Survival Analysis

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Jieping Ye - One of the best experts on this subject based on the ideXlab platform.

  • Multi-task Survival Analysis
    2017 IEEE International Conference on Data Mining (ICDM), 2017
    Co-Authors: Lu Wang, Yan Li, Jiayu Zhou, Jieping Ye
    Abstract:

    Collecting labeling information of time-to-event Analysis is naturally very time consuming, i.e., one has to wait for the occurrence of the event of interest, which may not always be observed for every instance. By taking advantage of censored instances, Survival Analysis methods internally consider more samples than standard regression methods, which partially alleviates this data insufficiency problem. Whereas most existing Survival Analysis models merely focus on a single Survival prediction task, when there are multiple related Survival prediction tasks, we may benefit from the tasks relatedness. Simultaneously learning multiple related tasks, multi-task learning (MTL) provides a paradigm to alleviate data insufficiency by bridging data from all tasks and improves generalization performance of all tasks involved. Even though MTL has been extensively studied, there is no existing work investigating MTL for Survival Analysis. In this paper, we propose a novel multi-task Survival Analysis framework that takes advantage of both censored instances and task relatedness. Specifically, based on two common used task relatedness assumptions, i.e., low-rank assumption and cluster structure assumption, we formulate two concrete models, COX-TRACE and COX-cCMTL, under the proposed framework, respectively. We develop efficient algorithms and demonstrate the performance of the proposed multi-task Survival Analysis models on the The Cancer Genome Atlas (TCGA) dataset. Our results show that the proposed approaches can significantly improve the prediction performance in Survival Analysis and can also discover some inherent relationships among different cancer types.

Lu Wang - One of the best experts on this subject based on the ideXlab platform.

  • Multi-task Survival Analysis
    2017 IEEE International Conference on Data Mining (ICDM), 2017
    Co-Authors: Lu Wang, Yan Li, Jiayu Zhou, Jieping Ye
    Abstract:

    Collecting labeling information of time-to-event Analysis is naturally very time consuming, i.e., one has to wait for the occurrence of the event of interest, which may not always be observed for every instance. By taking advantage of censored instances, Survival Analysis methods internally consider more samples than standard regression methods, which partially alleviates this data insufficiency problem. Whereas most existing Survival Analysis models merely focus on a single Survival prediction task, when there are multiple related Survival prediction tasks, we may benefit from the tasks relatedness. Simultaneously learning multiple related tasks, multi-task learning (MTL) provides a paradigm to alleviate data insufficiency by bridging data from all tasks and improves generalization performance of all tasks involved. Even though MTL has been extensively studied, there is no existing work investigating MTL for Survival Analysis. In this paper, we propose a novel multi-task Survival Analysis framework that takes advantage of both censored instances and task relatedness. Specifically, based on two common used task relatedness assumptions, i.e., low-rank assumption and cluster structure assumption, we formulate two concrete models, COX-TRACE and COX-cCMTL, under the proposed framework, respectively. We develop efficient algorithms and demonstrate the performance of the proposed multi-task Survival Analysis models on the The Cancer Genome Atlas (TCGA) dataset. Our results show that the proposed approaches can significantly improve the prediction performance in Survival Analysis and can also discover some inherent relationships among different cancer types.

Chandan K. Reddy - One of the best experts on this subject based on the ideXlab platform.

  • Machine Learning for Survival Analysis: A Survey
    ACM Computing Surveys, 2019
    Co-Authors: Ping Wang, Yan Li, Chandan K. Reddy
    Abstract:

    Survival Analysis is a subfield of statistics where the goal is to analyze and model data where the outcome is the time until an event of interest occurs. One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after a certain time point or when some instances do not experience any event during the monitoring period. This so-called censoring can be handled most effectively using Survival Analysis techniques. Traditionally, statistical approaches have been widely developed in the literature to overcome the issue of censoring. In addition, many machine learning algorithms have been adapted to deal with such censored data and tackle other challenging problems that arise in real-world data. In this survey, we provide a comprehensive and structured review of the statistical methods typically used and the machine learning techniques developed for Survival Analysis, along with a detailed taxonomy of the existing methods. We also discuss several topics that are closely related to Survival Analysis and describe several successful applications in a variety of real-world application domains. We hope that this article will give readers a more comprehensive understanding of recent advances in Survival Analysis and offer some guidelines for applying these approaches to solve new problems arising in applications involving censored data.

