AIMC Topic: Risk Assessment

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Auto detecting deliveries in elite cricket fast bowlers using microsensors and machine learning.

Journal of sports sciences
Cricket fast bowlers are at a high risk of injury occurrence, which has previously been shown to be correlated to bowling workloads. This study aimed to develop and test an algorithm that can automatically, reliably and accurately detect bowling deli...

Harnessing Population Pedigree Data and Machine Learning Methods to Identify Patterns of Familial Bladder Cancer Risk.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
BACKGROUND: Relatives of patients with bladder cancer have been shown to be at increased risk for kidney, lung, thyroid, and cervical cancer after correcting for smoking-related behaviors that may concentrate in some families. We demonstrate a novel ...

Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization: The LINK-HF Multicenter Study.

Circulation. Heart failure
BACKGROUND: Implantable cardiac sensors have shown promise in reducing rehospitalization for heart failure (HF), but the efficacy of noninvasive approaches has not been determined. The objective of this study was to determine the accuracy of noninvas...

Artificial intelligence in abdominal aortic aneurysm.

Journal of vascular surgery
OBJECTIVE: Abdominal aortic aneurysm (AAA) is a life-threatening disease, and the only curative treatment relies on open or endovascular repair. The decision to treat relies on the evaluation of the risk of AAA growth and rupture, which can be diffic...

Combination of compositional data analysis and machine learning approaches to identify sources and geochemical associations of potentially toxic elements in soil and assess the associated human health risk in a mining city.

Environmental pollution (Barking, Essex : 1987)
Mining activities change the chemical composition of the environment and have negative reflection on people's health and there is no single measure to deal with adverse consequences of mining activities, as each case is specific and needs to be under...

Detecting vulnerable plaque with vulnerability index based on convolutional neural networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Plaque rupture and subsequent thrombosis are major processes of acute cardiovascular events. The Vulnerability Index is a very important indicator of whether a plaque is ruptured, and these easily ruptured or fragile plaques can be detected early. Th...

Development of AOP relevant to microplastics based on toxicity mechanisms of chemical additives using ToxCastâ„¢ and deep learning models combined approach.

Environment international
Various additives are used in plastic products to improve the properties and the durability of the plastics. Their possible elution from the plastics when plastics are fragmented into micro- and nano-size in the environment is suspected to one of the...

Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects.

Circulation. Cardiovascular imaging
BACKGROUND: Epicardial adipose tissue (EAT) volume (cm) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate the prognostic value of fully automated deep learning-based EAT volume and attenua...

Predictability of drug-induced liver injury by machine learning.

Biology direct
BACKGROUND: Drug-induced liver injury (DILI) is a major concern in drug development, as hepatotoxicity may not be apparent at early stages but can lead to life threatening consequences. The ability to predict DILI from in vitro data would be a crucia...

Machine Learning to Predict Stent Restenosis Based on Daily Demographic, Clinical, and Angiographic Characteristics.

The Canadian journal of cardiology
BACKGROUND: Machine learning (ML) has arrived in medicine to deliver individually adapted medical care. This study sought to use ML to discriminate stent restenosis (SR) compared with existing predictive scores of SR. To develop an easily applicable ...