Big data and machine learning algorithms for health-care delivery

Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However, to effectively use machine learning tools in health care, several limitations must be addressed and key issues considered, such as its clinic...

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Vydáno v:	The lancet oncology Ročník 20; číslo 5; s. e262 - e273
Hlavní autoři:	Ngiam, Kee Yuan, Khor, Ing Wei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Elsevier Ltd 01.05.2019 Elsevier Limited
Témata:	Algorithms Artificial intelligence Biomarkers Biopsy Clinical medicine Clinical trials Colonoscopy Colorectal cancer Datasets Diagnosis Electronic health records Health care delivery Histology Learning algorithms Lung cancer Medical imaging Medical screening Melanoma Neural networks Oncology Pancreatic cancer Physicians Population Prostate cancer Skin cancer Vision systems Canada United States > US China Spain
ISSN:	1470-2045, 1474-5488, 1474-5488
On-line přístup:	Získat plný text
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Abstract	Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However, to effectively use machine learning tools in health care, several limitations must be addressed and key issues considered, such as its clinical implementation and ethics in health-care delivery. Advantages of machine learning include flexibility and scalability compared with traditional biostatistical methods, which makes it deployable for many tasks, such as risk stratification, diagnosis and classification, and survival predictions. Another advantage of machine learning algorithms is the ability to analyse diverse data types (eg, demographic data, laboratory findings, imaging data, and doctors' free-text notes) and incorporate them into predictions for disease risk, diagnosis, prognosis, and appropriate treatments. Despite these advantages, the application of machine learning in health-care delivery also presents unique challenges that require data pre-processing, model training, and refinement of the system with respect to the actual clinical problem. Also crucial are ethical considerations, which include medico-legal implications, doctors' understanding of machine learning tools, and data privacy and security. In this Review, we discuss some of the benefits and challenges of big data and machine learning in health care.
AbstractList	Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However, to effectively use machine learning tools in health care, several limitations must be addressed and key issues considered, such as its clinical implementation and ethics in health-care delivery. Advantages of machine learning include flexibility and scalability compared with traditional biostatistical methods, which makes it deployable for many tasks, such as risk stratification, diagnosis and classification, and survival predictions. Another advantage of machine learning algorithms is the ability to analyse diverse data types (eg, demographic data, laboratory findings, imaging data, and doctors' free-text notes) and incorporate them into predictions for disease risk, diagnosis, prognosis, and appropriate treatments. Despite these advantages, the application of machine learning in health-care delivery also presents unique challenges that require data pre-processing, model training, and refinement of the system with respect to the actual clinical problem. Also crucial are ethical considerations, which include medico-legal implications, doctors' understanding of machine learning tools, and data privacy and security. In this Review, we discuss some of the benefits and challenges of big data and machine learning in health care. Summary Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However, to effectively use machine learning tools in health care, several limitations must be addressed and key issues considered, such as its clinical implementation and ethics in health-care delivery. Advantages of machine learning include flexibility and scalability compared with traditional biostatistical methods, which makes it deployable for many tasks, such as risk stratification, diagnosis and classification, and survival predictions. Another advantage of machine learning algorithms is the ability to analyse diverse data types (eg, demographic data, laboratory findings, imaging data, and doctors' free-text notes) and incorporate them into predictions for disease risk, diagnosis, prognosis, and appropriate treatments. Despite these advantages, the application of machine learning in health-care delivery also presents unique challenges that require data pre-processing, model training, and refinement of the system with respect to the actual clinical problem. Also crucial are ethical considerations, which include medico-legal implications, doctors' understanding of machine learning tools, and data privacy and security. In this Review, we discuss some of the benefits and challenges of big data and machine learning in health care. Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However, to effectively use machine learning tools in health care, several limitations must be addressed and key issues considered, such as its clinical implementation and ethics in health-care delivery. Advantages of machine learning include flexibility and scalability compared with traditional biostatistical methods, which makes it deployable for many tasks, such as risk stratification, diagnosis and classification, and survival predictions. Another advantage of machine learning algorithms is the ability to analyse diverse data types (eg, demographic data, laboratory findings, imaging data, and doctors' free-text notes) and incorporate them into predictions for disease risk, diagnosis, prognosis, and appropriate treatments. Despite these advantages, the application of machine learning in health-care delivery also presents unique challenges that require data pre-processing, model training, and refinement of the system with respect to the actual clinical problem. Also crucial are ethical considerations, which include medico-legal implications, doctors' understanding of machine learning tools, and data privacy and security. In this Review, we discuss some of the benefits and challenges of big data and machine learning in health care.Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However, to effectively use machine learning tools in health care, several limitations must be addressed and key issues considered, such as its clinical implementation and ethics in health-care delivery. Advantages of machine learning include flexibility and scalability compared with traditional biostatistical methods, which makes it deployable for many tasks, such as risk stratification, diagnosis and classification, and survival predictions. Another advantage of machine learning algorithms is the ability to analyse diverse data types (eg, demographic data, laboratory findings, imaging data, and doctors' free-text notes) and incorporate them into predictions for disease risk, diagnosis, prognosis, and appropriate treatments. Despite these advantages, the application of machine learning in health-care delivery also presents unique challenges that require data pre-processing, model training, and refinement of the system with respect to the actual clinical problem. Also crucial are ethical considerations, which include medico-legal implications, doctors' understanding of machine learning tools, and data privacy and security. In this Review, we discuss some of the benefits and challenges of big data and machine learning in health care.
