Electrical Engineering and Computer Science, University of Missouri, USA.
Health Management and Informatics, University of Missouri, USA.
J Biomed Inform. 2019 Aug;96:103240. doi: 10.1016/j.jbi.2019.103240. Epub 2019 Jun 28.
With the increase in the population of older adults around the world, a significant amount of work has been done on in-home sensor technology to aid the elderly age independently. However, due to the large amounts of data generated by the sensors, it takes a lot of effort and time for the clinicians to makes sense of this data. In this work, we develop a system to help make this data more useful by presenting it in the form of natural language.
We start by identifying important attributes in the sensor data that are relevant to the health of the elderly. We then develop algorithms to extract these important health related features from the sensor parameters and summarize them in natural language. We focus on making the natural language summaries to be informative, accurate and concise.
We designed multiple surveys using real and synthetic data to validate the summaries produced by our algorithms. We show that the algorithms produce meaningful results comparable to human subjects. We also implemented our linguistic summarization system to produce summaries of data leading to health alerts derived from the sensor data. The system is running live in 110 apartments currently. By the means of retrospective case studies, we illustrate that the linguistic summaries are able to make the connection between changes in the sensor data and the health of the elderly.
We present a system that extracts important clinically relevant features from in-home sensor data generated in the apartments of the elderly and summarize those features in natural language. The preliminary testing of our summarization system shows that it has the potential to help the clinicians utilize this data effectively.
随着全球老年人口的增加，人们在家庭传感器技术方面做了大量工作，以帮助老年人独立生活。然而，由于传感器产生的大量数据，临床医生需要花费大量的精力和时间来理解这些数据。在这项工作中，我们开发了一个系统，通过以自然语言的形式呈现数据，帮助使这些数据更有用。
我们首先确定与老年人健康相关的传感器数据中的重要属性。然后，我们开发算法从传感器参数中提取这些重要的健康相关特征，并以自然语言对其进行总结。我们专注于使自然语言摘要具有信息性、准确性和简洁性。
我们使用真实和合成数据设计了多个调查来验证我们的算法生成的摘要。我们表明，算法产生的结果与人类受试者相当，具有有意义的结果。我们还实现了我们的语言总结系统，以生成源自传感器数据的健康警报的摘要。该系统目前正在 110 个公寓中运行。通过回顾性案例研究，我们说明语言摘要能够将传感器数据的变化与老年人的健康联系起来。
我们提出了一个从老年人公寓中生成的家庭传感器数据中提取重要的临床相关特征，并以自然语言对这些特征进行总结的系统。我们的总结系统的初步测试表明，它有可能帮助临床医生有效地利用这些数据。
