Big data analytics in healthcare

BioMed Research International

Volumn 2015, Issue , 2015, Pages

  Belle, Ashwin ^a   Thiagarajan, Raghuram ^c   Soroushmehr, S M Reza ^a   Navidi, Fatemeh ^a   Beard, Daniel A ^b   Najarian, Kayvan ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

^b UNIVERSITY OF MICHIGAN HEALTH SYSTEM   (United States)

^c UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTIC METHOD; ARTICLE; DATA ANALYSIS; DIAGNOSTIC IMAGING; GENE REGULATORY NETWORK; GENOMICS; HEALTH CARE DELIVERY; HEALTH CARE SYSTEM; HEALTH CARE UTILIZATION; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; INFORMATION PROCESSING; INFORMATION RETRIEVAL; MEDICAL INFORMATICS; MEDICAL RECORD; MEDICAL RESEARCH; SIGNAL PROCESSING; TELEMETRY; PROCEDURES; STATISTICS;

DATASETS AS TOPIC; DELIVERY OF HEALTH CARE; GENOMICS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; SIGNAL PROCESSING, COMPUTER-ASSISTED; STATISTICS AS TOPIC;

EID: 84937031726     PISSN: 23146133     EISSN: 23146141     Source Type: Journal    
DOI: 10.1155/2015/370194     Document Type: Article

References (181)

1. A. McAfee, E. Brynjolfsson, T. H. Davenport, D. J. Patil, and D. Barton, "Big data: the management revolution," Harvard Business Review, vol. 90, no. 10, pp. 60-68, 2012.
2. C. Lynch, "Big data: how do your data grow?" Nature, vol. 455, no. 7209, pp. 28-29, 2008.
3. A. Jacobs, "The pathologies of big data," Communications of the ACM, vol. 52, no. 8, pp. 36-44, 2009.
4. P. Zikopoulos, C. Eaton, D. deRoos, T. Deutsch, and G. Lapis, Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, McGraw-Hill Osborne Media, 2011.
5. J. Manyika, M. Chui, B. Brown et al., Big Data: The Next Frontier for Innovation, Competition, and Productivity, McKinsey Global Institute, 2011.
6. J. J. Borckardt, M. R. Nash, M. D. Murphy, M. Moore, D. Shaw, and P. O'Neil, "Clinical practice as natural laboratory for psychotherapy research: a guide to case-based time-series analysis," The American Psychologist, vol. 63, no. 2, pp. 77-95, 2008.
7. L. A. Celi, R. G. Mark, D. J. Stone, and R. A. Montgomery, "'Big data' in the intensive care unit: closing the data loop," American Journal of Respiratory and Critical Care Medicine, vol. 187, no. 11, pp. 1157-1160, 2013.
8. F. Ritter, T. Boskamp, A. Homeyer et al., "Medical image analysis," IEEE Pulse, vol. 2, no. 6, pp. 60-70, 2011.
9. J. A. Seibert, "Modalities and data acquisition," in Practical Imaging Informatics, pp. 49-66, Springer, New York, NY, USA, 2010.
10. B. J. Drew, P. Harris, J. K. Zègre-Hemsey et al., "Insights into the problem of alarm fatigue with physiologic monitor devices: a comprehensive observational study of consecutive intensive care unit patients," PLoS ONE, vol. 9, no. 10, Article IDe110274, 2014.
11. K. C. Grahamand M. Cvach, "Monitor alarmfatigue: standardizing use of physiological monitors and decreasing nuisance alarms,"The American Journal of Critical Care, vol. 19, no. 1, pp. 28-34, 2010.
12. M. Cvach, "Monitor alarm fatigue: an integrative review," Biomedical Instrumentation & Technology, vol. 46, no. 4, pp. 268-277, 2012.
13. J. M. Rothschild, C. P. Landrigan, J. W. Cronin et al., "The Critical Care Safety Study: the incidence and nature of adverse events and serious medical errors in intensive care," Critical Care Medicine, vol. 33, no. 8, pp. 1694-1700, 2005.
14. P. Carayon and A. P. Gürses, "A human factors engineering conceptual framework of nursing workload and patient safety in intensive care units," Intensive and Critical Care Nursing, vol. 21, no. 5, pp. 284-301, 2005.
15. P. Carayon, "Human factors of complex sociotechnical systems," Applied Ergonomics, vol. 37, no. 4, pp. 525-535, 2006.
16. E. S. Lander, L. M. Linton, B. Birren et al., "Initial sequencing and analysis of the human genome," Nature, vol. 409, no. 6822, pp. 860-921, 2001.
17. R. Drmanac, A. B. Sparks, M. J. Callow et al., "Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays," Science, vol. 327, no. 5961, pp. 78-81, 2010.
18. T. Caulfield, J. Evans, A. McGuire et al., "Reflections on the cost of 'Low-Cost' whole genome sequencing: framing the health policy debate," PLoS Biology, vol. 11, no. 11, Article ID e1001699, 2013.
19. F. E. Dewey, M. E. Grove, C. Pan et al., "Clinical interpretation and implications of whole-genome sequencing," JAMA, vol. 311, no. 10, pp. 1035-1045, 2014.
20. L. Hood and S. H. Friend, "Predictive, personalized, preventive, participatory (P4) cancer medicine," Nature Reviews Clinical Oncology, vol. 8, no. 3, pp. 184-187, 2011.