Author	Khor, Ing Wei Ngiam, Kee Yuan
Author_xml	– sequence: 1 givenname: Kee Yuan surname: Ngiam fullname: Ngiam, Kee Yuan email: kee_yuan_ngiam@nuhs.edu.sg organization: Department of Surgery, National University of Singapore, Singapore – sequence: 2 givenname: Ing Wei surname: Khor fullname: Khor, Ing Wei organization: Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31044724$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1145/3097983.3098149 10.1002/adtp.201800104 10.1111/j.1365-2753.2011.01720.x 10.1007/978-3-642-22887-2_45 10.1056/NEJM200006223422507 10.1038/s41591-018-0147-y 10.1038/nature14539 10.3389/fneur.2017.00489 10.1056/NEJMsr1503323 10.1377/hlthaff.2014.0041 10.1016/j.cmpb.2018.04.005 10.1038/nature21056 10.1038/s41746-018-0040-6 10.1001/jama.2016.17438 10.1080/17460441.2018.1465407 10.1056/NEJMra1615014 10.1371/journal.pmed.1002674 10.1109/ICASSP.2013.6638947 10.1093/nar/gkh061 10.1109/TMI.2016.2553401 10.1371/journal.pmed.1002686 10.1016/j.artmed.2014.06.004 10.1016/j.ijmedinf.2010.08.006 10.1001/jamaoncol.2015.1203 10.1109/TMI.2016.2526689 10.1126/scitranslmed.aab3719 10.1136/svn-2017-000101 10.1093/jamia/ocy072 10.1007/s11548-017-1627-0 10.1038/nbt.4233 10.21037/qims.2018.03.07 10.7326/M17-3008 10.1126/scitranslmed.aac5954 10.1148/radiol.2018180237 10.1007/s11886-018-0990-y 10.1038/s41591-018-0300-7 10.1073/pnas.1717139115 10.1136/leader-2018-000071 10.1147/rd.33.0210 10.1161/CIRCULATIONAHA.115.001593
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References	(bib53) 2018 Laranjo, Dunn, Tong (bib19) 2018; 25 (bib33) 2017 Ross, Swetlitz (bib49) 2018 Wainberg, Merico, Delong, Frey (bib7) 2018; 36 Bodenreider (bib26) 2004; 32 Graves A, Mohamed A-R, Hinton G. Speech recognition with deep recurrent neural networks. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver, BC, Canada; May 26–31, 2013. Jiang, Jiang, Zhi (bib52) 2017; 2 (bib56) 2012 Zarrinpar, Lee, Silva (bib59) 2016; 8 Greenspan, van Ginneken, Summers (bib31) 2016; 35 Silver DL. Machine lifelong learning: challenges and benefits for artificial general intelligence. Artificial General Intelligence (AGI) 2011; Mountain View, CA, USA; Aug 3–6, 2011. Pantuck, Lee, Kee (bib50) 2018; 1 Zheng KP, Gao J, Ngiam KY, Ooi BC, Yip WLJ. Resolving the bias in electronic medical records. 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining; Halifax, Nova Scotia, Canada; Aug 13–17, 2017. Mazzanti, Shirka, Gjergo, Hasimi (bib8) 2018; 20 Azizi, Bayat, Yan (bib47) 2017; 12 Voelker (bib39) 2018; 320 Nam, Park, Hwang (bib46) 2019; 290 Loh (bib60) 2018; 2 (bib32) 2017 Rajpurkar, Irvin, Ball (bib40) 2018; 15 Esteva, Kuprel, Novoa (bib17) 2017; 542 Henry, Hager, Pronovost, Saria (bib42) 2015; 7 Mobadersany, Yousefi, Amgad (bib48) 2018; 115 Marr (bib38) Jan 20, 2017 Kantarjian, Yu (bib6) 2015; 1 Hainc, Federau, Stieltjes, Blatow, Bink, Stippich (bib10) 2017; 8 Kerlikowske, Scott, Mahmoudzadeh (bib44) 2018; 168 van Grinsven, van Ginneken, Hoyng, Theelen, Sanchez (bib24) 2016; 35 Concato, Shah, Horwitz (bib14) 2000; 342 Ekeland, Bowes, Flottorp (bib15) 2010; 79 Faust, Hagiwara, Hong, Lih, Acharya (bib4) 2018; 161 Wang, Peng, Chang, Liang (bib23) 2018; 8 (bib55) 2019 Samuel (bib1) 1959; 3 (bib51) 2018 Gawehn, Hiss, Brown, Schneider (bib21) 2018; 13 (bib45) Feb 19, 2018 Marcus (bib27) 2018 Kowatsch T, Nissen M, Chen-Hsuan IS, et al. Text-based healthcare chatbots supporting patient and health professional teams: preliminary results of a randomized controlled trial on childhood obesity. Persuasive Embodied Agents for Behavior Change (PEACH2017) Workshop, co-located with the 17th International Conference on Intelligent Virtual Agents (IVA 2017); Stockholm, Sweden; Aug 27–30, 2017. Haendel, Chute, Robinson (bib25) 2018; 379 Daniel, Silcox, Sharma, Wright (bib62) 2019 McNair, Ottley (bib61) 1996; 10 LeCun, Bengio, Hinton (bib2) 2015; 521 Obeid NM, Atkinson IC, Thulborn KR, Hwu W-MW. GPU-accelerated gridding for rapid reconstruction of non-cartesian MRI. 19th Annual International Society for Magnetic Resonance in Medicine (ISMRM) Scientific Meeting and Exhibition 2011; Montreal, QC, Canada; May 7–13, 2011. Henry, Wongvibulsin, Zhan, Saria, Hager (bib43) 2017; 195 (bib58) 2017 Abràmoff, Lavin, Birch, Shahm, Folk (bib36) 2018; 1 (bib9) January 2019 (bib57) 2015 