AIMC Topic: Palliative Care

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Artificial intelligence for early palliative referral in adult oncology: opportunities, challenges and future directions.

BMJ supportive & palliative care
BACKGROUND: In oncology, early palliative care enhances quality of life and may increase survival; yet, because of resource limitations and overestimation of prognosis, referrals frequently happen late. Due to a shortage of specialised workers, this ...

Exploring Perspectives of Health Care Professionals on AI in Palliative Care: Qualitative Interview Study.

JMIR human factors
BACKGROUND: The use of artificial intelligence (AI) methods in palliative care research is increasing. Most AI palliative care research involves the use of routinely collected data from electronic health records; however, there are few data on the vi...

Public Perspectives on Palliative and Hospice Care: Social Media Content Analysis Using Topic Modeling and Multiclass Sentiment Analysis.

Journal of medical Internet research
BACKGROUND: Palliative care enhances dignity and quality of life for patients with serious illnesses by managing distressing symptoms and supporting families. However, inadequate awareness and misconceptions often hinder patients and their families f...

The predictive role of identifying frailty in assessing the need for palliative care in the elderly: the application of machine learning algorithm.

Journal of health, population, and nutrition
BACKGROUND: Palliative care is a key component of integrated care to improve care quality and reduce hospitalization costs for patients with chronic obstructive pulmonary disease (COPD). This study aims to use machine learning algorithms to create an...

Towards real-time conformal palliative treatment of spine metastases: A deep learning approach for Hounsfield Unit recovery of cone beam CT images.

Medical physics
BACKGROUND: The extension of onboard cone-beam CT (CBCT) imaging for real-time treatment planning is constrained by limitations in image quality. Synthetic CT (sCT) generation using deep learning provides a potential solution to these limitations.

Leveraging Artificial Intelligence to Uncover Symptom Burden in Palliative Care: Analysis of Nonscheduled Visits Using a Phi-3 Small Language Model.

JCO global oncology
PURPOSE: This study aimed to differentiate nonscheduled visits (NSVs) in an outpatient palliative care setting that are driven by or accompanied by uncontrolled symptoms from those that are administrative or routine, such as prescription refills and ...

Opportunities and Barriers to Artificial Intelligence Adoption in Palliative/Hospice Care for Underrepresented Groups: A Technology Acceptance Model-Based Review.

Journal of hospice and palliative nursing : JHPN : the official journal of the Hospice and Palliative Nurses Association
Underrepresented groups (URGs) in the United States, including African Americans, Latino/Hispanic Americans, Asian Pacific Islanders, and Native Americans, face significant barriers to accessing hospice and palliative care. Factors such as language b...

Towards clinical prediction with transparency: An explainable AI approach to survival modelling in residential aged care.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Scalable, flexible and highly interpretable tools for predicting mortality in residential aged care facilities for the purpose of informing and optimizing palliative care decisions, do not exist. This study is the first and ...

Leveraging Artificial Intelligence/Machine Learning Models to Identify Potential Palliative Care Beneficiaries: A Systematic Review.

Journal of gerontological nursing
PURPOSE: The current review examined the application of artificial intelligence (AI) and machine learning (ML) techniques in palliative care, specifically focusing on models used to identify potential beneficiaries of palliative services among indivi...