Kon AA, Shepard EK, Sederstrom NO, Swoboda SM, Marshall MF, Birriel B, Rincon F (2016) Defining futile and potentially inappropriate interventions: a policy statement from the Society of Critical Care Medicine Ethics Committee. Crit Care Med 44:1769–1774
Article
PubMed
Google Scholar
Anesi GL, Admon AJ, Halpern SD, Kerlin MP (2019) Understanding irresponsible use of intensive care unit resources in the USA. Lancet Respir Med 7:605–612
Article
PubMed
Google Scholar
Castela Forte J, Perner A, van der Horst ICC (2019) The use of clustering algorithms in critical care research to unravel patient heterogeneity. Intensive Care Med 45:1025–1028
Article
PubMed
Google Scholar
Kent DM, Steyerberg E, van Klaveren D (2018) Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ 363:k4245
Article
PubMed
PubMed Central
Google Scholar
Meiring C, Dixit A, Harris S, MacCallum NS, Brealey DA, Watkinson PJ, Jones A, Ashworth S, Beale R, Brett SJ, Singer M, Ercole A (2018) Optimal intensive care outcome prediction over time using machine learning. PLoS One 13:e0206862
Article
PubMed
PubMed Central
CAS
Google Scholar
Hinton G (2018) Deep learning - a technology with the potential to transform health care. JAMA 320:1101–1102
Article
PubMed
Google Scholar
McWilliams CJ, Lawson DJ, Santos-Rodriguez R, Gilchrist ID, Champneys A, Gould TH, Thomas MJ, Bourdeaux CP (2019) Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK. BMJ Open 9:e025925
Article
PubMed
PubMed Central
Google Scholar
Nanayakkara S, Fogarty S, Tremeer M, Ross K, Richards B, Bergmeir C, Xu S, Stub D, Smith K, Tacey M, Liew D, Pilcher D, Kaye DM (2018) Characterising risk of in-hospital mortality following cardiac arrest using machine learning. PLoS Med 15:e1002709
Article
PubMed
PubMed Central
Google Scholar
Pirracchio R, Petersen ML, Carone M, Rigon MR, Chevret S, van der Laan MJ (2015) Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study. Lancet Respir Med 3:42–52
Article
PubMed
Google Scholar
London AJ (2019) Artificial intelligence and black-box medical decisions: accuracy versus explainability. Hastings Cent Rep 49:15–21
Article
PubMed
Google Scholar
Jaderberg M, Czarnecki WM, Dunning I, Marris L, Lever G, Castañeda AG, Beattie C, Rabinowitz NC, Morcos AS, Ruderman A, Sonnerat N, Green T, Deason L, Leibo JZ, Silver D, Hassabis D, Kavukcuoglu K, Graepel T (2019) Human-level performance in 3D multiplayer games with population-based reinforcement learning. Science 364:859–865
Article
CAS
PubMed
Google Scholar
Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, Tan GSW, Schmetterer L, Keane PA, Wong TY (2019) Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 103:167–175
Article
PubMed
Google Scholar
Academy of Medical Royal Colleges (2019) Artificial Intelligence in healthcare.