Tuckson, Edmunds, Hodgkins (bib16) 2017; 377 Lin, Long, Ding (bib37) 2018; 15 Topol (bib11) 2019; 25 Titano, Badgeley, Schefflein (bib41) 2018; 24 Bates, Saria, Ohno-Machado, Shah, Escobar (bib3) 2014; 33 Gelhaus (bib12) 2011; 17 Kuo (bib28) 2016 Luxton (bib13) 2014; 62 Jha, Topol (bib5) 2016; 316 (bib54) 2018 Gepperth A, Hammer B. Incremental learning algorithms and applications. European Symposium on Artificial Neural Networks (ESANN) 2016; Bruges, Belgium; April 27–29, 2016. Deo (bib29) 2015; 132 Kerlikowske (10.1016/S1470-2045(19)30149-4_bib44) 2018; 168 Mobadersany (10.1016/S1470-2045(19)30149-4_bib48) 2018; 115 (10.1016/S1470-2045(19)30149-4_bib57) 2015 Henry (10.1016/S1470-2045(19)30149-4_bib42) 2015; 7 Bodenreider (10.1016/S1470-2045(19)30149-4_bib26) 2004; 32 (10.1016/S1470-2045(19)30149-4_bib9) 2019 Marcus (10.1016/S1470-2045(19)30149-4_bib27) Greenspan (10.1016/S1470-2045(19)30149-4_bib31) 2016; 35 Kuo (10.1016/S1470-2045(19)30149-4_bib28) Titano (10.1016/S1470-2045(19)30149-4_bib41) 2018; 24 Rajpurkar (10.1016/S1470-2045(19)30149-4_bib40) 2018; 15 Nam (10.1016/S1470-2045(19)30149-4_bib46) 2019; 290 Daniel (10.1016/S1470-2045(19)30149-4_bib62) Bates (10.1016/S1470-2045(19)30149-4_bib3) 2014; 33 Jiang (10.1016/S1470-2045(19)30149-4_bib52) 2017; 2 Zarrinpar (10.1016/S1470-2045(19)30149-4_bib59) 2016; 8 Henry (10.1016/S1470-2045(19)30149-4_bib43) 2017; 195 10.1016/S1470-2045(19)30149-4_bib20 Marr (10.1016/S1470-2045(19)30149-4_bib38) 2017 10.1016/S1470-2045(19)30149-4_bib22 (10.1016/S1470-2045(19)30149-4_bib58) 2017 Loh (10.1016/S1470-2045(19)30149-4_bib60) 2018; 2 Abràmoff (10.1016/S1470-2045(19)30149-4_bib36) 2018; 1 10.1016/S1470-2045(19)30149-4_bib18 Deo (10.1016/S1470-2045(19)30149-4_bib29) 2015; 132 (10.1016/S1470-2045(19)30149-4_bib51) 2018 Mazzanti (10.1016/S1470-2045(19)30149-4_bib8) 2018; 20 Jha (10.1016/S1470-2045(19)30149-4_bib5) 2016; 316 Concato (10.1016/S1470-2045(19)30149-4_bib14) 2000; 342 Esteva (10.1016/S1470-2045(19)30149-4_bib17) 2017; 542 Kantarjian (10.1016/S1470-2045(19)30149-4_bib6) 2015; 1 Wang (10.1016/S1470-2045(19)30149-4_bib23) 2018; 8 Pantuck (10.1016/S1470-2045(19)30149-4_bib50) 2018; 1 Lin (10.1016/S1470-2045(19)30149-4_bib37) 2018; 15 (10.1016/S1470-2045(19)30149-4_bib32) 2017 Wainberg (10.1016/S1470-2045(19)30149-4_bib7) 2018; 36 Faust (10.1016/S1470-2045(19)30149-4_bib4) 2018; 161 (10.1016/S1470-2045(19)30149-4_bib55) 2019 Laranjo (10.1016/S1470-2045(19)30149-4_bib19) 2018; 25 McNair (10.1016/S1470-2045(19)30149-4_bib61) 1996; 10 van Grinsven (10.1016/S1470-2045(19)30149-4_bib24) 2016; 35 (10.1016/S1470-2045(19)30149-4_bib33) 2017 Azizi (10.1016/S1470-2045(19)30149-4_bib47) 2017; 12 Samuel (10.1016/S1470-2045(19)30149-4_bib1) 1959; 3 Gawehn (10.1016/S1470-2045(19)30149-4_bib21) 2018; 13 Hainc (10.1016/S1470-2045(19)30149-4_bib10) 2017; 8 Tuckson (10.1016/S1470-2045(19)30149-4_bib16) 2017; 377 (10.1016/S1470-2045(19)30149-4_bib56) 2012 Gelhaus (10.1016/S1470-2045(19)30149-4_bib12) 2011; 17 Ekeland (10.1016/S1470-2045(19)30149-4_bib15) 2010; 79 Voelker (10.1016/S1470-2045(19)30149-4_bib39) 2018; 320 Topol (10.1016/S1470-2045(19)30149-4_bib11) 2019; 25 10.1016/S1470-2045(19)30149-4_bib30 Haendel (10.1016/S1470-2045(19)30149-4_bib25) 2018; 379 Luxton (10.1016/S1470-2045(19)30149-4_bib13) 2014; 62 10.1016/S1470-2045(19)30149-4_bib35 Ross (10.1016/S1470-2045(19)30149-4_bib49) LeCun (10.1016/S1470-2045(19)30149-4_bib2) 2015; 521 10.1016/S1470-2045(19)30149-4_bib34
References_xml	– year: 2012 ident: bib56 article-title: Personal Data Protection Act 2012 – year: 2017 ident: bib32 article-title: Changes to existing medical software policies resulting from section 3060 of the 21st Century Cures Act: draft guidance for industry and Food and Drug Administration staff – volume: 115 start-page: E2970 year: 2018 end-page: E2979 ident: bib48 article-title: Predicting cancer outcomes from histology and genomics using convolutional networks publication-title: Proc Natl Acad Sci USA – volume: 33 start-page: 1123 year: 2014 end-page: 1131 ident: bib3 article-title: Big data in health care: using analytics to identify and manage high-risk and high-cost patients publication-title: Health Aff (Millwood) – year: 2017 ident: bib33 article-title: Software as a medical device (SAMD): clinical evaluation. Guidance for