Predictive Analysis of Diabetes Dedicated Social Networks

Summary

According to the National Diabetes Statistics Report by the Center for Disease Control and Prevention released in 2017, 30.3 million people in the United States have diabetes mellitus (diabetes for short), a number that makes up 9.4% of the country’s population. Diabetes remains one of the leading causes of death in the United States and has incurred hundreds of billion dollars of economic loss (estimated to be $245 billion). In the face of such alarming statistics, the American Diabetes Association is treating diabetes as an epidemic that is projected to affect one in three Americans by 2050.

As the population of people with diabetes continues to grow, various online social networks dedicated to this disease have been emerging. The popularity of these networks can be attributed to the positive effects that they could have on patients in terms of forming support groups, exchanging personal experience, seeking help from one another, sharing news pertinent to diabetes, etc. To date, the state of practice of these diabetes dedicated social networks has been essentially devoted to such exploratory tasks, with each social network operating on its own. Despite the success of such practice, the predictive connection between a patient’s social activities on these networks and his/her measurements of biomarkers largely remains unknown.

The PI proposes a paradigm shift for diabetes dedicated social networks, from exploration to prediction. The overall goal of this project is to harness diabetes patients’ online social behaviors from multiple networks to predict their biomarker measurements. It consists of four complementary research thrusts, including (1) comprehensive social behavior feature extraction, (2) diabetes biomarker measurements extraction and densification, (3) connection between social behaviors and diabetes biomarkers, and (4) algorithmic and clinical evaluations.

Publications

Book

1. A. Nelakurthi, J. He. Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective. CRC Taylor and Francis Group, February 2020

Journals

1. X. Liu, J. He, W Min, and H. Yang. Missing Information Imputation for Disease-dedicated Social Networks with Heterogeneous Auxiliary Data. IISE Transactions on Healthcare Systems Engineering

2. Y. Zhou, L. Ying, and J. He. Multi-task Crowdsourcing via an Optimization Framework. ACM Transactions on Knowledge Discovery from Data 13(3): 27:1--27:26

Conferences

1. R. Deb, Y. Ban, S. Zuo, J. He, and A. Banerjee. Contextual Bandits with Online Neural Regression. ICLR 2024

2. Y. Ban, I. Agarwal, Z. Wu, Y. Zhu, K. Weldemariam, H. Tong, and J. He. Neural Active Learning Beyond Bandits. ICLR 2024

3. Y. Ban, Y. Qi, T. Wei, L. Liu, and J. He. Meta Clustering of Neural Bandits. KDD 2024

4. Y. Qi, Y. Ban, T. Wei, J. Zou, H. Yao, and J. He. Meta-Learning with Neural Bandit Scheduler. NeurIPS 2023

5. Y. Qi, Y. Ban, and J. He. Neural Bandit with Arm Group Graph. KDD 2022

6. Y. Ban, Y. Zhang, H. Tong, A. Banerjee, and J. He. Improved Algorithms for Neural Active Learning. NeurIPS 2022 [NeurIPS Scholar Award]

7. Z. Wu, and J. He. Fairness-aware Model-agnostic Positive and Unlabeled Learning. ACM FAccT 2022 Distinguished Paper Award

8. Y. Ban, Y. Yan, A. Banerjee, and J. He. EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits. ICLR 2022

9. Y. Zhou, J. Xu, J. Wu, Z. Taghavi, E. Korpeoglu, K. Achan, and J. He. PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network. KDD 2021

10. J. Wu, and J. He. Indirect Invisible Poisoning Attacks on Domain Adaptation. KDD 2021

11. Y. Ban, J. He, and C.B. Cook. Multi-facet Contextual Bandits: A Neural Network Perspective. KDD 2021 

12. L. Zheng, Y. Cheng, H. Yang, N. Cao, and J. He. Deep Co-Attention Network for Multi-View Subspace Learning. WWW 2021

13. Y. Ban, and J. He. Local Clustering in Contextual Multi-Armed Bandits. WWW 2021

14. H. Wang, C. Zhou, C. Yang, H. Yang, and J. He. Controllable Gradient Item Retrieval. WWW 2021

15. D. Fu, and J. He. SDG: A Simplified and Dynamic Graph Neural Network. SIGIR 2021

16. J. Li, L. Zheng, Y. Zhou, and J. He. Outlier Impact Characterization for Time Series Data. AAAI 2021

17. D. Zhou, L. Zheng, J. Han, and J. He. A Data Driven Graph Generative Model for Temporal Interaction Networks. KDD 2020

18. Y. Ban, and J. He. Generic Outlier Detection in Multi-Armed Bandit. KDD 2020

19. D. Fu, D. Zhou, and J. He. Local Motif Clustering on Time-Evolving Graphs. KDD 2020

20. D. Fu, Z. Xu, B. Li, H. Tong, and J. He. A View-Adversarial Framework for Multi-View Network Embedding. CIKM 2020

21. Y. Zhou, A. Nelakurthi, R. Maciejewski, W. Fan, and J. He. Crowd Teaching with Imperfect Labels. WWW 2020

22. D. Zhou, L. Zheng, Y. Zhu, J. Li, and J. He. Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting. WWW 2020

23. Z. Liu, D. Zhou, Y. Zhu, J. Gu, and J. He. Towards Fine-Grained Temporal Network Representation via Time-Reinforced Random Walk. AAAI 2020

