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Big Healthcare Data Analytics: Challenges and Applications

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Handbook of Large-Scale Distributed Computing in Smart Healthcare

Part of the book series: Scalable Computing and Communications ((SCC))

Abstract

Increasing demand and costs for healthcare, exacerbated by ageing populations and a great shortage of doctors, are serious concerns worldwide. Consequently, this has generated a great amount of motivation in providing better healthcare through smarter healthcare systems. Management and processing of healthcare data are challenging due to various factors that are inherent in the data itself such as high-dimensionality, irregularity and sparsity. A long stream of research has been proposed to address these problems and provide more efficient and scalable healthcare systems and solutions. In this chapter, we shall examine the challenges in designing algorithms and systems for healthcare analytics and applications, followed by a survey on various relevant solutions. We shall also discuss next-generation healthcare applications, services and systems, that are related to big healthcare data analytics.

Chonho Lee’s work was done while he was at National University of Singapore.

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Notes

  1. 1.

    http://www.toptenreviews.com/health/senior-care.

  2. 2.

    https://archive.ics.uci.edu/ml/datasets/Diabetes+130-US+hospitals+for+years+1999-2008.

  3. 3.

    http://www.kdd.org/kdd2016/topics/view/dimensionality-reduction.

  4. 4.

    https://labtestsonline.org/.

  5. 5.

    https://phekb.org/.

  6. 6.

    http://www.mobilehealthsummit.ca.

  7. 7.

    http://www.sonymobile.com/global-en/apps-services/lifelog.

References

  1. Apache storm. http://storm.apache.org.

  2. H. Alemdar and C. Ersoy. Wireless sensor networks for healthcare: A survey. Computer Networks, 54(15):2688–2710, 2010.

    Article  Google Scholar 

  3. M. R. Avendi, A. Kheradvar, and H. Jafarkhani. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac mri. Medical image analysis, 30:108–119, 2016.

    Article  Google Scholar 

  4. M. A. Balafar, A. R. Ramli, M. I. Saripan, and S. Mashohor. Review of brain MRI image segmentation methods. Artificial Intelligence Review, 33(3):261–274, 2010.

    Article  Google Scholar 

  5. H. Banaee, M. U. Ahmed, and A. Lout. Data mining for wearable sensors in health monitoring systems: A review of recent trends and challenges. Sensors, 13(12), 2013.

    Google Scholar 

  6. A. J. Bandodkar, I. Jeerapan, and J. Wang. Wearable chemical sensors: Present challenges and future prospects. ACS Sensors, 1:464–482, 2016.

    Article  Google Scholar 

  7. I. M. Baytas, K. Lin, F. Wang, et al. Stochastic convex sparse principal component analysis. EURASIP Journal on Bioinformatics and Systems Biology, 2016(1):1–11, 2016.

    Article  Google Scholar 

  8. Y. Bengio, A. Courville, and P. Vincent. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1798–1828, 2013.

    Article  Google Scholar 

  9. J. Bian, B. Gao, and T.-Y. Liu. Knowledge-powered deep learning for word embedding. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 132–148, 2014.

    Google Scholar 

  10. T. Botsis, G. Hartvigsen, F. Chen, and C. Weng. Secondary use of ehr: data quality issues and informatics opportunities. AMIA Summits Transl Sci Proc, 2010:1–5, 2010.

    Google Scholar 

  11. Y. Y. Broza and H. Haick. Nanomaterial-based sensors for detection of disease by volatile organic compounds. Nanomedicine (Lond), 8(5):785–806, 2013.

    Article  Google Scholar 

  12. A. Bulling, U. Blanke, and B. Schiele. A tutorial on human activity recognition using body-worn inertial sensors. ACM Computing Survey, 46(3):1–33, 2014.

    Article  Google Scholar 

  13. N. A. Capela, E. D. Lemaire, N. Baddour, et al. Evaluation of a smartphone human activity recognition application with able-bodied and stroke participants. NeuroEngineering and Rehabilitation, 13(5), 2016.