industry and Food and Drug Administration staff – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: bib2 article-title: Deep learning publication-title: Nature – year: 2018 ident: bib54 article-title: One of the largest AI platforms in healthcare is one you've never heard of, until now. The Pulse – year: 2015 ident: bib57 article-title: Human Biomedical Research Act 2015 – year: 2016 ident: bib28 article-title: Understanding convolutional neural networks with a mathematical model – volume: 195 start-page: A7016 year: 2017 ident: bib43 article-title: Can septic shock be identified early? Evaluating performance of a targeted real-time early warning score (TREWScore) for septic shock in a community hospital: global and subpopulation performance publication-title: Am J Resp Crit Care Med – volume: 542 start-page: 115 year: 2017 end-page: 118 ident: bib17 article-title: Dermatologist-level classification of skin cancer with deep neural networks publication-title: Nature – year: 2018 ident: bib27 article-title: Deep learning: a critical appraisal – volume: 35 start-page: 1153 year: 2016 end-page: 1159 ident: bib31 article-title: Deep learning in medical imaging: overview and future promise of an exciting new technique publication-title: IEEE Trans Med Imag – year: 2018 ident: bib49 article-title: IBM's Watson supercomputer recommended ‘unsafe and incorrect’ cancer treatments, internal documents show. STAT – volume: 290 start-page: 218 year: 2019 end-page: 228 ident: bib46 article-title: Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs publication-title: Radiology – volume: 25 start-page: 44 year: 2019 end-page: 56 ident: bib11 article-title: High-performance medicine: the convergence of human and artificial intelligence publication-title: Nat Med – volume: 342 start-page: 1887 year: 2000 end-page: 1892 ident: bib14 article-title: Randomized, controlled trials, observational studies, and the hierarchy of research designs publication-title: N Engl J Med – reference: Silver DL. Machine lifelong learning: challenges and benefits for artificial general intelligence. Artificial General Intelligence (AGI) 2011; Mountain View, CA, USA; Aug 3–6, 2011. – year: January 2019 ident: bib9 article-title: A Proposed model artificial intelligence governance framework – volume: 17 start-page: 883 year: 2011 end-page: 887 ident: bib12 article-title: Robot decisions: on the importance of virtuous judgment in clinical decision making publication-title: J Eval Clin Pract – year: 2019 ident: bib55 article-title: Predictive maintenance of medical devices based on years of experience and advanced analytics – volume: 132 start-page: 1920 year: 2015 end-page: 1930 ident: bib29 article-title: Machine learning in medicine publication-title: Circulation – volume: 377 start-page: 1585 year: 2017 end-page: 1592 ident: bib16 article-title: Telehealth publication-title: N Engl J Med – volume: 379 start-page: 1452 year: 2018 end-page: 1462 ident: bib25 article-title: Classification, ontology, and precision medicine publication-title: N Engl J Med – year: 2018 ident: bib51 article-title: FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems – volume: 3 start-page: 210 year: 1959 end-page: 229 ident: bib1 article-title: Some studies in machine learning using the game of checkers publication-title: IBM J Res Dev – volume: 8 start-page: 489 year: 2017 ident: bib10 article-title: The bright, artificial intelligence-augmented future of neuroimaging reading publication-title: Front Neurol – volume: 15 start-page: e1002686 year: 2018 ident: bib40 article-title: Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists publication-title: PLoS Med – volume: 36 start-page: 829 year: 2018 end-page: 838 ident: bib7 article-title: Deep learning in biomedicine publication-title: Nat Biotechnol – year: 2019 ident: bib62 article-title: Current state and near-term priorities for AI-enabled diagnostic support software in health care. Margolis Center for Health Policy – volume: 168 start-page: 757 year: 2018 end-page: 765 ident: bib44 article-title: Automated and clinical breast imaging reporting and data system density measures predict risk for screen-detected and interval cancers: a case-control study publication-title: Ann Intern Med – volume: 25 start-page: 1248 year: 2018 