PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models

IEEE Transactions on Visualization and Computer Graphics

Volumn 24, Issue 1, 2018, Pages 371-381

  Glueck, Michael ^a,b   Naeini, Mahdi Pakdaman ^c   Doshi Velez, Finale ^c   Chevalier, Fanny ^d   Khan, Azam ^a   Wigdor, Daniel ^b   Brudno, Michael ^b,e  

^a AUTODESK RESEARCH   (United States)

^b UNIVERSITY OF TORONTO   (Canada)

^c HARVARD UNIVERSITY   (United States)

^d INRIA   (France)

^e Hospital for Sick Children   (France)

Author keywords

Developmental disorder; Human Phenotype Ontology (HPO); Phenotypes; Topic models; Topology simplification

Indexed keywords

ANALYTICAL MODELS; ARTIFICIAL INTELLIGENCE; DATA STRUCTURES; DATA VISUALIZATION; DISEASES; FLOW VISUALIZATION; LEARNING SYSTEMS; METADATA; ONTOLOGY; TOOLS; TOPOLOGY; VISUALIZATION;

BIOLOGICAL SYSTEM MODELING; DEVELOPMENTAL DISORDERS; HUMAN PHENOTYPE ONTOLOGY (HPO); PHENOTYPES; TOPIC MODEL;

BIOLOGICAL SYSTEMS;

EID: 85028710555     PISSN: 10772626     EISSN: None     Source Type: Journal    
DOI: 10.1109/TVCG.2017.2745118     Document Type: Article

References (57)

