Deep learning for healthcare: Review, opportunities and challenges

Briefings in Bioinformatics

Volumn 19, Issue 6, 2017, Pages 1236-1246

  Miotto, Riccardo ^b   Wang, Fei ^a   Wang, Shuang ^c   Jiang, Xiaoqian ^c   Dudley, Joel T ^b  

^a WEILL CORNELL MEDICAL COLLEGE   (United States)

^b MOUNT SINAI SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Biomedical informatics; Deep learning; Electronic health records; Genomics; Health care; Translational bioinformatics

Indexed keywords

BIOLOGY; DATA MINING; DIAGNOSTIC IMAGING; ELECTRONIC HEALTH RECORD; GENOMICS; HEALTH CARE DELIVERY; HUMAN; ORGANIZATION AND MANAGEMENT; TELEMEDICINE;

COMPUTATIONAL BIOLOGY; DATA MINING; DEEP LEARNING; DELIVERY OF HEALTH CARE; DIAGNOSTIC IMAGING; ELECTRONIC HEALTH RECORDS; GENOMICS; HUMANS; TELEMEDICINE;

EID: 85050595396     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbx044     Document Type: Article

References (119)

1. Precision Medicine Initiative (NIH). https://www.nih.gov/pre cision-medicine-initiative-cohort-program (12 November 2016, date last accessed).
2. Lyman GH, Moses HL. Biomarker tests for molecularly targeted therapies - the key to unlocking precision medicine. N Engl J Med 2016;375:4-6.
3. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med 2015;372:793-5.
4. Xu R, Li L, Wang Q. dRiskKB: a large-scale disease-disease risk relationship knowledge base constructed from biomedical text. BMC Bioinformatics 2014;15:105.
5. Chen Y, Li L, Zhang G-Q, et al. Phenome-driven disease genetics prediction toward drug discovery. Bioinformatics 2015;31:i276-83.
6. Wang B, Mezlini AM, Demir F, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods 2014;11:333-7.
7. Tatonetti NP, Ye PP, Daneshjou R, et al. Data-driven prediction of drug effects and interactions. Sci Transl Med 2012;4:125ra31.
8. Miotto R, Weng C. Case-based reasoning using electronic health records efficiently identifies eligible patients for clinical trials. J Am Med Inform Assoc 2015;22:e141-50.
9. Li L, Cheng W-Y, Glicksberg BS, et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med 2015;7:311ra174.
10. Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet 2015;16:321-32.
11. Wang F, Zhang P, Wang X, et al. Clinical risk prediction by exploring high-order feature correlations. AMIA Annual Symposium 2014;2014:1170-9.
12. Bellazzi R, Zupan B. Predictive data mining in clinical medicine: current issues and guidelines. Int J Med Inform 2008;77:81-97.
13. Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc 2013;20:117-21.
14. Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet 2012;13:395-405.
15. Luo J, Wu M, Gopukumar D, et al. Big data application in biomedical research and health care: a literature review. Biomed Inform Insights 2016;8:1-10.
16. SNOMED CT. https://www.nlm.nih.gov/healthit/snomedct/index.html (15 August 2016, date last accessed).
17. Unified Medical Language System (UMLS). https://www.nlm.nih.gov/research/umls/ (15 August 2016, date last accessed).
18. ICD-9 Code. https://www.cms.gov/medicare-coverage-database/staticpages/icd-9-code-lookup.aspx (15 October 2016, date last accessed).
19. Mohan A, Blough DM, Kurc T, et al. Detection of conflicts and inconsistencies in taxonomy-based authorization policies. In: 2011 IEEE International Conference on Bioinformatics and Biomedicine, Atlanta, GA, USA, 2011, 590-4.
20. Gottlieb A, Stein GY, Ruppin E, et al. A method for inferring medical diagnoses from patient similarities. BMC Med 2013;11:194.
21. Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 2013;35:1798-828.
22. Farhan W, Wang Z, Huang Y, et al. A predictive model for medical events based on contextual embedding of temporal sequences. J Med Internet Res 2016;4:e39.
23. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-44.
24. Abdel-Hamid O, Mohamed A-R, Jiang H, et al. Convolutional neural networks for speech recognition. IEEE/ACM Trans Audio Speech Lang Process 2014;22:1533-45.
25. Deng L, Li X. Machine learning paradigms for speech recognition: an overview. IEEE Trans Audio Speech Lang Process 2013;21:1060-89.
26. Cho K, Courville A, Bengio Y. Describing multimedia content using attention-based encoder-decoder networks. arXiv 2015. http://arxiv.org/abs/1507.01053