end-page: 1258 ident: bib19 article-title: Conversational agents in healthcare: a systematic review publication-title: J Am Med Inform Assoc – volume: 8 start-page: 333ra49 year: 2016 ident: bib59 article-title: Individualizing liver transplant immunosuppression using a phenotypic personalized medicine platform publication-title: Sci Transl Med – year: 2018 ident: bib53 article-title: Philips launches AI platform for healthcare. Philips – volume: 24 start-page: 1337 year: 2018 end-page: 1341 ident: bib41 article-title: Automated deep-neural-network surveillance of cranial images for acute neurologic events publication-title: Nat Med – volume: 2 start-page: 59 year: 2018 end-page: 63 ident: bib60 article-title: Medicine and the rise of the robots: a qualitative review of recent advances of artificial intelligence in health publication-title: BMJ Leader – volume: 161 start-page: 1 year: 2018 end-page: 13 ident: bib4 article-title: Deep learning for healthcare applications based on physiological signals: a review publication-title: Comput Methods Programs Biomed – reference: Kowatsch T, Nissen M, Chen-Hsuan IS, et al. Text-based healthcare chatbots supporting patient and health professional teams: preliminary results of a randomized controlled trial on childhood obesity. Persuasive Embodied Agents for Behavior Change (PEACH2017) Workshop, co-located with the 17th International Conference on Intelligent Virtual Agents (IVA 2017); Stockholm, Sweden; Aug 27–30, 2017. – volume: 32 start-page: D267 year: 2004 end-page: D270 ident: bib26 article-title: The Unified Medical Language System (UMLS): integrating biomedical terminology publication-title: Nucleic Acids Res – volume: 1 start-page: 39 year: 2018 ident: bib36 article-title: Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices publication-title: npj Digit Med – volume: 10 start-page: 18 year: 1996 end-page: 20 ident: bib61 article-title: Negligence: who is the umpire publication-title: J Med Defence Union – volume: 35 start-page: 1273 year: 2016 end-page: 1284 ident: bib24 article-title: Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images publication-title: IEEE Trans Med Imag – year: Feb 19, 2018 ident: bib45 article-title: Arterys FDA clearance for Liver AI and Lung AI lesion spotting software. – volume: 8 start-page: 196 year: 2018 end-page: 208 ident: bib23 article-title: A survey of GPU-based acceleration techniques in MRI reconstructions publication-title: Quant Imaging Med Surg – reference: Zheng KP, Gao J, Ngiam KY, Ooi BC, Yip WLJ. Resolving the bias in electronic medical records. 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining; Halifax, Nova Scotia, Canada; Aug 13–17, 2017. – volume: 13 start-page: 579 year: 2018 end-page: 582 ident: bib21 article-title: Advancing drug discovery via GPU-based deep learning publication-title: Expert Opin Drug Discov – reference: Gepperth A, Hammer B. Incremental learning algorithms and applications. European Symposium on Artificial Neural Networks (ESANN) 2016; Bruges, Belgium; April 27–29, 2016. – year: 2017 ident: bib58 article-title: Human Biomedical Research Regulations 2017 – reference: Graves A, Mohamed A-R, Hinton G. Speech recognition with deep recurrent neural networks. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver, BC, Canada; May 26–31, 2013. – volume: 12 start-page: 1293 year: 2017 end-page: 1305 ident: bib47 article-title: Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations publication-title: Int J Comput Assist Radiol Surg – volume: 62 start-page: 1 year: 2014 end-page: 10 ident: bib13 article-title: Recommendations for the ethical use and design of artificial intelligent care providers publication-title: Artif Intell Med – volume: 1 start-page: 573 year: 2015 end-page: 574 ident: bib6 article-title: Artificial intelligence, big data, and cancer publication-title: JAMA Oncol – year: Jan 20, 2017 ident: bib38 article-title: First FDA approval for clinical cloud-based deep learning in healthcare – volume: 316 start-page: 2353 year: 2016 end-page: 2354 ident: bib5 article-title: Adapting to artificial intelligence: radiologists and pathologists as information specialists publication-title: JAMA – volume: 1 start-page: 1800104 year: 2018 ident: bib50 article-title: Modulating BET bromodomain