  • Machine Learning for Survival Analysis: A Survey
    arXiv: Learning, 2017
    Co-Authors: Ping Wang, Yan Li, Chandan K. Reddy
    Abstract:

    Accurately predicting the time of occurrence of an event of interest is a critical problem in longitudinal data Analysis. One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after a certain time point or when some instances do not experience any event during the monitoring period. Such a phenomenon is called censoring which can be effectively handled using Survival Analysis techniques. Traditionally, statistical approaches have been widely developed in the literature to overcome this censoring issue. In addition, many machine learning algorithms are adapted to effectively handle Survival data and tackle other challenging problems that arise in real-world data. In this survey, we provide a comprehensive and structured review of the representative statistical methods along with the machine learning techniques used in Survival Analysis and provide a detailed taxonomy of the existing methods. We also discuss several topics that are closely related to Survival Analysis and illustrate several successful applications in various real-world application domains. We hope that this paper will provide a more thorough understanding of the recent advances in Survival Analysis and offer some guidelines on applying these approaches to solve new problems that arise in applications with censored data.

  • Tensor-based Temporal Multi-Task Survival Analysis
    IEEE Transactions on Knowledge and Data Engineering, 1
    Co-Authors: Ping Wang, Chandan K. Reddy
    Abstract:

    Survival Analysis aims at predicting time to event of interest along with its probability on longitudinal data. It is commonly used to make predictions for a single specific event of interest at a given time point. However, predicting the occurrence of multiple events simultaneously and dynamically is needed in many applications. An intuitive way to solve this problem is to simply apply the regular Survival Analysis method independently to each task at each time point. However, it often leads to a suboptimal solution since the underlying dependencies between tasks are ignored, which motivates us to analyze these tasks jointly to select common features shared across all tasks. In this paper, we formulate a temporal Multi-Task learning framework (MTMT) using tensor representation. More specifically, given a Survival dataset and a sequence of time points, which are considered as the monitored time points, we model each task at each time point as a regular Survival Analysis problem and optimize them simultaneously. We demonstrate the performance of MTMT model on two real-world datasets. We show the superior performance of the MTMT model compared to several state-of-the-art models. We also provide the list of important features selected to demonstrate the interpretability of our model.

Jiayu Zhou - One of the best experts on this subject based on the ideXlab platform.

  • Multi-task Survival Analysis
    2017 IEEE International Conference on Data Mining (ICDM), 2017
    Co-Authors: Lu Wang, Yan Li, Jiayu Zhou, Jieping Ye
    Abstract:

    Collecting labeling information of time-to-event Analysis is naturally very time consuming, i.e., one has to wait for the occurrence of the event of interest, which may not always be observed for every instance. By taking advantage of censored instances, Survival Analysis methods internally consider more samples than standard regression methods, which partially alleviates this data insufficiency problem. Whereas most existing Survival Analysis models merely focus on a single Survival prediction task, when there are multiple related Survival prediction tasks, we may benefit from the tasks relatedness. Simultaneously learning multiple related tasks, multi-task learning (MTL) provides a paradigm to alleviate data insufficiency by bridging data from all tasks and improves generalization performance of all tasks involved. Even though MTL has been extensively studied, there is no existing work investigating MTL for Survival Analysis. In this paper, we propose a novel multi-task Survival Analysis framework that takes advantage of both censored instances and task relatedness. Specifically, based on two common used task relatedness assumptions, i.e., low-rank assumption and cluster structure assumption, we formulate two concrete models, COX-TRACE and COX-cCMTL, under the proposed framework, respectively. We develop efficient algorithms and demonstrate the performance of the proposed multi-task Survival Analysis models on the The Cancer Genome Atlas (TCGA) dataset. Our results show that the proposed approaches can significantly improve the prediction performance in Survival Analysis and can also discover some inherent relationships among different cancer types.

