Purchase one-time access:Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
One-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
- Three Greek tragedies in translation.The University of Chicago press, Chicago, Ill1942
- The Self-Fulfilling Prophecy.Antioch Rev. 1948; 8: 193-210
- The Mahābhārata.Penguin, New Delhi2009
- The self-fulfilling prophecy in intensive care.Theor Med Bioeth. 2009; 30: 401-410
- Ethical Machine Learning in Healthcare.Ann Rev Biomed Data Sci. 2021; 4
- Big Data's Disparate Impact.California Law Rev. 2016; 104: 671-732
- Algorithmic Fairness: Choices, Assumptions, and Definitions.Annu Rev Stat Appl. 2021; 8: 141-163
- Blinding in clinical trials and other studies.BMJ. 2000; 321: 504
- Chapter 19 - Machine Learning in Healthcare.in: Sheikh A. Cresswell K.M. Wright A. Bates D.W. Key Advances in Clinical Informatics. Academic Press, 2017: 279-291
- Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology.Clin Infect Dis. 2018; 66: 149-153
- Ann Int Med. 2020; 172: S79-S84
- Real-World Evidence - What Is It and What Can It Tell Us?.N Engl J Med. 2016; 375: 2293-2297
- Part 7: Systems of Care: 2020 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care.Circulation. 2020; 142: S580-S604
- Long-Term Outcomes of Out-of-Hospital Cardiac Arrest Care at Regionalized Centers.Ann Emerg Med. 2019; 73: 29-39
- Association for Computing Machinery, 2015: 1721-1730 Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission.
- Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability.Hastings Cent Rep. 2019; 49: 15-21
- Can we learn from hidden mistakes? Self-fulfilling prophecy and responsible neuroprognostic innovation.J Med Ethics. 2021;
- The Intracerebral Hemorrhage Score: A Self-Fulfilling Prophecy?.Neurosurgery. 2019; 84: 741-748
- Hospital usage of early do-not-resuscitate orders and outcome after intracerebral hemorrhage.Stroke. 2004; 35: 1130-1134
- Clinical nihilism in neuroemergencies.Emerg Med Clin North Am. 2009; 27: 27-37
Park SY, Kuo P-Y, Barbarin A, et al. Identifying Challenges and Opportunities in Human-AI Collaboration in Healthcare. Conference Companion Publication of the 2019 on Computer Supported Cooperative Work and Social Computing; 2019; Austin, TX, USA.
- Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff.Proc AAAI Conf Artif Intell. 2019; 33: 2429-2437
De-Arteaga M, Fogliato R, Chouldechova A. A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems; 2020; Honolulu, HI, USA.
- Algorithm aversion: People erroneously avoid algorithms after seeing them err.J Exp Psychol Gen. 2015; 144: 114-126
- Automation bias: a systematic review of frequency, effect mediators, and mitigators.J Am Med Inform Assoc. 2012; 19: 121-127
- To Incorporate or Not to Incorporate AI for Critical Judgments: The Importance of Ambiguity in Professionals’ Judgment Process.NYU Stern School Bus. 2020;
- Physicians' cognitive approach to prognostication after cardiac arrest.Resuscitation. 2022;
- Association Between Duration of Resuscitation and Favorable Outcome After Out-of-Hospital Cardiac Arrest: Implications for Prolonging or Terminating Resuscitation.Circulation. 2016; 134: 2084-2094
- A comparison of the universal TOR Guideline to the absence of prehospital ROSC and duration of resuscitation in predicting futility from out-of-hospital cardiac arrest.Resuscitation. 2017; 111: 96-102
- Dissecting racial bias in an algorithm used to manage the health of populations.Science. 2019; 366: 447-453
- Endovascular therapy for ischemic stroke with perfusion-imaging selection.N Engl J Med. 2015; 372: 1009-1018
- Thrombolysis with alteplase 3 to 4.5 hours after acute ischemic stroke.N Engl J Med. 2008; 359: 1317-1329
- Thrombectomy 6 to 24 Hours after Stroke with a Mismatch between Deficit and Infarct.N Engl J Med. 2018; 378: 11-21
- Changes in case fatality of aneurysmal subarachnoid haemorrhage over time, according to age, sex, and region: a meta-analysis.Lancet Neurol. 2009; 8: 635-642
- The need for a system view to regulate artificial intelligence/machine learning-based software as medical device.npj Digital Med. 2020; 3: 53
- Second opinion needed: communicating uncertainty in medical machine learning.npj Digital Med. 2021; 4: 4
- Direct Uncertainty Prediction for Medical Second Opinions.Proceedings of Machine Learning Research, 2019
- Performative Prediction.Proceedings of Machine Learning Research, 2020
Coston A, Kennedy EH, Chouldechova A. Counterfactual Predictions under Runtime Confounding. ArXiv. 2020;abs/2006.16916.
- Reliable decision support using counterfactual models.Adv Neural Inform Process Syst. 2017; : 1698-1709
- Helping Doctors and Patients Make Sense of Health Statistics.Psychol Sci Public Interest. 2007; 8: 53-96
- Standards for Studies of Neurological Prognostication in Comatose Survivors of Cardiac Arrest: A Scientific Statement From the American Heart Association.Circulation. 2019; 140: e517-e542
- Neurophysiology and neuroimaging accurately predict poor neurological outcome within 24 hours after cardiac arrest: The ProNeCA prospective multicentre prognostication study.Resuscitation. 2019; 143: 115-123
- Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nat Mach Intel. 2019; 1: 206-215
- What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use.Proceedings of Machine Learning Research, 2019
- Association for Computing Machinery, 2020: 1-14 Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning.
Lakkaraju H, Bastani O. “How do I fool you?”: Manipulating User Trust via Misleading Black Box Explanations. 2019:arXiv:1911.06473. https://ui.adsabs.harvard.edu/abs/2019arXiv191106473L. Accessed November 01, 2019.
- Manipulating and Measuring Model Interpretability.In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery. 2021 (Article 237)
De-Arteaga M, Dubrawski A, Chouldechova A. Learning under selective labels in the presence of expert consistency. ArXiv. 2018;abs/1807.00905.