Leveraging Heterogeneity in Time-to-Event Predictions- [electronic resource]
Leveraging Heterogeneity in Time-to-Event Predictions- [electronic resource]
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214100502
- ISBN
- 9798380855570
- DDC
- 004
- 저자명
- Nagpal, Chirag.
- 서명/저자
- Leveraging Heterogeneity in Time-to-Event Predictions - [electronic resource]
- 발행사항
- [S.l.]: : Carnegie Mellon University., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(167 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
- 주기사항
- Advisor: Dubrawski, Artur.
- 학위논문주기
- Thesis (Ph.D.)--Carnegie Mellon University, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Time-to-Event Regression, often referred to as Survival Analysis or Censored Regression involves learning of statistical estimators of the survival distribution of an individual given their covariates. As opposed to standard regression, survival analysis is challenging as it involves accounting for outcomes censored due to loss of follow up. This circumstance is common in, e.g., bio-statistics, predictive maintenance, and econometrics. With the recent advances in machine learning methodology, especially deep learning, it is now possible to exploit expressive representations to help model survival outcomes. My thesis contributes to this new body of work by demonstrating that problems in survival analysis often manifest inherent heterogeneity that can be effectively discovered, characterized, and modeled to learn better estimators of survival.Heterogeneity may arise in a multitude of settings in the context of survival analysis. Some examples include heterogeneity in the form of input features or covariates (for instance, static vs. streaming, time-varying data), or multiple outcomes of simultaneous interest (more commonly referred to as competing risks). Other sources of heterogeneity involve latent subgroups that manifest different base survival rates or diverse responses to an intervention or treatment.In this thesis, I aim to demonstrate that carefully modelling the inherent structure of heterogeneity can boost predictive power of survival analysis models while improving their specificity and precision of estimated survival at an individual level. An overarching methodological framework of this thesis is the application of graphical models to impose inherent structure in time-to-event problems that explicitly model heterogeneity, while employing advances in deep learning to learn powerful representations of data. Furthermore, through innovative probabilistic and numerical optimization techniques we explore how the learnt estimators can be made actionable tools for decision support. By enforcing constraints that improve model interpretability, we explore opportunities for enhancing the utility of such models, a requirement that is paramount in critical scenarios such as healthcare.
- 일반주제명
- Computer science.
- 일반주제명
- Biostatistics.
- 일반주제명
- Statistics.
- 일반주제명
- Bioinformatics.
- 키워드
- Deep learning
- 키워드
- Graphical models
- 키워드
- Time-to-event
- 기타저자
- Carnegie Mellon University Computer Science
- 기본자료저록
- Dissertations Abstracts International. 85-05B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.