    Google Scholar 

  14. R. Caruana. Multitask learning. Machine learning, 28(1):41–75, 1997.

    Article  MathSciNet  Google Scholar 

  15. R. D. Caytiles and S. Park. A study of the design of wireless medical sensor netork based u-healthcare system. International Journal of Bio-Science and Bio-Technology, 6(3):91–96, 2014.

    Article  Google Scholar 

  16. Z. Che, D. C. Kale, W. Li, et al. Deep computational phenotyping. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 507–516, 2015.

    Google Scholar 

  17. Z. Che, S. Purushotham, K. Cho, et al. Recurrent neural networks for multivariate time series with missing values. arXiv preprint arXiv: 1606.01865, 2016.

  18. Z. Che, S. Purushotham, R. Khemani, et al. Distilling knowledge from deep networks with applications to healthcare domain. arXiv preprint arXiv: 1512.03542, 2015.

  19. K. Cho, B. Van Merriënboer, C. Gulcehre, et al. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv: 1406.1078, 2014.

  20. D. A. Cohn. Neural network exploration using optimal experiment design. In NIPS, 1994.

    Google Scholar 

  21. R. Cort, X. Bonnaire, O. Marin, et al. Stream processing of healthcare sensor data: studying user traces to identify challenges from a big data perspective. In Proceedings of the 4th International Workshop on Body Area Sensor Networks, 2015.

    Google Scholar 

  22. B. Cui, H. Mei, and B. C. Ooi. Big data: the driver for innovation in databases. National Science Review, 1(1):27–30, 2014.

    Article  Google Scholar 

  23. A. G. Dent, T. G. Sutedja, and P. V. Zimmerman. Exhaled breath analysis for lung cancer. Journal of thoracic disease, 5:S540, 2013.

    Google Scholar 

  24. A. Doan, A. Halevy, and Z. Ives. Principles of data integration. Elsevier, 2012.

    Google Scholar 

  25. X. L. Dong and D. Srivastava. Big data integration. In Data Engineering (ICDE), 2013 IEEE 29th International Conference on, pages 1245–1248, 2013.

    Google Scholar 

  26. O. M. Doyle, E. Westman, A. F. Marquand, et al. Predicting progression of alzheimers disease using ordinal regression. PloS one, 9(8):e105542, 2014.

    Article  Google Scholar 

  27. S. Duchesne, A. Caroli, C. Geroldi, et al. Relating one-year cognitive change in mild cognitive impairment to baseline MRI features. Neuroimage, 47(4):1363–1370, 2009.

    Article  Google Scholar 

  28. A. S. Evani, B. Sreenivasan, J. S. Sudesh, et al. Activity recognition using wearable sensors for healthcare. In Proceedings of the 7th International Conference on Sensor Technologies and Appplications, 2013.

    Google Scholar 

  29. L. Filipe, F. Fdez-Riverola, N. Costa, et al. Wireless body area networks for healthcare applications: Protocol stack review. International Journal of Distributed Sensor Networks, 2015:1:1–1:1, 2015.

    Google Scholar 

  30. J. W. Gardner and T. A. Vincent. Electronic noses for well-being: Breath analysis and energy expenditure. Sensors, 16(7):947, 2016.

    Article  Google Scholar 

  31. N. D. Glenn. Cohort analysis. Sage, 2005.

    Google Scholar 

  32. D. Gomez-Cabrero, I. Abugessaisa, D. Maier, A. Teschendorff, M. Merkenschlager, A. Gisel, E. Ballestar, E. Bongcam-Rudloff, A. Conesa, and J. Tegnér. Data integration in the era of omics: current and future challenges. BMC Systems Biology, 8(2):1–10, 2014.

    Article  Google Scholar 

  33. P. Gupta and T. Dallas. Feature selection and activity recognition system using a single triaxial accelerometer. IEEE Transactions on Biomedical Engineering, 61(6):1780–1786, 2014.

