語系:
繁體中文
English
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
Learning accurate regressors for pre...
~
Lin, Hsiu-Chin.
FindBook
Google Book
Amazon
博客來
Learning accurate regressors for predicting survival times of individual cancer patients.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Learning accurate regressors for predicting survival times of individual cancer patients./
作者:
Lin, Hsiu-Chin.
面頁冊數:
127 p.
附註:
Source: Masters Abstracts International, Volume: 49-03, page: .
Contained By:
Masters Abstracts International49-03.
標題:
Artificial Intelligence. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR68893
ISBN:
9780494688939
Learning accurate regressors for predicting survival times of individual cancer patients.
Lin, Hsiu-Chin.
Learning accurate regressors for predicting survival times of individual cancer patients.
- 127 p.
Source: Masters Abstracts International, Volume: 49-03, page: .
Thesis (M.Sc.)--University of Alberta (Canada), 2011.
Survival prediction is the task of predicting the length of time that an individual patient will survive; accurate predictions can give doctors better guidelines on selecting treatments and planning futures. This differs from the standard survival analysis, which focuses on population-based studies and tries to discover the prognostic factors and/or analyze the median survival times of different groups of patients.
ISBN: 9780494688939Subjects--Topical Terms:
769149
Artificial Intelligence.
Learning accurate regressors for predicting survival times of individual cancer patients.
LDR
:03153nam 2200289 4500
001
1393314
005
20110311132659.5
008
130515s2011 ||||||||||||||||| ||eng d
020
$a
9780494688939
035
$a
(UMI)AAIMR68893
035
$a
AAIMR68893
040
$a
UMI
$c
UMI
100
1
$a
Lin, Hsiu-Chin.
$3
1671848
245
1 0
$a
Learning accurate regressors for predicting survival times of individual cancer patients.
300
$a
127 p.
500
$a
Source: Masters Abstracts International, Volume: 49-03, page: .
502
$a
Thesis (M.Sc.)--University of Alberta (Canada), 2011.
520
$a
Survival prediction is the task of predicting the length of time that an individual patient will survive; accurate predictions can give doctors better guidelines on selecting treatments and planning futures. This differs from the standard survival analysis, which focuses on population-based studies and tries to discover the prognostic factors and/or analyze the median survival times of different groups of patients.
520
$a
The objective of our work, survival prediction, is different: to find the most accurate model for predicting the survival times for each individual patient. We view this as a regression problem, where we try to map the features for each patient to his/her survival time. As the relationship between features and survival time is still not understood, we consider various ways to learn these models from historical patient records. This is challenging in medical/clinical data due to the presence of irrelevant features, outliers, and missing class labels. This dissertation describes our approach for overcoming these, and other challenges, producing techniques that can predict survival times.
520
$a
We focus our experiments on a data set of 2402 patients, including 1260 censored patients (i.e., whose survival time is not known). Our approach consists of two major steps. In the first step, we apply various grouping methods to divide the data set into smaller populations. In the second step, we apply different regression models to each sub-group we obtained from the first step. Our experiments show that the linear regression, the support vector regression, and the gating regression are effective: each predictor can obtain an average cross validated relative absolute error lower than 0.54 (where the average relative absolute error of a regressor is E &sqbl0;&vbm0;t-p&vbm0;p&sqbr0; where t is the true survival time and p is our prediction for each patient). We also use our regressors to classify each patient into "long survivor" versus "short survivor" where the classification boundary is the median survival time of the entire population; here, we show that several regressors can achieve at least 70% accuracy. These experimental results verify that we can effectively predict patients' survival times with a combination of statistical and machine learning approaches.
590
$a
School code: 0351.
650
4
$a
Artificial Intelligence.
$3
769149
650
4
$a
Computer Science.
$3
626642
650
4
$a
Health Sciences, Oncology.
$3
1018566
690
$a
0800
690
$a
0984
690
$a
0992
710
2
$a
University of Alberta (Canada).
$3
626651
773
0
$t
Masters Abstracts International
$g
49-03.
790
$a
0351
791
$a
M.Sc.
792
$a
2011
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR68893
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9156453
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入