Jeremic Alelksandar, Nikolic Dejan, Kostadinovic Milena, Milicevic Milena Santric
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5420-5423. doi: 10.1109/EMBC44109.2020.9175661.
Effective pain management can significantly improve quality of life and outcomes for various types of patients (e.g. elderly, adult, young) and often requires assisted living for a significant number of people worldwide. In order to improve our understanding of patients' response to pain and needs for assisted living we need to develop adequate data processing techniques that would enable us to understand underlying interdependencies. To this purpose in this paper we develop several different algorithms that can predict the need for medically assisted living outcomes using a large database obtained as a part of the national health survey. As a part of the survey the respondents provided detailed information about general health care state, acute and chronic problems as well as personal perception of pain associated with performing two simple talks: walking on the flat surface and walking upstairs. We model the correspondent responses using multinomial random variables and propose structured deep learning models based on maximum likelihood estimation and machine learning for information fusion. For comparison purposes we also implement fully connected deep learning network and use its results as benchmark measurements. We evaluate the performance of the proposed techniques using the national survey data and split them into two parts used for training and testing. Our preliminary results indicate that the proposed models can potentially be useful in forecasting the need for medically assisted living.
有效的疼痛管理可以显著提高各类患者（如老年人、成年人、年轻人）的生活质量和治疗效果，而且全球有相当数量的人常常需要辅助生活。为了更好地理解患者对疼痛的反应以及辅助生活需求，我们需要开发适当的数据处理技术，以便能够理解潜在的相互依存关系。为此，在本文中，我们开发了几种不同的算法，这些算法可以利用作为国家健康调查一部分获得的大型数据库来预测医疗辅助生活结果的需求。作为调查的一部分，受访者提供了有关总体医疗状况、急性和慢性问题以及与进行两项简单活动（在平坦表面行走和上楼梯）相关的疼痛个人感受的详细信息。我们使用多项随机变量对相应的反应进行建模，并基于最大似然估计和机器学习提出结构化深度学习模型用于信息融合。为了进行比较，我们还实现了全连接深度学习网络，并将其结果用作基准测量。我们使用国家调查数据评估所提出技术的性能，并将其分为两部分用于训练和测试。我们的初步结果表明，所提出的模型在预测医疗辅助生活需求方面可能会很有用。