27. Hannun A, Case C, Casper J, et al. Deep speech: scaling up end-to-end speech recognition. arXiv 2014. https://arxiv.org/abs/1412.5567
28. Google's DeepMind forms health unit to build medical software. https://www.bloomberg.com/news/articles/2016-0224/google-s-deepmind-forms-health-unit-to-build-medi cal-software (4 August 2016, date last accessed).
29. Enlitic uses deep learning to make doctors faster and more accurate. http://www.enlitic.com/index.html (29 November 2016, date last accessed).
30. Bengio Y, Lamblin P, Popovici D. Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst 2007;19:153-60
31. Bengio Y. Practical recommendations for gradient-based training of deep architectures. Neural Netw 2012;2;437-78.
32. Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw 2015;61:85-117.
33. Murphy Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
34. Bishop CM. Pattern Recognition and Machine Learning (Information Science and Statistics), 1st edn. 2006. corr. 2nd printing edn. Springer, New York, 2007.
35. Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science 2015; 349:255-60.
36. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature 1986;323:533-6.
37. Bengio Y. Learning deep architectures for AI. Found Trends Mach Learn 2009;2:1-127.
38. Erhan D, Bengio Y, Courville A, et al. Why does unsupervised pre-training help deep learning? J Mach Learn Res 2010;11:625-60.
39. Srivastava N, Hinton GE, Krizhevsky A. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 2014;15:1929-58.
40. Bastien F, Lamblin P, Pascanu R, et al. Theano: new features and speed improvements. arXiv 2012. http://arxiv.org/abs/1211.5590
41. Jia Y, Shelhamer E, Donahue J, et al. Caffe: convolutional architecture for fast feature embedding. In: ACM International Conference on Multimedia, Orlando, FL, USA, 2014, 675-8.
42. Abadi M, Agarwal A, Barham P, et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv 2016. http://arxiv.org/abs/1603.04467
43. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012;1097-1105.
44. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1-9.
45. Hinton G, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Process Mag 2012;29:82-97.
46. Collobert R, Weston J, Bottou L, et al. Natural language processing (almost) from scratch. J Mach Learn Res 2011;12:2493-537.
47. Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. Adv Neural Inf Process Syst 2014;27:3104-12.
48. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316:2402-10.
49. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-18.
50. Alipanahi B, Delong A, Weirauch MT, et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol 2015;33:831-8.
51. Liu S, Liu S, Cai W, et al. Early diagnosis of Alzheimer's disease with deep learning. In: International Symposium on Biomedical Imaging, Beijing, China 2014, 1015-18.
52. Brosch T, Tam R. Manifold learning of brain MRIs by deep learning. Med Image Comput Comput Assist Interv 2013;16:633-40.
53. Prasoon A, Petersen K, Igel C, et al. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. Med Image Comput Comput Assist Interv 2013;16:246-53.
54. Yoo Y, Brosch T, Traboulsee A, et al. Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation. In: International Workshop on Machine Learning in Medical Imaging, Boston, MA, USA, 2014, 117-24.
55. Cheng J-Z, Ni D, Chou Y-H, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep 2016;6:24454.
56. Liu C, Wang F, Hu J, et al. Risk prediction with electronic health records: a deep learning approach. In: ACM International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, 2015, 705-14.
57. Lipton ZC, Kale DC, Elkan C, et al. Learning to diagnose with LSTM recurrent neural networks. In: International Conference on Learning Representations, San Diego, CA, USA, 2015, 1-18.
58. Pham T, Tran T, Phung D, et al. DeepCare: a deep dynamic memory model for predictive medicine. arXiv 2016. https://arxiv.org/abs/1602.00357.
59. Miotto R, Li L, Kidd BA, et al. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep 2016;6:26094.
60. Miotto R, Li L, Dudley JT. Deep learning to predict patient future diseases from the electronic health records. In European Conference in Information Retrieval, 2016, 768-74.
61. Liang Z, Zhang G, Huang JX, et al. Deep learning for healthcare decision making with EMRs. In IEEE International Conference on Bioinformatics and Biomedicine, 2014, 556-9.