inhibitor ZEN-3694 and enzalutamide combination dosing in a metastatic prostate cancer patient using CURATE.AI, an artificial intelligence platform publication-title: Adv Therap – volume: 79 start-page: 736 year: 2010 end-page: 771 ident: bib15 article-title: Effectiveness of telemedicine: a systematic review of reviews publication-title: Int J Med Inform – volume: 15 start-page: e1002674 year: 2018 ident: bib37 article-title: Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: a retrospective, multicentre machine learning study publication-title: PLoS Med – reference: Obeid NM, Atkinson IC, Thulborn KR, Hwu W-MW. GPU-accelerated gridding for rapid reconstruction of non-cartesian MRI. 19th Annual International Society for Magnetic Resonance in Medicine (ISMRM) Scientific Meeting and Exhibition 2011; Montreal, QC, Canada; May 7–13, 2011. – volume: 320 start-page: 23 year: 2018 ident: bib39 article-title: Diagnosing fractures with AI publication-title: JAMA – volume: 20 start-page: 48 year: 2018 ident: bib8 article-title: Imaging, health record, and artificial intelligence: hype or hope? publication-title: Curr Cardiol Rep – volume: 2 start-page: 230 year: 2017 end-page: 243 ident: bib52 article-title: Artificial intelligence in healthcare: past, present and future publication-title: Stroke Vasc Neurol – volume: 7 start-page: 299ra122 year: 2015 ident: bib42 article-title: A targeted real-time early warning score (TREWScore) for septic shock publication-title: Sci Transl Med – ident: 10.1016/S1470-2045(19)30149-4_bib20 doi: 10.1145/3097983.3098149 – volume: 1 start-page: 1800104 year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib50 article-title: Modulating BET bromodomain inhibitor ZEN-3694 and enzalutamide combination dosing in a metastatic prostate cancer patient using CURATE.AI, an artificial intelligence platform publication-title: Adv Therap doi: 10.1002/adtp.201800104 – volume: 17 start-page: 883 year: 2011 ident: 10.1016/S1470-2045(19)30149-4_bib12 article-title: Robot decisions: on the importance of virtuous judgment in clinical decision making publication-title: J Eval Clin Pract doi: 10.1111/j.1365-2753.2011.01720.x – year: 2015 ident: 10.1016/S1470-2045(19)30149-4_bib57 – year: 2017 ident: 10.1016/S1470-2045(19)30149-4_bib38 – ident: 10.1016/S1470-2045(19)30149-4_bib35 doi: 10.1007/978-3-642-22887-2_45 – volume: 342 start-page: 1887 year: 2000 ident: 10.1016/S1470-2045(19)30149-4_bib14 article-title: Randomized, controlled trials, observational studies, and the hierarchy of research designs publication-title: N Engl J Med doi: 10.1056/NEJM200006223422507 – volume: 24 start-page: 1337 year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib41 article-title: Automated deep-neural-network surveillance of cranial images for acute neurologic events publication-title: Nat Med doi: 10.1038/s41591-018-0147-y – volume: 521 start-page: 436 year: 2015 ident: 10.1016/S1470-2045(19)30149-4_bib2 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 195 start-page: A7016 year: 2017 ident: 10.1016/S1470-2045(19)30149-4_bib43 article-title: Can septic shock be identified early? Evaluating performance of a targeted real-time early warning score (TREWScore) for septic shock in a community hospital: global and subpopulation performance publication-title: Am J Resp Crit Care Med – year: 2019 ident: 10.1016/S1470-2045(19)30149-4_bib55 – volume: 8 start-page: 489 year: 2017 ident: 10.1016/S1470-2045(19)30149-4_bib10 article-title: The bright, artificial intelligence-augmented future of neuroimaging reading publication-title: Front Neurol doi: 10.3389/fneur.2017.00489 – ident: 10.1016/S1470-2045(19)30149-4_bib18 – year: 2017 ident: 10.1016/S1470-2045(19)30149-4_bib33 – volume: 377 start-page: 1585 year: 2017 ident: 10.1016/S1470-2045(19)30149-4_bib16 article-title: Telehealth publication-title: N Engl J Med doi: 10.1056/NEJMsr1503323 – volume: 33 start-page: 1123 year: 2014 ident: 10.1016/S1470-2045(19)30149-4_bib3 article-title: Big data in health care: using analytics to identify and manage high-risk and high-cost patients publication-title: Health Aff (Millwood) doi: 10.1377/hlthaff.2014.0041 – volume: 161 start-page: 1 year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib4 article-title: Deep learning for healthcare applications