Electronic Health Records: Comparison
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Please note this is a comparison between Version 2 by Karina Chen and Version 1 by Neda Rostamzadeh.

The increasing use of electronic health record (EHR)-based systems has led to the generation of clinical data at an unprecedented rate, which produces an untapped resource for healthcare experts to improve the quality of care. Despite the growing demand for adopting EHRs, the large amount of clinical data has made some analytical and cognitive processes more challenging. The emergence of a type of computational system called visual analytics has the potential to handle information overload challenges in EHRs by integrating analytics techniques with interactive visualizations. 

  • electronic health records
  • visual analytics
  • interaction design
  • visual analytics tasks
  • analytics techniques
  • visualization
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References

  1. Murdoch, T.B.; Detsky, A.S. The Inevitable Application of Big Data to Health Care. JAMA J. Am. Med. Assoc. 2013, 309, 1351–1352.
  2. Doupi, P. Using EHR Data for Monitoring and Promoting Patient Safety: Reviewing the Evidence on Trigger Tools. Stud. Health Technol. Inf. 2012, 180, 786–790.
  3. Agrawal, A. Medication Errors: Prevention Using Information Technology Systems. Br. J. Clin. Pharmacol. 2009, 67, 681–686.
  4. Dey, S.; Luo, H.; Fokoue, A.; Hu, J.; Zhang, P. Predicting Adverse Drug Reactions through Interpretable Deep Learning Framework. BMC Bioinform. 2018, 19, 476.
  5. Abdullah, S.S.; Rostamzadeh, N.; Sedig, K.; Lizotte, D.J.; Garg, A.X.; McArthur, E. Machine Learning for Identifying Medication-Associated Acute Kidney Injury. Informatics 2020, 7, 18.
  6. Tang, P.C.; McDonald, C.J. Electronic health record systems. In Biomedical Informatics: Computer Applications in Health Care and Biomedicine; Shortliffe, E.H., Cimino, J.J., Eds.; Health Informatics; Springer: New York, NY, USA, 2006; pp. 447–475. ISBN 978-0-387-36278-6.
  7. Christensen, T.; Grimsmo, A. Instant Availability of Patient Records, but Diminished Availability of Patient Information: A Multi-Method Study of GP’s Use of Electronic Patient Records. BMC Med. Inform. Decis. Mak. 2008, 8, 12.
  8. Rostamzadeh, N.; Abdullah, S.S.; Sedig, K. Data-Driven Activities Involving Electronic Health Records: An Activity and Task Analysis Framework for Interactive Visualization Tools. Multimodal Technol. Interact. 2020, 4, 7.
  9. Heisey-Grove, D.; Danehy, L.N.; Consolazio, M.; Lynch, K.; Mostashari, F. A National Study of Challenges to Electronic Health Record Adoption and Meaningful Use. Med. Care 2014, 52, 144–148.
  10. Lau, F.; Price, M.; Boyd, J.; Partridge, C.; Bell, H.; Raworth, R. Impact of Electronic Medical Record on Physician Practice in Office Settings: A Systematic Review. BMC Med. Inform. Decis. Mak. 2012, 12, 10.
  11. Ola, O.; Sedig, K. The Challenge of Big Data in Public Health: An Opportunity for Visual Analytics. Online J. Public Health Inf. 2014, 5, 223.
  12. Keim, D.A.; Mansmann, F.; Thomas, J. Visual Analytics: How Much Visualization and How Much Analytics? ACM SIGKDD Explor. Newsl. 2010, 11, 5.
  13. Sedig, K.; Parsons, P.; Babanski, A. Towards a Characterization of Interactivity in Visual Analytics. J. Multimed. Process. Technol. 2012, 3, 12–28.
  14. Ribarsky, W.; Fisher, B.; Pottenger, W.M. Science of Analytical Reasoning. Inf. Vis. 2009.
  15. Vamathevan, J.; Clark, D.; Czodrowski, P.; Dunham, I.; Ferran, E.; Lee, G.; Li, B.; Madabhushi, A.; Shah, P.; Spitzer, M.; et al. Applications of Machine Learning in Drug Discovery and Development. Nat. Rev. Drug Discov. 2019, 18, 463–477.
  16. Cortez, P.; Embrechts, M.J. Using Sensitivity Analysis and Visualization Techniques to Open Black Box Data Mining Models. Inf. Sci. 2013, 225, 1–17.
  17. Keim, D.A.; Munzner, T.; Rossi, F.; Verleysen, M. Bridging Information Visualization with Machine Learning (Dagstuhl Seminar 15101). Dagstuhl Rep. 2015, 5, 1–27.
  18. Rajwan, Y.G.; Barclay, P.W.; Lee, T.; Sun, I.-F.; Passaretti, C.; Lehmann, H. Visualizing Central Line –Associated Blood Stream Infection (CLABSI) Outcome Data for Decision Making by Health Care Consumers and Practitioners—An Evaluation Study. Online J. Public Health Inf. 2013, 5, 218.