1. A. Arbabi. Personal communication, 2016-11-03.
2. D. M. Blei and J. D. Lafferty. Dynamic topic models. In Proceedings of the International Conference on Machine Learning, pages 113-120. ACM, 2006.
3. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan):993-1022, 2003.
4. O. Bodenreider. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Research, 32(suppl 1):D267-D270, 2004.
5. M. Bostock, V. Ogievetsky, and J. Heer. D3 data-driven documents. IEEE Transactions on Visualization and Computer Graphics, 17(12):2301-2309, 2011.
6. M. Brehmer and T. Munzner. A multi-level typology of abstract visualization tasks. IEEE Transactions on Visualization and Computer Graphics, 19(12):2376-2385, 2013.
7. J. Chang, J. L. Boyd-Graber, S. Gerrish, C. Wang, and D. M. Blei. Reading tea leaves: How humans interpret topic models. In Proceedings of the Conference on Neural Information Processing Systems, volume 31, pages 1-9, 2009.
8. Z. Che, D. Kale, W. Li, M. T. Bahadori, and Y. Liu. Deep computational phenotyping. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 507-516. ACM, 2015.
9. Y. Chen, L. Wang, M. Dong, and J. Hua. Exemplar-based visualization of large document corpus. IEEE Transactions on Visualization and Computer Graphics, 15(6), 2009.
10. L. Chittaro, C. Combi, and G. Trapasso. Data mining on temporal data: A visual approach and its clinical application to hemodialysis. Journal of Visual Languages & Computing, 14(6):591-620, 2003.
11. J. Chuang, S. Gupta, C. D. Manning, and J. Heer. Topic model diagnostics: Assessing domain relevance via topical alignment. In Proceedings of the International Conference on Machine Learning, pages 612-620, 2013.
12. J. Chuang, C. D. Manning, and J. Heer. Termite: Visualization techniques for assessing textual topic models. In Proceedings ofthe International Working Conference on Advanced Visual Interfaces, pages 74-77. ACM, 2012.
13. W. Cui, S. Liu, L. Tan, C. Shi, Y. Song, Z. Gao, H. Qu, and X. Tong. TextFlow: Towards better understanding of evolving topics in text. IEEE Transactions on Visualization and Computer Graphics, 17(12):2412-2421, 2011.
14. W. Cui, S. Liu, Z. Wu, and H. Wei. How hierarchical topics evolve in large text corpora. IEEE Transactions on Visualization and Computer Graphics, Volume: 20, Issue: 12:2281-2290, November 2014.
15. F. Dhombres and O. Bodenreider. Interoperability between phenotypes in research and healthcare terminologies-investigating partial mappings between HPO and SNOMED CT. Journal of Biomedical Semantics, 7(1):3, 2016.
16. F. Doshi-Velez, Y. Ge, and I. Kohane. Comorbidity clusters in autism spectrum disorders: An electronic health record time-series analysis. Pediatrics, 133(1):e54-e63, 2014.
17. F. Doshi-Velez, B. C. Wallace, and R. Adams. Graph-sparse lda: A topic model with structured sparsity. In Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.
18. W. Dou, X. Wang, R. Chang, and W. Ribarsky. ParallelTopics: A probabilistic approach to exploring document collections. In Proceedings ofthe IEEE Conference on Visual Analytics Science and Technology, pages 231-240. IEEE, 2011.
19. W. Dou, L. Yu, X. Wang, Z. Ma, and W. Ribarsky. HierarchicalTopics: Visually exploring large text collections using topic hierarchies. IEEE Transactions on Visualization and Computer Graphics, 19(12):2002-2011, 2013.
20. H. M. Elibol, V. Nguyen, S. Linderman, M. Johnson, A. Hashmi, and F. Doshi-Velez. Cross-corpora unsupervised learning of trajectories in autism spectrum disorders. Journal ofMachine Learning Research, 17(133):1-38, 2016.
21. A. Ganesan, K. Brantley, S. Pan, and J. Chen. LDAExplore: Visualizing topic models generated using latent dirichlet allocation. CoRR, abs/1507.06593, 2015.
22. M. J. Gardner, J. Lutes, J. Lund, J. Hansen, D. Walker, E. Ringger, and K. Seppi. The Topic Browser: An interactive tool for browsing topic models. In Conference on Neural Information Processing Systems, Workshop on Challenges ofData Visualization, volume 2, 2010.
23. M. Glueck, A. Gvozdik, F. Chevalier, A. Khan, M. Brudno, and D. Wigdor. PhenoStacks: cross-sectional cohort phenotype comparison visualizations. IEEE Transactions on Visualization and Computer Graphics, 23(1):191-200, 2017.
24. M. Glueck, P. Hamilton, F. Chevalier, S. Breslav, A. Khan, D. Wigdor, and M. Brudno. PhenoBlocks: Phenotype comparison visualizations. IEEE Transactions on Visualization and Computer Graphics, 22(1):101-110, 2016.
25. B. Gretarsson, J. Odonovan, S. Bostandjiev, T. Hollerer, A. Asuncion, D. Newman, and P. Smyth. TopicNets: Visual analysis of large text corpora with topic modeling. ACM Transactions on Intelligent Systems and Technology, 3(2):23, 2012.
26. T. Gschwandtner, W. Aigner, K. Kaiser, S. Miksch, and A. Seyfang. CareCruiser: exploring and visualizing plans, events, and effects interactively. In 2011 IEEE Pacific Visualization Symposium, pages 43-50. IEEE, 2011.
27. M. Harrower and C. A. Brewer. ColorBrewer.org: An online tool for selecting colour schemes for maps. The Cartographic Journal, 40(1):27-37, 2003.
28. V. Huddar, B. K. Desiraju, V. Rajan, S. Bhattacharya, S. Roy, and C. K. Reddy. Predicting complications in critical care using heterogeneous clinical data. IEEE Access, 4:7988-8001, 2016.
29. T. Iwata, T. Yamada, and N. Ueda. Probabilistic latent semantic visualization: Topic model for visualizing documents. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 363-371. ACM, 2008.
30. I. S. Kohane, D. R. Masys, and R. B. Altman. The incidentalome: A threat to genomic medicine. Journal ofthe American Medical Association, 296(2):212-215, 2006.
