research-article

Public Access

PopNet: Real-Time Population-Level Disease Prediction with Data Latency

Authors:

Lucas M. Glass,

Jimeng SunAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2552 - 2562

https://doi.org/10.1145/3485447.3512127

Published: 25 April 2022 Publication History

All formats PDF

Abstract

Population-level disease prediction estimates the number of potential patients of particular diseases in some location at a future time based on (frequently updated) historical disease statistics. Existing approaches often assume the existing disease statistics are reliable and will not change. However, in practice, data collection is often time-consuming and has time delays, with both historical and current disease statistics being updated continuously. In this work, we propose a real-time population-level disease prediction model which captures data latency (PopNet) and incorporates the updated data for improved predictions. To achieve this goal, PopNet models real-time data and updated data using two separate systems, each capturing spatial and temporal effects using hybrid graph attention networks and recurrent neural networks. PopNet then fuses the two systems using both spatial and temporal latency-aware attentions in an end-to-end manner. We evaluate PopNet on real-world disease datasets and show that PopNet consistently outperforms all baseline disease prediction and general spatial-temporal prediction models, achieving up to 47% lower root mean squared error and 24% lower mean absolute error compared with the best baselines.

References

[1]

Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Conguri Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, 2021. Spectral temporal graph neural network for multivariate time-series forecasting. arXiv preprint arXiv:2103.07719(2021).

[2]

Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F Stewart, and Jimeng Sun. 2016. Doctor ai: Predicting clinical events via recurrent neural networks. In Machine learning for healthcare conference. PMLR, 301–318.

[3]

Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F Stewart, and Jimeng Sun. 2017. GRAM: graph-based attention model for healthcare representation learning. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. 787–795.

Digital Library

[4]

Edward Choi, Andy Schuetz, Walter F Stewart, and Jimeng Sun. 2017. Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association 24, 2(2017), 361–370.

[5]

Edward Choi, Cao Xiao, Walter F Stewart, and Jimeng Sun. 2018. Mime: Multilevel medical embedding of electronic health records for predictive healthcare. arXiv preprint arXiv:1810.09593(2018).

[6]

Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555(2014).

[7]

Limin X Clegg, Eric J Feuer, Douglas N Midthune, Michael P Fay, and Benjamin F Hankey. 2002. Impact of reporting delay and reporting error on cancer incidence rates and trends. Journal of the National Cancer Institute 94, 20 (2002), 1537–1545.

[8]

Songgaojun Deng, Shusen Wang, Huzefa Rangwala, Lijing Wang, and Yue Ning. 2020. Cola-GNN: Cross-location Attention based Graph Neural Networks for Long-term ILI Prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 245–254.

Digital Library

[9]

Junyi Gao, Rakshith Sharma, Cheng Qian, Lucas M Glass, Jeffrey Spaeder, Justin Romberg, Jimeng Sun, and Cao Xiao. 2020. Stan: Spatio-temporal attention network for pandemic prediction using real world evidence. arXiv preprint arXiv:2008.04215(2020).

[10]

Jingyue Gao, Xiting Wang, Yasha Wang, Zhao Yang, Junyi Gao, Jiangtao Wang, Wen Tang, and Xing Xie. 2019. Camp: Co-attention memory networks for diagnosis prediction in healthcare. In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 1036–1041.

[11]

Junyi Gao, Cao Xiao, Lucas M Glass, and Jimeng Sun. 2020. Dr. Agent: Clinical predictive model via mimicked second opinions. Journal of the American Medical Informatics Association 27, 7(2020), 1084–1091.

[12]

Junyi Gao, Cao Xiao, Yasha Wang, Wen Tang, Lucas M Glass, and Jimeng Sun. 2020. StageNet: Stage-Aware Neural Networks for Health Risk Prediction. In Proceedings of The Web Conference 2020. 530–540.

Digital Library

[13]

Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 922–929.

Digital Library

[14]

Jie Hu, Li Shen, and Gang Sun. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 7132–7141.

[15]

Rongzhou Huang, Chuyin Huang, Yubao Liu, Genan Dai, and Weiyang Kong. 2020. LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks. In IJCAI. 2355–2361.

[16]

Amol Kapoor, Xue Ben, Luyang Liu, Bryan Perozzi, Matt Barnes, Martin Blais, and Shawn O’Banion. 2020. Examining covid-19 forecasting using spatio-temporal graph neural networks. arXiv preprint arXiv:2007.03113(2020).

[17]

William Ogilvy Kermack and Anderson G McKendrick. 1927. A contribution to the mathematical theory of epidemics. Proceedings of the royal society of london. Series A, Containing papers of a mathematical and physical character 115, 772 (1927), 700–721.

[18]

Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014).

[19]

Srijan Kumar, Xikun Zhang, and Jure Leskovec. 2019. Predicting dynamic embedding trajectory in temporal interaction networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1269–1278.

Digital Library

[20]

Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, and Jing Gao. 2017. Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. 1903–1911.

Digital Library

[21]

Liantao Ma, Junyi Gao, Yasha Wang, Chaohe Zhang, Jiangtao Wang, Wenjie Ruan, Wen Tang, Xin Gao, and Xinyu Ma. 2020. Adacare: Explainable clinical health status representation learning via scale-adaptive feature extraction and recalibration. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 825–832.

[22]

Nima Mohajerin and Steven L Waslander. 2017. State initialization for recurrent neural network modeling of time-series data. In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2330–2337.

[23]

Kris A Murray, Nicholas Preston, Toph Allen, Carlos Zambrana-Torrelio, Parviez R Hosseini, and Peter Daszak. 2015. Global biogeography of human infectious diseases. Proceedings of the National Academy of Sciences 112, 41(2015), 12746–12751.

[24]

Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016. Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499(2016).

