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Cancer Pain

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Tashinone II A-sulfoacid-natrum elevates the pain threshold through inhibiting nuclear factor kappa B pathway in neuropathic cancer pain.

Indian journal of cancer
OBJECTIVE: The purpose of this study was to evaluate the effects of Tashinone II A-sulfoacid-natrum on the pain threshold and potential molecular mechanism for neuropathic cancer pain.

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...

Machine-learned analysis of the association of next-generation sequencing-based genotypes with persistent pain after breast cancer surgery.

Pain
Cancer and its surgical treatment are among the most important triggering events for persistent pain, but additional factors need to be present for the clinical manifestation, such as variants in pain-relevant genes. In a cohort of 140 women undergoi...

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...

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...

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...

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...

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...