31. S. Kohler, M. H. Schulz, P. Krawitz, S. Bauer, S. Dolken, C. E. Ott, C. Mundlos, D. Horn, S. Mundlos, and P. N. Robinson. Clinical diagnostics in human genetics with semantic similarity searches in ontologies. The American Journal ofHuman Genetics, 85(4):457-464, 2009.
32. S. Kohler, N. A. Vasilevsky, M. Engelstad, E. Foster, J. McMurry, S. Ayme, G. Baynam, S. M. Bello, C. F. Boerkoel, K. M. Boycott, et al. The human phenotype ontology in 2017. Nucleic Acids Research, page gkw1039, 2016.
33. J. Krause, A. Perer, and H. Stavropoulos. Supporting iterative cohort construction with visual temporal queries. IEEE Transactions on Visualization and Computer Graphics, 22(1):91-100, 2016.
34. H. Lee, J. Kihm, J. Choo, J. Stasko, and H. Park. iVisClustering: An interactive visual document clustering via topic modeling. In Computer Graphics Forum, volume 31, pages 1155-1164. Wiley Online Library, 2012.
35. T. Lingren, P. Chen, J. Bochenek, F. Doshi-Velez, P. Manning-Courtney, J. Bickel, L. W. Welchons, J. Reinhold, N. Bing, Y. Ni, et al. Electronic health record based algorithm to identify patients with autism spectrum disorder. PloS one, 11(7):e0159621, 2016.
36. S. Liu, X. Wang, J. Chen, J. Zhu, and B. Guo. TopicPanorama: A full picture of relevant topics. In Proceedings ofthe IEEE Conference on Visual Analytics Science and Technology, pages 183-192. IEEE, 2014.
37. S. Liu, M. X. Zhou, S. Pan, Y. Song, W. Qian, W. Cai, and X. Lian. Tiara: Interactive, topic-based visual text summarization and analysis. ACM Transactions on Intelligent Systems and Technology, 3(2):25, 2012.
38. S. Malik, F. Du, M. Monroe, E. Onukwugha, C. Plaisant, and B. Shneiderman. Cohort comparison of event sequences with balanced integration of visual analytics and statistics. In Proceedings ofthe 20th International Conference on Intelligent User Interfaces, pages 38-49. ACM, 2015.
39. J. Munkres. Algorithms for the assignment and transportation problems. Journal ofthe Society for Industrial and Applied Mathematics, 5(1):32-38, 1957.
40. T. Munzner. A nested model for visualization design and validation. IEEE Transactions on Visualization and Computer Graphics, 15(6), 2009.
41. D. Newman, T. Baldwin, L. Cavedon, E. Huang, S. Karimi, D. Martinez, F. Scholer, and J. Zobel. Visualizing search results and document collections using topic maps. Web Semantics: Science, Services and Agents on the World Wide Web, 8(2):169-175, 2010.
42. R. Pivovarov, A. J. Perotte, E. Grave, J. Angiolillo, C. H. Wiggins, and N. Elhadad. Learning probabilistic phenotypes from heterogeneous EHR data. Journal of Biomedical Informatics, 58:156-165, 2015.
43. C. Plaisant, R. Mushlin, A. Snyder, J. Li, D. Heller, and B. Shneiderman. Lifelines: using visualization to enhance navigation and analysis of patient records. In Proceedings of the American Medical Informatics Association Symposium, page 76. American Medical Informatics Association, 1998.
44. R. Rehurek and P. Sojka. Software Framework for Topic Modelling with Large Corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pages 45-50, 2010.
45. A. Rind, W. Aigner, S. Miksch, S. Wiltner, M. Pohl, T. Turic, and F. Drexler. Visual exploration of time-oriented patient data for chronic diseases: Design study and evaluation. In Symposium of the Austrian HCI and Usability Engineering Group, pages 301-320. Springer, 2011.
46. A. Rind, T. D. Wang, W. Aigner, S. Miksch, K. Wongsuphasawat, C. Plaisant, and B. Shneiderman. Interactive information visualization to explore and query electronic health records. Foundations and Trends in Human-Computer Interaction, 5(3):207-298, 2011.
47. P. N. Robinson, C. J. Mungall, and M. Haendel. Capturing phenotypes for precision medicine. Molecular Case Studies, 1(1):a000372, 2015.
48. M. Sedlmair, M. Meyer, and T. Munzner. Design study methodology: Reflections from the trenches and the stacks. IEEE Transactions on Visualization and Computer Graphics, 18(12):2431-2440, 2012.
49. B. Shneiderman, C. Plaisant, and B. W. Hesse. Improving healthcare with interactive visualization. Computer, 46(5):58-66, 2013.
50. C. Sievert and K. E. Shirley. LDAvis: A method for visualizing and interpreting topics. In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, pages 63-70, 2014.
51. Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei. Sharing clusters among related groups: Hierarchical dirichlet processes. In Advances in Neural Information Processing Systems, pages 1385-1392, 2005.
52. R. Veras and C. Collins. Optimizing hierarchical visualizations with the minimum description length principle. IEEE Transactions on Visualization and Computer Graphics, 23(1):631-640, 2017.
53. T. D. Wang, C. Plaisant, A. J. Quinn, R. Stanchak, S. Murphy, and B. Shneiderman. Aligning temporal data by sentinel events: discovering patterns in electronic health records. In Proceedings ofthe SIGCHI Conference on Human Factors in Computing Systems, pages 457-466. ACM, 2008.
54. X. Wang and A. McCallum. Topics over time: A non-markov continuous-time model of topical trends. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 424-433. ACM, 2006.
55. R. Winnenburg and O. Bodenreider. Coverage of phenotypes in standard terminologies. Joint Bio-Ontologies and BioLINK ISMB, pages 41-44, 2014.
56. K. Wongsuphasawat, J. A. Guerra Gomez, C. Plaisant, T. D. Wang, M. Taieb-Maimon, and B. Shneiderman. LifeFlow: visualizing an overview of event sequences. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 1747-1756. ACM, 2011.
57. Z. Zhang, D. Gotz, and A. Perer. Iterative cohort analysis and exploration. Information Visualization, 14(4):289-307, 2015.