21. L. Hood and M. Flores, "A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory," New Biotechnology, vol. 29, no. 6, pp. 613-624, 2012.
22. L. Hood and N. D. Price, "Demystifying disease, democratizing health care," Science Translational Medicine, vol. 6, no. 225, Article ID 225ed5, 2014.
23. R. Chen, G. I. Mias, J. Li-Pook-Than et al., "Personal omics profiling reveals dynamic molecular and medical phenotypes," Cell, vol. 148, no. 6, pp. 1293-1307, 2012.
24. G. H. Fernald, E. Capriotti, R. Daneshjou, K. J. Karczewski, and R. B. Altman, "Bioinformatics challenges for personalized medicine," Bioinformatics, vol. 27, no. 13, Article ID btr295, pp. 1741-1748, 2011.
25. P. Khatri, M. Sirota, and A. J. Butte, "Ten years of pathway analysis: current approaches and outstanding challenges," PLoS Computational Biology, vol. 8, no. 2, Article ID e1002375, 2012.
26. J. Oyelade, J. Soyemi, I. Isewon, and O. Obembe, "Bioinformatics, healthcare informatics and analytics: an imperative for improved healthcare system," International Journal of Applied Information Systems, vol. 8, no. 5, pp. 1-6, 2015.
27. T. G. Kannampallil, A. Franklin, T. Cohen, and T. G. Buchman, "Sub-optimal patterns of information use: a rational analysis of information seeking behavior in critical care," in Cognitive Informatics in Health and Biomedicine, pp. 389-408, Springer, London, UK, 2014.
28. H. Elshazly, A. T. Azar, A. El-korany, and A. E. Hassanien, "Hybrid system for lymphatic diseases diagnosis," in Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI '13), pp. 343-347, IEEE, Mysore, India, August 2013.
29. G. Dougherty, Digital Image Processing forMedical Applications, Cambridge University Press, 2009.
30. R. C. Gessner, C. B. Frederick, F. S. Foster, and P. A. Dayton, "Acoustic angiography: a new imaging modality for assessing microvasculature architecture," International Journal of Biomedical Imaging, vol. 2013, Article ID 936593, 9 pages, 2013.
31. K. Bernatowicz, P. Keall, P. Mishra, A. Knopf, A. Lomax, and J. Kipritidis, "Quantifying the impact of respiratory-gated 4D CT acquisition on thoracic image quality: a digital phantom study," Medical Physics, vol. 42, no. 1, pp. 324-334, 2015.
32. I. Scholl, T. Aach, T. M. Deserno, and T. Kuhlen, "Challenges of medical image processing," Computer Science-Research and Development, vol. 26, no. 1-2, pp. 5-13, 2011.
33. D. S. Liebeskind and E. Feldmann, "Imaging of cerebrovascular disorders: precision medicine and the collaterome," Annals of the New York Academy of Sciences, 2015.
34. T. Hussain and Q. T. Nguyen, "Molecular imaging for cancer diagnosis and surgery," Advanced Drug Delivery Reviews, vol. 66, pp. 90-100, 2014.
35. G. Baio, "Molecular imaging is the key driver for clinical cancer diagnosis in the next century!," Journal of Molecular Imaging & Dynamics, vol. 2, article e102, 2013.
36. S. Mustafa, B. Mohammed, and A. Abbosh, "Novel preprocessing techniques for accurate microwave imaging of human brain," IEEE Antennas and Wireless Propagation Letters, vol. 12, pp. 460-463, 2013.
37. A. H. Golnabi, P. M. Meaney, and K. D. Paulsen, "Tomographic microwave imaging with incorporated prior spatial information," IEEE Transactions on Microwave Theory and Techniques, vol. 61, no. 5, pp. 2129-2136, 2013.
38. B. Desjardins, T. Crawford, E. Good et al., "Infarct architecture and characteristics on delayed enhanced magnetic resonance imaging and electroanatomic mapping in patients with postinfarction ventricular arrhythmia," Heart Rhythm, vol. 6, no. 5, pp. 644-651, 2009.
39. A. M. Hussain, G. Packota, P. W. Major, and C. Flores-Mir, "Role of different imaging modalities in assessment of temporomandibular joint erosions and osteophytes: a systematic review," Dentomaxillofacial Radiology, vol. 37, no. 2, pp. 63-71, 2014.
40. C. M. C. Tempany, J. Jayender, T. Kapur et al., "Multimodal imaging for improved diagnosis and treatment of cancers," Cancer, vol. 121, no. 6, pp. 817-827, 2015.
41. A. Widmer, R. Schaer, D. Markonis, and H. Müller, "Gesture interaction for content-based medical image retrieval," in Proceedings of the 4th ACMInternational Conference onMultimedia Retrieval, pp. 503-506, ACM, April 2014.
42. K. Shvachko, H. Kuang, S. Radia, and R. Chansler, "The Hadoop distributed file system," in Proceedings of the IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST '10), pp. 1-6, IEEE, May 2010.
43. D. Sobhy, Y. El-Sonbaty, and M. Abou Elnasr, "MedCloud: healthcare cloud computing system," in Proceedings of the International Conference for Internet Technology and Secured Transactions, pp. 161-166, IEEE, London, UK, December 2012.
44. J. Dean and S. Ghemawat, "MapReduce: simplified data processing on large clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008.