[25]

Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao Schardl, and Charles Leiserson. 2020. Evolvegcn: Evolving graph convolutional networks for dynamic graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 5363–5370.

[26]

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, 2019. Pytorch: An imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703(2019).

[27]

Sen Pei and Jeffrey Shaman. 2020. Initial Simulation of SARS-CoV2 Spread and Intervention Effects in the Continental US. medRxiv (2020).

[28]

Zhaozhi Qian, Ahmed M Alaa, and Mihaela van der Schaar. 2020. CPAS: the UK’s national machine learning-based hospital capacity planning system for COVID-19. Mach. Learn. (Nov. 2020), 1–21.

[29]

Constantinos I Siettos and Lucia Russo. 2013. Mathematical modeling of infectious disease dynamics. Virulence 4, 4 (2013), 295–306.

[30]

Ruiguang Song and Timothy A Green. 2012. An improved approach to accounting for reporting delay in case surveillance systems. JP J Biostat 7, 1 (2012), 1–14.

[31]

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1 (2014), 1929–1958.

Digital Library

[32]

Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, and Hongyuan Zha. 2019. Dyrep: Learning representations over dynamic graphs. In International conference on learning representations.

[33]

Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903(2017).

[34]

Richard J Verrall and Mario V Wüthrich. 2016. Understanding reporting delay in general insurance. Risks 4, 3 (2016), 25.

[35]

Yanbang Wang, Yen-Yu Chang, Yunyu Liu, Jure Leskovec, and Pan Li. 2021. Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks. arXiv preprint arXiv:2101.05974(2021).

[36]

Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020. Inductive representation learning on temporal graphs. arXiv preprint arXiv:2002.07962(2020).

[37]

Zifeng Yang, Zhiqi Zeng, Ke Wang, Sook-San Wong, Wenhua Liang, Mark Zanin, Peng Liu, Xudong Cao, Zhongqiang Gao, Zhitong Mai, 2020. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. Journal of Thoracic Disease 12, 3 (2020), 165.

[38]

Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, Yanwei Yu, and Zhenhui Li. 2018. Modeling spatial-temporal dynamics for traffic prediction. arXiv preprint arXiv:1803.01254(2018).

[39]

Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi. 2020. Gman: A graph multi-attention network for traffic prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 1234–1241.

Cited By

Wang JChen LLycett DVernon DZheng D(2024)Toward Population Health Intelligence: When Artificial Intelligence Meets Population Health ResearchComputer10.1109/MC.2023.328385757:6(62-72)Online publication date: Jun-2024
https://doi.org/10.1109/MC.2023.3283857
You TDang QLi QZhang PWu GHuang W(2024)TransLSTD: Augmenting hierarchical disease risk prediction model with time and context awareness via disease clusteringInformation Systems10.1016/j.is.2024.102390124(102390)Online publication date: Sep-2024
https://doi.org/10.1016/j.is.2024.102390
Zhou FHu SDu XWan XLu ZWu J(2023)Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing HomesElectronics10.3390/electronics1204100912:4(1009)Online publication date: 17-Feb-2023
https://doi.org/10.3390/electronics12041009
Show More Cited By

Index Terms

PopNet: Real-Time Population-Level Disease Prediction with Data Latency

Index terms have been assigned to the content through auto-classification.

Recommendations

Disease Occurrence Prediction Based on Spatio-temporal Characterization --- A Mesoscale Study for Knowledge and Pattern Discovery
DaWaK 2013: Proceedings of the 15th International Conference on Data Warehousing and Knowledge Discovery - Volume 8057

Many statistical modeling and simulation-based methods have been proposed to detect, simulate and predict disease in space and time. However, these models either make use of unprecedented amount of domain-related parameters, or suffer from issues of ...
Real-Time Neurodegenerative Disease Video Classification with Severity Prediction
Image Analysis and Processing – ICIAP 2019
Abstract
In this paper, an automatic diagnosis system for neurodegenerative diseases is presented. Starting with an existing neurodegenerative diseases gait dataset, namely the NDDGD dataset, classification and regression algorithms have been trained, with ...
Population Cost Prediction on Public Healthcare Datasets
DH '15: Proceedings of the 5th International Conference on Digital Health 2015

The increasing availability of digital health records should ideally improve accountability in healthcare. In this context, the study of predictive modeling of healthcare costs forms a foundation for accountable care, at both population and individual ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 April 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

NSF (National Science Foundation)

Conference

WWW '22

Sponsor:

SIGWEB

WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
620
Total Downloads

Downloads (Last 12 months)221
Downloads (Last 6 weeks)18

Reflects downloads up to 27 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wang JChen LLycett DVernon DZheng D(2024)Toward Population Health Intelligence: When Artificial Intelligence Meets Population Health ResearchComputer10.1109/MC.2023.328385757:6(62-72)Online publication date: Jun-2024
https://doi.org/10.1109/MC.2023.3283857
You TDang QLi QZhang PWu GHuang W(2024)TransLSTD: Augmenting hierarchical disease risk prediction model with time and context awareness via disease clusteringInformation Systems10.1016/j.is.2024.102390124(102390)Online publication date: Sep-2024
https://doi.org/10.1016/j.is.2024.102390
Zhou FHu SDu XWan XLu ZWu J(2023)Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing HomesElectronics10.3390/electronics1204100912:4(1009)Online publication date: 17-Feb-2023
https://doi.org/10.3390/electronics12041009
Wang XChen LWang JZheng D(2023)Machine Learning Approaches for Region-level Prescription Demand Forecasting2023 IEEE Smart World Congress (SWC)10.1109/SWC57546.2023.10449058(1-6)Online publication date: 28-Aug-2023
https://doi.org/10.1109/SWC57546.2023.10449058

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

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