AI in Health: State of the Art, Challenges, and Future Directions

Fei Wang; Anita Preininger

doi:10.1055/s-0039-1677908

Yearbook of Medical Informatics, Table of Contents

CC BY-NC-ND 4.0 · Yearb Med Inform 2019; 28(01): 016-026
DOI: 10.1055/s-0039-1677908

Special Section: Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications

Survey

Georg Thieme Verlag KG Stuttgart

AI in Health: State of the Art, Challenges, and Future Directions

Fei Wang

¹Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medicine, Cornell University, NY, USA

,

Anita Preininger∗

²IBM Watson Health, Cambridge, MA, USA

› Author Affiliations

Abstract

Summary

Introduction: Artificial intelligence (AI) technologies continue to attract interest from a broad range of disciplines in recent years, including health. The increase in computer hardware and software applications in medicine, as well as digitization of health-related data together fuel progress in the development and use of AI in medicine. This progress provides new opportunities and challenges, as well as directions for the future of AI in health.

Objective: The goals of this survey are to review the current state of AI in health, along with opportunities, challenges, and practical implications. This review highlights recent developments over the past five years and directions for the future.

Methods: Publications over the past five years reporting the use of AI in health in clinical and biomedical informatics journals, as well as computer science conferences, were selected according to Google Scholar citations. Publications were then categorized into five different classes, according to the type of data analyzed. Results: The major data types identified were multi-omics, clinical, behavioral, environmental and pharmaceutical research and development (R&D) data. The current state of AI related to each data type is described, followed by associated challenges and practical implications that have emerged over the last several years. Opportunities and future directions based on these advances are discussed.

Conclusion: Technologies have enabled the development of AI-assisted approaches to healthcare. However, there remain challenges. Work is currently underway to address multi-modal data integration, balancing quantitative algorithm performance and qualitative model interpretability, protection of model security, federated learning, and model bias.

Keywords

AI - health - deep learning - machine learning - natural language processing - federated learning

