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

Probabilistic Time-To-Event Modeling...

Chapfuwa, Paidamoyo.

FindBook

Google Book

Amazon

博客來

Probabilistic Time-To-Event Modeling Approaches for Risk Profiling.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Probabilistic Time-To-Event Modeling Approaches for Risk Profiling./
作者:	Chapfuwa, Paidamoyo.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	168 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
Contained By:	Dissertations Abstracts International82-12B.
標題:	Artificial intelligence. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28321565
ISBN:	9798738637810

Probabilistic Time-To-Event Modeling Approaches for Risk Profiling.
Chapfuwa, Paidamoyo.

Probabilistic Time-To-Event Modeling Approaches for Risk Profiling. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 168 p.

Source: Dissertations Abstracts International, Volume: 82-12, Section: B.

Thesis (Ph.D.)--Duke University, 2021.

This item must not be sold to any third party vendors.

Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice. Time-to-event analysis, also called survival analysis, stands as one of the most representative examples of such statistical models. Models for predicting the time of a future event are crucial for risk assessment, across a diverse range of applications, i.e., drug development, risk profiling, and clinical trials, and such data are also relevant in fields like manufacturing (e.g., for equipment monitoring). Existing time-to-event (survival) models have focused primarily on preserving the pairwise ordering of estimated event times (i.e., relative risk). In this dissertation, we propose neural time-to-event models that account for calibration and uncertainty, while predicting accurate absolute event times. Specifically, we introduce an adversarial nonparametric model for estimating matched time-to-event distributions for probabilistically concentrated and accurate predictions. We consider replacing the discriminator of the adversarial nonparametric model with a survival-function matching estimator that accounts for model calibration. The proposed estimator can be used as a means of estimating and comparing conditional survival distributions while accounting for the predictive uncertainty of probabilistic models.Moreover, we introduce a theoretically grounded unified counterfactual inference framework for survival analysis, which adjusts for bias from two sources, namely, confounding (from covariates influencing both the treatment assignment and the outcome) and censoring (informative or non-informative). To account for censoring biases, a proposed flexible and nonparametric probabilistic model is leveraged for event times. Then, we formulate a model-free nonparametric hazard ratio metric for comparing treatment effects or leveraging prior randomized real-world experiments in longitudinal studies. Further, the proposed model-free hazard-ratio estimator can be used to identify or stratify heterogeneous treatment effects. For stratifying risk profiles, we formulate an interpretable time-to-event driven clustering method for observations (patients) via a Bayesian nonparametric stick-breaking representation of the Dirichlet Process. Finally, through experiments on real-world datasets, consistent improvements in predictive performance and interpretability are demonstrated relative to existing state-of-the-art survival analysis models.

ISBN: 9798738637810Subjects--Topical Terms:

516317
Artificial intelligence.
Subjects--Index Terms:

Calibration and uncertainty

Probabilistic Time-To-Event Modeling Approaches for Risk Profiling.
LDR:03923nmm a2200433 4500 001 2282012
005 20210927083518.5
008 220723s2021 ||||||||||||||||| ||eng d
020 $a 9798738637810
035 $a (MiAaPQ)AAI28321565
035 $a AAI28321565
040 $a MiAaPQ $c MiAaPQ
100 1 $a Chapfuwa, Paidamoyo. $3 3560739
245 1 0 $a Probabilistic Time-To-Event Modeling Approaches for Risk Profiling.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 168 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
500 $a Advisor: Carin, Lawrence;Henao, Ricardo.
502 $a Thesis (Ph.D.)--Duke University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice. Time-to-event analysis, also called survival analysis, stands as one of the most representative examples of such statistical models. Models for predicting the time of a future event are crucial for risk assessment, across a diverse range of applications, i.e., drug development, risk profiling, and clinical trials, and such data are also relevant in fields like manufacturing (e.g., for equipment monitoring). Existing time-to-event (survival) models have focused primarily on preserving the pairwise ordering of estimated event times (i.e., relative risk). In this dissertation, we propose neural time-to-event models that account for calibration and uncertainty, while predicting accurate absolute event times. Specifically, we introduce an adversarial nonparametric model for estimating matched time-to-event distributions for probabilistically concentrated and accurate predictions. We consider replacing the discriminator of the adversarial nonparametric model with a survival-function matching estimator that accounts for model calibration. The proposed estimator can be used as a means of estimating and comparing conditional survival distributions while accounting for the predictive uncertainty of probabilistic models.Moreover, we introduce a theoretically grounded unified counterfactual inference framework for survival analysis, which adjusts for bias from two sources, namely, confounding (from covariates influencing both the treatment assignment and the outcome) and censoring (informative or non-informative). To account for censoring biases, a proposed flexible and nonparametric probabilistic model is leveraged for event times. Then, we formulate a model-free nonparametric hazard ratio metric for comparing treatment effects or leveraging prior randomized real-world experiments in longitudinal studies. Further, the proposed model-free hazard-ratio estimator can be used to identify or stratify heterogeneous treatment effects. For stratifying risk profiles, we formulate an interpretable time-to-event driven clustering method for observations (patients) via a Bayesian nonparametric stick-breaking representation of the Dirichlet Process. Finally, through experiments on real-world datasets, consistent improvements in predictive performance and interpretability are demonstrated relative to existing state-of-the-art survival analysis models.
590 $a School code: 0066.
650 4 $a Artificial intelligence. $3 516317
650 4 $a Biostatistics. $3 1002712
650 4 $a Statistics. $3 517247
653 $a Calibration and uncertainty
653 $a Counterfactual inference
653 $a Hazard ratio
653 $a Representation learning
653 $a Survival analysis
653 $a Calibration
653 $a Uncertainty
653 $a Absolute event times
653 $a Predictive performance
653 $a Interpretability
690 $a 0800
690 $a 0308
690 $a 0463
710 2 $a Duke University. $b Electrical and Computer Engineering. $3 1032075
773 0 $t Dissertations Abstracts International $g 82-12B.
790 $a 0066
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28321565