45. F. Wang, V. Ercegovac, T. Syeda-Mahmood et al., "Largescale multimodal mining for healthcare with mapreduce," inProceedings of the 1st ACM International Health Informatics Symposium, pp. 479-483, ACM, November 2010.
46. W.-S. Li, J. Yan, Y. Yan, and J. Zhang, "Xbase: cloud-enabled information appliance for healthcare," in Proceedings of the 13th International Conference on ExtendingDatabase Technology (EDBT '10), pp. 675-680, March 2010.
47. D. Markonis, R. Schaer, I. Eggel, H. Muller, and A. Depeursinge, "Using MapReduce for large-scale medical image analysis," in Proceedings of the 2nd IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology (HISB '12), p. 1, IEEE, San Diego, Calif, USA, September 2012.
48. K. Shackelford, "System & method for delineation and quantification of fluid accumulation in efast trauma ultrasound images," US Patent Application, 14/167,448, 2014.
49. H. Yang, J. Liu, J. Sui, G. Pearlson, and V. D. Calhoun, "A hybrid machine learning method for fusing fMRI and genetic data: combining both improves classification of schizophrenia," Frontiers in Human Neuroscience, vol. 4, 2010.
50. O. A. J. del Toro and H. Muller, "Multi atlas-based segmentation with data driven refinement," in Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI '14), pp. 605-608, IEEE, Valencia, Spain, June 2014.
51. A. Tsymbal, E. Meissner, M. Kelm, and M. Kramer, "Towards cloud-based image-integrated similarity search in big data," in Proceedings of the IEEE-EMBS International Conference on Biomedical andHealth Informatics (BHI '14), pp. 593-596, IEEE, Valencia, Spain, June 2014.
52. W. Chen, C. Cockrell, K. R. Ward, and K. Najarian, "Intracranial pressure level prediction in traumatic brain injury by extracting features from multiple sources and using machine learning methods," in Proceedings of the IEEE InternationalConference on Bioinformatics and Biomedicine (BIBM '10), pp. 510-515, IEEE, December 2010.
53. R. Weissleder, "Molecular imaging in cancer," Science, vol. 312, no. 5777, pp. 1168-1171, 2006.
54. T. Zheng, L. Cao, Q. He, and G. Jin, "Full-range in-plane rotation measurement for image recognition with hybrid digitaloptical correlator," Optical Engineering, vol. 53, no. 1, Article ID 011003, 2014.
55. L. Ohno-Machado, V. Bafna, A. A. Boxwala et al., "iDASH: integrating data for analysis, anonymization, and sharing," Journal of the AmericanMedical Informatics Association, vol. 19, no. 2, pp. 196-201, 2012.
56. C.-T. Yang, L.-T. Chen, W.-L. Chou, and K.-C. Wang, "Implementation of a medical image file accessing system on cloud computing," in Proceedings of the 13th IEEE International Conference on Computational Science and Engineering (CSE' 10), pp. 321-326, December 2010.
57. C. O. Rolim, F. L. Koch, C. B. Westphall, J. Werner, A. Fracalossi, and G. S. Salvador, "A cloud computing solution for patient's data collection in health care institutions," in Proceedings of the 2nd International Conference on eHealth, Telemedicine, and Social Medicine (ETELEMED '10), pp. 95-99, IEEE, February 2010.
58. C.-C. Teng, J. Mitchell, C. Walker et al., "A medical image archive solution in the cloud," in Proceedings of the IEEE International Conference on Software Engineering and Service Sciences (ICSESS '10), pp. 431-434, IEEE, July 2010.
59. A. Sandryhaila and J. M. F. Moura, "Big data analysis with signal processing on graphs: representation and processing of massive data sets with irregular structure," IEEE Signal Processing Magazine, vol. 31, no. 5, pp. 80-90, 2014.
60. J. G. Wolff, "Big data and the SP theory of intelligence," IEEE Access, vol. 2, pp. 301-315, 2014.
61. S. W. Jun, K. E. Fleming, M. Adler, and J. Emer, "ZIP-IO: architecture for application-specific compression of Big Data," in Proceedings of the International Conference on Field-Programmable Technology (FPT '12), pp. 343-351, December 2012.
62. B. Jalali and M. H. Asghari, "The anamorphic stretch transform: putting the squeezeon 'bigdata',"Optics and PhotonicsNews, vol. 25, no. 2, pp. 24-31, 2014.
63. D. Feldman, C. Sung, and D. Rus, "The single pixel GPS: learning big data signals fromtiny coresets," in Proceedings of the 20th International Conference onAdvances inGeographic Information Systems (SIGSPATIAL '12), pp. 23-32, ACM, Redondo Beach, Calif, USA, November 2012.
64. L. Chiron, M. A. Van Agthoven, B. Kieffer, C. Rolando, and M.-A. Delsuc, "Efficient denoising algorithms for large experimental datasets and their applications in Fourier transform ion cyclotron resonance mass spectrometry," Proceedings of the National Academy of Sciences of the United States of America, vol. 111, no. 4, pp. 1385-1390, 2014.
65. A. C. Gilbert, P. Indyk, M. Iwen, and L. Schmidt, "Recent developments in the sparse fourier transform: a compressed fourier transformfor big data," IEEE Signal ProcessingMagazine, vol. 31, no. 5, pp. 91-100, 2014.
