AIMC Topic: Risk Assessment

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Bridging a translational gap: using machine learning to improve the prediction of PTSD.

BMC psychiatry
BACKGROUND: Predicting Posttraumatic Stress Disorder (PTSD) is a pre-requisite for targeted prevention. Current research has identified group-level risk-indicators, many of which (e.g., head trauma, receiving opiates) concern but a subset of survivor...

Applying under-sampling techniques and cost-sensitive learning methods on risk assessment of breast cancer.

Journal of medical systems
Breast cancer is one of the most common cause of cancer mortality. Early detection through mammography screening could significantly reduce mortality from breast cancer. However, most of screening methods may consume large amount of resources. We pro...

An integrated breast cancer risk assessment and management model based on fuzzy cognitive maps.

Computer methods and programs in biomedicine
BACKGROUND: There is a growing demand for women to be classified into different risk groups of developing breast cancer (BC). The focus of the reported work is on the development of an integrated risk prediction model using a two-level fuzzy cognitiv...

A novel neural-inspired learning algorithm with application to clinical risk prediction.

Journal of biomedical informatics
Clinical risk prediction - the estimation of the likelihood an individual is at risk of a disease - is a coveted and exigent clinical task, and a cornerstone to the recommendation of life saving management strategies. This is especially important for...

African-American Men with Low-Risk Prostate Cancer: Modern Treatment and Outcome Trends.

Journal of racial and ethnic health disparities
OBJECTIVE: To investigate the clinical characteristics and treatment patterns for African-American (AA) men with low-risk prostate cancer (PCa) using a national, population-based dataset.

Identifying predictive features in drug response using machine learning: opportunities and challenges.

Annual review of pharmacology and toxicology
This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction ...

Robotic telepresence versus standardly supervised stroke alert team assessments.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association
BACKGROUND: Telemedicine has created access to emergency stroke care for patients in all communities, regardless of geography. We hypothesized that there is no difference in speed of assessment between vascular neurologist (VN) robotic telepresence a...

Laparoscopic nephrectomy and partial nephrectomy: intraperitoneal, retroperitoneal, single site.

The Urologic clinics of North America
The indication for use of laparoscopy, in the pediatric population, was initially for diagnostic purposes. As confidence with the technology and utility grew, it began to be applied for therapeutic indications. With equivalent surgical outcomes and d...

Artificial neural network and falls in community-dwellers: a new approach to identify the risk of recurrent falling?

Journal of the American Medical Directors Association
BACKGROUND: Identification of the risk of recurrent falls is complex in older adults. The aim of this study was to examine the efficiency of 3 artificial neural networks (ANNs: multilayer perceptron [MLP], modified MLP, and neuroevolution of augmenti...

Prediction of hospitalization due to heart diseases by supervised learning methods.

International journal of medical informatics
BACKGROUND: In 2008, the United States spent $2.2 trillion for healthcare, which was 15.5% of its GDP. 31% of this expenditure is attributed to hospital care. Evidently, even modest reductions in hospital care costs matter. A 2009 study showed that n...