Statistical Risk Assessment: Old Problems and New Applications

2006 ◽  
Vol 52 (1) ◽  
pp. 178-200 ◽  
Author(s):  
Stephen D. Gottfredson ◽  
Laura J. Moriarty
PEDIATRICS ◽  
1993 ◽  
Vol 92 (3) ◽  
pp. 509-509
Author(s):  
MICHAEL ASCHNER

To the Editor.— Dr Herbert Needleman's frightful description of his investigative ordeal, unnecessary as it may have been, will hopefully serve as an eye opener to the scientific community. It reemphasizes that where there is money at stake there is advocacy language on both sides. Anyone can seriously attack any statistical risk assessment on the premise that if a compound has no effect on a given measurement, about 1 of 20 studies to report such an association would be on the basis of change.


Author(s):  
Alexander Alekseev ◽  
Irina Alekseeva ◽  
Alexandra Noskova ◽  
Victoriya Kylosova ◽  
Alena Knyazeva

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6836
Author(s):  
José Gabriel Argañarás ◽  
Yan Tat Wong ◽  
Rezaul Begg ◽  
Nemai Chandra Karmakar

Radar technology is constantly evolving, and new applications are arising, particularly for the millimeter wave bands. A novel application for radar is gait monitoring for fall prevention, which may play a key role in maintaining the quality of life of people as they age. Alarming statistics indicate that one in three adults aged 65 years or older will experience a fall every year. A review of the sensors used for gait analysis and their applications to technology-based fall prevention interventions was conducted, focusing on wearable devices and radar technology. Knowledge gaps were identified, such as wearable radar development, application specific signal processing and the use of machine learning algorithms for classification and risk assessment. Fall prevention through gait monitoring in the natural environment presents significant opportunities for further research. Wearable radar could be useful for measuring gait parameters and performing fall risk-assessment using statistical methods, and could also be used to monitor obstacles in real-time.


2007 ◽  
Vol 35 (2) ◽  
pp. 173-176
Author(s):  
Andrijana Eđed ◽  
Dražen Horvat

2019 ◽  
Vol 34 (6) ◽  
pp. 4773-4783 ◽  
Author(s):  
Xi Chen ◽  
Jing Qiu ◽  
Luke Reedman ◽  
Zhao Yang Dong

2020 ◽  
Vol 0 ◽  
pp. 1-4
Author(s):  
Amitha Abraham ◽  
K. Sobhanakumari ◽  
Athira Mohan

Artificial intelligence (AI) refers to the ability of a machine to communicate, reason, and operate independently. There is a need to understand this technology’s progress for future medical care. AI has many applications in the field of medicine, but its use in dermatology is comparatively new. Applications that commonly analyse and classify images and tools like risk assessment calculators are available. Even though many applications exist, the important implementation barriers inclue difficulty in standardization, interpretability, and acceptance by patient and doctor.


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