Department of Computer Science, College of Science and Mathematics, United States of America; University of Massachusetts Boston, Boston, MA, United States of America.
Departments of Nursing, College of Nursing and Health Sciences, United States of America; University of Massachusetts Boston, Boston, MA, United States of America; Department of Medicine, Harvard Medical School, Boston, MA, United States of America; Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States of America.
Artif Intell Med. 2022 May;127:102280. doi: 10.1016/j.artmed.2022.102280. Epub 2022 Mar 18.
Falls are a complex problem and play a leading role in the development of disabilities in the older population. While fall detection systems are important, it is also essential to work on fall preventive strategies, which will have the most significant impact in reducing disability in the elderly. In this work, we explore a prospective cohort study, specifically designed for examining novel risk factors for falls in community-living older adults. Various types of data were acquired that are common for real-world applications. Learning from multiple data sources often leads to more valuable findings than any of the data sources can provide alone. However, simply merging features from disparate datasets usually will not produce a synergy effect. Hence, it becomes crucial to properly manage the synergy, complementarity, and conflicts that arise in multi-source learning. In this work, we propose a multi-source learning approach called the Synergy LSTM model, which exploits complementarity among textual fall descriptions together with people's physical characteristics. We further use the learned complementarities to evaluate fall risk factors present in the data. Experiment results show that our Synergy LSTM model can significantly improve classification performance and capture meaningful relations between data from multiple sources.
跌倒问题复杂，是老年人失能的主要原因。虽然跌倒检测系统很重要，但也必须重视预防跌倒策略，这对于减少老年人失能影响最大。在这项工作中，我们探索了一项前瞻性队列研究，专门针对社区居住的老年人的新跌倒风险因素进行研究。我们获取了各种常用于实际应用的数据。从多个数据源中学习通常比任何单一数据源都能提供更有价值的发现。但是，简单地合并来自不同数据集的特征通常不会产生协同效应。因此，正确管理多源学习中出现的协同、互补和冲突变得至关重要。在这项工作中，我们提出了一种名为协同 LSTM 的多源学习方法，该方法利用文本跌倒描述与人们的身体特征之间的互补性。我们进一步利用学到的互补性来评估数据中存在的跌倒风险因素。实验结果表明，我们的协同 LSTM 模型可以显著提高分类性能，并捕捉来自多个源的数据之间的有意义关系。
