Visually defining and querying consistent multi-granular clinical temporal abstractions

References 

1. Chittaro L. Information visualization and its application to medicine. Artificial Intelligence in Medicine. 2001;22(2):81–88
   • Abstract
   • Full Text
   • Full-Text PDF (70 KB)
   • CrossRef
2. Combi C, Keravnou-Papailiou E, Shahar Y. Temporal information systems in medicine. New York: Springer Publishing Company, Incorporated; 2010;
3. Aigner W, Miksch S. Carevis: integrated visualization of computerized protocols and temporal patient data. Artificial Intelligence in Medicine. 2006;37(3):203–218
   • Abstract
   • Full Text
   • Full-Text PDF (602 KB)
4. Aigner W, Miksch S, Müller W, Schumann H, Tominski C. Visual methods for analyzing time-oriented data. IEEE Transactions on Visualization and Computer Graphics. 2008;14(1):47–60
   • CrossRef
5. Combi C, Portoni L, Pinciroli F. Visualizing temporal clinical data on the WWW. In: Horn W, Shahar Y, Lindberg G, Andreassen S, Wyatt JC editor. Proceedings of the joint European conference on artificial intelligence in medicine and medical decision making, Vol. 1620 of Lecture Notes in Computer Science. London, UK: Springer-Verlag; 1999;p. 301–314
6. Plaisant C, Mushlin R, Snyder A, Li J, Heller D, Shneiderman B, et al. Lifelines: Using visualization to enhance navigation and analysis of patient records. In: Proceedings of the 1998 American Medical Informatic Association annual fall symposium. Philadelphia, PA, USA: Hanley & Belfus; 1998;p. 76–80
7. Chittaro L, Combi C. Visualizing queries on databases of temporal histories: new metaphors and their evaluation. Data & Knowledge Engineering. 2003;44(2):239–264
8. Chittaro L, Combi C, Trapasso G. Data mining on temporal data: a visual approach and its clinical application to hemodialysis. Journal of Visual Languages and Computing. 2003;14(6):591–620
9. Klimov D, Shahar Y, Taieb-Maimon M. Intelligent visualization and exploration of time-oriented data of multiple patients. Artificial Intelligence in Medicine. 2010;49(1):11–31
   • Abstract
   • Full Text
   • Full-Text PDF (1542 KB)
   • CrossRef
10. Wang TD, Plaisant C, Shneiderman B, Spring N, Roseman D, Marchand G, et al. Temporal summaries: supporting temporal categorical searching, aggregation and comparison. IEEE Transactions on Visualization and Computer Graphics. 2009;15(6):1049–1056
    • CrossRef
11. Shahar Y, Musen MA. Knowledge-based temporal abstraction in clinical domains. Artificial Intelligence in Medicine. 1996;8(3):267–298
    • Abstract
    • Full-Text PDF (2568 KB)
    • CrossRef
12. Combi C, Chittaro L. Abstraction on clinical data sequences: an object-oriented data model and a query language based on the event calculus. Artificial Intelligence in Medicine. 1999;17(3):271–301
    • Abstract
    • Full Text
    • Full-Text PDF (967 KB)
    • CrossRef
13. Shahar Y, Cheng C. Model-based visualization of temporal abstractions. Computational Intelligence. 2000;16(2):279–306
14. Pane JF, Myers BA. Tabular and textual methods for selecting objects from a group. In: Proceedings of the 2000 IEEE international symposium on visual languages (VL’00). Washington, DC, USA: IEEE Computer Society; 2000;p. 157–164
15. Combi C, Franceschet M, Peron A. Representing and reasoning about temporal granularities. Journal of Logic and Computation. 2004;14(1):51–77
16. Bettini C, Wang XS, Jajodia S. A general framework for time granularity and its application to temporal reasoning. Annals of Mathematics and Artificial Intelligence. 1998;22(1–2):29–58
17. Bettini C, Wang XS, Jajodia S. Temporal granularity. In: Liu L, Özsu MT editor. Encyclopedia of database systems. Springer US; 2009;p. 2968–2973
18. Goralwalla IA, Leontiev Y, Özsu MT, Szafron D, Combi C. Temporal granularity: completing the puzzle. Journal of Intelligent Information Systems. 2001;16(1):41–63