66. W.-Y. Hsu, "Segmentation-based compression: new frontiers of telemedicine in telecommunication," Telematics and Informatics, vol. 32, no. 3, pp. 475-485, 2015.
67. F. P. M. Oliveira and J. M. R. S. Tavares, "Medical image registration: a review," Computer Methods in Biomechanics and Biomedical Engineering, vol. 17, no. 2, pp. 73-93, 2014.
68. L. Qu, F. Long, and H. Peng, "3D registration of biological images and models: registration of microscopic images and its uses in segmentation and annotation," IEEE Signal Processing Magazine, vol. 32, no. 1, pp. 70-77, 2015.
69. M. Ulutas, G. Ulutas, and V. V. Nabiyev, "Medical image security and EPR hiding using shamir's secret sharing scheme," Journal of Systems and Software, vol. 84, no. 3, pp. 341-353, 2011.
70. H. Satoh, N. Niki, K. Eguchi et al., "Teleradiology network system on cloud using the WEB medical image conference system with a new information security solution," in Medical Imaging : Advanced PACS-based Imaging Informatics and Therapeutic Applications, vol. 8674 of Proceedings of SPIE, International Society for Optics and Photonics, March 2013.
71. C. K. Tan, J. C. Ng, X. Xu, C. L. Poh, Y. L. Guan, and K. Sheah, "Security protection of DICOM medical images using duallayer reversiblewatermarkingwith tamper detection capability," Journal of Digital Imaging, vol. 24, no. 3, pp. 528-540, 2011.
72. F. Wang, R. Lee, Q. Liu, A. Aji, X. Zhang, and J. Saltz, "Hadoopgis: a high performance query system for analytical medical imaging with mapreduce," Tech. Rep., Emory University, Atlanta, Ga, USA, 2011.
73. N. Koutsouleris, S. Borgwardt, E. M. Meisenzahl, R. Bottlender, H.-J. Möller, and A. Riecher-Rössler, "Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the fepsy study," Schizophrenia Bulletin, vol. 38, no. 6, Article IDsbr145, pp. 1234-1246, 2012.
74. K. W. Bowyer, "Validation of medical image analysis techniques," in Handbook of Medical Imaging, vol. 2, pp. 567-607, 2000.
75. P. Jannin, E. Krupinski, and S. Warfield, "Guest editorial: validation in medical image processing," IEEE Transactions on Medical Imaging, vol. 25, no. 11, pp. 1405-1409, 2006.
76. A. Popovic, M. de la Fuente, M. Engelhardt, and K. Radermacher, "Statistical validation metric for accuracy assessment in medical image segmentation," International Journal of Computer Assisted Radiology and Surgery, vol. 2, no. 3-4, pp. 169-181, 2007.
77. C. F. Mackenzie, P. Hu, A. Sen et al., "Automatic pre-hospital vital signs waveform and trend data capture fills quality management, triage and outcome prediction gaps," AMIA Annual Symposium Proceedings, vol. 2008, pp. 318-322, 2008.
78. M. Bodo, T. Settle, J. Royal, E. Lombardini, E. Sawyer, and S. W. Rothwell, "Multimodal noninvasive monitoring of soft tissue wound healing," Journal of Clinical Monitoring and Computing, vol. 27, no. 6, pp. 677-688, 2013.
79. P. Hu, S.M. Galvagno Jr., A. Sen et al., "Identificationof dynamic prehospital changes with continuous vital signs acquisition," Air Medical Journal, vol. 33, no. 1, pp. 27-33, 2014.
80. D. Apiletti, E. Baralis, G. Bruno, and T. Cerquitelli, "Real-time analysis of physiological data to support medical applications," IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 3, pp. 313-321, 2009.
81. J. Chen, E. Dougherty, S. S. Demir, C. P. Friedman, C. S. Li, and S. Wong, "Grand challenges for multimodal bio-medical systems," IEEE Circuits and Systems Magazine, vol. 5, no. 2, pp. 46-52, 2005.
82. N. Menachemi, A. Chukmaitov, C. Saunders, and R. G. Brooks, "Hospital quality of care: does information technology matter? Therelationship between information technology adoption and quality of care," Health Care Management Review, vol. 33, no. 1, pp. 51-59, 2008.
83. C. M. DesRoches, E. G. Campbell, S. R. Rao et al., "Electronic health records in ambulatory care-a national survey of physicians," The New England Journal of Medicine, vol. 359, no. 1, pp. 50-60, 2008.
84. J. S. McCullough, M. Casey, I. Moscovice, and S. Prasad, "The effect of health information technology on quality in U.S. hospitals," Health Affairs, vol. 29, no. 4, pp. 647-654, 2010.
85. J. M. Blum, H. Joo, H. Lee, and M. Saeed, "Design and implementation of a hospital wide waveform capture system," Journal of ClinicalMonitoring and Computing, vol. 29, no. 3, pp. 359-362, 2015.
86. D. Freeman, "The future of patient monitoring," Health Management Technology, vol. 30, no. 12, article 26, 2009.
87. B. Muhsin and A. Sampath, "Systems and methods for storing, analyzing, retrieving and displaying streaming medical data," US Patent 8,310,336, 2012.
88. D. Malan, T. Fulford-Jones, M. Welsh, and S. Moulton, "Codeblue: an ad hoc sensor network infrastructure for emergency medical care," in Proceedings of the International Workshop on Wearable and Implantable Body SensorNetworks, vol. 5, London, UK, 2004.
