Pain Management

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 opioid requirements in patients undergoing ambulatory surgery. To develop the models, we used a perioperative dataset of 13,700 patients (≥ 18 years) undergoing ambulatory surgery between the years 2016-2018. The data, comprising of patient, procedure a...

Modeling motivation for alcohol in humans using traditional and machine learning approaches.

Given the significant cost of alcohol use disorder (AUD), identifying risk factors for alcohol seeking represents a research priority. Prominent addiction theories emphasize the role of motivation in the alcohol seeking process, which has largely been studied using preclinical models. In order to bridge the gap between preclinical and clinical studies, this study examined predictors of motivation ...

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.

Ontological model of multi-agent Smart-system for predicting drug properties based on modified algorithms of artificial immune systems.

BACKGROUND: Currently, due to the huge progress in the field of information technologies and computer equipment, it is important to use modern approac...

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...

Non-Invasive Estimation of Intracranial Pressure by Diffuse Optics: A Proof-of-Concept Study.

Intracranial pressure (ICP) is an important parameter to monitor in several neuropathologies. However, because current clinically accepted methods are...

Automatic Midline Identification in Transverse 2-D Ultrasound Images of the Spine.

Effective epidural needle placement and injection involves accurate identification of the midline of the spine. Ultrasound, as a safe pre-procedural i...

Is there any association between low level of serum nesfatin-1 and fibromyalgia syndrome?

OBJECTIVES: This study aims to investigate the relationship between serum level of nesfatin-1 and fibromyalgia syndrome (FMS) clinical parameters such...

Automated histologic diagnosis of CNS tumors with machine learning.

The discovery of a new mass involving the brain or spine typically prompts referral to a neurosurgeon to consider biopsy or surgical resection. Intrao...

Selective Neuronal Vulnerability in Alzheimer's Disease: A Network-Based Analysis.

A major obstacle to treating Alzheimer's disease (AD) is our lack of understanding of the molecular mechanisms underlying selective neuronal vulnerabi...

Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach.

Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioe...

Value of Machine Learning-based Coronary CT Fractional Flow Reserve Applied to Triple-Rule-Out CT Angiography in Acute Chest Pain.

PURPOSE: To evaluate the additional value of noninvasive artificial intelligence (AI)-based CT-derived fractional flow reserve (CT FFR), derived from ...

Touching the social robot PARO reduces pain perception and salivary oxytocin levels.

Human-human social touch improves mood and alleviates pain. No studies have so far tested the effect of human-robot emotional touch on experimentally ...

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 ...

Altered resting-state functional connectivity and effective connectivity of the habenula in irritable bowel syndrome: A cross-sectional and machine learning study.

Irritable bowel syndrome (IBS) is a disorder involving dysfunctional brain-gut interactions characterized by chronic recurrent abdominal pain, altered...

An artificial intelligence decision support system for the management of type 1 diabetes.

Type 1 diabetes (T1D) is characterized by pancreatic beta cell dysfunction and insulin depletion. Over 40% of people with T1D manage their glucose thr...

Deep learning for predicting the occurrence of cardiopulmonary diseases in Nanjing, China.

The efficiency of disease prevention and medical care service necessitated the prediction of incidence. However, predictive accuracy and power were la...