19. Combi C, Cucchi G, Pinciroli F. Applying object-oriented technologies in modeling and querying temporally oriented clinical databases dealing with temporal granularity and indeterminacy. IEEE Transactions on Information Technology in Biomedicine. 1997;1(2):100–127
20. Combi C, Montanari A, Pozzi G. The t4sql temporal query language. In: Silva MJ, Laender AHF, Baeza-Yates RA, McGuinness DL, Olstad B, Olsen ØH, Falcão AO editor. Proceedings of the sixteenth ACM conference on information and knowledge management. New York, NY, USA: ACM; 2007;p. 193–202
21. Combi C, Pozzi G. Hmap – a temporal data model managing intervals with different granularities and indeterminacy from natural language sentences. Very Large Data Bases Journal. 2001;9(4):294–311
22. Shahar Y. A framework for knowledge-based temporal abstraction. Artificial Intelligence. 1997;90(1–2):79–133
23. Shneiderman B. The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of the 1996 IEEE symposium on visual languages. Washington, DC, USA. IEEE Computer Society; 1996;p. 336–343
24. Combi C, Pinciroli F, Musazzi G, Ponti C. Managing and displaying different time granularities of clinical information. In: Ozbolt J editors. 18th annual symposium on computer applications in medical care. Philadelphia, PA, USA: Hanley & Belfus; 1994;p. 954–958
25. Cousins SB, Kahn MG. The visual display of temporal information. Artificial Intelligence in Medicine. 1991;3:341–357
    • CrossRef
26. Wohlfart E, Aigner W, Bertone A, Miksch S. Comparing information visualization tools focusing on the temporal dimensions. In: Proceedings of the 2008 12th international conference information visualisation. Washington, DC, USA: IEEE Computer Society; 2008;p. 69–74
27. Bueno R, Razente HL, Kaster DS, Barioni MCN, Traina AJM, C.T. . Metric data analysis enhanced through temporal visualization. In: Banissi E, Bertschi S, Burkhard RA, Counsell J, Dastbaz M, Eppler MJ, Forsell C, Grinstein GG, Johansson J, Jern M, Khosrowshahi F, Marchese FT, Maple C, Laing R, Cvek U, Trutschl M, Sarfraz M, Stuart LJ, Ursyn A, Wyeld TG editor. 14th international conference on information visualisation, IV 2010. 26–29 July 2010, London, UK. Washington, DC, USA: IEEE Computer Society; 2010;p. 116–121
28. Noah SA, Yaakob S, Shahar S. Application of information visualization techniques in representing patients’ temporal personal history data. In: Zaman HB, Robinson P, Petrou M, Olivier P, Schröder H, Shih TK editor. Proceedings of visual informatics: bridging research and practice, first international visual informatics conference, vol. 5857 of Lecture Notes in Computer Science. 2009;p. 168–179
29. Ropinski T, Oeltze S, Preim B. Survey of glyph-based visualization techniques for spatial multivariate medical data. Computers & Graphics. 2011;35(2):392–401
30. Aigner W, Miksch S, Schumann H, Tominski C. Visualization of time-oriented data. London: Springer Publishing Company, Incorporated; 2011;
31. Pinciroli F, Portoni L, Combi C, Violante F. WWW-based access to object-oriented clinical databases: the KHOSPAD project. Computers in Biology and Medicine. 1998;28(5):531–552
    • Abstract
    • Full Text
    • Full-Text PDF (1629 KB)
    • CrossRef
32. Aigner W, Miksch S, Thurnher B, Biffl S. Planninglines: novel glyphs for representing temporal uncertainties and their evaluation. In: 9th international conference on information visualisation, IV. 6–8 July 2005, London, UK. Washington, DC, USA: IEEE Computer Society; 2005;p. 457–463
33. Plaisant C, Milash B, Rose A, Widoff S, Shneiderman B. Lifelines: visualizing personal histories. In: Proceedings of the SIGCHI conference on Human factors in computing systems: common ground, CHI ’96. New York, NY, USA. ACM; 1996;p. 221–227
34. Tatu A, Albuquerque G, Eisemann M, Schneidewind J, Theisel H, Magnor MA, et al. Combining automated analysis and visualization techniques for effective exploration of high-dimensional data. In: Proceedings of the IEEE symposium on visual analytics science and technology. IEEE VAST 2009, part of VisWeek 2009. Washington, DC, USA: IEEE Computer Society.