Komorowski M (2019) Artificial intelligence in intensive care: are we there yet? Intensive Care Med. 45:1298–1300
Article
PubMed
Google Scholar
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444
Article
CAS
PubMed
Google Scholar
Ghahramani Z (2015) Probabilistic machine learning and artificial intelligence. Nature 521:452–459
Article
CAS
PubMed
Google Scholar
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780
Article
CAS
PubMed
Google Scholar
Steinruecken C, Smith E, Janz D, Lloyd J, Ghahramani Z (2019) The automatic statistician. In: Kotthoff L, Vanschoren J (eds) Hutter F. Springer, Automated Machine Learning
Google Scholar
Begoli E, Bhattacharya T, Kusnezov D (2019) The need for uncertainty quantification in machine-assisted medical decision making. Nat Machine Intell 1:20–23
Article
Google Scholar
Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, Liu PJ, Liu X, Marcus J, Sun M, Sundberg P, Yee H, Zhang K, Zhang Y, Flores G, Duggan GE, Irvine J, Le Q, Litsch K, Mossin A, Tansuwan J, Wang D, Wexler J, Wilson J, Ludwig D, Volchenboum SL, Chou K, Pearson M, Madabushi S, Shah NH, Butte AJ, Howell MD, Cui C, Corrado GS, Dean J (2018) Scalable and accurate deep learning with electronic health records. npj Digital Med 1:18
Article
Google Scholar
Cahan EM, Hernandez-Boussard T, Thadaney-Israni S, Rubin DL (2019) Putting the data before the algorithm in big data addressing personalized healthcare. NPJ Digit Med 2:78
Article
PubMed
PubMed Central
Google Scholar
Andersen FH, Flaatten H, Klepstad P, Romild U, Kvale R (2015) Long-term survival and quality of life after intensive care for patients 80 years of age or older. Ann Intensive Care 5:53
Article
PubMed
Google Scholar
Vest MT, Murphy TE, Araujo KL, Pisani MA (2011) Disability in activities of daily living, depression, and quality of life among older medical ICU survivors. Health Qual Life Outcomes 9:9
Article
PubMed
PubMed Central
Google Scholar
Vermeulen J, Neyens JC, van Rossum E, Spreeuwenberg MD, de Witte LP (2011) Predicting ADL disability in community-dwelling elderly people using physical frailty indicators. BMC Geriatr 11:33
Article
PubMed
PubMed Central
Google Scholar
Lawson RA, Yarnall AJ, Duncan GW, Breen DP, Khoo TK, Williams-Gray CH, Barker RA, Collerton D, Taylor JP, Burn DJ, ICICLE-PD study group (2016) Cognitive decline and quality of life in incident Parkinson’s disease. Parkinsonism Relat Disord 27:47–53
Article
PubMed
PubMed Central
Google Scholar
Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L (2016) The ethics of algorithms: mapping the debate. Big Data & Society 2:1–21
Google Scholar
Gomez E (2018) Assessing the impact of machine intelligence on human behaviour. Proceedings of 1st HUMAINT workshop, Barcelona, Spain, March 5-6, 2018. Luxembourg: Publications Office of the European Union.
Finlayson SG, Chung HW, Kohane IS, Beam AL (2019) Adversarial attacks against medical deep learning systems. arXiv:1804.05296v3
Ovadia Y, Fertig E, Ren J, Nado Z, Sculley D, Nowozon S, Dillon JV, Lakshminarayanan B, Snoek J (2019) Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift. arXiv:1906.02530v1
Winfield AF, Michael K, Pitt J, Evers V (2019) Machine ethics: the design and governance of ethical AI and autonomous systems. Proc IEEE 107:509–517
Article
Google Scholar
High-Level Expert Group on Artificial Intelligence (2019) Ethics guidelines for trustworthy AI. European Commission, Brussels
Google Scholar
Gillon R (2015) Defending the four principles approach as a good basis for good medical practice and therefore for good medical ethics. J Med Ethics 41:111–116
Article
PubMed
Google Scholar
Hwang DY, White DB (2018) Prognostication and ethics. In: Shutter L, Molyneaux BJ (eds) Neurocritical Care. Oxford University Press
Reddy BK, Delen D (2018) Predicting hospital readmission for lupus patients: an RNN-LSTM-based deep-learning methodology. Comput Biol Med. 101:199–209
Article
PubMed
Google Scholar
Dumas F, Bougouin W, Cariou A (2019) Cardiac arrest: prediction models in the early phase of hospitalization. Curr Opin Crit Care 25:204–210
Article
PubMed
Google Scholar
Le Gall JR, Neumann A, Hemery F, Bleriot JP, Fulgencio JP, Garrigues B, Gouzes C, Lepage E, Moine P, Villers D (2005) Mortality prediction using SAPS II: an update for French intensive care units. Crit Care. 9:R645–R652
Article
PubMed
PubMed Central
Google Scholar
Seymour CW, Kennedy JN, Wang S, Chang CH, Elliott CF, Xu Z, Berry S, Clermont G, Cooper G, Gomez H, Huang DT, Kellum JA, Mi Q, Opal SM, Talisa V, van der Poll T, Visweswaran S, Vodovotz Y, Weiss JC, Yealy DM, Yende S, Angus DC (2019) Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA. 2019 May 19.
