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Opioid-Related Disorders

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

AMIA ... Annual Symposium proceedings. AMIA Symposium
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 a...

The Role of Gabapentinoids in the Substance Use Pattern of Adult Germans Seeking Inpatient Detoxification Treatment - A Pilot Study.

Journal of psychoactive drugs
To shed more light on the addictive power of the gabapentinoids (GPTs) gabapentin and pregabalin, we performed a structured face-to-face interview with GPT-users about DSM-IV-dependence-criteria (sedatives), consume-motives and cessation-needs. Among...

How prevalent and severe is addiction on GABAmimetic drugs in an elderly German general hospital population? Focus on gabapentinoids, benzodiazepines, and z-hypnotic drugs.

Human psychopharmacology
OBJECTIVE: Gabapentinoids (GPT) are reported to be increasingly misused by opioid- and polydrug-users, but the addictive potential of GPT outside of these populations remains understudied. Investigations comparing GPT abuse and dependence liability t...

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

Journal of medical Internet research
BACKGROUND: In recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to suicidal ideation; this may often result in overdose. However, these fatal overdoses are difficult ...

Development of a machine learning algorithm for early detection of opioid use disorder.

Pharmacology research & perspectives
BACKGROUND: Opioid use disorder (OUD) affects an estimated 16 million people worldwide. The diagnosis of OUD is commonly delayed or missed altogether. We aimed to test the utility of machine learning in creating a prediction model and algorithm for e...

Big data and predictive modelling for the opioid crisis: existing research and future potential.

The Lancet. Digital health
A need exists to accurately estimate overdose risk and improve understanding of how to deliver treatments and interventions in people with opioid use disorder in a way that reduces such risk. We consider opportunities for predictive analytics and rou...

Clinical Performance of a Gene-Based Machine Learning Classifier in Assessing Risk of Developing OUD in Subjects Taking Oral Opioids: A Prospective Observational Study.

Annals of clinical and laboratory science
OBJECTIVE: To reduce the incidence of Opioid Use Disorder (OUD), multiple guidelines recommend assessing the risk of OUD prior to prescribing oral opioids. Although subjective risk assessments are available to help classify subjects at risk for OUD, ...

Identifying risk of opioid use disorder for patients taking opioid medications with deep learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early c...

Classifying Characteristics of Opioid Use Disorder From Hospital Discharge Summaries Using Natural Language Processing.

Frontiers in public health
BACKGROUND: Opioid use disorder (OUD) is underdiagnosed in health system settings, limiting research on OUD using electronic health records (EHRs). Medical encounter notes can enrich structured EHR data with documented signs and symptoms of OUD and s...