Full Text

References

References
1 Hinton G. Deep Learning-A Technology with the Potential to Transform Health Care. JAMA 2018; 320 (11) 1101-2
2 Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med 2016; 375 (13) 1216-9
3 LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521 (7553): 436-44
4 Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol 2017; 18 (01) 83
5 Sun S. A survey of multi-view machine learning. Neural Comput Appl 2013; 23 (7-8): 2031-2038
6 Fridley BL, Lund S, Jenkins GD, Wang L. A Bayesian integrative genomic model for pathway analysis of complex traits. Genet Epidemiol 2012; 36 (04) 352-9
7 Mankoo PK, Shen R, Schultz N, Levine DA, Sander C. Time to recurrence and survival in serous ovarian tumors predicted from integrated genomic profiles. PLoS One 2011; 6 (11) e24709
8 Shen R, Mo Q, Schultz N, Seshan VE, Olshen AB, Huse J. , et al. Integrative subtype discovery in glioblastoma using iCluster. PloS One 2012; 7 (04) e35236
9 Holzinger ER, Dudek SM, Frase AT, Pendergrass SA, Ritchie MD. ATHENA: the analysis tool for heritable and environmental network associations. Bioinformatics 2014; 30 (05) 698-705
10 Kim S, Yeganova L, Comeau DC, Wilbur WJ, Lu Z. PubMed Phrases, an open set of coherent phrases for searching biomedical literature. Sci Data 2018; 5: 180104
11 Turner SD, Dudek SM, Ritchie MD. ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci. BioData Min 2010; 3 (01) 5
12 Wang B, Mezlini AM, Demir F, Fiume M, Tu Z, Brudno M. , et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods 2014; 11 (03) 333
13 Draghici S, Potter RB. Predicting HIV drug resistance with neural networks. Bioinformatics 2003; 19 (01) 98-107
14 Kidd BA, Readhead BP, Eden C, Parekh S, Dudley JT. Integrative network modeling approaches to personalized cancer medicine. Per Med 2015; 12 (03) 245-57
15 Zhu X, Ghahramani Z. Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University; 2002
16 Wang F, Zhang C. Label propagation through linear neighborhoods. IEEE Trans Knowl Data Eng 2008; 20 (01) 55-67
17 Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M. , et al. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261. 2018
18 Baytas IM, Xiao Wang F, Jain AK, Zhou J. Heterogeneous Hyper-Network Embedding. In: 2018 IEEE International Conference on Data Mining (ICDM); 2018. p. 875–80
19 Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A. , et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316 (22) 2402-10
20 Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016. p. 2818-26
21 Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM. , et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542 (7639): 115-8
22 Kermany DS, Goldbaum M, Cai W, Valentim CS, Liang H, Baxter SL. , et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell 2018; 172 (05) 1122-31
23 De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S. , et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018; 24 (09) 1342-50
24 Cicek O, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2016(Oct. 17). p. 424-32
25 Quer G, Muse ED, Nikzad N, Topol EJ, Steinhubl SR. Augmenting diagnostic vision with AI. Lancet 2017; 390 (10091): 221
26 Henglin M, Stein G, Hushcha PV, Snoek J, Wiltschko AB, Cheng S. Machine learning approaches in cardiovascular imaging. Circ Cardiovasc Imaging 2017; 10 (10) e005614
27 Adamson AS, Smith A. Machine learning and health care disparities in dermatology. JAMA Dermatol 2018; 154 (11) 1247-8
28 Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet 2012; 13 (06) 395-405
29 Wu J, Roy J, Stewart WF. Prediction modeling using EHR data: challenges, strategies, and a comparison of machine learning approaches. Med Care 2010; S106-13
30 Sun J, Hu J, Luo D, Markatou M, Wang F, Edabollahi S. , et al. Combining knowledge and data driven insights for identifying risk factors using electronic health records. AMIA Annu Symp Proc 2012; 2012: 901-10
31 Wang FN, Lee N, Hu J, Sun J, Ebadollahi S, Laine AF. A framework for mining signatures from event sequences and its applications in healthcare data. IEEE Trans Pattern Anal Mach Intell 2013; 35 (02) 272-85
