Hsiao-Ying Lu
ABSTRACT
Cooperation among teams or individuals of healthcare professionals (HCPs) is one of the crucial factors towards patients’ survival outcome. However, it is challenging to uncover and understand such factors in the complex Multiteam System (MTS) communication networks representing daily HCP cooperation. In this paper, we present a study on MTS communication networks constructed with real-world cancer patients’ Electronic Health Record (EHR) access logs. We adopt a visual analytics workflow to extract associations between semantic characteristics of MTS communication networks and the patients’ survival outcomes. The workflow consists of a neural network learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. We provide the insights found using this workflow with two case studies and an expert interview.
- M. L. Barnett, N. A. Christakis, J. O’Malley, J. P. Onnela, N. L. Keating, and B. E. Landon. 2012. Physician patient-sharing networks and the cost and intensity of care in US hospitals. Med Care 50, 2 (2012), 152–60. https://doi.org/10.1097/MLR.0b013e31822dcef7Google Scholar
Cross Ref
- Eta S. Berner and Tonya J. La Lande. 2016. Overview of Clinical Decision Support Systems. Springer Int. Publishing, Chapter 1, 1–17.Google Scholar
- Derun Cai, Chenxi Sun, Moxian Song, Baofeng Zhang, Shenda Hong, and Hongyan Li. 2022. Hypergraph contrastive learning for electronic health records. In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM). SIAM, 127–135.Google ScholarCross Ref
- Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A scalable tree boosting system. In Proc. SIGKDD. 785–794.Google ScholarDigital Library
- Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart. 2016. RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism. In Proc. NIPS. 3504–3512.Google Scholar
- Jerome H. Friedman. 2001. Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics 29, 5 (2001), 1189–1232.Google ScholarCross Ref
- Michael Gleicher. 2013. Explainers: Expert explorations with crafted projections. IEEE Trans Vis Comput Graph 19, 12 (2013), 2042–2051.Google ScholarDigital Library
- Rongchen Guo, Takanori Fujiwara, Yiran Li, Kelly M Lima, Soman Sen, Nam K Tran, and Kwan-Liu Ma. 2020. Comparative visual analytics for assessing medical records with sequence embedding. Visual Informatics 4, 2 (2020), 72–85.Google ScholarCross Ref
- Shunan Guo, Zhuochen Jin, David Gotz, Fan Du, Hongyuan Zha, and Nan Cao. 2018. Visual progression analysis of event sequence data. IEEE Trans. on Visualization and Computer Graphics 25, 1 (2018), 417–426.Google ScholarDigital Library
- F. Hackl, M. Hummer, and G. J. Pruckner. 2015. Old boys’ network in general practitioners’ referral behavior?J Health Econ 43 (2015), 56–73. https://doi.org/10.1016/j.jhealeco.2015.06.005Google ScholarCross Ref
- Leora I. Horwitz and Allan S. Detsky. 2011. Physician Communication in the 21st Century: To Talk or to Text?JAMA 305, 11 (2011), 1128–1129. https://doi.org/10.1001/jama.2011.324Google ScholarCross Ref
- X. Ji, H. Shen, A. Ritter, R. Machiraju, and P. Yen. 2019. Visual Exploration of Neural Document Embedding in Information Retrieval: Semantics and Feature Selection. IEEE Trans. on Visualization and Computer Graphics 25, 6 (2019), 2181–2192.Google ScholarCross Ref
- Zhuochen Jin, Shuyuan Cui, Shunan Guo, David Gotz, Jimeng Sun, and Nan Cao. 2019. CarePre: An Intelligent Clinical Decision Assistance System. ACM Trans. on Computing for Healthcare (2019).Google Scholar
- Nancy Katz, David Lazer, Holly Arrow, and Noshir Contractor. 2004. Network Theory and Small Groups. Small Group Research 35, 3 (2004), 307–332. https://doi.org/10.1177/1046496404264941Google ScholarCross Ref
- M. Kilduff and D. J. Brass. 2010. Organizational Social Network Research: Core Ideas and Key Debates. Academy of Management Annals 4, 1 (2010), 317–357. https://doi.org/10.1080/19416520.2010.494827Google ScholarCross Ref
- Bum Chul Kwon, Vibha Anand, Kristen A Severson, Soumya Ghosh, Zhaonan Sun, Brigitte I Frohnert, Markus Lundgren, and Kenney Ng. 2019. DPVis: Visual Exploration of Disease Progression Pathways. arXiv:1904.11652 (2019).Google Scholar
- Bum Chul Kwon, Min-Je Choi, Joanne Taery Kim, Edward Choi, Young Bin Kim, Soonwook Kwon, Jimeng Sun, and Jaegul Choo. 2018. RetainVis: Visual analytics with interpretable and interactive recurrent neural networks on electronic medical records. IEEE Trans. on Visualization and Computer Graphics 25, 1 (2018), 299–309.Google ScholarDigital Library
- B. E. Landon, N. L. Keating, M. L. Barnett, and et al.2012. Variation in patient-sharing networks of physicians across the united states. JAMA 308, 3 (2012), 265–273. https://doi.org/10.1001/jama.2012.7615Google ScholarCross Ref
- Oscar Li, Hao Liu, Chaofan Chen, and Cynthia Rudin. 2017. Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains its Predictions. arXiv:1710.04806 (2017).Google Scholar
- Yiran Li, Takanori Fujiwara, Yong K Choi, Katherine K Kim, and Kwan-Liu Ma. 2020. A visual analytics system for multi-model comparison on clinical data predictions. Visual Informatics 4, 2 (2020), 122–131.Google ScholarCross Ref
