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001 on1105587039;

003 OCoLC;

005 20190828020454.0;

006 m o d ;

007 cr unu||||||||;

008 190624s2019 ncua obm 000 0 eng d;

035 a| (Sirsi) o1105587039;

035 a| (OCoLC)1105587039;

049 a| EREE;

090 a| Q325.5;

100 1 a| Gudivada, Akhil,e| author.?| UNAUTHORIZED;

245 10 a| Machine learning based medical information retrieval systems /c| by Akhil Gudivada.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2019.;

300 a| 53 pages :b| illustrations (some color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 1.757 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2019.;

500 a| Presented to the faculty of the Department of Computer Science;

500 a| Advisor: Nasseh Tabrizi;

500 a| Title from PDF t.p. (viewed August 27, 2019).;

520 3 a| As many fields progress with the assistance of cognitive computing, the field of health care is also adapting, providing many benefits to all users. However, advancements in this area are hindered by several challenges such as the void between user queries and the knowledge base, query mismatches, and range of domain knowledge in users. In this research, we explore existing methodologies as well as look into existing real-life applications that are used in the medical field today. We also look into specific challenges and techniques that can be used to overcome these barriers, specifically related to cognitive computing in the medical domain. Future information retrieval (IR) models that can be tailored specifically for medically intensive applications which can handle large amounts of data are explored as well. The purpose of this work is to give the reader an in-depth understanding of artificial intelligence being used in the medical field today, as well as future possibilities in the domain. The models and techniques designed and discussed in this research can help provide a framework, or starting point for those interested in effectively developing, maintaining, and using these models to help improve the quality of health-care. Furthermore, we explore the development process of such a model and discuss the steps including data collection, processing, model creation, and also improvement.;

504 a| Includes bibliographical references.;

650 0 a| Machine learning.=| ^A145211;

650 0 a| Big data.=| ^A1157100;

653 a| medical computing;

700 1 a| Tabrizi, M. H. N.,e| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/7256;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5111019;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5111019-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 6/24/2019x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5111019-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 6/24/2019x| ETDz| HERESOURCE;

Machine learning based medical information retrieval systems / by Akhil Gudivada.

Author/creator	Gudivada, Akhil author.
Other author	Tabrizi, M. H. N., degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2019.
Description	53 pages : illustrations (some color)
Supplemental Content	Access via ScholarShip
Subjects	Machine learning. Big data.

Summary	As many fields progress with the assistance of cognitive computing, the field of health care is also adapting, providing many benefits to all users. However, advancements in this area are hindered by several challenges such as the void between user queries and the knowledge base, query mismatches, and range of domain knowledge in users. In this research, we explore existing methodologies as well as look into existing real-life applications that are used in the medical field today. We also look into specific challenges and techniques that can be used to overcome these barriers, specifically related to cognitive computing in the medical domain. Future information retrieval (IR) models that can be tailored specifically for medically intensive applications which can handle large amounts of data are explored as well. The purpose of this work is to give the reader an in-depth understanding of artificial intelligence being used in the medical field today, as well as future possibilities in the domain. The models and techniques designed and discussed in this research can help provide a framework, or starting point for those interested in effectively developing, maintaining, and using these models to help improve the quality of health-care. Furthermore, we explore the development process of such a model and discuss the steps including data collection, processing, model creation, and also improvement.
General note	Presented to the faculty of the Department of Computer Science
General note	Advisor: Nasseh Tabrizi
General note	Title from PDF t.p. (viewed August 27, 2019).
Dissertation note	M.S. East Carolina University 2019.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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Machine learning based medical information retrieval systems / by Akhil Gudivada.

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