Ethical considerations about artificial intelligence for prognostication in intensive care

[1] Kon AA(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

[2] Shepard EK(2019)Understanding irresponsible use of intensive care unit resources in the USA Lancet Respir Med 7 605-612

[3] Sederstrom NO(2019)The use of clustering algorithms in critical care research to unravel patient heterogeneity Intensive Care Med 45 1025-1028

[4] Swoboda SM(2018)Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects BMJ 363 k4245-1102

[5] Marshall MF(2018)Optimal intensive care outcome prediction over time using machine learning PLoS One 13 e0206862-52

[6] Birriel B(2018)Deep learning - a technology with the potential to transform health care JAMA 320 1101-21

[7] Rincon F(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 e025925-865

[8] Anesi GL(2018)Characterising risk of in-hospital mortality following cardiac arrest using machine learning PLoS Med 15 e1002709-175

[9] Admon AJ(2015)Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study Lancet Respir Med 3 42-1300

[10] Halpern SD(2019)Artificial intelligence and black-box medical decisions: accuracy versus explainability Hastings Cent Rep 49 15-444

[1] Kon AA(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

[2] Shepard EK(2019)Understanding irresponsible use of intensive care unit resources in the USA Lancet Respir Med 7 605-612

[3] Sederstrom NO(2019)The use of clustering algorithms in critical care research to unravel patient heterogeneity Intensive Care Med 45 1025-1028

[4] Swoboda SM(2018)Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects BMJ 363 k4245-1102

[5] Marshall MF(2018)Optimal intensive care outcome prediction over time using machine learning PLoS One 13 e0206862-52

[6] Birriel B(2018)Deep learning - a technology with the potential to transform health care JAMA 320 1101-21

[7] Rincon F(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 e025925-865

[8] Anesi GL(2018)Characterising risk of in-hospital mortality following cardiac arrest using machine learning PLoS Med 15 e1002709-175

[9] Admon AJ(2015)Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study Lancet Respir Med 3 42-1300

[10] Halpern SD(2019)Artificial intelligence and black-box medical decisions: accuracy versus explainability Hastings Cent Rep 49 15-444