842594 |
| 035 | |
▼a (MiAaPQ)sunyalb:12438 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 020 |
| 100 | 1 |
▼a Tao, Mingzhe. |
| 245 | 10 |
▼a Clinical Information Extraction from Unstructured Free-Texts. |
| 260 | |
▼a [S.l.]:
▼b State University of New York at Albany.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 142 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: A. |
| 500 | |
▼a Advisers: Ozlem Uzuner |
| 502 | 1 |
▼a Thesis (Ph.D.)--State University of New York at Albany, 2018. |
| 520 | |
▼a 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, |
| 520 | |
▼a 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 |
| 520 | |
▼a (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 |
| 520 | |
▼a (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 |
| 520 | |
▼a (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 |
| 520 | |
▼a (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. |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0668. |
| 650 | 4 |
▼a Information science. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Bioinformatics. |
| 690 | |
▼a 0723 |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0715 |
| 710 | 20 |
▼a State University of New York at Albany.
▼b Information Science. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12A(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0668 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999857
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |