research-article

Open access

Deep State-Space Generative Model For Correlated Time-to-Event Predictions

Authors:

Claire CuiAuthors Info & Claims

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 1552 - 1562

https://doi.org/10.1145/3394486.3403206

Published: 20 August 2020 Publication History

Abstract

Capturing the inter-dependencies among multiple types of clinically-critical events is critical not only to accurate future event prediction, but also to better treatment planning. In this work, we propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events (e.g., kidney failure, mortality) by explicitly modeling the temporal dynamics of patients' latent states. Based on these learned patient states, we further develop a new general discrete-time formulation of the hazard rate function to estimate the survival distribution of patients with significantly improved accuracy. Extensive evaluations over real EMR data show that our proposed model compares favorably to various state-of-the-art baselines. Furthermore, our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.

References

[1]

Martín Abadi and et al. 2015. TensorFlow: A System for Large-Scale Machine Learning.

[2]

Ahmed M. Alaa and Mihaela van der Schaar. 2017. Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks. In NIPS. 2329--2337.

[3]

Ahmed M. Alaa and Mihaela van der Schaar. 2019. Attentive State-Space Modeling of Disease Progression. In NeurIPS.

[4]

James E. Barretta and Anthony C. C. Coolena. 2010. Gaussian process regression for survival data with competing risks.

[5]

Alexis Bellot and Mihaela van der Schaar. 2018. Multitask Boosting for Survival Analysis with Competing Risks. In NeurIPS.

[6]

Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li, Courtney Page, Benjamin Goldstein, Lawrence Carin, and Ricardo Henao. 2018. Adversarial Time-to-Event Modeling. In ICML.

[7]

Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and YanLiu. 2018. Recurrent Neural Networks for Multivariate Time Series with Missing Values. Sci. Rep.8, 1 (2018).

[8]

Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F Stewart, and Jimeng Sun. 2015. Doctor AI: Predicting Clinical Events via Recurrent Neural Networks. (Nov. 2015). arXiv:1511.05942

[9]

D. R. Cox. 1992. Regression models and life-tables. In Breakthroughs in statistics. 527--541.

[10]

Tamara Fernandez, Nicolas Rivera, and Yee Whye Teh. 2016. Gaussian Processes for Survival Analysis. In NIPS. 5021--5029.

[11]

J. P. Fine and R. J. Gray. 1999. A Proportional Hazards Model for the Sub distribution of a Competing Risk. Journal of the American statistical association 94, 446(1999), 496--509.

[12]

Marco Fraccaro, Simon Kamronn, Ulrich Paquet, and Ole Winther. 2017. A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning. In NIPS.

[13]

Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, and Ole Winther. 2016. Sequential Neural Models with Stochastic Layers. In NIPS.

Digital Library

[14]

Marzyeh Ghassemi, Mike Wu, Michael C. Hughes, Peter Szolovits, and Finale Doshi-Velez. 2017. Predicting intervention onset in the ICU with switching state space models. American Medical Informatics Association (AMIA),.

[15]

E. Giunchiglia, A. Nemchenko, and M. van der Schaar. 2018. RNN-SURV: A Deep Recurrent Model for Survival Analysis. In International Conference on Artificial Neural Networks (ICANN).

[16]

Alistair E.W. Johnson, Tom J. Pollard, Lu Shen, Li wei H. Lehman, Mengling Feng,Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, andRoger G. Mark. 2016. MIMIC-III, A Freely Accessible Critical Care Database. Scientific Data 3 (2016). Article number: 160035.

[17]

Alan E. Jones, Stephen Trzeciak, and MD Jeffrey A. Kline. 2010. The Sequential Organ Failure Assessment score for predicting outcome in patients with severe sepsis and evidence of hypoper fusion at the time of emergency department presentation.

[18]

Maximilian Karl, Maximilian Soelch, Justin Bayer, and Patrick van der Smagt. 2017. Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data. In ICLR.

[19]

Jared L. Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, and Yuval Kluger. 2018. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Medical Research Methodology 18, 1 (2018), 24.

[20]

Rahul G. Krishnan, Uri Shalit, and David Sontag. 2015. Deep Kalman Filters. CoRRabs/1511.05121 (2015).

[21]

Rahul G Krishnan, Uri Shalit, and David Sontag. 2017. Structured Inference Networks for Nonlinear State Space Models. In AAAI.

[22]

Changhee Lee, William R. Zame, Jinsung Yoon, and Mihaela van der Schaar. 2018. DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks. In AAAI.

[23]

Zachary C Lipton, David C Kale, Charles Elkan, and Randall Wetzel. 2016. Learning to Diagnose with LSTM Recurrent Neural Networks. In International Conference on Learning Representations (ICLR).

[24]

Zitao Liu and Milos Hauskrecht. 2013. Clinical Time Series Prediction with a Hierarchical Dynamical System.Artificial Intelligence in Medicine(2013), 227--237.

[25]

Zitao Liu and Milos Hauskrecht. 2016. Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data. In AAAI.