32 Cheng Y, Wang F, Zhang P, Hu J. Risk prediction with electronic health records: A deep learning approach. Proceedings of the 2016 SIAM International Conference on Data Mining; 2016. p. 432-40
33 Mikolov T, Karafiat M, Burget L, Cernocky J, Khudanpur S. Recurrent neural network-based language model. 11th Annual Conference of the International Speech Communication Association: 2010
34 Choi E, Schuetz A, Stewart WF, Sun J. Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc 2016; 24 (02) 361-70
35 Choi E, Bahadori MT, Sun J, Kulas J, Schuetz A, Stewart W. An interpretable predictive model for healthcare using reverse time attention mechanism. Advances in Neural Information Processing Systems 2016; 3504-12
36 Bekhet LR, Wu Y, Wang N, Geng X, Zheng WJ, Wang F. , et al. A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set. J Biomed Inform 2018; 11-6
37 Xiao C, Ma T, Dieng AB, Blei DM, Wang F. Readmission prediction via deep contextual embedding of clinical concepts. PLoS One 2016; 13 (04) e0195024
38 Dieng AB, Wang C, Gao J, Paisley J. A recurrent neural network with long-range semantic dependency. arXiv preprint, 2016. arXiv(1611.01702)
39 Kaur H, Sohn S, Wi CI, Ryu E, Park MA, Bachman K. , et al. Automated chart review utilizing natural language processing algorithm for asthma predictive index. BMC Pulm Med 2018; 18 (01) 34
40 Luo Y, Xin Y, Hochberg E, Joshi R, Uzuner O, Szolovits P. Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text. J Am Med Inform Assoc 2015; 22 (05) 1009-19
41 Li Y, Jin R, Luo Y. Classifying relations in clinical narratives using segment graph convolutional and recurrent neural networks (Seg-GCRNs). J Am Med Inform Assoc 2018; 26 (03) 262-8
42 Luo Y, Cheng Y, Uzuner Ö, Szolovits P, Starren J. Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes. J Am Med Inform Assoc 2017; 25 (01) 93-8
43 Filannino M, Uzuner O. Advancing the State of the Art in Clinical Natural Language Processing through Shared Tasks. Yearb Med Inform 2018; 27 (01) 184-92
44 Soysal E, Wang J, Jiang M, Wu Y, Pakhomov S, Liu H. , et al. CLAMP - a toolkit for efficiently building customized clinical natural language processing pipelines. J Am Med Inform Assoc 2017
45 Du M, Liu N, Hu X. Techniques for interpretable machine learning. Commun ACM. In print 2019
46 Polat K, Güneş S. Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl Math Comput 2007; 187 (02) 1017-26
47 Adeli H, Zhou Z, Dadmehr N. Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods 2003; 123 (01) 69-87
48 Shaker MM. EEG waves classifier using wavelet transform and Fourier transform. International Scholarly and Scientific Research & Innovation 2007; 1 (03) 169-74
49 Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP. , et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med 2019; 25 (01) 65-9
50 Schwab P, Scebba GC, Zhang J, Delai M, Karlen W. Beat by Beat: Classifying cardiac arrhythmias with recurrent neural networks. In: Computing in Cardiology (CinC); 2017. p. 1-4
51 Schirrmeister RT, Springenberg JT, Fiederer LDJ, Glasstetter M, Eggensperger K, Tangermann M. , et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp 2017; 38 (11) 5391-420
52 Liang J, Lu R, Zhang C, Wang F. Predicting seizures from electroencephalography recordings: a knowledge transfer strategy. Healthcare Informatics (ICHI) 2016 IEEE International Conference, 2016 (Oct. 4). p. 184-191
53 Zhang K, Gong M, Ramsey J, Batmanghelich K, Spirtes P, Glymour C. Causal discovery in the presence of measurement error: Identifiability conditions. Proceedings of The Conference on Uncertainty in Artificial Intelligence (UAI); 2017
54 Xu Y, Siddharth B, Shriprasad RD, Kevin OM, Jimeng S. RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD); 2018. p. 2565-73
55 Sinnenberg L, DiSilvestro CL, Mancheno C, Dailey K, Tufts C, Buttenheim AM. , et al. Twitter as a Potential Data Source for Cardiovascular Disease Research. JAMA Cardiol 2016; 1 (09) 1032-6