based on physiological signals: a review publication-title: Comput Methods Programs Biomed doi: 10.1016/j.cmpb.2018.04.005 – volume: 542 start-page: 115 year: 2017 ident: 10.1016/S1470-2045(19)30149-4_bib17 article-title: Dermatologist-level classification of skin cancer with deep neural networks publication-title: Nature doi: 10.1038/nature21056 – volume: 1 start-page: 39 year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib36 article-title: Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices publication-title: npj Digit Med doi: 10.1038/s41746-018-0040-6 – volume: 316 start-page: 2353 year: 2016 ident: 10.1016/S1470-2045(19)30149-4_bib5 article-title: Adapting to artificial intelligence: radiologists and pathologists as information specialists publication-title: JAMA doi: 10.1001/jama.2016.17438 – volume: 13 start-page: 579 year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib21 article-title: Advancing drug discovery via GPU-based deep learning publication-title: Expert Opin Drug Discov doi: 10.1080/17460441.2018.1465407 – volume: 379 start-page: 1452 year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib25 article-title: Classification, ontology, and precision medicine publication-title: N Engl J Med doi: 10.1056/NEJMra1615014 – volume: 15 start-page: e1002674 year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib37 article-title: Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: a retrospective, multicentre machine learning study publication-title: PLoS Med doi: 10.1371/journal.pmed.1002674 – year: 2017 ident: 10.1016/S1470-2045(19)30149-4_bib58 – ident: 10.1016/S1470-2045(19)30149-4_bib28 – ident: 10.1016/S1470-2045(19)30149-4_bib30 doi: 10.1109/ICASSP.2013.6638947 – volume: 32 start-page: D267 year: 2004 ident: 10.1016/S1470-2045(19)30149-4_bib26 article-title: The Unified Medical Language System (UMLS): integrating biomedical terminology publication-title: Nucleic Acids Res doi: 10.1093/nar/gkh061 – year: 2017 ident: 10.1016/S1470-2045(19)30149-4_bib32 – volume: 35 start-page: 1153 year: 2016 ident: 10.1016/S1470-2045(19)30149-4_bib31 article-title: Deep learning in medical imaging: overview and future promise of an exciting new technique publication-title: IEEE Trans Med Imag doi: 10.1109/TMI.2016.2553401 – volume: 15 start-page: e1002686 year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib40 article-title: Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists publication-title: PLoS Med doi: 10.1371/journal.pmed.1002686 – ident: 10.1016/S1470-2045(19)30149-4_bib34 – ident: 10.1016/S1470-2045(19)30149-4_bib27 – ident: 10.1016/S1470-2045(19)30149-4_bib62 – ident: 10.1016/S1470-2045(19)30149-4_bib49 – volume: 62 start-page: 1 year: 2014 ident: 10.1016/S1470-2045(19)30149-4_bib13 article-title: Recommendations for the ethical use and design of artificial intelligent care providers publication-title: Artif Intell Med doi: 10.1016/j.artmed.2014.06.004 – volume: 79 start-page: 736 year: 2010 ident: 10.1016/S1470-2045(19)30149-4_bib15 article-title: Effectiveness of telemedicine: a systematic review of reviews publication-title: Int J Med Inform doi: 10.1016/j.ijmedinf.2010.08.006 – volume: 1 start-page: 573 year: 2015 ident: 10.1016/S1470-2045(19)30149-4_bib6 article-title: Artificial intelligence, big data, and cancer publication-title: JAMA Oncol doi: 10.1001/jamaoncol.2015.1203 – volume: 35 start-page: 1273 year: 2016 ident: 10.1016/S1470-2045(19)30149-4_bib24 article-title: Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images publication-title: IEEE Trans Med Imag doi: 10.1109/TMI.2016.2526689 – volume: 7 start-page: 299ra122 year: 2015 ident: 10.1016/S1470-2045(19)30149-4_bib42 article-title: A targeted real-time early warning score (TREWScore) for septic shock publication-title: Sci Transl Med doi: 10.1126/scitranslmed.aab3719 – volume: 2 start-page: 230 year: 2017 ident: 10.1016/S1470-2045(19)30149-4_bib52 article-title: Artificial intelligence in healthcare: past, present and future publication-title: Stroke Vasc Neurol doi: 10.1136/svn-2017-000101 – volume: 10 start-page: 18 year: 1996 ident: 10.1016/S1470-2045(19)30149-4_bib61 article-title: Negligence: who is the umpire publication-title: J Med Defence Union – volume: 25 start-page: 1248 