BoyerA.The Undying: Pain, vulnerability, mortality, medicine, art, time, dreams, data, exhaustion, cancer, and care. Farrar, Straus and Giroux, 2019, p.72.
2.
MasukawaKAoyamaMYokotaS, et al. Machine learning models to detect social distress, spiritual pain, and severe physical psychological symptoms in terminally ill patients with cancer from unstructured text data in electronic medical records. Palliative Medicine 2022; 36(8): 1207–1216. DOI: 10.1177/02692163221105595.
3.
LindvallCCasselCKPantilatSZ, et al. Ethical considerations in the use of AI mortality predictions in the care of people with serious illness. Health Affairs. September 16, 2020. 10.1377/forefront.20200911.401376 accessed august 17, 2022
4.
National Consensus Project for Quality Palliative Care. Clinical practice guidelines for quality palliative care. 4th ed.Richmond, VA: National Coalition for Hospice and Palliative Care, 2018.
5.
MurdochTBDetskyAS.The inevitable application of big data to health care. JAMA2013; 309: 1351–1352.
6.
LindvallCDengCYAgaronnikND, et al. Deep learning for cancer symptoms monitoring on the basis of electronic health record unstructured clinical notes. JCO Clin Cancer Inform2022; 6: e2100136.
7.
BernackiRPaladinoJNevilleBA, et al. Effect of the serious illness care program in outpatient oncology: a cluster randomized clinical trial. JAMA Intern Med2019; 179(6): 751–759.
8.
MitchellSLVolandesAEGutmanR, et al. Advance care planning video intervention among long-stay nursing home residents: a pragmatic cluster randomized clinical trial. JAMA Intern Med2020; 180(8): 1070–1078.
9.
DurieuxBNBerrierACatzenHZ, et al. “I think that she would have wanted. . .”: qualitative interviews with bereaved caregivers reveal complexity in measuring goal-concordant care at the end of life. Palliative Medicine2022; 36(4): 742–750.
10.
SureshHGuttagJA. Framework for understanding sources of harm throughout the machine learning life cycle. In: Equity and access in algorithms, mechanisms, and optimization [Internet]. New York, NY: Association for Computing Machinery (EAAMO ‘21), 2021. https://doi.org/10.1145/3465416.3483305accessed august 17, 2022
11.
BeachMCSahaSParkJ, et al. Testimonial injustice: linguistic bias in the medical records of Black patients and women. J Gen Intern Med2021; 36(6): 1708–1714.