62. Tran T, Nguyen TD, Phung D, et al. Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM). J Biomed Inform 2015;54:96-105.
63. Che Z, Kale D, Li W, et al. Deep computational phenotyping. In ACM International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, 2015, 507-16.
64. Lasko TA, Denny JC, Levy MA. Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. PLoS One 2013;8:e66341.
65. Choi E, Bahadori MT, Schuetz A, et al. Doctor AI: predicting clinical events via recurrent neural networks. arXiv 2015. http://arxiv.org/abs/1511.05942v11
66. Nguyen P, Tran T, Wickramasinghe N, et al. Deepr: a Convolutional Net for Medical Records. IEEE J Biomed Health Inform 2017;21:22-30.
67. Razavian N, Marcus J, Sontag D. Multi-task prediction of disease onsets from longitudinal laboratory tests. In Proceedings of the 1st Machine Learning for Healthcare Conference, Los Angeles, CA, USA, 2016, 73-100.
68. Dernoncourt F, Lee JY, Uzuner O, et al. De-identification of patient notes with recurrent neural networks. J Am Med Inform Assoc 2016; doi: 10.1093/jamia/ocw156.
69. Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods 2015;12:931-4.
70. Kelley DR, Snoek J, Rinn JL. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res 2016;26:990-9.
71. Angermueller C, Lee H, Reik W, et al. Accurate prediction of single-cell DNA methylation states using deep learning. bioRxiv 2016. http://dx.doi.org/10.1101/055715.
72. Koh PW, Pierson E, Kundaje A. Denoising genome-wide his-tone ChIP-seq with convolutional neural networks. bioRxiv 2016. http://dx.doi.org/10.1101/052118
73. Fakoor R, Ladhak F, Nazi A, et al. Using deep learning to enhance cancer diagnosis and classification. In: International Conference on Machine Learning, Atlanta, GA, USA, 2013.
74. Lyons J, Dehzangi A, Heffernan R, et al. Predicting backbone Ca angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network. J Comput Chem 2014;35:2040-6.
75. Hammerla NY, Halloran S, Ploetz T. Deep, convolutional, and recurrent models for human activity recognition using wearables. arXiv preprint arXiv:1604.08880.
76. Zhu J, Pande A, Mohapatra P, et al. Using deep learning for energy expenditure estimation with wearable sensors. In: 17th International Conference on E-health Networking, Application Services (HealthCom), Cambridge, MA, USA, 2015, 501-6.
77. Jindal V, Birjandtalab J, Pouyan MB, et al. An adaptive deep learning approach for PPG-based identification. In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 2016, 6401-4.
78. Nurse E, Mashford BS, Yepes AJ, et al. Decoding EEG and LFP signals using deep learning: heading TrueNorth. In: ACM International Conference on Computing Frontiers, 2016, 259-66.
79. Sathyanarayana A, Joty S, Fernandez-Luque L, et al. Correction of: sleep quality prediction from wearable data using deep learning. JMIR Mhealth Uhealth 2016;4:e130.
80. Gerstung M, Papaemmanuil E, Martincorena I, et al. Precision oncology for acute myeloid leukemia using a knowledge bank approach. Nat Genet 2017;49:332-40.
81. Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE 1998;86:2278-324.
82. Williams RJ, Zipser D. A learning algorithm for continually running fully recurrent neural networks. Neural Comput 1989;1:270-80.
83. Smolensky P. Information processing in dynamical systems: Foundations of harmony theory (No. CU-CS-321-86). Colorado Univ at Boulder Dept of Computer Science 1986.
84. Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science 2006;313:504-7.
85. Hubel DH, Wiesel TN. Receptive fields and functional architecture of monkey striate cortex. J. Physiol 1968;195:215-43.
86. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9:1735-80.
87. Cho K, van Merrienboer B, Bahdanau D, et al. On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259.
88. Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning, 2007, 791-8.
89. Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Comput 2006;18:1527-54.
90. Vincent P, Larochelle H, Lajoie I, et al. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 2010;11:3371-408.
91. Manning CD, Raghavan P, Schütze H. Introduction to information retrieval, Vol. 3. Cambridge: Cambridge university press, 2008.