24. J. Wu, and J. He. Scalable Manifold-Regularized Attributed Network Embedding via Maximum Mean Discrepancy. CIKM 2019

25. Z. Liu, D. Zhou, and J. He. Towards Explainable Representation of Time-Evolving Graphs via Spatial-Temporal Graph Attention Networks. CIKM 2019

26. X. Liu, J. He, S. Duddy, and L. O’Sullivan. Convolution-Consistent Collective Matrix Completion. CIKM 2019

27. J. Wu, J. He, and J. Xu. DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification. KDD 2019

28. P. Yang, Q. Tan, H. Tong, and J. He. Task-Adversarial Co-Generative Nets. KDD 2019

29. L. Zheng, Y. Cheng, and J. He. Deep Multimodality Model for Multi-task Multi-view Learning. SDM 2019

30. A. Nelakurthi, R. Maciejewski, J. He. Source Free Domain Adaptation Using an Off-the-Shelf Classifier. IEEE BigData 2018

31. D. Zhou, J. He, H. Davulcu, R. Maciejewski. Motif-Preserving Dynamic Local Graph Cut. IEEE BigData 2018

32. J. Wu, J. He, Y. Liu. ImVerde: Vertex-Diminished Random Walk for Learning Imbalanced Network Representation. IEEE BigData 2018

33. J. Li, J. He, and Y. Zhu. E-tail Product Return Prediction via Hypergraph-based Local Graph Cut. KDD 2018

34. D. Zhou, J. He, H. Yang, and W. Fan. SPARC: Self-Paced Network Representation for Few-Shot Rare Category Characterization. KDD 2018

35. Y. Zhou, A. Nelakurthi, and J. He. Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners. KDD 2018

36. A. Nelakurthi, A. Pinto, C. Cook, L. Jones, M. Boyle, J. Ye, T. Lappas, and J. He. Should Patients with Diabetes Be Encouraged to Integrate Social Media into Their Care Plan? Future Science OA, July 2018

Tutorials

1. Heterogeneous Contrastive Learning for Foundation Models and Beyond, KDD 2024

2. Neural Contextual Bandits for Personalized Recommendation, WWW 2024

3. Trustworthy Transfer Learning: Transferability and Trustworthiness, KDD 2023

4. Learning from Non-IID Data: Centralized vs. Federated Learning, IJCAI 2023

5. Natural and Artificial Dynamics in GNNs, WSDM 2023

6. Contrastive Learning: A Heterogeneous Perspective, SDM 2023

7. Learning from Non-IID Data: Centralized vs. Federated Learning, IEEE BigData 2022

8. Exploring Rare Categories on Graphs: Representation, Inference, and Generalization, IJCAI 2020

9. Recent Advances in Machine Teaching: From Machine to Human, AAAI 2020

10. Gold Panning from the Mess: Rare Category Exploration, Exposition, Representation and Interpretation, KDD 2019

11. Optimize the Wisdom of the Crowd: Inference, Learning, and Teaching, KDD 2019

12. Analysis of Complex Rare Categories, SDM 2019

13. Analysis of Complex Rare Categories, IEEE Big Data 2018

Source Code

1. Ban et al., KDD'24: https://github.com/banyikun/MCNB

2. Qi et al., NeurIPS'23: https://github.com/yunzhe0306/Bandit_Task_Scheduler

3. Qi et al., KDD'23: https://github.com/yunzhe0306/GNB-Pytorch 

4. Ban et al., NeurIPS'22: https://github.com/matouk98/I-NeurAL 

5. Qi et al., KDD'22: https://github.com/yunzhe0306/AGG-UCB 

6. Wu et al., FAccT'22: https://github.com/hhhiddleston/fairpul 

7. Ban et al., ICLR'22: https://github.com/banyikun/EE-Net-ICLR-2022 

8. Zhou et al., KDD'21: https://drive.google.com/file/d/1f82giul6W1nAtposWdVy9z1G6lXwkHxT/view

9. Ban et al., KDD'21: https://github.com/banyikun/KDD2021_MuFasa

10. Wu et al., KDD'21: https://github.com/jwu4sml/I2Attack

11. Ban et al., WWW'21: https://github.com/banyikun/LOCB

12. Zheng et al., WWW'21: https://github.com/Leo02016/ANTS

13. Wang et al., WWW'21: https://github.com/haonan3/CGIR

14. Fu et al., SIGIR'21: https://github.com/DongqiFu/SDG 

15. Fu et al., KDD'20: https://github.com/DongqiFu/L-MEGA

16. Ban et al., KDD'20: https://anonymous.4open.science/r/8f096b64-734d-4718-b96b-c3f8ec6d338d/

17. Zhou et al., KDD'20: https://github.com/davidchouzdw/TagGen

18. Fu et al., CIKM'20: https://github.com/DongqiFu/VANE

19. Zhou et al., WWW'20: https://github.com/arunreddy/vader 

20. Liu et al., AAAI'20 paper: http://publish.illinois.edu/daweizhou/files/2020/02/FiGTNE.zip 

21. Wu et al., CIKM'19: https://github.com/jwu4sml/MARINE 

22. Liu et al., CIKM'19: http://publish.illinois.edu/daweizhou/files/2019/11/STANE.zip