MEvA-X: a hybrid multiobjective evolutionary tool using an XGBoost classifier for biomarkers discovery on biomedical datasets.

Motivation: Biomarker discovery is one of the most frequent pursuits in bioinformatics and is crucial for precision medicine, disease prognosis, and drug discovery. A common challenge of biomarker discovery applications is the low ratio of samples over features for the selection of a reliable not-redundant subset of features, but despite the development of efficient tree-based classification methods, such as the extreme gradient boosting (XGBoost), this limitation is still relevant. Moreover, existing approaches for optimizing XGBoost do not deal effectively with the class imbalance nature of the biomarker discovery problems, and the presence of multiple conflicting objectives, since they focus on the training of a single-objective model.

Improvement of Pain and Function After Use of a Topical Pain Relieving Patch: Results of the RELIEF Study.

Purpose: Pain is the most common reason for patients to consult primary care providers. Identification of effective treatments with minimal adverse events is critical to safer opioid-sparing and multi-modal approaches to pain treatment. Topical analgesic patches target medication to peripheral sites of pain while potentially avoiding adverse effects associated with systemic medications.

Reducing Opioid Prescriptions by Identifying Responders on Topical Analgesic Treatment Using an Individualized Medicine and Predictive Analytics Approach.

Purpose: Chronic pain is a life changing condition, and non-opioid treatments have been lately introduced to overcome the addictive nature of opioid therapies and their side effects. In the present study, we explore the potential of machine learning methods to discriminate chronic pain patients into ones who will benefit from such a treatment and ones who will not, aiming to personalize their treatment.

Patients And Methods: In the current study, data from the OPERA study were used, with 631 chronic pain patients answering the Brief Pain Inventory (BPI) validated questionnaire along with supplemental questions before and after a follow-up period.

Reduction of opioid use and improvement in chronic pain in opioid-experienced patients after topical analgesic treatment: an exploratory analysis.

Objective: There is a need to identify safe and effective opioid-sparing multimodal alternative treatment strategies and approaches, including topical analgesics, for opioid-experienced chronic pain patients to mitigate the risk of addiction, misuse, and abuse of opioids.

Methods: This subset analysis from a prospective, observational study evaluated changes in opioid use, other concurrent medication use, and pain severity and interference in opioid-experienced patients (OEP) treated with topical analgesics for chronic pain with measures obtained at baseline and 3- and 6- month follow-up.

Results: The 3-month opioid-experienced patient (3-month OEP) group included 121 patients who completed baseline and 3-month follow-up assessments; 27 opioid-experienced patients completed baseline and 6-month follow-up assessments (6-month OEP).

Changes in pain and concurrent pain medication use following compounded topical analgesic treatment for chronic pain: 3- and 6-month follow-up results from the prospective, observational Optimizing Patient Experience and Response to Topical Analgesics study.

Background: Opioids and other controlled substances prescribed for chronic pain are associated with abuse, addiction, and death, prompting national initiatives to identify safe and effective pain management strategies including topical analgesics.

Methods: This prospective, observational study evaluated changes from baseline in overall mean severity and interference scores on the Brief Pain Inventory scale and the use of concurrent pain medications at 3- and 6-month follow-up assessments in chronic pain patients treated with topical analgesics. Changes in pain severity and interference and medication usage were compared between treated patients and unmatched and matched controls.