56 Ra CK, Cho J, Stone MD, De La Cerda J, Goldenson NI, Moroney E. , et al. Association of Digital Media Use With Subsequent Symptoms of Attention-Deficit/Hyperactivity Disorder Among Adolescents. JAMA 2018; 320 (03) 255-63
57 Birnbaum M, Rizvi A, De Choudhury M, Ernala S, Cecchi G, Kane J. Identifying psychotic symptoms and predicting relapse through social media. Schizophr Bull 2018; 44 (01) S100
58 Birnbaum ML, Ernala SK, Rizvi AF, De Choudhury M, Kane JM. A collaborative approach to identifying social media markers of schizophreniz by employing machine learning and clinical appraisals. J Med Internet Res 2017; 19: 8
59 De Choudhury M, Kiciman E. The language of social support in social media and its effect on suicidal ideation risk. Proc Int AAAI Conf Weblogs Soc Media 2017; 2017: 32-41
60 De Choudhury M, Gamon M, Counts S, Horvitz E. Predicting depression via social media. Seventh international AAAI conference on weblogs and social media (ICWSM) 2013; 8 (13) 128-37
61 Forbes. Tencent Aims to Train AI to Spot Parkinson’s in 3 Minutes, in https://www.forbes.com/sites/samshead/2018/10/08/ten-cent-aims-to-train-ai-to-spot-parkinsons-in-3-minutes/#6db6d3f06f36 ; 2018
62 Dodge HH, Zhu J, Mattek NC, Bowman M, Ybarra O, Wild KV. , et al. Web-enabled Conversational Interactions as a Means to Improve Cognitive Functions: Results of a 6-Week Randomized Controlled Trial. Alzheimers Dement (N Y) 2015; 1 (01) 1-12
63 Asgari M, Kaye J, Dodge H. Predicting mild cognitive impairment from spontaneous spoken utterances. Alzheimers Dement (N Y) 2017; 3 (02) 219-28
64 Tang F, Lin K, Uchendu I, Dodge HH, Zhou J. Improving mild cognitive impairment prediction via reinforcement learning and dialogue simulation. arXiv preprint ar Xiv, 2018. 1802.06428
65 Sutton RS, Barto AG. Introduction to reinforcement learning. Vol. Mar. 1, 1998. Cambridge: MIT Press; 1998
66 Kumar S, Nilsen WJ, Abernethy A, Atienza A, Patrick K, Pavel M. , et al. Mobile health technology evaluation: the mHealth evidence workshop. Am J Prev Med 2013; 45 (02) 228-36
67 Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME, Kording KP. , et al. Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study. J Med Internet Res 2015; 17 (07) e175
68 Selter A, Tsangouri C, Ali SB, Freed D, Vatchinsky A, Kizer J. , et al. An mHealth App for Self-Management of Chronic Lower Back Pain (Limbr): Pilot Study. JMIR Mhealth Uhealth 2018; 6 (09) e179
69 Zhan A, Mohan S, Tarolli C, Schneider RB, Adams JL, Sharma S. , et al. Using smartphones and machine learning to quantify Parkinson disease severity: the mobile Parkinson disease score. JAMA Neurol 2018; 75 (07) 876-80
70 Turakhia MP, Kaiser DW. Transforming the care of atrial fibrillation with mobile health. J Interv Card Electrophysiol 2018; 47 (01) 45-50
71 Kumar S, Abowd GD, Abraham WT, al’Absi M, Beck JG, Chau DH. , et al. Center of excellence for mobile sensor data-to-knowledge (MD2K). J Am Med Inform Assoc 2015; 22 (06) 1137-42
72 Insel TR. Digital Phenotyping: Technology for a New Science of Behavior. JAMA 2017; 318 (13) 1215-6
73 Yeung S, Downing NL, Li FF, Milstein A. Bedside Computer Vision - Moving Artificial Intelligence from Driver Assistance to Patient Safety. N Engl J Med 2018; 378 (14) 1271-3
74 Cosselman KE, Navas-Acien A, Kaufman JD. Environmental factors in cardiovascular disease. Nat Rev Cardiol 2015; 12 (11) 627-42
75 MacNee W, Donaldson K. Exacerbations of COPD: environmental mechanisms. Chest 2000; 117 (5 Suppl 2): 390S-7S
76 Tanner CM. The role of environmental toxins in the etiology of Parkinson’s disease. Trends Neurosci 1989; 12 (02) 49-54
77 Guloksuz S, van Os J, Rutten BPF. The Exposome Paradigm and the Complexities of Environmental Research in Psychiatry. JAMA Psychiatry 2018; 75 (10) 985-6
78 Boffetta P, Nyberg F. Contribution of environmental factors to cancer risk. Br Med Bull 2003; 68: 71-94
79 Song Y, Wang F, Wang B, Tao S, Zhang H, Liu S. , et al. Time series analyses of hand, foot and mouth disease integrating weather variables. PLoS One 2015; 10 (03) e0117296