- Zachary Chase Lipton, David C. Kale, Charles Elkan, and Randall C. Wetzel. 2016. Learning to Diagnose with LSTM Recurrent Neural Networks. In Proc. Int. Conf. on Learning Representations.Google Scholar
- Zheng Liu, Xiaohan Li, Hao Peng, Lifang He, and S Yu Philip. 2020. Heterogeneous similarity graph neural network on electronic health records. In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 1196–1205.Google ScholarCross Ref
- Hsiao-Ying Lu, Takanori Fujiwara, Ming-Yi Chang, Yang-chih Fu, Anders Ynnerman, and Kwan-Liu Ma. 2023. Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction. Visual Informatics forthcoming (2023).Google Scholar
- Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Proc. NIPS, Vol. 30.Google Scholar
- Yao Ming, Panpan Xu, Huamin Qu, and Liu Ren. 2019. Interpretable and Steerable Sequence Learning via Prototypes. Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining (2019).Google ScholarDigital Library
- Peter R Monge and Noshir S Contractor. 2003. Theories of communication networks. Oxford University Press, New York, NY.Google Scholar
- C. E. Pollack, G. Weissman, J. Bekelman, K. Liao, and K. Armstrong. 2012. Physician social networks and variation in prostate cancer treatment in three cities. Health Serv Res 47, 1 Pt 2 (2012), 380–403. https://doi.org/10.1111/j.1475-6773.2011.01331.xGoogle ScholarCross Ref
- J. Ross Quinlan. 1983. Learning Efficient Classification Procedures and Their Application to Chess End Games. In Machine Learning: An Artificial Intelligence Approach. Springer, 463–482.Google Scholar
- Jen Rogers, Nicholas Spina, Ashley Neese, Rachel Hess, Darrel Brodke, and Alexander Lex. 2018. Composer: Visual Cohort Analysis of Patient Outcomes. In Proceedings of 9th workshop on Visual Analytics in Healthcare.Google Scholar
- N. D. Soulakis, M. B. Carson, Y. J. Lee, D. H. Schneider, C. T. Skeehan, and D. M. Scholtens. 2015. Visualizing collaborative electronic health record usage for hospitalized patients with heart failure. J Am Med Inform Assoc 22, 2 (2015), 299–311. https://doi.org/10.1093/jamia/ocu017Google ScholarCross Ref
- Stephen H Taplin, Sallie Weaver, Eduardo Salas, Veronica Chollette, Heather M Edwards, Suanna S Bruinooge, and Michael P Kosty. 2015. Reviewing cancer care team effectiveness. Journal of oncology practice 11, 3 (2015), 239–246.Google ScholarCross Ref
- Tin Kam Ho. 1995. Random decision forests. In Proc. Int. Conf. on Document Analysis and Recognition, Vol. 1. 278–282 vol.1.Google Scholar
- Chun-Fu Wang, Jianping Li, Kwan-Liu Ma, Chih-Wei Huang, and Yu-Chuan Li. 2014. A Visual Analysis Approach to Cohort Study of Electronic Patient Records. In Proceedings of IEEE BIBM (to appear).Google ScholarCross Ref
- Qianwen Wang, Kexin Huang, Payal Chandak, Marinka Zitnik, and Nils Gehlenborg. 2022. Extending the Nested Model for User-Centric XAI: A Design Study on GNN-based Drug Repurposing. IEEE Transactions on Visualization and Computer Graphics (2022).Google Scholar
- A. C. Weaver, M. Callaghan, A. L. Cooper, J. Brandman, and K. J. O’Leary. 2015. Assessing Interprofessional Teamwork in Inpatient Medical Oncology Units. J Oncol Pract 11, 1 (2015), 19–22. https://doi.org/10.1200/jop.2014.001536Google ScholarCross Ref
- Robert Wu, Peter Rossos, Sherman Quan, Scott Reeves, Vivian Lo, Brian Wong, Mark Cheung, and Dante Morra. 2011. An evaluation of the use of smartphones to communicate between clinicians: a mixed-methods study. Journal of Medical Internet Research 13, 3 (2011), e59.Google ScholarCross Ref
- Tong Wu, Yunlong Wang, Yue Wang, Emily Zhao, Yilian Yuan, and Zhi Yang. 2019. Representation learning of ehr data via graph-based medical entity embedding. arXiv preprint arXiv:1910.02574 (2019).Google Scholar
- Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang. 2018. Network representation learning: A survey. IEEE Trans Big Data 6, 1 (2018), 3–28.Google ScholarCross Ref
Index Terms
- A Study of Healthcare Team Communication Networks using Visual Analytics
Index terms have been assigned to the content through auto-classification.
Recommendations
Improving Healthcare with Interactive Visualization
Visualization and visual analytics re-searchers can contribute substantial technological advances to support the reliable, effective, safe, and validated systems required for personal health, clinical healthcare, and public health policymaking. The Web ...
Data Analytics in Healthcare: A Tertiary Study
AbstractThe field of healthcare has seen a rapid increase in the applications of data analytics during the last decades. By utilizing different data analytic solutions, healthcare areas such as medical image analysis, disease recognition, outbreak ...
The Strengths, Weaknesses, Opportunities, and Threats Analysis of Big Data Analytics in Healthcare
Improving the performance and reducing the cost of healthcare have been a great concern and a huge challenge for healthcare organizations and governments at every level in the US. Measures taken have included laws, regulations, policies, and ...
Comments