Back Pain

Using Natural Language Processing and Machine Learning to Identify Hospitalized Patients with Opioid Use Disorder.

Opioid use disorder (OUD) represents a global public health crisis that challenges classic clinical decision making. As existing hospital screening methods are resource-intensive, patients with OUD are significantly under-detected. An automated and accurate approach is needed to improve OUD identification so that appropriate care can be provided to these patients in a timely fashion. In this study...

Lower, Variable Intrathecal Opioid Doses, and the Incidence of Prolonged Fetal Heart Rate Decelerations After Combined Spinal Epidural Analgesia for Labor: A Quality Improvement Analysis.

BACKGROUND: Combined spinal-epidurals with low-dose intrathecal opioids and local anesthetics are commonly used to initiate labor analgesia due to the fast onset of analgesia and good patient satisfaction. Intrathecal fentanyl has been associated with fetal bradycardia, and the rate may be higher at doses of 25 mcg and above. As such, our institution limits intrathecal fentanyl doses to less than ...

Trace element, immune and opioid biomarkers of unstable angina, increased atherogenicity and insulin resistance: Results of machine learning.

BACKGROUND: Aberrations in endothelial cells, immune and oxidative pathways are associated with atherosclerosis (ATS) and unstable angina (UA). The ro...

Detection of Suicidality Among Opioid Users on Reddit: Machine Learning-Based Approach.

BACKGROUND: In recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to...

Plasma Concentrations of Pro-inflammatory Cytokine IL-6 and Antiinflammatory Cytokine IL-10 in Short- and Long-term Opioid Users with Noncancer Pain.

INTRODUCTION: Little is known whether the duration of opioid use influences the concentrations of pro- and anti-inflammatory cytokines.

Analysis of 17 fentanyls in plasma and blood by UPLC-MS/MS with interpretation of findings in surgical and postmortem casework.

The opioid crisis is linked to an increased misuse of fentanyl as well as fentanyl analogs that originate from the illicit drug market. Much of our cu...

Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery.

Machine learning is a well-known approach for virtual screening. Recently, deep learning, a machine learning algorithm in artificial neural networks, ...

Artificial intelligence interventions focused on opioid use disorders: A review of the gray literature.

BACKGROUND: With the artificial intelligence (AI) paradigm shift comes momentum toward the development and scale-up of novel AI interventions to aid i...

Investigation of MDMA Inhibitory Effect on CytochromeP450 3A4 in Isolated Perfused Rat Liver Model Using Tramadol.

MDMA (methylenedioxymethamphetamine) is a synthetic compound, which is a structurally derivative of amphetamine. Also, it acts like an amphetamine, s...

Machine learning approach to predict postoperative opioid requirements in ambulatory surgery patients.

Opioids play a critical role in acute postoperative pain management. Our objective was to develop machine learning models to predict postoperative opi...

Acceptance and Commitment Therapy Delivered via a Mobile Phone Messaging Robot to Decrease Postoperative Opioid Use in Patients With Orthopedic Trauma: Randomized Controlled Trial.

BACKGROUND: Acceptance and commitment therapy (ACT) is a pragmatic approach to help individuals decrease avoidable pain.

Predictors of emergency department opioid administration and prescribing: A machine learning approach.

INTRODUCTION: The opioid epidemic has altered normative clinical perceptions on addressing both acute and chronic pain, particularly within the Emerge...

Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization.

BACKGROUND: Chronic spinal pain conditions affect millions of US adults and carry a high healthcare cost burden, both direct and indirect. Conservativ...

Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic study.

OBJECTIVE: To develop and validate a machine-learning algorithm to improve prediction of incident OUD diagnosis among Medicare beneficiaries with ≥1 o...

Robotic Versus Open Ureteroneocystostomy: Is There a Robotic Benefit?

We sought to compare the outcomes of patients who underwent an open robotic ureteroneocystostomy for ureteral obstruction. Retrospective review was...

Chemical Degradation of Intravenous Chemotherapy Agents and Opioids by a Novel Instrument.

To assess chemical degradation of various liquid chemotherapy and opioid drugs in the novel RxDestruct™ instrument. Intravenous (IV) drug solutions ...

Is P&T Ready to Add Rapid Cycle Analytics to Formulary?

The intent of this article is to evaluate a novel approach, using rapid cycle analytics and real world evidence, to optimize and improve the medicati...

Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients.

BACKGROUND: Automated de-identification methods for removing protected health information (PHI) from the source notes of the electronic health record ...

Robot-Assisted Radical Prostatectomy Associated with Decreased Persistent Postoperative Opioid Use.

Minimally invasive surgery offers reduced pain and opioid use postoperatively compared with open surgery, but large-scale comparative studies are lac...

Machine Learning Based Opioid Overdose Prediction Using Electronic Health Records.

Opioid addiction in the United States has come to national attention as opioid overdose (OD) related deaths have risen at alarming rates. Combating op...