89. A. Page, O. Kocabas, S. Ames, M. Venkitasubramaniam, and T. Soyata, "Cloud-based secure health monitoring: optimizing fully-homomorphic encryption for streaming algorithms," in Proceedings of the IEEE Globecom Workshops (GC Wkshps '14), pp. 48-52, IEEE, Austin, Tex, USA, December 2014.
90. J. Bange, M. Gryzwa, K. Hoyme, D. C. Johnson, J. LaLonde, and W. Mass, "Medical data transport over wireless life critical network," US Patent 7,978,062, 2011.
91. N. Kara and O. A. Dragoi, "Reasoning with contextual data in telehealth applications," in Proceedings of the 3rd IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMoB '07), p. 69, IEEE, October 2007.
92. G. Li, J. Liu, X. Li, L. Lin, and R. Wei, "A multiple biomedical signals synchronous acquisition circuit based on over-sampling and shaped signal for the application of the ubiquitous health care," Circuits, Systems, and Signal Processing, vol. 33, no. 10, pp. 3003-3017, 2014.
93. A. Bar-Or, J. Healey, L. Kontothanassis, and J. M. van Thong, "BioStream: a system architecture for real-time processing of physiological signals," in Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC' 04), vol. 2, pp. 3101-3104, September 2004.
94. W. Raghupathi and V. Raghupathi, "Big data analytics in healthcare: promise and potential," Health Information Science and Systems, vol. 2, article 3, 2014.
95. S. Ahmad, T. Ramsay, L. Huebsch et al., "Continuous multiparameter heart rate variability analysis heralds onset of sepsis in adults," PLoS ONE, vol. 4, no. 8, Article ID e6642, 2009.
96. A. L. Goldberger, L. A. Amaral, L. Glass et al., "Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals," Circulation, vol. 101, no. 23, pp. e215-e220, 2000.
97. E. J. Siachalou, I. K. Kitsas, K. J. Panoulas et al., "ICASP: an intensive-care acquisition and signal processing integrated framework," Journal of Medical Systems, vol. 29, no. 6, pp. 633-646, 2005.
98. M. Saeed, C. Lieu, G. Raber, and R. G. Mark, "Mimic II: a massive temporal icu patient database to support research in intelligent patient monitoring," in Proceedings of the Computers in Cardiology, pp. 641-644, IEEE, September 2002.
99. A. Burykin, T. Peck, and T. G. Buchman, "Using 'off-theshelf ' tools for terabyte-scale waveform recording in intensive care: computer system design, database description and lessons learned," Computer Methods and Programs in Biomedicine, vol. 103, no. 3, pp. 151-160, 2011.
100. G. Adrián, G. E. Francisco, M. Marcela, A. Baum, L. Daniel, and G. B. de Quirós Fernán, "Mongodb: an open source alternative for HL7-CDA clinical documents management," in Proceedings of the Open Source International Conference (CISL '13), Buenos Aires, Argentina, 2013.
101. K. Kaur and R. Rani, "Managing data in healthcare information systems: many models, one solution," Computer, vol. 48, no. 3, pp. 52-59, 2015.
102. S. Prasad and M. S. N. Sha, "NextGen data persistence pattern in healthcare: polyglot persistence," in Proceedings of the 4th International Conference on Computing, Communications and Networking Technologies (ICCCNT '13), pp. 1-8, July 2013.
103. W. D. Yu, M. Kollipara, R. Penmetsa, and S. Elliadka, "A distributed storage solution for cloud based e-Healthcare Information System," in Proceedings of the IEEE 15th International Conference on e-Health Networking, Applications & Services (Healthcom '13), pp. 476-480, Lisbon, Portugal, October 2013.
104. M. Santos and F. Portela, "Enabling ubiquitous Data Mining in intensive care: features selection and data pre-processing," in Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS' 11), pp. 261-266, June 2011.
105. D. J. Berndt, J.W. Fisher, A. R. Hevner, and J. Studnicki, "Healthcare data warehousing and quality assurance," Computer, vol. 34, no. 12, pp. 28-65, 2001.
106. O. Uzuner, B. R. South, S. Shen, and S. L. DuVall, "2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text," Journal of the AmericanMedical Informatics Association, vol. 18, no. 5, pp. 552-556, 2011.
107. B. D. Athey, M. Braxenthaler, M. Haas, and Y. Guo, "tranSMART: an open source and community-driven informatics and data sharing platform for clinical and translational research," AMIA Summits on Translational Science Proceedings, vol. 2013, pp. 6-8, 2013.
108. M. Saeed, M. Villarroel, A. T. Reisner et al., "Multiparameter intelligent monitoring in intensive care II: a public-access intensive care unit database," Critical CareMedicine, vol. 39, no. 5, pp. 952-960, 2011.
109. D. J. Scott, J. Lee, I. Silva et al., "Accessing the public MIMICII intensive care relational database for clinical research," BMC Medical Informatics andDecisionMaking, vol. 13, no. 1, article 9, 2013.
110. A. Belle, M. A. Kon, and K. Najarian, "Biomedical informatics for computer-aided decision support systems: a survey," The Scientific World Journal, vol. 2013, Article ID 769639, 8 pages, 2013.
