東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Prediction of Traditional Chinese Me...

Mist, Scott David.

FindBook

Google Book

Amazon

博客來

Prediction of Traditional Chinese Medicine diagnosis from psychosocial questionnaires.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Prediction of Traditional Chinese Medicine diagnosis from psychosocial questionnaires./
作者:	Mist, Scott David.
面頁冊數:	219 p.
附註:	Source: Dissertation Abstracts International, Volume: 70-01, Section: B, page: 0206.
Contained By:	Dissertation Abstracts International70-01B.
標題:	Health Sciences, Medicine and Surgery. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3343773
ISBN:	9780549988762

Prediction of Traditional Chinese Medicine diagnosis from psychosocial questionnaires.
Mist, Scott David.

Prediction of Traditional Chinese Medicine diagnosis from psychosocial questionnaires. - 219 p.

Source: Dissertation Abstracts International, Volume: 70-01, Section: B, page: 0206.

Thesis (Ph.D.)--Portland State University, 2008.

Astonishingly, most Traditional Chinese Medicine (CM) research in the West proceeds without CM diagnoses, perhaps because CM diagnosis is time consuming or not considered relevant. One way to improve the feasibility of incorporating CM diagnosis would be to prescreen participants using questionnaires. This would allow cost savings in recruitment, and balancing of treatments by CM diagnosis. Consequently, the hypothesis of this research was that pre-treatment questionnaires can predict CM diagnosis.

ISBN: 9780549988762Subjects--Topical Terms:

1017756
Health Sciences, Medicine and Surgery.

Prediction of Traditional Chinese Medicine diagnosis from psychosocial questionnaires.
LDR:03216nam 2200277 4500 001 1402782
005 20111103090533.5
008 130515s2008 ||||||||||||||||| ||eng d
020 $a 9780549988762
035 $a (UMI)AAI3343773
035 $a AAI3343773
040 $a UMI $c UMI
100 1 $a Mist, Scott David. $3 1681998
245 1 0 $a Prediction of Traditional Chinese Medicine diagnosis from psychosocial questionnaires.
300 $a 219 p.
500 $a Source: Dissertation Abstracts International, Volume: 70-01, Section: B, page: 0206.
502 $a Thesis (Ph.D.)--Portland State University, 2008.
520 $a Astonishingly, most Traditional Chinese Medicine (CM) research in the West proceeds without CM diagnoses, perhaps because CM diagnosis is time consuming or not considered relevant. One way to improve the feasibility of incorporating CM diagnosis would be to prescreen participants using questionnaires. This would allow cost savings in recruitment, and balancing of treatments by CM diagnosis. Consequently, the hypothesis of this research was that pre-treatment questionnaires can predict CM diagnosis.
520 $a Baseline questionnaires from 195 participants with temporomandibular joint disorder (TMD) were examined to test the hypothesis. Two methods, logistic regression (LR) and reconstructability analysis (RA), were used in conjunction to test the hypothesis. Models were created that predicted CM diagnosis from pre-treatment questionnaires. First, LR models were prepared to predict the diagnosis for each subject using direct effects only. Then variable-based (VBRA) and state-based RA (SBRA) were used to select potentially important interaction terms. These terms were then introduced into the original LR model and assessed for clinical relevance, model simplification, and improved diagnosis prediction.
520 $a Prediction equations were identified for each of the four CM diagnoses that increased prediction accuracy by 3.9% to 22.2% compared to a naive model that simply used population prevalence. VBRA successfully identified one clinically significant interaction term that improved classification by 1.8% and simplified the model. SBRA identified a state that improved classification by 3.2% and further simplified the original model. The classification errors were reduced between 23.9% and 56.0% when adjusting for baseline prevalence. When assessing the trade-off of specificity and sensitivity, the model efficiencies for the diagnoses ranged from 0.61 to 0.83 where 0.50 indicates a model with no improvement over a model that simply assumes population prevalence and 1.00 indicates a perfect model.
520 $a Since the study identified successful prediction algorithms for three of the most common diagnoses, the research successfully demonstrated the use of questionnaires to help select patients with specific CM diagnoses. The use of VBRA and SBRA with LR to find interaction terms was also demonstrated. Future studies of CM diagnoses should build upon the current study.
590 $a School code: 0180.
650 4 $a Health Sciences, Medicine and Surgery. $3 1017756
690 $a 0564
710 2 $a Portland State University. $3 1020459
773 0 $t Dissertation Abstracts International $g 70-01B.
790 $a 0180
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3343773