AIMC Topic: Palliative Care

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Robotic technology for palliative and supportive care: Strengths, weaknesses, opportunities and threats.

Palliative medicine
BACKGROUND: Medical robots are increasingly used for a variety of applications in healthcare. Robots have mainly been used to support surgical procedures, and for a variety of assistive uses in dementia and elderly care. To date, there has been limit...

Improving palliative care with deep learning.

BMC medical informatics and decision making
BACKGROUND: Access to palliative care is a key quality metric which most healthcare organizations strive to improve. The primary challenges to increasing palliative care access are a combination of physicians over-estimating patient prognoses, and a ...

Identifying in Palliative Care Consultations: A Tandem Machine-Learning and Human Coding Method.

Journal of palliative medicine
Systematic measurement of conversational features in the natural clinical setting is essential to better understand, disseminate, and incentivize high quality serious illness communication. Advances in machine-learning (ML) classification of human s...

Perception and sentiment analysis of palliative care in Chinese social media: Qualitative studies based on machine learning.

Social science & medicine (1982)
BACKGROUND: Traditional Chinese culture makes death a sensitive and taboo topic, leading patients and family members to refuse to choose palliative care.

Systematic literature review on the application of explainable artificial intelligence in palliative care studies.

International journal of medical informatics
BACKGROUND: As machine learning models become increasingly prevalent in palliative care, explainability has become a critical factor in their successful deployment in this sensitive field, where decisions can profoundly impact patient health and qual...

Needs of bereaved families of patients with cancer towards artificial intelligence in palliative care: A web-based survey.

European journal of oncology nursing : the official journal of European Oncology Nursing Society
PURPOSE: Artificial intelligence (AI) systems in palliative care have garnered attention and popularity in recent years. Understanding patient and family needs is crucial for developing and implementing AI systems in palliative care. Few studies in p...

Machine learning model for prediction of palliative care phases in patients with advanced cancer: a retrospective study.

BMC palliative care
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...

Lexical associations can characterize clinical documentation trends related to palliative care and metastatic cancer.

Scientific reports
Palliative care is known to improve quality of life in advanced cancer. Natural language processing offers insights to how documentation around palliative care in relation to metastatic cancer has changed. We analyzed inpatient clinical notes using u...

Multidisciplinary clinician perceptions on utility of a machine learning tool (ALERT) to predict 6-month mortality and improve end-of-life outcomes for advanced cancer patients.

Cancer medicine
BACKGROUND: There are significant disparities in outcomes at the end-of-life (EOL) for minoritized patients with advanced cancer, with most dying without a documented serious illness conversation (SIC). This study aims to assess clinician perceptions...

Using voice recognition and machine learning techniques for detecting patient-reported outcomes from conversational voice in palliative care patients.

Japan journal of nursing science : JJNS
AIM: Patient-reported outcome measures (PROMs) are increasingly used in palliative care to evaluate patients' symptoms and conditions. Healthcare providers often collect PROMs through conversations. However, the manual entry of these data into electr...