43,919 research outputs found
Reducing fall risk with combined motor and cognitive training in elderly fallers
Background. Falling is a major clinical problem in elderly people, demanding effective solutions. At present, the only effective intervention is motor training of balance and strength. Executive function-based training (EFt) might be effective at preventing falls according to evidence showing a relationship between executive functions and gait abnormalities. The aim was to assess the effectiveness of a motor and a cognitive treatment developed within the EU co-funded project I-DONT-FALL. Methods. In a sample of 481 elderly people at risk of falls recruited in this multicenter randomised controlled trial, the effectiveness of a motor treatment (pure motor or mixed with EFt) of 24 one-hour sessions delivered through an i-Walker with a non-motor treatment (pure EFt or control condition) was evaluated. Similarly, a 24 one-hour session cognitive treatment (pure EFt or mixed with motor training), delivered through a touch-screen computer was compared with a non-cognitive treatment (pure motor or control condition). Results. Motor treatment, particularly when mixed with EFt, reduced significantly fear of falling (F(1,478) = 6.786, p = 0.009) although to a limited extent (ES -0.25) restricted to the period after intervention. Conclusions. This study suggests the effectiveness of motor treatment empowered by EFt in reducing fear of falling.Peer ReviewedPostprint (published version
The Application of Integrated Knowledge-based Systems for the Biomedical Risk Assessment Intelligent Network (BRAIN)
One of NASA's goals for long duration space flight is to maintain acceptable levels of crew health, safety, and performance. One way of meeting this goal is through the Biomedical Risk Assessment Intelligent Network (BRAIN), an integrated network of both human and computer elements. The BRAIN will function as an advisor to flight surgeons by assessing the risk of in-flight biomedical problems and recommending appropriate countermeasures. This paper describes the joint effort among various NASA elements to develop BRAIN and an Infectious Disease Risk Assessment (IDRA) prototype. The implementation of this effort addresses the technological aspects of the following: (1) knowledge acquisition; (2) integration of IDRA components; (3) use of expert systems to automate the biomedical prediction process; (4) development of a user-friendly interface; and (5) integration of the IDRA prototype and Exercise Countermeasures Intelligent System (ExerCISys). Because the C Language, CLIPS (the C Language Integrated Production System), and the X-Window System were portable and easily integrated, they were chosen as the tools for the initial IDRA prototype. The feasibility was tested by developing an IDRA prototype that predicts the individual risk of influenza. The application of knowledge-based systems to risk assessment is of great market value to the medical technology industry
Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction
As algorithms are increasingly used to make important decisions that affect
human lives, ranging from social benefit assignment to predicting risk of
criminal recidivism, concerns have been raised about the fairness of
algorithmic decision making. Most prior works on algorithmic fairness
normatively prescribe how fair decisions ought to be made. In contrast, here,
we descriptively survey users for how they perceive and reason about fairness
in algorithmic decision making.
A key contribution of this work is the framework we propose to understand why
people perceive certain features as fair or unfair to be used in algorithms.
Our framework identifies eight properties of features, such as relevance,
volitionality and reliability, as latent considerations that inform people's
moral judgments about the fairness of feature use in decision-making
algorithms. We validate our framework through a series of scenario-based
surveys with 576 people. We find that, based on a person's assessment of the
eight latent properties of a feature in our exemplar scenario, we can
accurately (> 85%) predict if the person will judge the use of the feature as
fair.
Our findings have important implications. At a high-level, we show that
people's unfairness concerns are multi-dimensional and argue that future
studies need to address unfairness concerns beyond discrimination. At a
low-level, we find considerable disagreements in people's fairness judgments.
We identify root causes of the disagreements, and note possible pathways to
resolve them.Comment: To appear in the Proceedings of the Web Conference (WWW 2018). Code
available at https://fate-computing.mpi-sws.org/procedural_fairness
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