AIMC Topic: Cancer Pain

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Leading predictors and their associations with combination opioid pain therapy in older adults with cancer: Application of machine learning approaches.

PloS one
Combined use of opioids and other pharmacological therapies used for pain management, such as non-steroidal anti-inflammatory drugs (NSAIDs), benzodiazepines, gabapentinoids, and/or skeletal muscle relaxants (SMRs), in older adult cancer survivors ca...

Synergistic analgesic effects of astaxanthin combined with celecoxib on a mouse bone cancer pain model: From behavioral validation to target prediction.

International immunopharmacology
Bone cancer pain (BCP) is a complex condition that severely affects patients' quality of life, and its treatment remains challenging. Astaxanthin, a potent antioxidant with anti-inflammatory and neuroprotective effects, and celecoxib, a selective COX...

Facilitators and barriers for using artificial intelligence in cancer pain assessment: a qualitative study.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
OBJECTIVES: This study qualitatively explored healthcare providers' perception regarding the use of modern technology, specifically artificial intelligence (AI), in the assessment and management of cancer pain. It also aimed to identify barriers to a...

Data - Knowledge driven machine learning model for cancer pain medication decisions.

International journal of medical informatics
BACKGROUND: Cancer pain is one of the most common symptoms in cancer patients, and drug decision-making in cancer pain management remains challenges. This study aims to develop machine learning models using real-world clinical data and prior knowledg...

Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review.

Journal of pain and symptom management
BACKGROUND/OBJECTIVES: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain m...

Opioid Nonadherence Risk Prediction of Patients with Cancer-Related Pain Based on Five Machine Learning Algorithms.

Pain research & management
OBJECTIVES: Opioid nonadherence represents a significant barrier to cancer pain treatment efficacy. However, there is currently no effective prediction method for opioid adherence in patients with cancer pain. We aimed to develop and validate a machi...

Clinical relevance of deep learning models in predicting the onset timing of cancer pain exacerbation.

Scientific reports
Cancer pain is a challenging clinical problem that is encountered in the management of cancer pain. We aimed to investigate the clinical relevance of deep learning models that predict the onset of cancer pain exacerbation in hospitalized patients. We...

Artificial neural networks for simultaneously predicting the risk of multiple co-occurring symptoms among patients with cancer.

Cancer medicine
Patients with cancer often exhibit multiple co-occurring symptoms which can impact the type of treatment received, recovery, and long-term health. We aim to simultaneously predict the risk of three symptoms: severe pain, moderate-severe depression, a...

Prediction of opioid dose in cancer pain patients using genetic profiling: not yet an option with support vector machine learning.

BMC research notes
OBJECTIVE: Use of opioids for pain management has increased over the past decade; however, inadequate analgesic response is common. Genetic variability may be related to opioid efficacy, but due to the many possible combinations and variables, statis...