    Article  Google Scholar 

  34. I. Guyon and A. Elisseeff. An introduction to variable and feature selection. Journal of machine learning research, 3:1157–1182, 2003.

    MATH  Google Scholar 

  35. M. Haghighi, P. Woznowski, N. Zhu, et al. Agent-based decentralised data-acquisition and time-synchronisation in critical healthcare applications. In Proceedings of the IEEE 2nd World Forum on Inernet of Things, 2015.

    Google Scholar 

  36. A. Halevy, A. Rajaraman, and J. Ordille. Data integration: The teenage years. In Proceedings of the 32nd International Conference on Very Large Data Bases, pages 9–16, 2006.

    Google Scholar 

  37. W. R. Hersh, M. G. Weiner, P. J. Embi, et al. Caveats for the use of operational electronic health record data in comparative effectiveness research. Medical care, 51:S30–S37, 2013.

    Article  Google Scholar 

  38. J. S. Hirsch, J. S. Tanenbaum, S. Lipsky Gorman, et al. Harvest, a longitudinal patient record summarizer. Journal of the American Medical Informatics Association, 22(2):263–274, 2014.

    Google Scholar 

  39. J. C. Ho, J. Ghosh, and J. Sun. Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 115–124, 2014.

    Google Scholar 

  40. S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.

    Article  Google Scholar 

  41. G. Hripcsak and D. J. Albers. Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association, 20(1):117–121, 2013.

    Article  Google Scholar 

  42. G. Hripcsak, D. J. Albers, and A. Perotte. Parameterizing time in electronic health record studies. Journal of the American Medical Informatics Association, 22(4):794–804, 2015.

    Article  Google Scholar 

  43. J. Hu, A. Perer, and F. Wang. Data driven analytics for personalized healthcare. In Healthcare Information Management Systems, pages 529–554. Springer, 2016.

    Google Scholar 

  44. C. H. Jackson, L. D. Sharples, S. G. Thompson, et al. Multistate markov models for disease progression with classification error. Journal of the Royal Statistical Society: Series D (The Statistician), 52(2):193–209, 2003.

    MathSciNet  Google Scholar 

  45. H. Jagadish. Challenges and opportunities with big data, 2012.

    Google Scholar 

  46. D. Jiang, Q. Cai, G. Chen, et al. Cohort query processing. Proceedings of the VLDB Endowment, 10(1), 2017.

    Google Scholar 

  47. D. Jiang, G. Chen, B. C. Ooi, et al. epic: an extensible and scalable system for processing big data. Proceedings of the VLDB Endowment, 7(7):541–552, 2014.

    Article  Google Scholar 

  48. K. Kalantar-Zadeh, C. K. Yao, K. J. Berean, et al. Intestinal gas capsules: A proof-of-concept demonstration. Gastroenterology, 150(1):37–39, 2016.

    Article  Google Scholar 

  49. D. C. Kale, Z. Che, M. T. Bahadori, et al. Causal phenotype discovery via deep networks. In AMIA Annual Symposium Proceedings, pages 677–686, 2015.

    Google Scholar 

  50. A. Karpathy and L. Fei-Fei. Deep visual-semantic alignments for generating image descriptions. arXiv preprint  arXiv: 1412.2306, 2014.

  51. A. Kaushik, R. D. Jayant, S. Tiwari, et al. Nano-biosensors to detect beta-amyloid for alzheimer’s disease management. Biosensors and Bioelectronics, 80(15):273–287, 2016.

    Article  Google Scholar 

  52. R. Korchiyne, S. M. Farssi, A. Sbihi, R. Touahni, and M. T. Alaoui. A combined method of fractal and GLCM features for MRI and CT scan images classification. arXiv preprint  arXiv: 1409.4559, 2014.

  53. H. Krumholz, S.-L. Normand, P. Keenan, et al. 30-day heart failure readmission measure methodology. Technical report, Yale University/Yale-New Haven Hospital Center for Outcomes Research And Evaluation (YNHH-CORE), 2008.