  19. Goldsmith, M.-R.; Transue, T.R.; Chang, D.T.; Tornero-Velez, R.; Breen, M.S.; Dary, C.C. PAVA: Physiological and Anatomical Visual Analytics for Mapping of Tissue-Specific Concentration and Time-Course Data. J. Pharm. Pharm. 2010, 37, 277–287.
  20. Perer, A.; Sun, J. MatrixFlow: Temporal Network Visual Analytics to Track Symptom Evolution during Disease Progression. AMIA Annu. Symp. Proc. 2012, 2012, 716–725.
  21. Lo, Y.-S.; Lee, W.-S.; Liu, C.-T. Utilization of Electronic Medical Records to Build a Detection Model for Surveillance of Healthcare-Associated Urinary Tract Infections. J. Med. Syst. 2013, 37, 9923.
  22. Gotz, D.; Stavropoulos, H. Decisionflow: Visual Analytics for High-Dimensional Temporal Event Sequence Data. IEEE Trans. Vis. Comput. Graph. 2014, 20, 1783–1792.
  23. Kwon, B.C.; Choi, M.-J.; Kim, J.T.; Choi, E.; Kim, Y.B.; Kwon, S.; Sun, J.; Choo, J. Retainvis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records. IEEE Trans. Vis. Comput. Graph. 2018, 25, 299–309.
  24. Kwon, B.C.; Anand, V.; Severson, K.A.; Ghosh, S.; Sun, Z.; Frohnert, B.I.; Lundgren, M.; Ng, K. DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways. IEEE Trans. Vis. Comput. Graph. 2020.
  25. Ledieu, T.; Bouzille, G.; Plaisant, C.; Thiessard, F.; Polard, E.; Cuggia, M. Mining Clinical Big Data for Drug Safety: Detecting Inadequate Treatment with a DNA Sequence Alignment Algorithm. AMIA Annu. Symp. Proc. 2018, 2018, 1368–1376.
  26. Gotz, D.; Wang, F.; Perer, A. A Methodology for Interactive Mining and Visual Analysis of Clinical Event Patterns Using Electronic Health Record Data. J. Biomed. Inform. 2014, 48, 148–159.
  27. Ayres, J.; Flannick, J.; Gehrke, J.; Yiu, T. Sequential Pattern Mining Using a Bitmap Representation. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, AB, Canada, 23 July 2002; pp. 429–435.
  28. Simpao, A.F.; Ahumada, L.M.; Desai, B.R.; Bonafide, C.P.; Galvez, J.A.; Rehman, M.A.; Jawad, A.F.; Palma, K.L.; Shelov, E.D. Optimization of Drug-Drug Interaction Alert Rules in a Pediatric Hospital’s Electronic Health Record System Using a Visual Analytics Dashboard. J. Am. Med. Inform. Assoc. 2014, 22, 361–369.
  29. Dagliati, A.; Sacchi, L.; Tibollo, V.; Cogni, G.; Teliti, M.; Martinez-Millana, A.; Traver, V.; Segagni, D.; Posada, J.; Ottaviano, M.; et al. A Dashboard-Based System for Supporting Diabetes Care. J. Am. Med. Inf. Assoc. 2018, 25, 538–547.
  30. Sacchi, L.; Capozzi, D.; Bellazzi, R.; Larizza, C. JTSA: An Open Source Framework for Time Series Abstractions. Comput. Methods Programs Biomed. 2015, 121, 175–188.
  31. Dagliati, A.; Sacchi, L.; Zambelli, A.; Tibollo, V.; Pavesi, L.; Holmes, J.H.; Bellazzi, R. Temporal Electronic Phenotyping by Mining Careflows of Breast Cancer Patients. J. Biomed. Inf. 2017, 66, 136–147.
  32. Mane, K.K.; Bizon, C.; Schmitt, C.; Owen, P.; Burchett, B.; Pietrobon, R.; Gersing, K. VisualDecisionLinc: A Visual Analytics Approach for Comparative Effectiveness-Based Clinical Decision Support in Psychiatry. J. Biomed. Inform. 2012, 45, 101–106.
  33. Perer, A.; Wang, F.; Hu, J. Mining and Exploring Care Pathways from Electronic Medical Records with Visual Analytics. J. Biomed. Inform. 2015, 56, 369–378.
  34. Dingen, D.; van’t Veer, M.; Houthuizen, P.; Mestrom, E.H.J.; Korsten, E.H.H.M.; Bouwman, A.R.A.; van Wijk, J. RegressionExplorer: Interactive Exploration of Logistic Regression Models with Subgroup Analysis. IEEE Trans. Vis. Comput. Graph. 2019, 25, 246–255.
  35. Mica, L.; Niggli, C.; Bak, P.; Yaeli, A.; McClain, M.; Lawrie, C.M.; Pape, H.-C. Development of a Visual Analytics Tool for Polytrauma Patients: Proof of Concept for a New Assessment Tool Using a Multiple Layer Sankey Diagram in a Single-Center Database. World J. Surg. 2020, 44, 764–772.
  36. Klimov, D.; Shknevsky, A.; Shahar, Y. Exploration of Patterns Predicting Renal Damage in Patients with Diabetes Type II Using a Visual Temporal Analysis Laboratory. J. Am. Med. Inform. Assoc. 2015, 22, 275–289.
  37. Moskovitch, R.; Shahar, Y. Classification of Multivariate Time Series via Temporal Abstraction and Time Intervals Mining. Knowl. Inf. Syst. 2015, 45, 35–74.
  38. Moskovitch, R.; Shahar, Y. Fast Time Intervals Mining Using the Transitivity of Temporal Relations. Knowl. Inf. Syst. 2015, 42, 21–48.
  39. Ha, H.; Lee, J.; Han, H.; Bae, S.; Son, S.; Hong, C.; Shin, H.; Lee, K. Dementia Patient Segmentation Using EMR Data Visualization: A Design Study. Int. J. Environ. Res. Public Health 2019, 16, 3438.
  40. Sun, J.; McNaughton, C.D.; Zhang, P.; Perer, A.; Gkoulalas-Divanis, A.; Denny, J.C.; Kirby, J.; Lasko, T.; Saip, A.; Malin, B.A. Predicting Changes in Hypertension Control Using Electronic Health Records from a Chronic Disease Management Program. J. Am. Med. Inf. Assoc. 2014, 21, 337–344.
  41. Guo, R.; Fujiwara, T.; Li, Y.; Lima, K.M.; Sen, S.; Tran, N.K.; Ma, K.-L. Comparative Visual Analytics for Assessing Medical Records with Sequence Embedding. Vis. Inform. 2020, 4, 72–85.
  42. Gower, J.C.; Warrens, M.J. Similarity, Dissimilarity, and Distance, Measures Of. Wiley StatsRef Stat. Ref. Online 2014, 1–11.
  43. Kramer, M.A. Nonlinear Principal Component Analysis Using Autoassociative Neural Networks. AICHE J. 1991, 37, 233–243.
  44. Sutskever, I.; Vinyals, O.; Le, Q.V. Sequence to Sequence Learning with Neural Networks. Adv. Neural Inf. Process. Syst. 2014, 27, 3104–3112.
  45. Hund, M.; Böhm, D.; Sturm, W.; Sedlmair, M.; Schreck, T.; Ullrich, T.; Keim, D.A.; Majnaric, L.; Holzinger, A. Visual Analytics for Concept Exploration in Subspaces of Patient Groups. Brain Inf. 2016, 3, 233–247.
  46. Müller, E.; Günnemann, S.; Assent, I.; Seidl, T. Evaluating Clustering in Subspace Projections of High Dimensional Data. Proc. VLDB Endow. 2009, 2, 1270–1281.
  47. Cox, M.A.A.; Cox, T.F. Multidimensional Scaling. In Handbook of Data Visualization; Chen, C., Härdle, W., Unwin, A., Eds.; Springer Handbooks Comp. Statistics; Springer: Berlin/Heidelberg, Germany, 2008; pp. 315–347. ISBN 978-3-540-33037-0.
  48. Rao, R.; Card, S.K. The Table Lens: Merging Graphical and Symbolic Representations in an Interactive Focus+ Context Visualization for Tabular Information. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Boston, MA, USA, 24–28 April 1994; pp. 318–322.
  49. Huang, C.-W.; Lu, R.; Iqbal, U.; Lin, S.-H.; Nguyen, P.A.A.; Yang, H.-C.; Wang, C.-F.; Li, J.; Ma, K.-L.; Li, Y.-C.J.; et al. A Richly Interactive Exploratory Data Analysis and Visualization Tool Using Electronic Medical Records. BMC Med. Inform. Decis. Mak. 2015, 15, 92.
  50. Jin, Z.; Cui, S.; Guo, S.; Gotz, D.; Sun, J.; Cao, N. CarePre: An Intelligent Clinical Decision Assistance System. ACM Trans. Comput. Healthc. 2020, 1, 1–20.
  51. Kwon, B.C.; Verma, J.; Perer, A. Peekquence: Visual Analytics for Event Sequence Data. In Proceedings of the ACM SIGKDD 2016 Workshop on Interactive Data Exploration and Analytics, San Francisco, CA, USA, 14 August 2016; Volume 1.
  52. Baytas, I.M.; Lin, K.; Wang, F.; Jain, A.K.; Zhou, J. PhenoTree: Interactive Visual Analytics for Hierarchical Phenotyping from Large-Scale Electronic Health Records. IEEE Trans. Multimed. 2016, 18, 2257–2270.
  53. Abdullah, S.S.; Rostamzadeh, N.; Sedig, K.; Garg, A.X.; McArthur, E. Visual Analytics for Dimension Reduction and Cluster Analysis of High Dimensional Electronic Health Records. Informatics 2020, 7, 17.
  54. Abdullah, S.S.; Rostamzadeh, N.; Sedig, K.; Garg, A.X.; McArthur, E. Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3. Data 2020, 5, 33.
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