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Interactive visualization to assist fall-risk assessment of community-dwelling elderly people
Information Visualization ( IF 2.3 ) Pub Date : 2017-08-02 , DOI: 10.1177/1473871617721243
Tien-Lung Sun, Chien-Hua Huang

In fall-risk assessment, clinical experts have to provide accurate assessment of high-risk individuals using vast amounts of collected data. In this article, we propose an interactive visualization approach for clinical experts to improve their interpretation of assessment scores and facilitate the decision-making process. Fall-risk assessment data on a total of 356 community-dwelling elders were collated. The Short-Form Berg Balance Scale and 3-Meter Timed Up and Go test were used to screen elderly people with high fall risks. A series of interactive visualization techniques were conducted. After grouping by the literature and a statistical 5% outlier method, some disputed elderly people were examined through interactive visualization. Finally, receiver operating characteristic analysis was conducted using previous fall experience (faller or non-faller) and the three methods. Receiver operating characteristic analysis revealed that the area under the curve was the highest (0.87, 95% confidence interval: 0.80–0.94) for the interactive visualization process compared to the other methods (literature, 0.81 (95% confidence interval: 0.71–0.90); statistical 5% outlier, 0.80 (95% confidence interval: 0.70–0.90)). Through the interactive visualization approach, the clinical experts were able to determine the screening results and their relationship with the decision boundary more rapidly and accurately, demonstrating that this approach is useful for risk assessment in the medical domain.

中文翻译:

交互式可视化辅助社区老年人跌倒风险评估

在跌倒风险评估中,临床专家必须使用大量收集的数据对高危个体进行准确评估。在本文中,我们为临床专家提出了一种交互式可视化方法,以改善他们对评估分数的解释并促进决策过程。对总共 356 名社区长者的跌倒风险评估数据进行了整理。简短形式的伯格平衡量表和 3 米计时起床测试被用来筛查具有高跌倒风险的老年人。进行了一系列交互式可视化技术。在通过文献和统计 5% 异常值方法分组后,通过交互式可视化对一些有争议的老年人进行了检查。最后,使用先前的跌倒经验(跌倒者或非跌倒者)和三种方法进行接受者操作特征分析。接收器操作特征分析显示,与其他方法相比,交互式可视化过程的曲线下面积最高(0.87,95% 置信区间:0.80-0.94)(文献,0.81(95% 置信区间:0.71-0.90) ;统计 5% 异常值,0.80(95% 置信区间:0.70–0.90))。通过交互式可视化方法,临床专家能够更快、更准确地确定筛查结果及其与决策边界的关系,证明该方法可用于医学领域的风险评估。接收器操作特征分析显示,与其他方法相比,交互式可视化过程的曲线下面积最高(0.87,95% 置信区间:0.80-0.94)(文献,0.81(95% 置信区间:0.71-0.90) ;统计 5% 异常值,0.80(95% 置信区间:0.70–0.90))。通过交互式可视化方法,临床专家能够更快、更准确地确定筛查结果及其与决策边界的关系,证明该方法可用于医学领域的风险评估。接收器操作特征分析显示,与其他方法相比,交互式可视化过程的曲线下面积最高(0.87,95% 置信区间:0.80-0.94)(文献,0.81(95% 置信区间:0.71-0.90) ;统计 5% 异常值,0.80(95% 置信区间:0.70–0.90))。通过交互式可视化方法,临床专家能够更快、更准确地确定筛查结果及其与决策边界的关系,证明该方法可用于医学领域的风险评估。0.70–0.90))。通过交互式可视化方法,临床专家能够更快、更准确地确定筛查结果及其与决策边界的关系,证明该方法可用于医学领域的风险评估。0.70–0.90))。通过交互式可视化方法,临床专家能够更快、更准确地确定筛查结果及其与决策边界的关系,证明该方法可用于医学领域的风险评估。
更新日期:2017-08-02
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