    Google Scholar 

  54. Z. Kuang, J. Thomson, M. Caldwell, et al. Computational drug repositioning using continuous self-controlled case series. arXiv preprint  arXiv: 1604.05976, 2016.

  55. S. Kumar, M. Willander, J. G. Sharma, et al. A solution processed carbon nanotube modified conducting paper sensor for cancer detection. Journal of Materials Chemistry B, 3:9305–9314, 2015.

    Article  Google Scholar 

  56. O. D. Lara and M. A. Labrador. A survey on human activity recognition using wearable sensors. IEEE Communications Surveys and Tutorials, 15(3):1192–1209, 2013.

    Article  Google Scholar 

  57. T. A. Lasko, J. C. Denny, and M. A. Levy. Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. PloS one, 8(6):1–13, 2013.

    Article  Google Scholar 

  58. M. Lenzerini. Data integration: A theoretical perspective. In Proceedings of the 21st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS ’02, pages 233–246. ACM, 2002.

    Google Scholar 

  59. D. D. Lewis and W. A. Gale. A sequential algorithm for training text classifiers. In Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’94, pages 3–12, New York, NY, USA, 1994. Springer-Verlag New York, Inc.

    Google Scholar 

  60. Q. Lin, B. C. Ooi, Z. Wang, et al. Scalable distributed stream join processing. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pages 811–825, 2015.

    Google Scholar 

  61. Z. J. Ling, Q. T. Tran, J. Fan, et al. GEMINI: An integrative healthcare analytics system. Proceedings of the VLDB Endowment, 7(13):1766–1771, 2014.

    Article  Google Scholar 

  62. Z. C. Lipton, D. C. Kale, C. Elkan, et al. Learning to diagnose with lstm recurrent neural networks. arXiv preprint  arXiv: 1511.03677, 2015.

  63. X. Liu, M. Lu, B. C. Ooi, et al. CDAS: a crowdsourcing data analytics system. Proceedings of the VLDB Endowment, 5(10):1040–1051, 2012.

    Article  Google Scholar 

  64. J. W. Lockhart, T. Pulickal, and G. M. Weiss. Applications of mobile activity recognition. In ACM Conference on Ubiquitous Computing, pages 1054–1058, 2012.

    Google Scholar 

  65. J. W. Lockhart, G. M. Weiss, J. C. Xue, et al. Design considerations for the wisdm smart phone-based sensor mining architecture. In Proceedings of the 5th International Workshop on Knowledge Discovery from Sensor Data, pages 25–33, 2011.

    Google Scholar 

  66. P. Lorwongtragool, E. Sowade, N. Watthanawisuth, et al. A novel wearable electronic nose for healthcare based on flexible printed chemical sensor array. Sensors, 14(10):19700, 2014.

    Article  Google Scholar 

  67. V. Loscrí, L. Matekovits, I. Peter, et al. In-body network biomedical applications: From modeling to experimentation. IEEE Transactions on Nanobioscience, 15(1):53–61, 2016.

    Article  Google Scholar 

  68. L. L. Low, K. H. Lee, M. E. Hock Ong, et al. Predicting 30-day readmissions: performance of the lace index compared with a regression model among general medicine patients in singapore. BioMed research international, 2015.

    Google Scholar 

  69. D. Malak and O. B. Akan. Molecular communication nanonetworks inside human body. Nano Communication Networks, 3(1):19–35, 2012.

    Article  Google Scholar 

  70. C. Manjarrs, D. Garizado, M. Obregon, et al. Chemical sensor network for ph monitoring. Journal of Applied Research and Technology, 14(1):1–8, 2016.

    Article  Google Scholar 

  71. J. Margarito, R. Helaoui, A. M. Bianchi, et al. User-independent recognition of sports activities from a single wrist-worn accelerometer: A template-matching-based approach. IEEE Transactions on Biomedical Engineering, 63(4):788–796, 2016.

