AIMC Topic: Risk Assessment

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Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method.

The Lancet. Digital health
BACKGROUND: Current lung cancer screening guidelines use mean diameter, volume or density of the largest lung nodule in the prior computed tomography (CT) or appearance of new nodule to determine the timing of the next CT. We aimed at developing a mo...

Clinical-learning versus machine-learning for transdiagnostic prediction of psychosis onset in individuals at-risk.

Translational psychiatry
Predicting the onset of psychosis in individuals at-risk is based on robust prognostic model building methods including a priori clinical knowledge (also termed clinical-learning) to preselect predictors or machine-learning methods to select predicto...

Extreme Gradient Boosting Model Has a Better Performance in Predicting the Risk of 90-Day Readmissions in Patients with Ischaemic Stroke.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECT: Ischemic stroke readmission within 90 days of hospital discharge is an important quality of care metric. The readmission rates of ischemic stroke patients are usually higher than those of patients with other chronic diseases. Our aim was to i...

An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.

British journal of anaesthesia
BACKGROUND: Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores ...

The ethical, legal and social implications of using artificial intelligence systems in breast cancer care.

Breast (Edinburgh, Scotland)
Breast cancer care is a leading area for development of artificial intelligence (AI), with applications including screening and diagnosis, risk calculation, prognostication and clinical decision-support, management planning, and precision medicine. W...

Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome.

Scientific reports
Most risk stratification methods use expert opinion to identify a fixed number of clinical variables that have prognostic significance. In this study our goal was to develop improved metrics that utilize a variable number of input parameters. We firs...

Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction.

JACC. Heart failure
OBJECTIVES: This study sought to develop models for predicting mortality and heart failure (HF) hospitalization for outpatients with HF with preserved ejection fraction (HFpEF) in the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with...