東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Using network analysis to identify f...

Zamani Esfahlani, Farnaz.

Linked to FindBook

Google Book

Amazon

博客來

Using network analysis to identify factors differentiating antipsychotic treatment-resistant and treatment-responsive patients with psychosis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Using network analysis to identify factors differentiating antipsychotic treatment-resistant and treatment-responsive patients with psychosis./
Author:	Zamani Esfahlani, Farnaz.
Description:	67 p.
Notes:	Source: Masters Abstracts International, Volume: 55-02.
Contained By:	Masters Abstracts International55-02(E).
Subject:	Biomedical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1601655
ISBN:	9781339137513

Using network analysis to identify factors differentiating antipsychotic treatment-resistant and treatment-responsive patients with psychosis.
Zamani Esfahlani, Farnaz.

Using network analysis to identify factors differentiating antipsychotic treatment-resistant and treatment-responsive patients with psychosis. - 67 p.

Source: Masters Abstracts International, Volume: 55-02.

Thesis (M.S.)--State University of New York at Binghamton, 2015.

There is an increasing evidence that suggest mental disorders are caused by the complex interaction of various symptoms rather than isolated effects of a limited number of latent factors. The complex interactions among symptoms of mental disorders can be studied using network science. In this study, we applied analytical tools of network science to understand the mechanism of symptoms interaction in treatment-resistant schizophrenia patients. In this regard, we constructed a network of schizophrenia risk factors and symptoms using the CATIE dataset and analyzed different network properties to infer the difference of symptomatic interaction between treatment-resistant and treatment-responsive patients. According to the results of our study, "Preoccupation" is the most central node in the symptom interaction network of treatment-resistant patients, whereas "Unusual Thought Content" and "Stereotyped Thinking" are the most central symptoms in treatment-responsive patients. These central symptoms are worth further analysis for effective planning of treatment strategy for schizophrenia patients.

ISBN: 9781339137513Subjects--Topical Terms:

535387
Biomedical engineering.

Using network analysis to identify factors differentiating antipsychotic treatment-resistant and treatment-responsive patients with psychosis.
LDR:02033nmm a2200277 4500 001 2078418
005 20161122122601.5
008 170521s2015 ||||||||||||||||| ||eng d
020 $a 9781339137513
035 $a (MiAaPQ)AAI1601655
035 $a AAI1601655
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zamani Esfahlani, Farnaz. $3 3194004
245 1 0 $a Using network analysis to identify factors differentiating antipsychotic treatment-resistant and treatment-responsive patients with psychosis.
300 $a 67 p.
500 $a Source: Masters Abstracts International, Volume: 55-02.
500 $a Adviser: Hiroki Sayama.
502 $a Thesis (M.S.)--State University of New York at Binghamton, 2015.
520 $a There is an increasing evidence that suggest mental disorders are caused by the complex interaction of various symptoms rather than isolated effects of a limited number of latent factors. The complex interactions among symptoms of mental disorders can be studied using network science. In this study, we applied analytical tools of network science to understand the mechanism of symptoms interaction in treatment-resistant schizophrenia patients. In this regard, we constructed a network of schizophrenia risk factors and symptoms using the CATIE dataset and analyzed different network properties to infer the difference of symptomatic interaction between treatment-resistant and treatment-responsive patients. According to the results of our study, "Preoccupation" is the most central node in the symptom interaction network of treatment-resistant patients, whereas "Unusual Thought Content" and "Stereotyped Thinking" are the most central symptoms in treatment-responsive patients. These central symptoms are worth further analysis for effective planning of treatment strategy for schizophrenia patients.
590 $a School code: 0792.
650 4 $a Biomedical engineering. $3 535387
650 4 $a Systems science. $3 3168411
690 $a 0541
690 $a 0790
710 2 $a State University of New York at Binghamton. $b Bioengineering. $3 1059042
773 0 $t Masters Abstracts International $g 55-02(E).
790 $a 0792
791 $a M.S.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1601655