35. Woodring J, Shen H-W. Multiscale time activity data exploration via temporal clustering visualization spreadsheet. IEEE Transactions on Visualization and Computer Graphics. 2009;15(1):123–137
    • CrossRef
36. Javed W, Elmqvist N. Stack zooming for multi-focus interaction in time-series data visualization. In: IEEE pacific visualization symposium PacificVis 2010. 2–5 March, Taipei, Taiwan. Washington, DC, USA: IEEE Computer Society; 2010;
37. Hinum K, Miksch S, Aigner W, Ohmann S, Popow C, Pohl M, et al. Gravi++: interactive information visualization to explore highly structured temporal data. Journal of Universal Computer Science. 2005;11(11):1792–1805
38. Lowe A, Jones RW, Harrison MJ. The graphical presentation of decision support information in an intelligent anaesthesia monitor. Artificial Intelligence in Medicine. 2001;22(2):173–191
    • Abstract
    • Full Text
    • Full-Text PDF (852 KB)
    • CrossRef
39. Aigner W, Miksch S, Müller W, Schumann H, Tominski C. Visualizing time-oriented data – a systematic view. Computers & Graphics. 2007;31(3):401–409
40. Aigner W, Miksch S, Müller W, Schumann H, Tominski C. Visual methods for analyzing time-oriented data. IEEE Transactions on Visualization and Computer Graphics. 2008;14(1):47–60
    • CrossRef
41. Shahar Y, Goren-Bar D, Boaz D, Tahan G. Distributed, intelligent, interactive visualization and exploration of time-oriented clinical data and their abstractions. Artificial Intelligence in Medicine. 2006;38(2):115–135
    • Abstract
    • Full Text
    • Full-Text PDF (2022 KB)
    • CrossRef
42. Klimov D, Shahar Y, Taieb-Maimon M. Intelligent selection and retrieval of multiple time-oriented records. Journal of Intelligent Information Systems. 2010;35(2):261–300
43. Wang TD, Plaisant C, Quinn AJ, Stanchak R, Murphy S, Shneiderman B. Aligning temporal data by sentinel events: discovering patterns in electronic health records. In: Czerwinski M, Lund AM, Tan DS editor. Proceedings of the 2008 conference on human factors in computing systems. New York, NY, USA: ACM; 2008;p. 457–466
44. Bade R, Schlechtweg S, Miksch S. Connecting time-oriented data and information to a coherent interactive visualization. In: Dykstra-Erickson E, Tscheligi M editor. Proceedings of the 2004 conference on human factors in computing systems. New York, NY, USA: ACM; 2004;p. 105–112
45. Hochheiser H, Shneiderman B. Dynamic query tools for time series data sets: timebox widgets for interactive exploration. Information Visualization. 2004;3(1):1–18
46. Fails JA, Karlson AK, Shahamat L, Shneiderman B. A visual interface for multivariate temporal data: finding patterns of events across multiple histories. In: Wong PC, Keim DA editor. IEEE symposium on visual analytics science and technology. IEEE VAST 2006. 31 October–2 November, 2006, Baltimore, Maryland, USA. Washington, DC, USA: IEEE Computer Society; 2006;p. 167–174
47. Wongsuphasawat K, Shneiderman B. Finding comparable temporal categorical records: a similarity measure with an interactive visualization. In: Proceedings of the IEEE symposium on visual analytics science and technology. IEEE VAST 2009. Atlantic City, NJ, USA. 2009;p. 27–34
48. Wang TD, Wongsuphasawat K, Plaisant C, Shneiderman B. Visual information seeking in multiple electronic health records: design recommendations and a process model. In: Veinot TC, Çatalyürek ÜV, Luo G, Andrade H, Smalheiser NR editor. Proceedings of the ACM international health informatics symposium. New York, NY, USA: ACM Press; 2010;p. 46–55
49. Wang TD, Deshpande A, Shneiderman B. A temporal pattern search algorithm for personal history event visualization. IEEE Transactions on Knowledge and Data Engineering 99 (PrePrints). doi:http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.257.
50. Lammarsch T, Aigner W, Bertone A, Gärtner J, Mayr E, Miksch S, et al. Hierarchical temporal patterns and interactive aggregated views for pixel-based visualizations. In: Banissi E, Stuart LJ, Wyeld TG, Jern M, Andrienko GL, Memon N, Alhajj R, Burkhard RA, Grinstein GG, Groth DP, Ursyn A, Johansson J, Forsell C, Cvek U, Trutschl M, Marchese FT, Maple C, Cowell AJ, Moere AV editor. 13th international conference on information visualisation, IV. Barcelona, Spain. Washington, DC, USA: IEEE Computer Society; 2009;p. 44–50
51. Lee T-Y, Shen H-W. Visualization and exploration of temporal trend relationships in multivariate time-varying data. IEEE Transactions on Visualization and Computer Graphics. 2009;15(6):1359–1366
    • CrossRef
52. Dudzic S, Godwin JA, Kilgore RM. Visualization of temporal relationships within coordinated views. In: Proceedings of the IEEE conference on visual analytics science and technology. IEEE VAST 2010. Salt Lake City, UT, USA. Washington, DC, USA: IEEE Computer Society; 2010;p. 219–220
53. Jin J, Szekely PA. Interactive querying of temporal data using a comic strip metaphor. In: Proceedings of the IEEE conference on visual analytics science and technology. IEEE VAST 2010, part of VisWeek 2010. Washington, DC, USA: IEEE Computer Society.