111. B. S. Bloom, "Crossing the quality chasm: a new health system for the 21st century (committee on quality of health care in America, institute of medicine)," The Journal of the American Medical Association (International Edition), vol. 287, no. 5, p. 645, 2002.
112. S. Eta Berner, "Clinical decision support systems: state of the art," AHRQ Publication (090069), 2009.
113. H. Han, H.C. Ryoo, and H. Patrick, "An infrastructure of stream data mining, fusion and management for monitored patients," in Proceedings of the 19th IEEE International Symposium on Computer-Based Medical Systems (CBMS '06), pp. 461-468, IEEE, Salt Lake City, Utah, USA, June 2006.
114. N. Bressan, A. James, and C. McGregor, "Trends and opportunities for integrated real time neonatal clinical decision support," in Proceedings of the IEEE-EMBS International Conference on Biomedical andHealth Informatics (BHI '12), pp. 687-690, IEEE, Hong Kong, January 2012.
115. A. J. E. Seely, A. Bravi, C. Herry et al., "Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?" Critical Care, vol. 18, no. 2, article R65, 2014.
116. M. Attin, G. Feld, H. Lemus et al., "Electrocardiogram characteristics prior to in-hospital cardiac arrest," Journal of Clinical Monitoring and Computing, vol. 29, no. 3, pp. 385-392, 2015.
117. J. Lee and R. G. Mark, "A hypotensive episode predictor for intensive care based on heart rate and blood pressure time series," in Computing in Cardiology, pp. 81-84, IEEE, 2010.
118. J. Sun, D. Sow, J. Hu, and S. Ebadollahi, "A system for mining temporal physiological data streams for advanced prognostic decision support," in Proceedings of the 10th IEEE International Conference on Data Mining (ICDM' 10), pp. 1061-1066, December 2010.
119. H. Cao, L. Eshelman, N. Chbat, L. Nielsen, B. Gross, and M. Saeed, "Predicting icu hemodynamic instability using continuous multiparameter trends," in Proceedings of the IEEE International Conference on Engineering inMedicine and Biology Society (EMBS '08), pp. 3803-3806, August 2008.
120. D. L. Reich, Monitoring in Anesthesia and Perioperative Care, Cambridge University Press, 2011.
121. A. Smolinska, A.-Ch. Hauschild, R. R. R. Fijten, J. W. Dallinga, J. Baumbach, and F. J. van Schooten, "Current breathomics-a reviewon data pre-processing techniques and machine learning in metabolomics breath analysis," Journal of Breath Research, vol. 8, no. 2, Article ID 027105, 2014.
122. P. Le Roux, D. K. Menon, G. Citerio et al., "Consensus summary statement of the internationalmultidisciplinary consensus conference on multimodality monitoring in neurocritical care," Intensive Care Medicine, vol. 40, no. 9, pp. 1189-1209, 2014.
123. M. M. Tisdall and M. Smith, "Multimodal monitoring in traumatic brain injury: current status and future directions," British Journal of Anaesthesia, vol. 99, no. 1, pp. 61-67, 2007.
124. J. C. Hemphill, P. Andrews, and M. de Georgia, "Multimodal monitoring and neurocritical care bioinformatics," Nature Reviews Neurology, vol. 7, no. 8, pp. 451-460, 2011.
125. A. Pantelopoulos and N. G. Bourbakis, "A survey on wearable sensor-based systems for health monitoring and prognosis," IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 40, no. 1, pp. 1-12, 2010.
126. S. Winkler, M. Schieber, S. Lücke et al., "A new telemonitoring system intended for chronic heart failure patients using mobile telephone technology-feasibility study," International Journal of Cardiology, vol. 153, no. 1, pp. 55-58, 2011.
127. D. Sow, D. S. Turaga, and M. Schmidt, "Mining of sensor data in healthcare: a survey," in Managing and Mining Sensor Data, pp. 459-504, Springer, 2013.
128. J. W. Davey, P. A. Hohenlohe, P. D. Etter, J. Q. Boone, J. M. Catchen, and M. L. Blaxter, "Genome-wide genetic marker discovery and genotyping using next-generation sequencing," Nature Reviews Genetics, vol. 12, no. 7, pp. 499-510, 2011.
129. T. J. Treangen and S. L. Salzberg, "Repetitive DNA and nextgeneration sequencing: computational challenges and solutions," Nature Reviews Genetics, vol. 13, no. 1, pp. 36-46, 2012.
130. D. C. Koboldt, K. M. Steinberg, D. E. Larson, R. K. Wilson, and E. R. Mardis, "The next-generation sequencing revolution and its impact on genomics," Cell, vol. 155, no. 1, pp. 27-38, 2013.
131. Institute of Medicine, Informatics Needs and Challenges in Cancer Research: Workshop Summary, The National Academies Press, Washington, DC, USA, 2012.
132. E. M. van Allen, N. Wagle, and M. A. Levy, "Clinical analysis and interpretation of cancer genome data," Journal of Clinical Oncology, vol. 31, no. 15, pp. 1825-1833, 2013.
133. A. Tabchy, C. X. Ma, R. Bose, and M. J. Ellis, "Incorporating genomics into breast cancer clinical trials and care," Clinical Cancer Research, vol. 19, no. 23, pp. 6371-6379, 2013.