Yan Li - One of the best experts on this subject based on the ideXlab platform.

  • Machine Learning for Survival Analysis: A Survey
    ACM Computing Surveys, 2019
    Co-Authors: Ping Wang, Yan Li, Chandan K. Reddy
    Abstract:

    Survival Analysis is a subfield of statistics where the goal is to analyze and model data where the outcome is the time until an event of interest occurs. One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after a certain time point or when some instances do not experience any event during the monitoring period. This so-called censoring can be handled most effectively using Survival Analysis techniques. Traditionally, statistical approaches have been widely developed in the literature to overcome the issue of censoring. In addition, many machine learning algorithms have been adapted to deal with such censored data and tackle other challenging problems that arise in real-world data. In this survey, we provide a comprehensive and structured review of the statistical methods typically used and the machine learning techniques developed for Survival Analysis, along with a detailed taxonomy of the existing methods. We also discuss several topics that are closely related to Survival Analysis and describe several successful applications in a variety of real-world application domains. We hope that this article will give readers a more comprehensive understanding of recent advances in Survival Analysis and offer some guidelines for applying these approaches to solve new problems arising in applications involving censored data.

  • Machine Learning for Survival Analysis: A Survey
    arXiv: Learning, 2017
    Co-Authors: Ping Wang, Yan Li, Chandan K. Reddy
    Abstract:

    Accurately predicting the time of occurrence of an event of interest is a critical problem in longitudinal data Analysis. One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after a certain time point or when some instances do not experience any event during the monitoring period. Such a phenomenon is called censoring which can be effectively handled using Survival Analysis techniques. Traditionally, statistical approaches have been widely developed in the literature to overcome this censoring issue. In addition, many machine learning algorithms are adapted to effectively handle Survival data and tackle other challenging problems that arise in real-world data. In this survey, we provide a comprehensive and structured review of the representative statistical methods along with the machine learning techniques used in Survival Analysis and provide a detailed taxonomy of the existing methods. We also discuss several topics that are closely related to Survival Analysis and illustrate several successful applications in various real-world application domains. We hope that this paper will provide a more thorough understanding of the recent advances in Survival Analysis and offer some guidelines on applying these approaches to solve new problems that arise in applications with censored data.

  • Multi-task Survival Analysis
    2017 IEEE International Conference on Data Mining (ICDM), 2017
    Co-Authors: Lu Wang, Yan Li, Jiayu Zhou, Jieping Ye
    Abstract:

    Collecting labeling information of time-to-event Analysis is naturally very time consuming, i.e., one has to wait for the occurrence of the event of interest, which may not always be observed for every instance. By taking advantage of censored instances, Survival Analysis methods internally consider more samples than standard regression methods, which partially alleviates this data insufficiency problem. Whereas most existing Survival Analysis models merely focus on a single Survival prediction task, when there are multiple related Survival prediction tasks, we may benefit from the tasks relatedness. Simultaneously learning multiple related tasks, multi-task learning (MTL) provides a paradigm to alleviate data insufficiency by bridging data from all tasks and improves generalization performance of all tasks involved. Even though MTL has been extensively studied, there is no existing work investigating MTL for Survival Analysis. In this paper, we propose a novel multi-task Survival Analysis framework that takes advantage of both censored instances and task relatedness. Specifically, based on two common used task relatedness assumptions, i.e., low-rank assumption and cluster structure assumption, we formulate two concrete models, COX-TRACE and COX-cCMTL, under the proposed framework, respectively. We develop efficient algorithms and demonstrate the performance of the proposed multi-task Survival Analysis models on the The Cancer Genome Atlas (TCGA) dataset. Our results show that the proposed approaches can significantly improve the prediction performance in Survival Analysis and can also discover some inherent relationships among different cancer types.