80 Stingone JA, Pandey OP, Claudio L, Pandey G. Using machine learning to identify air pollution exposure profiles associated with early cognitie skills among us children. Environmental Pollution 2017; 230: 730-40
81 Park SK, Zhao Z, Mukherjee B. Construction of environmental risk score beyond standard linear models using machine learning methods: Application to metal mixtures, oxidative stress and cardiovascular disease in NHANES. Environ Health 2017; 16 (01) 102-19
82 Hahn LW, Ritchie MD, Moore JH. Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 2003; 19 (03) 376-82
83 Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 2009; 37 (Web Server issue): W623-33
84 Zhang P, Wang F, Hu J, Sorrentino R. Towards personalized medicine: leveraging patient similarity and drug similarity analytics. AMIA Jt Summits Transl Sci Proc 2014; 132-6
85 Zhang P, Wang F, Hu J. Towards drug repositioning: a unified computational framework for integrating multiple aspects of drug similarity and disease similarity. AMIA Annu Symp Proc 2014; 1258-67
86 Bronstein MM, Bruna J, Le Cun Y, Szlam A. Vandergheynst P Geometric deep learning: Going beyond Euclidean data. IEEE Signal Process Mag 2017; 34 (04) 18-42
87 Duvenaud DK, Maclaurin D, Aguilera-Iparraguirre J, Gomez-Bombarelli R, Hirzel T, Aspuru-Guzik A. , et al. Conventional networks on graphs for learning molecular fingerprints. Adv Neural Inf Process Syst 2015; 2224-32
88 Kearnes S, McCloskey K, Berndl M, Pande V, Riley P. Molecular graph convolutions: moving beyond fingerprints. J Comput Aided Mol Des 2016; 30 (08) 595-608
89 Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK, Trivedi MH. , et al. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 2016; 3 (03) 243-50
90 Kohannim O, Hua X, Hibar DP, Lee S, Chou YY, Toga AW. , et al; Alzheimer’s Disease Neuroimaging Initiative. Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiol Aging 2010; 31 (08) 1429-42
91 FDA, The FDA Adverse Event Reporting System. https://www.fda.gov/drugs/guidancecompliance-regulatoryinformation/Surveillance/AdverseDrugEffects/default.htm
92 Sakaeda T, Tamon A, Kadoyama K, Okuno Y. Data mining of the public version of the FDA Adverse Event Reporting System. Int J Med Sci 2013; 10 (07) 796-803
93 Tatonetti NP, Fernald GH, Altman RB. A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports. J Am Med Inform Assoc 2013; 19 (01) 79-85
94 Zhang P, Wang F, Hu J, Sorrentino R. Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects. Sci Rep 2015; 5: 12339
95 Banda JM, Evans L, Vanguri RS, Tatonetti NP, Ryan PB, Shah NH. A curated and standardized adverse drug event resource to accelerate drug safety research. Sci Data 2016; 3: 160026
96 US Food and Drug Administration. Framework for FDA’s Real-World Evidence Program 2018; 2019
97 Cohen AM, Hersh WR. A survey of current work in biomedical text mining. Brief Bioinform 2005; 6 (01) 57-71
98 Leaman R, RDo an RI, Lu Z. DNorm: disease name normalization with pairwise learning to rank. Bioinformatics 2013; 29 (22) 2909-17
99 Aronson AR. Metamap: Mapping text to the UMLS metathesaurus. Bethesda, MD: NLM, NIH, DHHS; 2006. p. 1-26
100 McCandless M, Hatcher E, Gospodnetic O. Lucene in action: covers Apache Lucene 3.0. Manning Publications Co.; 2010
101 Leaman R, Lu Z. TaggerOne: joint named entity recognition and normalization with semi-Markov Models. Bioinformatics 2016; 32 (18) 2839-46
102 Zhao S, Wang F, Zhao S, Liu T. A Neural MultiTask Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI); 2019
103 Singhal A, Simmons M, Lu Z. Text mining genotype-phenotype relationships from biomedical literature for database curation and precision medicine. PLoS Comput Biol 2016; 12 (11) e1005017
104 Peng Y, Lu Z. Deep learning for extracting protein-protein interactions from biomedical literature. Proceedings of the ACL BioNLP 2017 workshop; 2017. p. 29-38
105 Zhang X, Chen D, Zhu Y, Che C, Su C, Zhao S. , et al. Multi-view ensemble classification for clinically actionable genetic mutations. The NIPS’17 Competition: Building intelligent systems; 2018. p. 79-99