Liu R, Greenstein JL, Granite SJ, Fackler JC, Bembea MM, Sarma SV, Winslow RL (2019) Data-driven discovery of a novel sepsis pre-shock state predicts impending septic shock in the ICU. Sci Rep 9:6145
Article
PubMed
PubMed Central
CAS
Google Scholar
Beauchamps TL, Childress JF (1994) Principles of biomedical ethics. Med Clin North Amer 80:225–243
Google Scholar
Bailey J, Burch M (2013) Ethics for behavior analysts, 2nd edn. Routledge, New York
Book
Google Scholar
Bosslet GT, Pope TM, Rubenfeld GD, Lo B, Truog RD, Rushton CH, Curtis JR, Ford DW, Osborne M, Misak C, Au DH, Azoulay E, Brody B, Fahy BG, Hall JB, Kesecioglu J, Kon AA, Lindell KO, White DB (2015) An official ATS/AACN/ACCP/ESICM/SCCM policy statement: responding to requests for potentially inappropriate treatments in intensive care units. Am J Respir Crit Care Med 191:1318–1330
Article
PubMed
Google Scholar
Scheunemann LP, Ernecoff NC, Buddadhumaruk P, Carson SS, Hough CL, Curtis JR, Anderson WG, Steingrub J, Lo B, Matthay M, Arnold RM, White DB (2019) Clinician-family communication about patients’ values and preferences in intensive care units. JAMA Intern Med.;179(5):676-684.
Article
PubMed
PubMed Central
Google Scholar
Zier LS, Burack JH, Micco G, Chipman AK, Frank JA, White DB (2009) Surrogate decision makers’ responses to physicians’ predictions of medical futility. Chest 136:110–117
Article
PubMed
PubMed Central
Google Scholar
Joynt GM, Lipman J, Hartog C, Guidet B, Paruk F, Feldman C, Kissoon N, Sprung CL (2015) The Durban World Congress Ethics Round Table IV: health care professional end-of-life decision making. J Crit Care 30:224–230
Article
PubMed
Google Scholar
Cannesson M, Shafer SL (2016) All boxes are black. Anesth Analg. 122:309–317
Article
PubMed
Google Scholar
Frosst N, Hinton G (2017) Distilling a neural network into a soft decision tree. arXiv:1711.09784
Li Y, Richtarik P, Ding L, Gao X (2018) On the decision boundary of deep neural networks. arXiv:1808.05385
Zhang Z, Beck MW, Winkler DA, Huang B, Sibanda W, Goyal H (2018) Opening the black box of neural networks: methods for interpreting neural network models in clinical applications. Ann Transl Med. 6:216
Article
PubMed
PubMed Central
Google Scholar
Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Proceedings of the Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.
Raghu M, Blumer K, Sayres R, Obermeyer Z, Kleinberg R, Mullainathan S, Kleinberg J (2019) Direct uncertainty prediction for medical second opinions. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA.
Whittlestone J, Alexandrova A, Nyrup, R, Cave, S (2019) The role and limits of principles in AI ethics. Proceedings 2019 AAAI/ACM Conference on AI, Ethics, and Society.
UK Government (2019) Code of conduct for data-driven health and care technology. https://www.gov.uk/government/publications/code-of-conduct-for-data-driven-health-and-care-technology/initial-code-of-conduct-for-data-driven-health-and-care-technology. Accessed 14 Aug 2019.
Biller-Andorno N, Biller A (2019) Algorithm-aided prediction of patient preferences - an ethics sneak peek. N Engl J Med. 381:1480–1485
Article
PubMed
Google Scholar