    Google Scholar 

  72. B. M. Marlin, D. C. Kale, R. G. Khemani, et al. Unsupervised pattern discovery in electronic health care data using probabilistic clustering models. In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, pages 389–398, 2012.

    Google Scholar 

  73. M. E. Matheny, R. A. Miller, T. A. Ikizler, et al. Development of inpatient risk stratification models of acute kidney injury for use in electronic health records. Medical Decision Making, 30(6):639–650, 2010.

    Article  Google Scholar 

  74. A. McLeod, E. M. Bochniewicz, P. S. Lum, et al. Using wearable sensors and machine learning models to separate functional upper extremity use from walking-associated arm movements. Physical Medicine and Rehabilitation., 97(2):224–231, 2016.

    Article  Google Scholar 

  75. N. Q. Mehmood, R. Culmone, and L. Mostarda. A flexible and scalable architecture for real-time ANT+ sensor data acquisition and nosql storage. International Journal of Distributed Sensor Networks, 12(5), 2016.

    Google Scholar 

  76. D. Mould. Models for disease progression: new approaches and uses. Clinical Pharmacology & Therapeutics, 92(1):125–131, 2012.

    Article  Google Scholar 

  77. M. Mun, S. Reddy, K. Shilton, et al. Peir, the personal environmental impact report, as a platform for participatory sensing systems research. In Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, pages 55–68, 2009.

    Google Scholar 

  78. I. Muslea, S. Minton, and C. A. Knoblock. Selective sampling with redundant views. In AAAI/IAAI, pages 621–626, 2000.

    Google Scholar 

  79. T. D. Nguyen, T. Tran, D. Phung, et al. Latent patient profile modelling and applications with mixed-variate restricted boltzmann machine. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 123–135, 2013.

    Google Scholar 

  80. L. Nie, L. Zhang, Y. Yang, et al. Beyond doctors: Future health prediction from multimedia and multimodal observations. In Proceedings of the 23rd ACM international conference on Multimedia, pages 591–600, 2015.

    Google Scholar 

  81. B. C. Ooi, K. L. Tan, Q. T. Tran, et al. Contextual crowd intelligence. ACM SIGKDD Explorations Newsletter, 16(1):39–46, 2014.

    Article  Google Scholar 

  82. B. C. Ooi, K.-L. Tan, S. Wang, et al. SINGA: A distributed deep learning platform. In Proceedings of the 23rd ACM International Conference on Multimedia, pages 685–688, 2015.

    Google Scholar 

  83. F. J. Ordez and D. Roggen. Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors (Basel, Switzerland), 16(1):115, 2016.

    Google Scholar 

  84. F. J. Ordonez, G. Englebienne, P. de Toledo, et al. In-home activity recognition: Bayesian inference for hidden markov models. IEEE Pervasive Computing, 13(3):67–75, 2014.

    Article  Google Scholar 

  85. R. K. Pearson, R. J. Kingan, and A. Hochberg. Disease progression modeling from historical clinical databases. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pages 788–793, 2005.

    Google Scholar 

  86. T. Pham, T. Tran, D. Phung, et al. Deepcare: A deep dynamic memory model for predictive medicine. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 30–41, 2016.

    Google Scholar 

  87. R. Pivovarov, D. J. Albers, J. L. Sepulveda, et al. Identifying and mitigating biases in ehr laboratory tests. Journal of biomedical informatics, 51:24–34, 2014.

    Article  Google Scholar 

  88. R. Pivovarov, A. J. Perotte, E. Grave, et al. Learning probabilistic phenotypes from heterogeneous ehr data. Journal of biomedical informatics, 58:156–165, 2015.

    Article  Google Scholar 

  89. S. R. and C. L. Stress detection using physiological sensors. IEEE Computer, 48(10):26–33, 2015.

    Google Scholar 

  90. N. Roy and A. McCallum. Toward optimal active learning through monte carlo estimation of error reduction. ICML, Williamstown, pages 441–448, 2001.