54. Chu WW, Hsu C-C, Cardenas AF, Taira RK. Knowledge-based image retrieval with spatial and temporal constructs. IEEE Transactions on Knowledge and Data Engineering. 1998;10(6):872–888
55. Dionisio JDN, Cardenas AF. Unified data model for representing multimedia, timeline, and simulation data. IEEE Transactions on Knowledge and Data Engineering. 1998;10(5):746–767
56. Dionisio JDN, Cardenas AF. Mquery: a visual query language for multimedia, timeline and simulation data. Journal of Visual Languages and Computing. 1996;7(4):377–401
57. Cardenas AF, Ieong IT, Taira RK, Barker R, Breant CM. The knowledge-based object-oriented picquery+ language. IEEE Transactions on Knowledge and Data Engineering. 1993;5(4):644–657
58. Bui AAT, Aberle DR, Kangarloo H. Timeline: visualizing integrated patient records. IEEE Transactions on Information Technology in Biomedicine. 2007;11(4):462–473
59. Aoyama DA, Hsiao J-TT, Cardenas AF, Pon RK. Timeline and visualization of multiple-data sets and the visualization querying challenge. Journal of Visual Languages and Computing. 2007;18(1):1–21
60. Ding J, Hughes LM, Berleant D, Fulmer AW, Wurtele ES. Pubmed assistant: a biologist-friendly interface for enhanced pubmed search. Bioinformatics. 2006;22(3):378–380
    • MEDLINE
    • CrossRef
61. Murray N, Paton NW, Goble CA. Kaleidoquery: a visual query language for object databases. In: Catarci T, Costabile MF, Santucci G, Tarantino L editor. Proceedings of the working conference on advanced visual interfaces 1998. New York, NY, USA: ACM Press; 1998;p. 247–257
62. Polyviou S, Samaras G, Evripidou P. A relationally complete visual query language for heterogeneous data sources and pervasive querying. In: Proceedings of the 21st international conference on data engineering, ICDE. 5–8 April 2005, Tokyo, Japan. Washington, DC, USA: IEEE Computer Society; 2005;p. 471–482
63. Jones S, McInnes S, Staveley MS. A graphical user interface for boolean query specification. International Journal on Digital Libraries. 1999;2(2–3):207–223
64. Spoerri A. Infocrystal: a visual tool for information retrieval & management. In: Bhargava BK, Finin TW, Yesha Y editor. Proceedings of the second international conference on information and knowledge management. New York, NY, USA: ACM Press; 1993;p. 11–20
65. Huo J. KMVQL: a visual query interface based on karnaugh map. In: Levialdi S editors. Proceedings of the working conference on advanced visual interfaces. New York, NY, USA: ACM Press; 2008;p. 243–250
66. Paolino L, Sebillo M, Tortora G, Vitiello G. The predicate tree – a metaphor for visually describing complex boolean queries. In: Qiu G, Leung C, Xue X, Laurini R editor. Proceedings of the 9th international conference on advances in visual information systems, vol. 4781 of Lecture Notes in Computer Science. Berlin/Heidelberg: Springer-Verlag; 2007;p. 524–536
67. Huo J, Cowan W. Comprehending boolean queries. In: Creem-Regehr SH, Myszkowski K editor. Proceedings of the 5th symposium on applied perception in graphics and visualization. ACM international conference proceeding series. New York, NY, USA: ACM; 2008;p. 179–186
68. Chui M, Dillon A. Speed and accuracy using four boolean query systems. In: Priss U editors. Tenth midwest artificial intelligence and cognitive science conference (MAICS 99). Indiana University, Bloomington, IN. CA, USA: AAAI Press; 1999;p. 36–42
69. Bettini C, Wang XS, Jajodia S. Solving multi-granularity temporal constraint networks. Artificial Intelligence. 2002;140(1/2):107–152
70. Cormen TH, Leiserson CE, Rivest RL. Introduction to algorithms. Cambridge: The MIT Press/McGraw-Hill Book Company; 1989;
71. Booch G, Rumbaugh J, Jacobson I. The unified modeling language reference manual. Boston, MA: Addison-Wesley; 2004;
72. Chin JP, Diehl VA, Norman KL. Development of an instrument measuring user satisfaction of the human–computer interface. In: Proceedings of the SIGCHI conference on Human factors in computing systems. Washington, DC, USA. New York, NY, USA: ACM; 1988;p. 213–218
73. Golfarelli M, Rizzi S. A survey on temporal data warehousing. International Journal of Data Warehousing and Mining. 2009;5(1):1–17
74. Bellazzi R, Larizza C, Magni P, Bellazzi R. Temporal data mining for the quality assessment of hemodialysis services. Artificial Intelligence in Medicine. 2005;34(1):25–39
    • Abstract
    • Full Text
    • Full-Text PDF (440 KB)
    • CrossRef