AIMC Topic: Palliative Care
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Explainable Machine Learning Model to Predict Overall Survival in Patients Treated With Palliative Radiotherapy for Bone Metastases.
JCO clinical cancer informatics
Jun 1, 2024
PURPOSE: The estimation of prognosis and life expectancy is critical in the care of patients with advanced cancer. To aid clinical decision making, we build a prognostic strategy combining a machine learning (ML) model with explainable artificial int...
Advanced Care Planning Content Encoding with Natural Language Processing.
Studies in health technology and informatics
Jan 25, 2024
While advanced care planning (ACP) is an essential practice for ensuring patient-centered care, its adoption remains poor and the completeness of its documentation variable. Natural language processing (NLP) approaches hold promise for supporting ACP...
Maintaining High-Touch in High-Tech Digital Health Monitoring and Multi-Omics Prognostication: Ethical, Equity, and Societal Considerations in Precision Health for Palliative Care.
Omics : a journal of integrative biology
Oct 1, 2023
Advances in digital health, systems biology, environmental monitoring, and artificial intelligence (AI) continue to revolutionize health care, ushering a precision health future. More than disease treatment and prevention, precision health aims at ma...
ENRICHing medical imaging training sets enables more efficient machine learning.
Journal of the American Medical Informatics Association : JAMIA
May 19, 2023
OBJECTIVE: Deep learning (DL) has been applied in proofs of concept across biomedical imaging, including across modalities and medical specialties. Labeled data are critical to training and testing DL models, but human expert labelers are limited. In...
Use of machine learning to transform complex standardized nursing care plan data into meaningful research variables: a palliative care exemplar.
Journal of the American Medical Informatics Association : JAMIA
Nov 25, 2021
The aim of this article was to describe a novel methodology for transforming complex nursing care plan data into meaningful variables to assess the impact of nursing care. We extracted standardized care plan data for older adults from the electronic ...
Improving the delivery of palliative care through predictive modeling and healthcare informatics.
Journal of the American Medical Informatics Association : JAMIA
Jun 12, 2021
OBJECTIVE: Access to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address...
Independent Validation of a Comprehensive Machine Learning Approach Predicting Survival After Radiotherapy for Bone Metastases.
Anticancer research
Mar 1, 2021
BACKGROUND/AIM: The aim of this study was to analyze the survival predictions obtained from a web platform allowing for computation of the so-called Bone Metastases Ensemble Trees for Survival (BMETS). This prediction model is based on a machine lear...
External Validation of the Bone Metastases Ensemble Trees for Survival (BMETS) Machine Learning Model to Predict Survival in Patients With Symptomatic Bone Metastases.
JCO clinical cancer informatics
Mar 1, 2021
PURPOSE: The Bone Metastases Ensemble Trees for Survival (BMETS) model uses a machine learning algorithm to estimate survival time following consultation for palliative radiation therapy for symptomatic bone metastases (SBM). BMETS was developed at a...
[Artificial intelligence and palliative care: opportunities and limitations.].
Recenti progressi in medicina
Nov 1, 2020
The so-called artificial intelligence tools applied to palliative care (machine learning, natural language processing) have great potential to support clinicians in improving decision-making processes and in identifying those who are at high risk of ...
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