國立虎尾科技大學 |

Advanced Information Retrieval and Extraction in Research and Clinical Settings.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Advanced Information Retrieval and Extraction in Research and Clinical Settings./
Author:	Nagy, Mate Levente.
Description:	1 online resource (141 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Subject:	Bioinformatics. -
Online resource:	click for full text (PQDT)
ISBN:	9780355105520

Advanced Information Retrieval and Extraction in Research and Clinical Settings.
Nagy, Mate Levente.

Advanced Information Retrieval and Extraction in Research and Clinical Settings. - 1 online resource (141 pages)

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)--Yale University, 2017.

Includes bibliographical references

Due to the seemingly endless stream of publications and the tremendous amount of medical data collected. the development of advanced information extraction (IE) and information retrieval (IR) tools is becoming important to ensure continued access to meaningful information from an increasing amount of accumulated data. In this dissertation, IR. and IE are explored in three data domains: biomedical publications data, with a focus on figure search through the Yale Image Finder (YIF), an online biomedical figure discovery tool; clinical claims data, with a focus on finding similar patients using innovative similarity metrics: and linked data, with a focus on data representation and search. The first thesis contribution is a set of advanced YIF IR tools. including a new text region segmentation algorithm. the introduction of search by image type, and improved context-sensitive related figure retrieval. The second thesis contribution is the development of a novel Needleman-Wunsch-based alignment method that facilitates the retrieval of complex sequence-bused data by establishing trajectory similarity. Using the method, claims-based sequential patient data are compared, clustered, and used for the prediction of 30-day readmission after heart failure hospitalization. The third thesis contribution is the successful use of semantics to aid in biomedical information retrieval. Semantic search over VIF content is demonstrated using a new ontology that defines semantic relationships between figures and their data additionally, a schema for the generation of nanopublications is introduced as a means to convey basic scientific findings in an informal way.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355105520Subjects--Topical Terms:

583857
Bioinformatics.
Index Terms--Genre/Form:

554714
Electronic books.

Advanced Information Retrieval and Extraction in Research and Clinical Settings.
LDR:02793ntm a2200313K 4500 001 912411
005 20180608141653.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355105520
035 $a (MiAaPQ)AAI10633260
035 $a AAI10633260
040 $a MiAaPQ $b eng $c MiAaPQ
100 1 $a Nagy, Mate Levente. $3 1184759
245 1 0 $a Advanced Information Retrieval and Extraction in Research and Clinical Settings.
264 0 $c 2017
300 $a 1 online resource (141 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Adviser: Michael Krauthanmmer.
502 $a Thesis (Ph.D.)--Yale University, 2017.
504 $a Includes bibliographical references
520 $a Due to the seemingly endless stream of publications and the tremendous amount of medical data collected. the development of advanced information extraction (IE) and information retrieval (IR) tools is becoming important to ensure continued access to meaningful information from an increasing amount of accumulated data. In this dissertation, IR. and IE are explored in three data domains: biomedical publications data, with a focus on figure search through the Yale Image Finder (YIF), an online biomedical figure discovery tool; clinical claims data, with a focus on finding similar patients using innovative similarity metrics: and linked data, with a focus on data representation and search. The first thesis contribution is a set of advanced YIF IR tools. including a new text region segmentation algorithm. the introduction of search by image type, and improved context-sensitive related figure retrieval. The second thesis contribution is the development of a novel Needleman-Wunsch-based alignment method that facilitates the retrieval of complex sequence-bused data by establishing trajectory similarity. Using the method, claims-based sequential patient data are compared, clustered, and used for the prediction of 30-day readmission after heart failure hospitalization. The third thesis contribution is the successful use of semantics to aid in biomedical information retrieval. Semantic search over VIF content is demonstrated using a new ontology that defines semantic relationships between figures and their data additionally, a schema for the generation of nanopublications is introduced as a means to convey basic scientific findings in an informal way.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Bioinformatics. $3 583857
650 4 $a Information science. $3 561178
655 7 $a Electronic books. $2 local $3 554714
690 $a 0715
690 $a 0723
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Yale University. $3 1178968
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10633260 $z click for full text (PQDT)