    Google Scholar 

  91. M. Salai, I. Vassnyi, and I. Ksa. Stress detection using low cost heart rate sensors. Journal of Healthcare Engineering, 2, 2016.

    Google Scholar 

  92. Y. Sasaya and T. Nakamoto. Study of halitosis-substance sensing at low concentration using an electrochemical sensor array combined with a preconcentrator. IEEE Journal of Transactions on Sensors and Micromachines, 126, 2006.

    Google Scholar 

  93. J. L. Schafer and J. W. Graham. Missing data: our view of the state of the art. Psychological methods, 7(2):147, 2002.

    Article  Google Scholar 

  94. P. Schulam, F. Wigley, and S. Saria. Clustering longitudinal clinical marker trajectories from electronic health data: Applications to phenotyping and endotype discovery. In Proceedings of the 29th AAAI Conference on Artificial Intelligence, pages 2956–2964, 2015.

    Google Scholar 

  95. M. B. Schulze, K. Hoffmann, H. Boeing, et al. An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes care, 30(3):510–515, 2007.

    Article  Google Scholar 

  96. B. Settles and M. Craven. An analysis of active learning strategies for sequence labeling tasks. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP ’08, pages 1070–1079, Stroudsburg, PA, USA, 2008. Association for Computational Linguistics.

    Google Scholar 

  97. H. S. Seung, M. Opper, and H. Sompolinsky. Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT ’92, pages 287–294, New York, NY, USA, 1992. ACM.

    Google Scholar 

  98. M. J. Sewitch, K. Leffondré, and P. L. Dobkin. Clustering patients according to health perceptions: relationships to psychosocial characteristics and medication nonadherence. Journal of psychosomatic research, 56(3):323–332, 2004.

    Article  Google Scholar 

  99. M. Shoaib, S. Bosch, O. D. Incel, et al. A survey of online activity recognition using mobile phones. Sensors, 15(1):2059–2085, 2015.

    Article  Google Scholar 

  100. C. M. Stonnington, C. Chu, S. Klöppel, et al. Predicting clinical scores from magnetic resonance scans in alzheimer’s disease. Neuroimage, 51(4):1405–1413, 2010.

    Article  Google Scholar 

  101. N. Street. A neural network model for prognostic prediction. In Proceedings of the 15th International Conference on Machine Learning, pages 540–546, 1998.

    Google Scholar 

  102. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. J. Mach. Learn. Res., 2:45–66, Mar. 2002.

    Google Scholar 

  103. S. N. Topkaya and D. Ozkan-Ariksoysal. Prostate cancer biomarker detection with carbon nanotubes modified screen printed electrodes. Electroanalysis, 28(5), 2016.

    Google Scholar 

  104. C. Torres-Huitzil and A. Alvarez-Landero. Accelerometer-Based Human Activity Recognition in Smartphones for Healthcare Services, pages 147–169. Springer, 2015.

    Google Scholar 

  105. T. Tran, T. D. Nguyen, D. Phung, et al. Learning vector representation of medical objects via emr-driven nonnegative restricted boltzmann machines (enrbm). Journal of biomedical informatics, pages 96–105, 2015.

    Google Scholar 

  106. C. van Walraven, I. A. Dhalla, C. Bell, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. Canadian Medical Association Journal, 182(6):551–557, 2010.

    Article  Google Scholar 

  107. P. Vemuri, H. Wiste, S. Weigand, et al. MRI and CSF biomarkers in normal, MCI, and AD subjects predicting future clinical change. Neurology, 73(4):294–301, 2009.

    Article  Google Scholar 

  108. F. Wang, N. Lee, J. Hu, et al. Towards heterogeneous temporal clinical event pattern discovery: a convolutional approach. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 453–461, 2012.

    Google Scholar 

  109. W. Wang, G. Chen, A. T. T. Dinh, et al. SINGA: Putting deep learning in the hands of multimedia users. In Proceedings of the 23rd ACM International Conference on Multimedia, pages 25–34, 2015.