[26]

Jiayu Zhou Dongxiao Zhu Lu Wang, Yan Li and Jieping Ye. 2017. Multi-task Survival Analysis. In IEEE International Conference on Data Mining.

[27]

Wu M, Ghassemi M, Feng M, Celi LA, Szolovits P, and Doshi-Velez F. 2017. Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database. J Am Med Inform Assoc(2017),488--495.

[28]

Aniruddh Raghu, Matthieu Komorowski, Leo Anthony Celi, Peter Szolovits, and Marzyeh Ghassemi. 2017. Continuous State-Space Models for Optimal Sepsis Treatment: a Deep Reinforcement Learning Approach. In Proceedings of the 2nd Machine Learning for Healthcare Conference. 147--163.

[29]

Alvin Rajkomar and et al. 2018. Scalable and Accurate Deep Learning with Electronic Health Records. Digital Medicine1 (2018). Article number: 18.

[30]

Rajesh Ranganath, Adler Perotte, Noémie Elhadad, and David Blei. 2016. Deep Survival Analysis. In Proceedings of Machine Learning Research. Vol. 56. 101--114.

[31]

Kan Ren, Jiarui Qin, Lei Zheng, Zhengyu Yang, Weinan Zhang, Lin Qiu, and Yong Yu. 2019. Deep Recurrent Survival Analysis. In AAAI.

[32]

Ying Sha and May D Wang. 2017. Interpretable Predictions of Clinical Out-comes with An Attention-based Recurrent Neural Network. In Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics.

Digital Library

[33]

Huan Song, Deepta Rajan, Jayaraman J Thiagarajan, and Andreas Spanias. 2018.Attend and Diagnose: Clinical Time Series Analysis Using Attention Models. InAAAI.

[34]

Yuan Xue, Denny Zhou, Nan Du, Andrew M. Dai, Zhen Xu, Kun Zhang, and Claire Cui. 2019. Deep Physiological State Space Model for Clinical Forecasting. In NeurIPS Workshop on Machine Learning for Health.

[35]

Jieping Ye Yan Li, Jie Wang and Chandan K. Reddy. 2016. A Multi-Task Learning Formulation for Survival Analysis. In 22nd ACM SIGKDD.

[36]

Quan Zhang and Mingyuan Zhou. 2018. Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks. In NeurIPS.

Cited By

Fujiwara RAl Hasan MXiong L(2022)C-Cast: A Real-Time Forecasting Model for a Controlled SequenceProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557817(5112-5115)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557817
Ng KGhalwash MChakraborty PSow DKoseki AYanagisawa HKudo M(2022)Data-Driven Disease Progression ModelingHealthcare Information Management Systems10.1007/978-3-031-07912-2_17(247-276)Online publication date: 25-Nov-2022
https://doi.org/10.1007/978-3-031-07912-2_17

Index Terms

Deep State-Space Generative Model For Correlated Time-to-Event Predictions
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning

Recommendations

Multi-view learning via multiple graph regularized generative model

Topic models, such as probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), have shown impressive success in many fields. Recently, multi-view learning via probabilistic latent semantic analysis (MVPLSA), is also designed ...
Variational Bayesian analysis of survival data using a log-logistic accelerated failure time model
Abstract
The log-logistic regression model is one of the most commonly used accelerated failure time (AFT) models in survival analysis, for which statistical inference methods are mainly established under the frequentist framework. Recently, Bayesian ...
Bayes linear kinematics in a dynamic survival model

Bayes linear kinematics and Bayes linear Bayes graphical models provide an extension of Bayes linear methods so that full conditional updates may be combined with Bayes linear belief adjustment. In this paper we investigate the application of this ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

August 2020

3664 pages

ISBN:9781450379984

DOI:10.1145/3394486

General Chairs:
Rajesh Gupta
UC San Diego, USA
,
Yan Liu
USC, USA
,
Program Chairs:
Mohak Shah
LG Electronics, USA
,
Suju Rajan
Linkedin, USA
,
Publications Chairs:
Jiliang Tang
Michigan State, USA
,
B. Aditya Prakash
Georgia Tech, USA

Copyright © 2020 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2020

Check for updates

Author Tags

Qualifiers

Research-article

Conference

KDD '20

Sponsor:

KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

July 6 - 10, 2020

CA, Virtual Event, USA

Acceptance Rates

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '24

Sponsor:
sigkdd
sigkdd

The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 25 - 29, 2024

Barcelona , Spain

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
2,557
Total Downloads

Downloads (Last 12 months)381
Downloads (Last 6 weeks)36

Reflects downloads up to 18 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Fujiwara RAl Hasan MXiong L(2022)C-Cast: A Real-Time Forecasting Model for a Controlled SequenceProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557817(5112-5115)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557817
Ng KGhalwash MChakraborty PSow DKoseki AYanagisawa HKudo M(2022)Data-Driven Disease Progression ModelingHealthcare Information Management Systems10.1007/978-3-031-07912-2_17(247-276)Online publication date: 25-Nov-2022
https://doi.org/10.1007/978-3-031-07912-2_17

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents