Scalable predictive analysis in critically ill patients using a visual open data analysis platform

PLoS ONE

Volumn 11, Issue 1, 2016, Pages

  Van Poucke, Sven ^a   Zhang, Zhongheng ^b   Schmitz, Martin ^c   Vukicevic, Milan ^d   Laenen, Margot Vander ^a   Celi, Leo Anthony ^e   De Deyne, Cathy ^a,f  

^a Emergency Medicine and Pain Therapy ^*  (Belgium)

^b Jinhua Hospital of Zhejiang University   (China)

^c RAPIDMINER GMBH   (Germany)

^d UNIVERSITY OF BELGRADE   (Serbia)

^e Massachusetts Institute of Technology Cambridge   (United States)

^f HASSELT UNIVERSITY   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

CLINICAL STUDY; CRITICALLY ILL PATIENT; DATA ANALYSIS; DATA BASE; DATA MINING; EXTRACT; HUMAN; INTENSIVE CARE; MEANINGFUL USE CRITERIA; MEDICAL RESEARCH; MODEL; PLANT LEAF; QUANTITATIVE STUDY; THROMBOCYTE COUNT; ALGORITHM; COMPUTER LANGUAGE; CRITICAL ILLNESS; FACTUAL DATABASE; INFORMATION RETRIEVAL; INTENSIVE CARE UNIT; PROCEDURES; REPRODUCIBILITY; STATISTICS AND NUMERICAL DATA; THEORETICAL MODEL;

ALGORITHMS; CRITICAL CARE; CRITICAL ILLNESS; DATA MINING; DATABASES, FACTUAL; HUMANS; INFORMATION STORAGE AND RETRIEVAL; INTENSIVE CARE UNITS; MODELS, THEORETICAL; PROGRAMMING LANGUAGES; REPRODUCIBILITY OF RESULTS;

EID: 84953931466     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0145791     Document Type: Article

References (63)

1. Van Poucke S, Thomeer M, Hadzic A. 2015, Big data in healthcare: for whom the bell tolls? Crit Care [Internet]. 2015; 19(1): 2013-4. Available: http://ccforum.com/content/19/1/171.
2. Ghassemi M, Celi LA, Stone DJ. State of the art review: the data revolution in critical care. Crit Care [Internet]. 2015; 19(1). Available: http://ccforum.com/content/19/1/118.
3. Mohammadzadeh N, Safdari R, Baraani A, Mohammadzadeh F. Intelligent data analysis: the best approach for chronic heart failure (CHF) follow up management. Acta Inform Med. 2014; 22(4): 263-7. Available: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4216425/pdf/AIM-22-263.pdf. doi: 10.5455/aim.2014.22.263-267
4. Golubnitschaja O, Kinkorova J, Costigliola V. Predictive, Preventive and Personalised Medicine as the hardcore of "Horizon 2020": EPMA position paper. EPMA J [Internet]. 2014; 5(1):6. doi: 10.1186/1878-5085-5-6
5. Koliopoulos AK, Yiapanis P, Tekiner F, Nenadic G, Keane J. A Parallel Distributed Weka Framework for Big Data Mining using Spark. Big Data (BigData Congress), 2015 IEEE International Congress on. IEEE. 2015: 9-16.
6. Wimmer H, Powell LM. A comparison of open source tools for sentiment analysis. 2015;1-9. Available: http://fotiad.is/blog/sentiment-Analysis-comparison/.
7. Jovic, A, Brkic K, Bogunovic N. An overview of free software tools for general data mining. Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014 37th International Convention on. IEEE. 2014: 1112-1117.
8. Herschel G, Linden A, Kart L. Magic quadrant for advanced analytics platforms. Available: http://www. gartner.com/technology/reprints.do?id=1-2A881DN&ct=150219&st=sb.
9. Landset S, Khoshgoftaar TM, Richter AN, Hasanin T. A survey of open source tools for machine learning with big data in the Hadoop ecosystem. J Big Data [Internet]. Springer International Publishing; 2015; 2(1):24. Available: http://www.journalofbigdata.com/content/2/1/24.
10. Dean J, Ghemawat S. MapReduce: Simplified Data Processing on Large Clusters. Commun ACM [Internet]. 2008; 51(1):1-13. Available: http://www.usenix.org/events/osdi04/tech/full-papers/dean/dean-html/.
11. Apache Hadoop. Available: http://hadoop.apache.org/.
12. Thusoo A, Sarma JS, Jain N, Shao Z, Chakka P, N. Z. 0002, Anthony S, Liu H, Murthy R. Hive-A petabyte scale data warehouse using Hadoop. In F. Li, M. M. Moro, S. Ghandeharizadeh, J. R. Haritsa, G. Weikum, M. J. Carey, F. Casati, E. Y. Chang, I. Manolescu, S. Mehrotra, U. Dayal, and V. J. Tsotras, editors, Proceedings of the 26th International Conference on Data Engineering, ICDE 2010, March 1-6, 2010, Long Beach, California, USA, pages 996-1005. IEEE. 2010.
13. Vavilapalli VK, Murthy AC, Douglas C, Agarwali S, Konar M, Evans R, et al. Apache Hadoop YARN: Yet Another Resource Negotiator. ACM Symp Cloud Comput [Internet]. 2013; 16. Available: http://www.socc2013.org/home/program/a5-vavilapalli.pdf.
14. Olston C, Reed B, Srivastava U, Kumar R, Tomkins A. Pig Latin: A Not-So-Foreign Language for Data Processing. Proc 2008 ACM SIGMOD Int Conf Manag data-SIGMOD '08. 2008; 1099.
15. Apache Mahout. Available: http://mahout.apache.org/.
16. Sparks ER, Talwalkar A, Smith V, Kottalam J, Pan X, Gonzalez J, et al. MLI: An API for Distributed Machine Learning. 13th IEEE International Conference on Data Mining [Internet]. 2013. p. 1187-92. Available: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6729619 MLib.
17. Zaharia M, Chowdhury M, Das T, Dave A. Fast and interactive analytics over Hadoop data with Spark. USENIX Login. 2012; 37(4):45-51.
18. Ni Z. Comparative Evaluation of Spark and Stratosphere. Thesis, KTH Royal Institute of Technology; 2013.
19. MLib. Available: https://spark.apache.org/mllib/.
20. Zheng J, Dagnino A. An initial study of predictive machine learning analytics on large volumes of historical data for power system applications. In: 2014 IEEE International Conference on Big Data. 2014. 952-59.
21. Katsipoulakis NR, Tian Y, Reinwald B, Pirahesh H. A Generic Solution to Integrate SQL and Analytics for Big Data. In: 18th International Conference on Extending Database Technology (EDBT). 2015. 671-6.
22. SparkR. Available: http://amplab-extras.github.io/SparkR-pkg/.
23. PySpark. Available: https://spark.apache.org/docs/0.9.0/python-programming-guide.html.
24. Ritthoff O, Klinkenberg R, Fisher S, Mierswa I, Felske S. YALE: Yet Another Learning Environment. LLWA'01 -Tagungsband der GI-Workshop-Woche Lernen-Lehren-Wissen Adaptivitat. University of Dortmund, Dortmund, Germany. Technical Report 763. 2001: 84-92.
25. Hall M. Weka and Hadoop. 2013. Available: http://markahall.blogspot.co.uk/2013/10/weka-Andhadooppart-1.html/.
26. Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, et al. KNIME: The Konstanz Information Miner. In Preisach C, Burkhardt H, Schmidt-Thieme L, Decker R, editors. Data Analysis, Machine Learning and Applications (Studies in Classification, Data Analysis, and Knowledge Organization). Berlin Heidelberg: Springer.2008. 319-326.
27. Saeed M, Villarroel M, Reisner AT, Clifford G, Lehman L, Moody GB, Heldt T, Kyaw TH, Moody TE, Mark RG. Multiparameter intelligent monitoring in intensive care II (MIMIC-II): A public-Access ICU database. Crit Care Med. 2011; 39(5): 952-960. doi: 10.1097/CCM.0b013e31820a92c6
28. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ch P, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet. Circulation. 2000; 101(23): E215-20.
29. Le Gall JR, Loirat P, et al. A simplified acute physiology score for ICU patients. Crit Care Med. 1984; 12: 975-977.
30. Le Gall JR, Lemeshow S, Saulnier F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American Multicenter Study. JAMA. 1993; 270: 2957-2963.
31. Vincent JL, de Mendonça A, Cantraine F, Moreno R. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on "sepsis-related problems" of the European Society of Intensive Care Medicine. Crit Care Med. 1998; 26:1793-1800.
32. Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. Communications of the ACM-50th anniversary issue: 1958-2008 [Internet]. 2008;51(1): 1-13. Available: http://www. usenix.org/events/osdi04/tech/full-papers/dean/dean-html/.
33. Mohammed EA, Far BH, Naugler C. Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends. BioData Min [Internet]. 2014; 7(1): 1-23. Available: http://link.springer.com/article/10.1186/1756-0381-7-22. doi: 10.1186/1756-0381-7-22
34. Shearer C. The CRISP-DM model: the new blueprint for data mining. J Data Warehousing. 2000; 5:13-22.
35. Russell S, Norvig P. Artificial intelligence: a modern approach. 2nd ed. Prentice Hall Series; 2003.
36. Rish I. An empirical study of the naive bayes classifier. In: IJCAI 2001 Workshop on empirical methods in artificial intelligence; 2001: 41-66. Available: http://www.cc.gatech.edu/~isbell/reading/papers/Rish. pdf.
37. Wolfson J, Bandyopadhyay S, Elidrisi M, Vazquez-Benitez G, Musgrove D, Adomavicius G, et al. A Naive Bayes machine learning approach to risk prediction using censored, time-To-event data. ARXIV [Internet]. 2014; Apr 8. Available: http://arxiv.org/abs/1404.2124.
38. Hand DJ, Yu K. Idiot's Bayes-not so stupid after all? International Statistical Review. 2001; 69 (3): 385-398.
39. Chao CM, Yu YW, Cheng BW, & Kuo YL. Construction the model on the breast cancer survival analysis use support vector machine, logistic regression and decision tree. J Med Syst. 2014; 38(10): 1-7. Available: http://link.springer.com/article/10.1007%2Fs10916-014-0106-1.
40. Ting H, Mai YT, Hsu HC, Wu HC, & Tseng MH. Decision tree based diagnostic system for moderate to severe obstructive sleep apnea. J Med Syst. 2014; 38(9):1-10.
41. Quinlan JR. Induction of Decision Trees. Machine Learning. 1986; 1: 81-106.
42. Hastie T, Tibshirani R, Friedman J, Franklin J. The elements of statistical learning: data mining, inference and prediction. Math Intell. 2005; 27:83-5.
43. Druss BG, Marcus SC, Rosenheck RA, Olfson M, Tanielian T, Pincus HA. Understanding disability in mental and general medical conditions. Am J Psychiatry. 2000; 157(9):1485-91.
44. Radovanovic, S, Vukicevic, M, Kovacevic, A, Sliglic, G, Obradovic, Z (2015) Domain knowledge based hierarchical feature selection for 30-day hospital readmission prediction. Proceedings of the 15th Conference on Artificial Intelligence in Medicine; 2015 June 17-20; Pavia, Italy. Available: http://link. springer.com/chapter/10.1007%2F978-3-319-19551-3-11.
45. Post RM, Altshuler L, Leverich GS, Frye MA, Suppes T, McElroy SL, et al. Relationship of clinical course of illness variables to medical comorbidities in 900 adult outpatients with bipolar disorder. Compr Psychiatry. 2015; 56: 21-28. Available: http://www.sciencedirect.com/science/article/pii/S0010440X14002429. doi: 10.1016/j.comppsych.2014.08.050
46. Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM TIST. 2011; 2(3):1-27. Available: http://dl.acm.org/citation.cfm?doid=1961189.1961199.
47. Cortes C, Vapnik V. Support vector networks. Machine Learning. 1995; 20: 273-297. Available: http://link.springer.com/article/10.1023%2FA%3A1022627411411.
48. Kuncheva LI, Whitaker CJ. Measures of diversity in classifier ensembles. Machine Learning. 2003; 51: 181-207 Available: http://pages.bangor.ac.uk/~mas00a/papers/lkml.pdf.
49. Breiman L. Random Forests. Machine Learning. 2001; 45:5-32. Available: http://link.springer.com/article/10.1023%2FA%3A1010933404324.
50. Freund Y, Schapire R, Abe N. A short introduction to boosting. Journal of JSAI.1999; 14(5): 771-780. Available: http://cseweb.ucsd.edu/~yfreund/papers/IntroToBoosting.pdf.
51. Breiman L. Bagging predictors. Machine Learning. 1999; 24(2):123-140. Available: http://link.springer. com/article/10.1023%2FA%3A1018054314350.
52. Breiman L. Stacked regressions. Machine learning. 1996; 24(1):49-64. Available: http://link.springer. com/article/10.1007%2FBF00117832.
53. Kononenko I. Estimating Attributes: Analysis and Extensions of RELIEF. In: European Conference on Machine Learning, 171-182, 1994. Available: http://link.springer.com/chapter/10.1007%2F3-540-57868-4-57.
54. Kohavi R, John GH. Wrappers for feature subset selection. Artificial Intelligence. 1997; 97(1): 273-324. doi: http://dx.doi.org/10.1016/S0004-3702(97)00043-X
55. Mao KZ. Orthogonal forward selection and backward elimination algorithms for feature subset selection. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on. 2004; 34(1): 629-634. Available: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1262531.
56. Goldberg DE. Genetic algorithms in search, optimization, and machine learning. 1st Ed. Boston: Addison-Wesley Longman; 1989.
57. Friedrichs F, Igel C. Evolutionary tuning of multiple SVM parameters. Neurocomputing. 2005; 64:107-117. Available: http://www.sciencedirect.com/science/article/pii/S0925231204005223.
58. Grau J, Keilwagen J. Precision-Recall and ROC Curves for Weighted and Unweighted Data; 2015. Repository: CRAN package repository [Internet].Available: https://cran.r-project.org/web/packages/PRROC/PRROC.pdf.
59. Riley RD, Ahmed I, Debray TPA, Willis BH, Noordzij JP, Higgins JPT, Deeks J. Summarising and validating test accuracy results across multiple studies for use in clinical practice. Statist. Med. 2015; 34 (13):1097-0258. Available: http://onlinelibrary.wiley.com/doi/10.1002/sim.6471/abstract.
60. Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves. In Proceedings of the 23rd international conference on Machine learning (ICML 2006). ACM, New York, NY, USA, 233-240. Available: http://doi.acm.org/10.1145/1143844.1143874J.
61. Kamkar I, Gupta SK, Phung D, Venkatesh S. Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso. J Biomed Inform. 2015; 53: 277-290. Available: http://www.jbiomed-inform.com/article/S1532-0464(14)00263-9/abstract. doi: 10.1016/j.jbi.2014.11.013
62. Zhang Z, Xu X, Ni H, Deng H. Predictive value of ionized calcium in critically ill patients: An analysis of a large clinical database MIMIC II. PLoS ONE. 2014; 9(4): e95204. doi: 10.1371/journal.pone.0095204
63. Scott DJ, Lee J, Silva I, Park S, Moody GB, Celi LA, et al. Accessing the public MIMIC-II intensive care relational database for clinical research. BMC Med Inform Decis Mak [Internet]. 2013 Jan; 13(1): 9. Available: http://www.biomedcentral.com/1472-6947/13/9.