AIMC Topic: Risk Assessment

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Prediction model for thyrotoxic atrial fibrillation: a retrospective study.

BMC endocrine disorders
BACKGROUND: Thyrotoxic atrial fibrillation (TAF) is a recognized significant complication of hyperthyroidism. Early identification of the individuals predisposed to TAF would improve thyrotoxic patients' management. However, to our knowledge, an inst...

Derivation, characterisation and analysis of an adverse outcome pathway network for human hepatotoxicity.

Toxicology
Adverse outcome pathways (AOPs) and their networks are important tools for the development of mechanistically based non-animal testing approaches, such as in vitro and/or in silico assays, to assess toxicity induced by chemicals. In the present study...

Machine learning for selecting patients with Crohn's disease for abdominopelvic computed tomography in the emergency department.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: Patients with Crohn's disease (CD) frequently undergo abdominopelvic computed tomography (APCT) in the emergency department (ED). It's essential to diagnose clinically actionable findings (CAF) as they may need immediate intervention, fre...

Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence.

BMJ open
INTRODUCTION: The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were both published to improve the reporting and criti...

From everyday life predictions to suicide prevention: Clinical and ethical considerations in suicide predictive analytic tools.

Journal of clinical psychology
Advances in artificial intelligence and machine learning have fueled growing interest in the application of predictive analytics to identify high-risk suicidal patients. Such application will require the aggregation of large-scale, sensitive patient ...

Augmented intelligence to predict 30-day mortality in patients with cancer.

Future oncology (London, England)
An augmented intelligence tool to predict short-term mortality risk among patients with cancer could help identify those in need of actionable interventions or palliative care services. An algorithm to predict 30-day mortality risk was developed us...

Safer chemicals using less animals: kick-off of the European ONTOX project.

Toxicology
The 3Rs concept, calling for replacement, reduction and refinement of animal experimentation, is receiving increasing attention around the world, and has found its way to legislation, in particular in the European Union. This is aligned by continuing...

Assessing the utility of deep neural networks in predicting postoperative surgical complications: a retrospective study.

The Lancet. Digital health
BACKGROUND: Early detection of postoperative complications, including organ failure, is pivotal in the initiation of targeted treatment strategies aimed at attenuating organ damage. In an era of increasing health-care costs and limited financial reso...

Machine Learning for Predicting the 3-Year Risk of Incident Diabetes in Chinese Adults.

Frontiers in public health
We aimed to establish and validate a risk assessment system that combines demographic and clinical variables to predict the 3-year risk of incident diabetes in Chinese adults. A 3-year cohort study was performed on 15,928 Chinese adults without dia...