Abstract: Palliative care is an active practice of whole-person care, aimed at alleviating the physical and psychological suffering of patients, their families, and caregivers, thereby improving quality of life. Against the backdrop of an aging population, patients with non-cancer chronic diseases have become the primary recipients of palliative care services. These patients typically experience prolonged disease courses, significant fluctuations in condition, and difficulty in identifying the terminal phase. Traditional manual assessment models lead to delayed care interventions, suboptimal resource allocation, and insufficient precision in interventions. Machine learning has the potential to deeply mine multi-source medical data, thereby facilitating the intelligent transformation of palliative care. This review summarizes the current applications and existing challenges of machine learning in palliative care of non-cancer patients, aiming to provide a reference for clinical practice and research in this field.

Key words: machine learning, artificial intelligence, palliative care, non-cancer chronic disease