Machine learning to allocate palliative care consultations during cancer treatment
JC He, GT Moffat, S Podolsky, F Khan, N Liu… - Journal of Clinical …, 2024 - ascopubs.org
PURPOSE For patients with advanced cancer, early consultations with palliative care (PC)
specialists reduce costs, improve quality of life, and prolong survival. However, capacity …
specialists reduce costs, improve quality of life, and prolong survival. However, capacity …
[引用][C] Use of Machine Learning to Optimize Referral for Early Palliative Care: Are Prognostic Predictions Enough?
GE Weissman, JA Greer, JS Temel - Journal of Clinical Oncology, 2024 - ascopubs.org
Early and longitudinal provision of palliative care services delivered by specialty-trained
clinicians for patients with an advanced cancer diagnosis and limited life expectancy …
clinicians for patients with an advanced cancer diagnosis and limited life expectancy …
Leveraging advances in artificial intelligence to improve the quality and timing of palliative care
Cancers | Free Full-Text | Leveraging Advances in Artificial Intelligence to Improve the Quality
and Timing of Palliative Care Next Article in Journal In Vitro Organotypic Systems to Model …
and Timing of Palliative Care Next Article in Journal In Vitro Organotypic Systems to Model …
Impact of augmented intelligence on utilization of palliative care services in a real-world oncology setting
A Gajra, ME Zettler, KA Miller, JG Frownfelter… - JCO oncology …, 2022 - ascopubs.org
PURPOSE: For patients with advanced cancer, timely referral to palliative care (PC) services
can ensure that end-of-life care aligns with their preferences and goals. Overestimation of …
can ensure that end-of-life care aligns with their preferences and goals. Overestimation of …
[HTML][HTML] Predictive models for palliative care needs of advanced cancer patients receiving chemotherapy
A Kawashima, T Furukawa, T Imaizumi… - Journal of pain and …, 2024 - Elsevier
Context Early palliative care is recommended within eight-week of diagnosing advanced
cancer. Although guidelines suggest routine screening to identify cancer patients who could …
cancer. Although guidelines suggest routine screening to identify cancer patients who could …
Improving time to palliative care review with predictive modeling in an inpatient adult population: study protocol for a stepped-wedge, pragmatic randomized controlled …
Background Palliative care is a medical specialty centered on improving the quality of life
(QOL) of patients with complex or life-threatening illnesses. The need for palliative care is …
(QOL) of patients with complex or life-threatening illnesses. The need for palliative care is …
Machine learning approaches to predict 6-month mortality among patients with cancer
Importance Machine learning algorithms could identify patients with cancer who are at risk of
short-term mortality. However, it is unclear how different machine learning algorithms …
short-term mortality. However, it is unclear how different machine learning algorithms …
Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction
Background Ex-ante identification of the last year in life facilitates a proactive palliative
approach. Machine learning models trained on electronic health records (EHR) demonstrate …
approach. Machine learning models trained on electronic health records (EHR) demonstrate …
[HTML][HTML] Precision palliative care as a pragmatic solution for a care delivery problem
ASCO and National Comprehensive Cancer Network guidelines recommend all patients
with advanced cancer receive early palliative care (PC), within 8 weeks of diagnosis, 1, 2 on …
with advanced cancer receive early palliative care (PC), within 8 weeks of diagnosis, 1, 2 on …
Clinician perspectives on machine learning prognostic algorithms in the routine care of patients with cancer: a qualitative study
Purpose Oncologists may overestimate prognosis for patients with cancer, leading to
delayed or missed conversations about patients' goals and subsequent low-quality end-of …
delayed or missed conversations about patients' goals and subsequent low-quality end-of …