134. F. Andre, E. Mardis, M. Salm, J. C. Soria, L. L. Siu, and C. Swanton, "Prioritizing targets for precision cancer medicine," Annals of Oncology, vol. 25, no. 12, pp. 2295-2303, 2014.
135. G. Karlebach and R. Shamir, "Modelling and analysis of gene regulatory networks," Nature Reviews Molecular Cell Biology, vol. 9, no. 10, pp. 770-780, 2008.
136. J. Lovén, D. A. Orlando, A. A. Sigova et al., "Revisiting global gene expression analysis," Cell, vol. 151, no. 3, pp. 476-482, 2012.
137. J. T. Leek, R. B. Scharpf, H. C. Bravo et al., "Tackling the widespread and critical impact of batch effects in highthroughput data," Nature Reviews Genetics, vol. 11, no. 10, pp. 733-739, 2010.
138. D. W. Huang, B. T. Sherman, and R. A. Lempicki, "Bioinformatics enrichment tools: paths toward the comprehensivefunctional analysis of large gene lists," Nucleic Acids Research, vol. 37, no. 1, pp. 1-13, 2009.
139. P. Khatri, S. Draghici, G. C. Ostermeier, and S. A. Krawetz, "Profiling gene expression using Onto-Express," Genomics, vol. 79, no. 2, pp. 266-270, 2001.
140. S. Draghici, P. Khatri, R. P. Martins, G. C. Ostermeier, and S. A. Krawetz, "Global functional profiling of gene expression," Genomics, vol. 81, no. 2, pp. 98-104, 2003.
141. S. Draghici, P. Khatri, R. P. Martins, G. C. Ostermeier, and S. A. Krawetz, "Global functional profiling of gene expression," Genomics, vol. 81, no. 2, pp. 98-104, 2003.
142. B. R. Zeeberg, W. Feng, G. Wang et al., "GoMiner: a resource for biological interpretation of genomic and proteomic data," Genome Biology, vol. 4, no. 4, article R28, 2003.
143. K. L. Poguegeile, J. A. MacKey, R. D. George et al., "A new microarray, enriched in pancreas and pancreatic cancer cdnas to identify genes relevant to pancreatic cancer," Cancer Genomics and Proteomics, vol. 1, no. 5-6, pp. 371-386, 2004.
144. G. Bindea, B. Mlecnik, H. Hackl et al., "Cluego: a cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks," Bioinformatics, vol. 25, no. 8, pp. 1091-1093, 2009.
145. G. Bindea, J. Galon, and B. Mlecnik, "CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data," Bioinformatics, vol. 29, no. 5, pp. 661-663, 2013.
146. A. Subramanian, P. Tamayo, V. K. Mootha et al., "Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles," Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 43, pp. 15545-15550, 2005.
147. V. K. Mootha, C. M. Lindgren, K.-F. Eriksson et al., "PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes," Nature Genetics, vol. 34, no. 3, pp. 267-273, 2003.
148. S. Draghici, P. Khatri, A. L. Tarca et al., "A systems biology approach for pathway level analysis," Genome Research, vol. 17, no. 10, pp. 1537-1545, 2007.
149. M.-H. Teiten, S. Eifes, S. Reuter, A. Duvoix, M. Dicato, and M. Diederich, "Gene expression profiling related to antiinflammatory properties of curcumin in K562 leukemia cells," Annals of the New York Academy of Sciences, vol. 1171, pp. 391-398, 2009.
150. I. Thiele, N. Swainston, R. M. T. Fleming et al., "A communitydriven global reconstruction of human metabolism," Nature Biotechnology, vol. 31, no. 5, pp. 419-425, 2013.
151. O. Folger, L. Jerby, C. Frezza, E. Gottlieb, E. Ruppin, and T. Shlomi, "Predicting selective drug targets in cancer through metabolic networks," Molecular Systems Biology, vol. 7, no. 1, 2011.
152. D. Marbach, J. C. Costello, R. Küffner et al., "Wisdomof crowds for robust gene network inference," Nature Methods, vol. 9, no. 8, pp. 796-804, 2012.
153. R.-S. Wang, A. Saadatpour, and R. Albert, "Booleanmodeling in systems biology: an overviewof methodology and applications," Physical Biology, vol. 9, no. 5, Article ID 055001, 2012.
154. W. Gong, N. Koyano-Nakagawa, T. Li, and D. J. Garry, "Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data," BMC Bioinformatics, vol. 16, no. 1, article 74, 2015.
155. K. C. Chen, L. Calzone, A. Csikasz-Nagy, F. R. Cross, B. Novak, and J. J. Tyson, "Integrative analysis of cell cycle control in budding yeast," Molecular Biology of the Cell, vol. 15, no. 8, pp. 3841-3862, 2004.
156. S. Kimura, K. Ide, A. Kashihara et al., "Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm," Bioinformatics, vol. 21, no. 7, pp. 1154-1163, 2005.
157. J. Gebert, N. Radde, and G.-W. Weber, "Modeling gene regulatory networks with piecewise linear differential equations," European Journal of Operational Research, vol. 181, no. 3, pp. 1148-1165, 2007.
158. J. N. Bazil, K. D. Stamm, X. Li et al., "The inferred cardiogenic gene regulatory network in the mammalian heart," PLoS ONE, vol. 9, no. 6, Article ID e100842, 2014.
