AIMC Topic: Palliative Care

Clear Filters Showing 11 to 20 of 70 articles

Artificial intelligence and large language models in palliative medicine clinical practice and education.

BMJ supportive & palliative care
As we approach 2034, we anticipate significant advancements in digital technologies and their impact across various domains, including palliative and end-of-life care and perhaps higher education more generally. Predicting technological breakthroughs...

Machine Learning Reveals Demographic Disparities in Palliative Care Timing Among Patients With Traumatic Brain Injury Receiving Neurosurgical Consultation.

Neurocritical care
BACKGROUND: Timely palliative care (PC) consultations offer demonstrable benefits for patients with traumatic brain injury (TBI), yet their implementation remains inconsistent. This study employs machine learning methods to identify distinct patient ...

Large Language Models to Identify Advance Care Planning in Patients With Advanced Cancer.

Journal of pain and symptom management
CONTEXT: Efficiently tracking Advance Care Planning (ACP) documentation in electronic heath records (EHRs) is essential for quality improvement and research efforts. The use of large language models (LLMs) offers a novel approach to this task.

A model for integrating palliative care into Eastern Mediterranean health systems with a primary care approach.

BMC palliative care
BACKGROUND AND AIMS: Palliative care in the Eastern Mediterranean Region (EMR) faces challenges despite the high number of patients in need. To provide accessible, affordable, and timely services, it is crucial to adopt a suitable care model. World h...

AI-Generated Content in Cancer Symptom Management: A Comparative Analysis Between ChatGPT and NCCN.

Journal of pain and symptom management
BACKGROUND: Artificial intelligence-driven tools, like ChatGPT, are prevalent sources for online health information. Limited research has explored the congruity between AI-generated content and professional treatment guidelines. This study seeks to c...

Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction.

BMC palliative care
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 promising performance in cancer prognostication. However, gaps in ...

Machine learning-based model to predict delirium in patients with advanced cancer treated with palliative care: a multicenter, patient-based registry cohort.

Scientific reports
This study aimed to present a new approach to predict to delirium admitted to the acute palliative care unit. To achieve this, this study employed machine learning model to predict delirium in patients in palliative care and identified the significan...

Consumer satisfaction, palliative care and artificial intelligence (AI).

BMJ supportive & palliative care
The scope of artificial intelligence (AI) in healthcare is promising, and AI has the potential to revolutionise the field of palliative care services also. Consumer satisfaction in palliative care is a critical aspect of providing high-quality end-of...

Looking Beyond Mortality Prediction: Primary Care Physician Views of Patients' Palliative Care Needs Predicted by a Machine Learning Tool.

Applied clinical informatics
OBJECTIVES:  To assess primary care physicians' (PCPs) perception of the need for serious illness conversations (SIC) or other palliative care interventions in patients flagged by a machine learning tool for high 1-year mortality risk.