    Google Scholar 

  110. X. Wang, D. Sontag, and F. Wang. Unsupervised learning of disease progression models. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 85–94, 2014.

    Google Scholar 

  111. Y. Wang, R. Chen, J. Ghosh, et al. Rubik: Knowledge guided tensor factorization and completion for health data analytics. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1265–1274, 2015.

    Google Scholar 

  112. B. J. Wells, A. S. Nowacki, K. Chagin, et al. Strategies for handling missing data in electronic health record derived data. eGEMs (Generating Evidence & Methods to improve patient outcomes), 1(3):7, 2013.

    Google Scholar 

  113. Z. Xiang, R. M. Minter, X. Bi, et al. minituba: medical inference by network integration of temporal data using bayesian analysis. Bioinformatics, 23(18):2423–2432, 2007.

    Article  Google Scholar 

  114. T. Yokota, P. Zalar, M. Kaltenbrunner, et al. Ultraflexible organic photonic skin. Science Advances Online Edition, 2(4), 2016.

    Google Scholar 

  115. H. Zhang, G. Chen, B. C. Ooi, et al. In-memory big data management and processing: A survey. IEEE Transactions on Knowledge and Data Engineering, 27(7):1920–1948, 2015.

    Article  Google Scholar 

  116. X. Zhang, B. Hu, L. Zhou, et al. An eeg based pervasive depression detection for females. In Proceedings of the 2012 International Conference on Pervasive Computing and the Networked World, pages 848–861, 2013.

    Google Scholar 

  117. J. Zhou, J. Liu, V. A. Narayan, et al. Modeling disease progression via fused sparse group lasso. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1095–1103, 2012.

    Google Scholar 

  118. J. Zhou, F. Wang, J. Hu, et al. From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 135–144, 2014.

    Google Scholar 

  119. J. Zhou, L. Yuan, J. Liu, et al. A multi-task learning formulation for predicting disease progression. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 814–822, 2011.

    Google Scholar 

  120. T. Zhu, S. Xiao, Q. Zhang, et al. Emergent technologies in big data sensing: A survey. International Journal of Distributed Sensor Networks, 2015(8):1–13, 2015.

    Google Scholar 

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Acknowledgements

This work is supported by National Research Foundation, Prime Ministers Office, Singapore under its Competitive Research Programme (CRP Award No. NRF-CRP8-2011-08). Gang Chen’s work is supported by National Natural Science Foundation of China (NSFC) Grant No. 61472348. Meihui Zhang is supported by SUTD Start-up Research Grant under Project No. SRG ISTD 2014 084. We would like to thank Jinyang Gao and Gerald Koh for the discussion and useful suggestions that help to improve the chapter.

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Authors and Affiliations

  1. Osaka University, Suita, Japan

    Chonho Lee

  2. National University of Singapore, Singapore, Singapore

    Zhaojing Luo, Kee Yuan Ngiam, Kaiping Zheng, Beng Chin Ooi & Wei Luen James Yip

  3. National University Hospital, Singapore, Singapore

    Kee Yuan Ngiam & Wei Luen James Yip

  4. Singapore University of Technology and Design, Singapore, Singapore

    Meihui Zhang

  5. Zhejiang University, Hangzhou, China

    Gang Chen

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  1. Chonho Lee
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Correspondence to Meihui Zhang .

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  1. Department of Electrical and Computer Engineering, North Dakota State University , Fargo, North Dakota, USA

    Samee U. Khan

  2. School of Information Technologies, University of Sydney, Sydney, New South Wales, Australia

    Albert Y. Zomaya

  3. Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan

    Assad Abbas

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Lee, C. et al. (2017). Big Healthcare Data Analytics: Challenges and Applications. In: Khan, S., Zomaya, A., Abbas, A. (eds) Handbook of Large-Scale Distributed Computing in Smart Healthcare. Scalable Computing and Communications. Springer, Cham. https://doi.org/10.1007/978-3-319-58280-1_2

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