Deep learning for healthcare: review, opportunities and challenges
Tóm tắt
Từ khóa
Tài liệu tham khảo
Precision Medicine Initiative (NIH), 12 2016
Lyman, 2016, Biomarker tests for molecularly targeted therapies — the key to unlocking precision medicine, N Engl J Med, 375, 4, 10.1056/NEJMp1604033
Xu, 2014, dRiskKB: a large-scale disease-disease risk relationship knowledge base constructed from biomedical text, BMC Bioinformatics, 15, 105., 10.1186/1471-2105-15-105
Chen, 2015, Phenome-driven disease genetics prediction toward drug discovery, Bioinformatics, 31, i276, 10.1093/bioinformatics/btv245
Wang, 2014, Similarity network fusion for aggregating data types on a genomic scale, Nat Methods, 11, 333, 10.1038/nmeth.2810
Tatonetti, 2012, Data-driven prediction of drug effects and interactions, Sci Transl Med, 4, 125ra31., 10.1126/scitranslmed.3003377
Miotto, 2015, Case-based reasoning using electronic health records efficiently identifies eligible patients for clinical trials, J Am Med Inform Assoc, 22, e141, 10.1093/jamia/ocu050
Li, 2015, Identification of type 2 diabetes subgroups through topological analysis of patient similarity, Sci Transl Med, 7, 311ra174., 10.1126/scitranslmed.aaa9364
Libbrecht, 2015, Machine learning applications in genetics and genomics, Nat Rev Genet, 16, 321, 10.1038/nrg3920
Wang, 2014, Clinical risk prediction by exploring high-order feature correlations, AMIA Annual Symposium, 2014, 1170
Bellazzi, 2008, Predictive data mining in clinical medicine: current issues and guidelines, Int J Med Inform, 77, 81, 10.1016/j.ijmedinf.2006.11.006
Hripcsak, 2013, Next-generation phenotyping of electronic health records, J Am Med Inform Assoc, 20, 117, 10.1136/amiajnl-2012-001145
Jensen, 2012, Mining electronic health records: towards better research applications and clinical care, Nat Rev Genet, 13, 395, 10.1038/nrg3208
Luo, 2016, Big data application in biomedical research and health care: a literature review, Biomed Inform Insights, 8, 1, 10.4137/BII.S31559
SNOMED CT, 15 2016
Unified Medical Language System (UMLS), 15 2016
ICD-9 Code, 15 2016
Mohan, 2011, 2011 IEEE International Conference on Bioinformatics and Biomedicine, 590, 10.1109/BIBM.2011.79
Gottlieb, 2013, A method for inferring medical diagnoses from patient similarities, BMC Med, 11, 194., 10.1186/1741-7015-11-194
Bengio, 2013, Representation learning: a review and new perspectives, IEEE Trans Pattern Anal Mach Intell, 35, 1798, 10.1109/TPAMI.2013.50
Farhan, 2016, A predictive model for medical events based on contextual embedding of temporal sequences, J Med Internet Res, 4, e39.
Abdel-Hamid, 2014, Convolutional neural networks for speech recognition, IEEE/ACM Trans Audio Speech Lang Process, 22, 1533, 10.1109/TASLP.2014.2339736
Deng, 2013, Machine learning paradigms for speech recognition: an overview, IEEE Trans Audio Speech Lang Process, 21, 1060, 10.1109/TASL.2013.2244083
Cho, 2015
Hannun, 2014
Google’s DeepMind forms health unit to build medical software, 4 2016
Enlitic uses deep learning to make doctors faster and more accurate, 29 2016
Bengio, 2007, Adv Neural Inf Process Syst, 19, 153
Bengio, 2012, Neural Netw, 2, 437
Schmidhuber, 2015, Deep learning in neural networks: an overview, Neural Netw, 61, 85, 10.1016/j.neunet.2014.09.003
Murphy, 2012
Bishop, 2007
Jordan, 2015, Machine learning: trends, perspectives, and prospects, Science, 349, 255, 10.1126/science.aaa8415
Rumelhart, 1986, Learning representations by back-propagating errors, Nature, 323, 533, 10.1038/323533a0
Erhan, 2010, Why does unsupervised pre-training help deep learning?, J Mach Learn Res, 11, 625
Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, J Mach Learn Res, 15, 1929
Bastien, 2012
Jia, 2014, 675
Abadi, 2016
Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, Adv Neural Inf Process Syst, 1097
Szegedy, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1
Hinton, 2012, Deep neural networks for acoustic modeling in speech recognition, IEEE Signal Process Mag, 29, 82, 10.1109/MSP.2012.2205597
Collobert, 2011, Natural language processing (almost) from scratch, J Mach Learn Res, 12, 2493
Sutskever, 2014, Sequence to sequence learning with neural networks, Adv Neural Inf Process Syst, 27, 3104
Gulshan, 2016, Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, JAMA, 316, 2402, 10.1001/jama.2016.17216
Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056
Alipanahi, 2015, Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning, Nat Biotechnol, 33, 831, 10.1038/nbt.3300
Liu, 2014, 1015
Brosch, 2013, Manifold learning of brain MRIs by deep learning, Med Image Comput Comput Assist Interv, 16, 633
Prasoon, 2013, Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network, Med Image Comput Comput Assist Interv, 16, 246
Yoo, 2014, Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation, International Workshop on Machine Learning in Medical Imaging, 117, 10.1007/978-3-319-10581-9_15
