BACKGROUND: Developing an accurate predictive model for palliative care phases is crucial for improving cancer patient management, enabling healthcare providers to identify those in need of specific care plans and streamlining decision-making process...
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...
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 ...
BACKGROUND: High quality serious illness communication requires good understanding of patients' values and beliefs for their treatment at end of life. Natural Language Processing (NLP) offers a reliable and scalable method for measuring and analyzing...
OBJECTIVES: Patients in oncological and palliative care (PC) often have complex needs, which require a comprehensive treatment approach. The assessment of patient-reported outcomes (PROs) has been shown to improve identification of patient needs and ...