year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib19 article-title: Conversational agents in healthcare: a systematic review publication-title: J Am Med Inform Assoc doi: 10.1093/jamia/ocy072 – volume: 12 start-page: 1293 year: 2017 ident: 10.1016/S1470-2045(19)30149-4_bib47 article-title: Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations publication-title: Int J Comput Assist Radiol Surg doi: 10.1007/s11548-017-1627-0 – volume: 320 start-page: 23 year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib39 article-title: Diagnosing fractures with AI publication-title: JAMA – volume: 36 start-page: 829 year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib7 article-title: Deep learning in biomedicine publication-title: Nat Biotechnol doi: 10.1038/nbt.4233 – volume: 8 start-page: 196 year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib23 article-title: A survey of GPU-based acceleration techniques in MRI reconstructions publication-title: Quant Imaging Med Surg doi: 10.21037/qims.2018.03.07 – volume: 168 start-page: 757 year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib44 article-title: Automated and clinical breast imaging reporting and data system density measures predict risk for screen-detected and interval cancers: a case-control study publication-title: Ann Intern Med doi: 10.7326/M17-3008 – volume: 8 start-page: 333ra49 year: 2016 ident: 10.1016/S1470-2045(19)30149-4_bib59 article-title: Individualizing liver transplant immunosuppression using a phenotypic personalized medicine platform publication-title: Sci Transl Med doi: 10.1126/scitranslmed.aac5954 – year: 2012 ident: 10.1016/S1470-2045(19)30149-4_bib56 – volume: 290 start-page: 218 year: 2019 ident: 10.1016/S1470-2045(19)30149-4_bib46 article-title: Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs publication-title: Radiology doi: 10.1148/radiol.2018180237 – volume: 20 start-page: 48 year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib8 article-title: Imaging, health record, and artificial intelligence: hype or hope? publication-title: Curr Cardiol Rep doi: 10.1007/s11886-018-0990-y – volume: 25 start-page: 44 year: 2019 ident: 10.1016/S1470-2045(19)30149-4_bib11 article-title: High-performance medicine: the convergence of human and artificial intelligence publication-title: Nat Med doi: 10.1038/s41591-018-0300-7 – ident: 10.1016/S1470-2045(19)30149-4_bib22 – volume: 115 start-page: E2970 year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib48 article-title: Predicting cancer outcomes from histology and genomics using convolutional networks publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.1717139115 – volume: 2 start-page: 59 year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib60 article-title: Medicine and the rise of the robots: a qualitative review of recent advances of artificial intelligence in health publication-title: BMJ Leader doi: 10.1136/leader-2018-000071 – volume: 3 start-page: 210 year: 1959 ident: 10.1016/S1470-2045(19)30149-4_bib1 article-title: Some studies in machine learning using the game of checkers publication-title: IBM J Res Dev doi: 10.1147/rd.33.0210 – volume: 132 start-page: 1920 year: 2015 ident: 10.1016/S1470-2045(19)30149-4_bib29 article-title: Machine learning in medicine publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.115.001593 – year: 2019 ident: 10.1016/S1470-2045(19)30149-4_bib9 – year: 2018 ident: 10.1016/S1470-2045(19)30149-4_bib51
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Snippet	Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data. However,... Summary Analysis of big data by machine learning offers considerable advantages for assimilation and evaluation of large amounts of complex health-care data....
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SubjectTerms	Algorithms Artificial intelligence Biomarkers Biopsy Clinical medicine Clinical trials Colonoscopy Colorectal cancer Datasets Diagnosis Electronic health records Health care delivery Histology Learning algorithms Lung cancer Medical imaging Medical screening Melanoma Neural networks Oncology Pancreatic cancer Physicians Population Prostate cancer Skin cancer Vision systems
Title	Big data and machine learning algorithms for health-care delivery
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