Cheng, 2016, Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans, Sci Rep, 6, 24454, 10.1038/srep24454
Liu, 2015, 705
Lipton, 2015, 1
Pham, 2016
Miotto, 2016, Deep patient: an unsupervised representation to predict the future of patients from the electronic health records, Sci Rep, 6, 26094, 10.1038/srep26094
Miotto, 2016, 768
Liang, 2014, 556
Tran, 2015, Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM), J Biomed Inform, 54, 96, 10.1016/j.jbi.2015.01.012
Che, 2015, ACM International Conference on Knowledge Discovery and Data Mining, 507
Lasko, 2013, Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data, PLoS One, 8, e66341., 10.1371/journal.pone.0066341
Choi, 2015
Nguyen, 2017, Deepr: a Convolutional Net for Medical Records, IEEE J Biomed Health Inform, 21, 22, 10.1109/JBHI.2016.2633963
Razavian, 2016, 73
Dernoncourt, 2016, De-identification of patient notes with recurrent neural networks, J Am Med Inform Assoc, 10.1093/jamia/ocw156
Zhou, 2015, Predicting effects of noncoding variants with deep learning-based sequence model, Nat Methods, 12, 931, 10.1038/nmeth.3547
Kelley, 2016, Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks, Genome Res, 26, 990, 10.1101/gr.200535.115
Angermueller, 2016
Koh, 2016
Fakoor, 2013
Lyons, 2014, Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network, J Comput Chem, 35, 2040, 10.1002/jcc.23718
Hammerla
Zhu, 2015, 17th International Conference on E-health Networking, Application Services (HealthCom), 501, 10.1109/HealthCom.2015.7454554
Jindal, 2016, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 6401
Sathyanarayana, 2016, Correction of: sleep quality prediction from wearable data using deep learning, JMIR Mhealth Uhealth, 4, e130., 10.2196/mhealth.6953
Gerstung, 2017, Precision oncology for acute myeloid leukemia using a knowledge bank approach, Nat Genet, 49, 332, 10.1038/ng.3756
Lecun, 1998, Gradient-based learning applied to document recognition, Proc IEEE, 86, 2278, 10.1109/5.726791
Williams, 1989, A learning algorithm for continually running fully recurrent neural networks, Neural Comput, 1, 270, 10.1162/neco.1989.1.2.270
Smolensky, 1986
Hinton, 2006, Reducing the dimensionality of data with neural networks, Science, 313, 504, 10.1126/science.1127647
Hubel, 1968, Receptive fields and functional architecture of monkey striate cortex, J. Physiol, 195, 215, 10.1113/jphysiol.1968.sp008455
Cho
Salakhutdinov, 2007, 791
Hinton, 2006, A fast learning algorithm for deep belief nets, Neural Comput, 18, 1527, 10.1162/neco.2006.18.7.1527
Vincent, 2010, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion, J Mach Learn Res, 11, 3371
Manning, 2008
Choi, 2016
Mamoshina, 2016, Applications of deep learning in biomedicine, Mol Pharm, 13, 1445, 10.1021/acs.molpharmaceut.5b00982
Angermueller, 2016, Deep learning for computational biology, Mol Syst Biol, 12, 878., 10.15252/msb.20156651
Leung, 2016, Machine learning in genomic medicine: a review of computational problems and data sets, Proc IEEE, 104, 176, 10.1109/JPROC.2015.2494198
Xiong, 2015, The human splicing code reveals new insights into the genetic determinants of disease, Science, 347, 1254806., 10.1126/science.1254806
Ma, 2015, Deep neural nets as a method for quantitative structure – activity relationships, J Chem Inf Model, 55, 263, 10.1021/ci500747n
Shameer, 2017, Translational bioinformatics in the era of real-time biomedical, healthcare and wellness data streams, Brief Bioinform, 18, 1105, 10.1093/bib/bbv118
Piwek, 2016, The rise of consumer health wearables: promises and barriers, PLoS Med, 13, e1001953., 10.1371/journal.pmed.1001953
Ravi, 2017, A deep learning approach to on-node sensor data analytics for mobile or wearable devices, IEEE J Biomed Health Inform, 21, 56, 10.1109/JBHI.2016.2633287
Lane, 2015, International Workshop on Mobile Computing Systems and Applications, 117
Lane, 2016, ACM/IEEE International Conference on Information Processing in Sensor Networks, 1
Correia, 2016, Monitoring potential drug interactions and reactions via network analysis of instagram user timelines, Pac Symp Biocomput, 21, 492
Nikfarjam, 2015, Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features, J Am Med Inform Assoc, 22, 671, 10.1093/jamia/ocu041
Gilad-Bachrach, 2016, International Conference on Machine Learning, 201
Yao, 1982, 23rd Annual Symposium on Foundations of Computer Science (SFCS 1982), 160, 10.1109/SFCS.1982.38
Tramèr, 2016
Dwork, 2011, Differential privacy, Encyclopedia of Cryptography and Security, 338, 10.1007/978-1-4419-5906-5_752
Leoni, 2012, 40
McSherry, 2007, 94
Chaudhuri, 2011, Differentially private empirical risk minimization, J Mach Learn Res, 12, 1069
Abadi, 2016, 308
Phan, 2016, Differential privacy preservation for deep auto-encoders: an application of human behavior prediction, 1309
Shokri, 2015, 1310
Hermann, 2015, Teaching machines to read and comprehend, Adv Neural Inf Process Syst, 201, 1693
Lei, 2016
Ribeiro, 2016, 1135
The Michael J, 29 2016