TY - JOUR
T1 - A Novel Tensor-Based Temporal Multi-Task Survival Analysis Model
AU - Wang, Ping
AU - Shi, Tian
AU - Reddy, Chandan K.
N1 - Publisher Copyright:
© 1989-2012 IEEE.
PY - 2021/9/1
Y1 - 2021/9/1
N2 - Survival analysis aims at predicting the 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 of interest simultaneously and dynamically is needed in many real-world applications. An intuitive way to solve this problem is to simply apply the standard survival analysis method independently to each prediction task at each time point. However, it often leads to a sub-optimal solution since the underlying dependencies between these tasks are ignored. This motivates us to analyze these prediction tasks jointly in order to select the common features shared across all the tasks. In this paper, we formulate a temporal (Multiple Time points) Multi-Task learning framework (MTMT) for survival analysis problems using tensor representation. More specifically, given a survival dataset and a sequence of time points, which are considered as the monitored time points for the events of interest, we reformulate the survival analysis problem to jointly handle each task at each time point and optimize them simultaneously. We demonstrate the performance of the proposed MTMT model on important real-world datasets, including employee attrition and medical records. We show the superior performance of the MTMT model compared to several state-of-the-art models using standard metrics. We also provide the list of important features selected by our MTMT model thus demonstrating the interpretability of the proposed model.
AB - Survival analysis aims at predicting the 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 of interest simultaneously and dynamically is needed in many real-world applications. An intuitive way to solve this problem is to simply apply the standard survival analysis method independently to each prediction task at each time point. However, it often leads to a sub-optimal solution since the underlying dependencies between these tasks are ignored. This motivates us to analyze these prediction tasks jointly in order to select the common features shared across all the tasks. In this paper, we formulate a temporal (Multiple Time points) Multi-Task learning framework (MTMT) for survival analysis problems using tensor representation. More specifically, given a survival dataset and a sequence of time points, which are considered as the monitored time points for the events of interest, we reformulate the survival analysis problem to jointly handle each task at each time point and optimize them simultaneously. We demonstrate the performance of the proposed MTMT model on important real-world datasets, including employee attrition and medical records. We show the superior performance of the MTMT model compared to several state-of-the-art models using standard metrics. We also provide the list of important features selected by our MTMT model thus demonstrating the interpretability of the proposed model.
KW - Multi-task learning
KW - regression analysis
KW - regularization
KW - survival analysis
KW - temporal models
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U2 - 10.1109/TKDE.2020.2967700
DO - 10.1109/TKDE.2020.2967700
M3 - Article
AN - SCOPUS:85112773546
SN - 1041-4347
VL - 33
SP - 3311
EP - 3322
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 9
M1 - 8962039
ER -