AIMC Topic: Palliative Care

Clear Filters Showing 31 to 40 of 70 articles

Natural Language Processing to Identify Advance Care Planning Documentation in a Multisite Pragmatic Clinical Trial.

Journal of pain and symptom management
CONTEXT: Large multisite clinical trials studying decision-making when facing serious illness require an efficient method for abstraction of advance care planning (ACP) documentation from clinical text documents. However, the current gold standard me...

Using natural language processing to explore heterogeneity in moral terminology in palliative care consultations.

BMC palliative care
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...

Effectiveness of Radiofrequency Ablation in the Treatment of Painful Osseous Metastases: A Correlation Meta-Analysis with Machine Learning Cluster Identification.

Journal of vascular and interventional radiology : JVIR
A systematic review and meta-analysis of pain response after radiofrequency (RF) ablation over time for osseous metastases was conducted in 2019. Analysis used a random-effects model with GOSH plots and meta-regression. Fourteen studies comprising 42...

Identifying Goals of Care Conversations in the Electronic Health Record Using Natural Language Processing and Machine Learning.

Journal of pain and symptom management
CONTEXT: Goals-of-care discussions are an important quality metric in palliative care. However, goals-of-care discussions are often documented as free text in diverse locations. It is difficult to identify these discussions in the electronic health r...

Developing an Improved Statistical Approach for Survival Estimation in Bone Metastases Management: The Bone Metastases Ensemble Trees for Survival (BMETS) Model.

International journal of radiation oncology, biology, physics
PURPOSE: To determine whether a machine learning approach optimizes survival estimation for patients with symptomatic bone metastases (SBM), we developed the Bone Metastases Ensemble Trees for Survival (BMETS) to predict survival using 27 prognostic ...

The views of physicians and nurses on the potentials of an electronic assessment system for recognizing the needs of patients in palliative care.

BMC palliative care
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 ...

Story Arcs in Serious Illness: Natural Language Processing features of Palliative Care Conversations.

Patient education and counseling
OBJECTIVE: Serious illness conversations are complex clinical narratives that remain poorly understood. Natural Language Processing (NLP) offers new approaches for identifying hidden patterns within the lexicon of stories that may reveal insights abo...

Documentation of Palliative and End-of-Life Care Process Measures Among Young Adults Who Died of Cancer: A Natural Language Processing Approach.

Journal of adolescent and young adult oncology
Few studies have investigated palliative and end-of-life care processes among young adults (YAs), aged 18-34 years, who died of cancer. This retrospective study used a natural language processing algorithm to identify documentation and timing of four...

Development and Validation of a Deep Learning Algorithm for Mortality Prediction in Selecting Patients With Dementia for Earlier Palliative Care Interventions.

JAMA network open
IMPORTANCE: Early palliative care interventions drive high-value care but currently are underused. Health care professionals face challenges in identifying patients who may benefit from palliative care.