92. Choi Y, Ms CY-IC, Sontag D. Learning low-dimensional representations of medical concepts. In: Proceedings of the AMIA Summit on Clinical Research Informatics, 2016.
93. Mamoshina P, Vieira A, Putin E, et al. Applications of deep learning in biomedicine. Mol Pharm 2016;13:1445-54.
94. Angermueller C, Parnamaa € T, Parts L, et al. Deep learning for computational biology. Mol Syst Biol 2016;12:878.
95. Park Y, Kellis M. Deep learning for regulatory genomics. Nat Biotechnol 2015;33:825-6.
96. Leung MKK, Delong A, Alipanahi B, et al. Machine learning in genomic medicine: a review of computational problems and data sets. Proc IEEE 2016;104:176-97.
97. Xiong HY, Alipanahi B, Lee LJ, et al. The human splicing code reveals new insights into the genetic determinants of disease. Science 2015;347:1254806.
98. Ma J, Sheridan RP, Liaw A, et al. Deep neural nets as a method for quantitative structure - activity relationships. J Chem Inf Model 2015;55:263-74.
99. Shameer K, Badgeley MA, Miotto R, et al. Translational bioinformatics in the era of real-time biomedical, healthcare and wellness data streams. Brief Bioinform 2017; 18:1105-124.
100. Piwek L, Ellis DA, Andrews S, et al. The rise of consumer health wearables: promises and barriers. PLoS Med 2016; 13:e1001953.
101. Ravi D, Wong C, Lo B, et al. A deep learning approach to on-node sensor data analytics for mobile or wearable devices. IEEE J Biomed Health Inform 2017;21:56-64.
102. Lane ND, Georgiev P. Can deep learning revolutionize mobile sensing? In International Workshop on Mobile Computing Systems and Applications, Santa Fe, NM, USA, 2015, 117-22.
103. Lane ND, Bhattacharya S, Georgiev P, et al. DeepX: a software accelerator for low-power deep learning inference on mobile devices. In ACM/IEEE International Conference on Information Processing in Sensor Networks, 2016, 1-12.
104. Correia RB, Li L, Rocha LM. Monitoring potential drug interactions and reactions via network analysis of instagram user timelines. Pac Symp Biocomput 2016;21:492-503.
105. Nikfarjam A, Sarker A, O'Connor K, et al. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. J Am Med Inform Assoc 2015;22:671-81.
106. Gilad-Bachrach R, Dowlin N, Laine K, et al. CryptoNets: applying neural networks to encrypted data with high throughput and accuracy. In: International Conference on Machine Learning, New York, NY, USA, 2016, 201-10.
107. Yao AC. Protocols for secure computations. In: 23rd Annual Symposium on Foundations of Computer Science (SFCS 1982), Los Angeles, CA, USA, 1982, 160-4.
108. Tramèr F, Zhang F, Juels A, et al. Stealing machine learning models via prediction apis. In: USENIX Security Symposium, Austin, TX, USA, 2016.
109. Dwork C. Differential privacy. In: Encyclopedia of Cryptography and Security, Springer, Berlin, Germany, 2011, 338-40.
110. Leoni D. Non-interactive differential privacy: a survey. In Proceedings of the First International Workshop on Open Data, 2012, 40-52.
111. McSherry F, Talwar K. Mechanism design via differential privacy. In: 48th Annual IEEE Symposium on Foundations of Computer Science, 2007, 2007, 94-103.
112. Chaudhuri K, Monteleoni C, Sarwate A. Differentially private empirical risk minimization. J Mach Learn Res 2011;12:1069-109.
113. Abadi M, Chu A, Goodfellow I, et al. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 2016, 308-18.
114. Phan N, Wang Y, Wu X, et al. Differential privacy preservation for deep auto-encoders: an application of human behavior prediction. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, 2016:1309-16.
115. Shokri R, Shmatikov V. Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, Denver, CO, USA, 2015, 1310-21.
116. Hermann KM, Kocisky T, Grefenstette E, et al. Teaching machines to read and comprehend. Adv Neural Inf Process Syst 2015;201:1693-701.
117. Lei T, Barzilay R, Jaakkola T. Rationalizing neural predictions. arXiv 2016. http://arxiv.org/abs/1606.04155
118. Ribeiro MT, Singh S, Guestrin C. Why should I trust you? Explaining the predictions of any classifier. In: ACM Conferences on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, 1135-44.
119. The Michael J. Fox Foundation for Parkinson's Research: subtyping Parkinson's disease with deep learning models. https://www.michaeljfox.org/foundation/grant-detail.php? grant_id¼1518 (29 November 2016, date last accessed).