106 Kim S, Yeganova L, Comeau DC, Wilbur WJ, Lu Z. PubMed Phrases, an open set of coherent phrases for searching biomedical literature. Sci Data 2018; 5: 180104
107 Fiorini N, Canese K, Bryzgunov R, Radetska I, Gindulyte A, Latterner M. , et al. PubMed Labs: an experimental system for improving biomedical literature search. Database (Oxford) 2018;2018
108 Allot A, Peng Y, Wei CH, Lee K, Phan L, Lu Z. LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC. Nucleic Acids Res 2018; 46 (W1): W530-W536
109 Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med 2015; 372 (09) 793-5
110 Zhang X, Chou J, Wang F. Integrative analysis of patient health records and neuroimages via memory-based graph convolutional network. In: Proceedings of IEEE International Conference on Data Mining (ICDM); 2018. p. 767-76
111 McCarty CA, Chisholm RL, Chute CG, Kullo IJ, Jarvik GP, Larson EB, Li R. , et al., The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med Genomics, 2011. 4: p. 13
112 Gottesman O, Kuivaniemi H, Tromp G, Faucett WA, Li R, Manolio TA. , et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet Med 2013; 15 (10) 761-71
113 Li L, Cheng WY, Glicksberg BS, Gottesman O, Tamler R, Chen R. , et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med 2013; 7 (311): 311ra174
114 Simonite T. Google’s AI Guru Wants Computers to Think More Like Brains. Wired Business. 12/12/2018. https://www.wired.com/story/googles-ai-guru-computers-think-more-like-brains/
115 Poursabzi-Sangdeh F, Goldstein DG, Hofman JM, Wortman Vaughan J, Wallach H. Manipulating and measuring model interpretability. arXiv preprint arXiv:1802.07810;2018
116 Holm EA. In Defense of the Black Box. Science 2019; 364 (6435): 26-7
117 Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M. , et al. Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine 2018; 1 (01) 18
118 Min X, Yu B, Wang F. Predictive Modeling of the Hospital Readmission Risk from Patients’ Claims Data Using Machine Learning: A Case Study on COPD. Sci Rep 2019; 9 (01) 2362
119 Wang F, Casalino LP, Khullar D. Deep Learning in Medicine–Promise, Progress, and Challenges. JAMA Intern Med 2019; 179 (03) 293-4
120 Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531; 2015
121 Shah ND, Steyerberg EW, Kent DM. Big data and predictive analytics: recalibrating expectations. JAMA 2018; 320 (01) 27-8
122 Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019; 25 (01) 44-56
123 Parikh RB, Obermeyer Z, Navathe AS. Regulation of predictive analytics in medicine. Science 2019; 363 (6429): 810-2
124 Sitawarin C, Bhagoji AN, Mosenia A, Chiang M, Mittal P. Darts: Deceiving autonomous cars with toxic signs. arXiv preprint arXiv:1802.06430: 2018
125 Sun M, Tang F, Yi J, Wang F, Zhou J. Identify susceptible locations in medical records via adversarial attacks on deep predictive models. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD); 2018. p. 793-801
126 Finlayson SG, Bowers JD, Ito J, Zittrain JL, Beam AL, Kohane IS. Adversarial attacks on medical machine learning. Science 2019; 363 (6433): 1287-9
127 Konecny J, McMahan HB, Yu FX, Richtarik P, Suresh AT, Bacon D. Federated learning: Strategies for improving communication efficiency ArXiv preprint. arXiv(1610.05492); 2016: p.18
128 Lee J, Sun J, Wang F, Wang S, Jun CH, Jiang X. Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis. JMIR Med Inform 2018; 6 (02) e20
129 Johnson AE, Pollard TJ, Shen L, Li-wei HL, Feng M, Ghassemi M. , et al. MIMIC-III, a freely accessible critical care database. Sci Data 2016; 3: 160035
130 Khullar D. A.I. could worsen health disparities. The New York Times. Jan. 31, 2019. https://www nytimes.com/2019/01/31/opinion/ai-bias-healthcare.html
131 Hripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ. , et al. Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers. Stud Health Technol Inform 2015; 216: 574-8
132 PCORI: Patient-Centered Outcomes Research Institute. https://www.pcori.org/
133 Schulam P, Saria S. Reliable decision support using counterfactual models. Adv Neural Inf Process Syst 2017; 1697-708