159. B. O. Palsson, Systems Biology, Cambridge University Press, 2006.
160. D. Marbach, R. J. Prill, T. Schaffter, C. Mattiussi, D. Floreano, and G. Stolovitzky, "Revealing strengths and weaknesses of methods for gene network inference," Proceedings of the National Academy of Sciences of the United States of America, vol. 107, no. 14, pp. 6286-6291, 2010.
161. N. C. Duarte, S. A. Becker, N. Jamshidi et al., "Global reconstruction of the human metabolic network based on genomic and bibliomic data," Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 6, pp. 1777-1782, 2007.
162. K. Raman and N. Chandra, "Flux balance analysis of biological systems: applications and challenges," Briefings in Bioinformatics, vol. 10, no. 4, pp. 435-449, 2009.
163. C. S. Henry, M. Dejongh, A. A. Best, P.M. Frybarger, B. Linsay, and R. L. Stevens, "High-throughput generation, optimization and analysis of genome-scale metabolic models," Nature Biotechnology, vol. 28, no. 9, pp. 977-982, 2010.
164. K. Radrich, Y. Tsuruoka, P. Dobson et al., "Integration of metabolic databases for the reconstruction of genome-scale metabolic networks," BMC Systems Biology, vol. 4, article 114, 2010.
165. K. Yizhak, T. Benyamini, W. Liebermeister, E. Ruppin, and T. Shlomi, "Integrating quantitative proteomics and metabolomics with a genome-scalemetabolic networkmodel," Bioinformatics, vol. 26, no. 12, Article ID btq183, pp. i255-i260, 2010.
166. C. R. Haggart, J. A. Bartell, J. J. Saucerman, and J. A. Papin, "Whole-genome metabolic network reconstruction and constraint-based modeling," in Methods in Systems Biology, M. Verma, D. Jameson, and H. V. Westerhoff, Eds., vol. 500 of Methods in Enzymology, chapter 21, pp. 411-433, Academic Press, 2011.
167. D. McCloskey, B.O. Palsson, and A.M. Feist, "Basic andapplied uses of genome-scale metabolic network reconstructions of Escherichia coli," Molecular Systems Biology, vol. 9, article 661, 2013.
168. E. P. Gianchandani, A. K. Chavali, and J. A. Papin, "The application of flux balance analysis in systems biology," Wiley Interdisciplinary Reviews: Systems Biology and Medicine, vol. 2, no. 3, pp. 372-382, 2010.
169. N. E. Lewis, H. Nagarajan, and B. O. Palsson, "Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods," Nature Reviews Microbiology, vol. 10, no. 4, pp. 291-305, 2012.
170. W. Zhang, F. Li, and L. Nie, "Integrating multiple 'omics' analysis for microbial biology: application and methodologies," Microbiology, vol. 156, no. 2, pp. 287-301, 2010.
171. A. S. Blazier and J. A. Papin, "Integration of expression data in genome-scale metabolic network reconstructions," Frontiers in Physiology, vol. 3, article 299, 2012.
172. P. A. Jensen and J. A. Papin, "Functional integration of a metabolic networkmodel and expression datawithout arbitrary thresholding," Bioinformatics, vol. 27, no. 4, pp. 541-547, 2011.
173. R. L. Chang, L. Xie, L. Xie, P. E. Bourne, and B. O. Palsson, "Drug off-target effects predicted using structural analysis in the context of ametabolic networkmodel," PLoS Computational Biology, vol. 6, no. 9, Article ID e1000938, 2010.
174. V. A. Huynh-Thu, A. Irrthum, L. Wehenkel, and P. Geurts, "Inferring regulatory networks from expression data using treebasedmethods," PLoSONE, vol. 5, no.9, Article IDe12776, 2010.
175. R. Küffner, T. Petri, P. Tavakkolkhah, L. Windhager, and R. Zimmer, "Inferring gene regulatory networks by ANOVA," Bioinformatics, vol. 28, no. 10, Article ID bts143, pp. 1376-1382, 2012.
176. R. J. Prill, J. Saez-Rodriguez, L. G. Alexopoulos, P.K. Sorger, and G. Stolovitzky, "Crowdsourcing network inference: the dream predictive signaling network challenge," Science Signaling, vol. 4, no. 189, 2011.
177. T. Saithong, S. Bumee, C. Liamwirat, and A. Meechai, "Analysis and practical guideline of constraint-based boolean method in genetic network inference," PLoS ONE, vol. 7, no. 1, Article ID e30232, 2012.
178. S. Martin, Z. Zhang, A. Martino, and J.-L. Faulon, "Boolean dynamics of genetic regulatory networks inferred from microarray time series data," Bioinformatics, vol. 23, no. 7, pp. 866-874, 2007.
179. J. N. Bazil, F. Qi, and D. A. Beard, "A parallel algorithm for reverse engineering of biological networks," Integrative Biology, vol. 3, no. 12, pp. 1215-1223, 2011.
180. A. Belle, S.-Y. Ji, W. Chen, T. Huynh, and K. Najarian, "Rulebased computer aided decision making for traumatic brain injuries," inMachine Learning in Healthcare Informatics, vol. 56, pp. 229-259, Springer, Berlin, Germany, 2014.
181. I. Yoo, P. Alafaireet, M. Marinov et al., "Data mining in healthcare and biomedicine: a survey of the literature," Journal of Medical Systems, vol. 36, no. 4, pp. 2431-2448, 2012.