Research Area:  Machine Learning
Context computing is a branch of ambient intelligence (AmI) research, which has been rapidly emerging in the support of intelligent smart health platform solution. To develop reliable ambient computing using the hybrid peer-to-peer network and Internet of Things, machine learning, deep learning, artificial intelligence, and context awareness have been applied. This study proposes an ambient context-based modeling for a health risk assessment using deep neural network. In the proposed method, we collected medical information from chronic disease patients such as EMR, PHR, and medical histories, as well as environmental data from a health platform. Subsequently, heterogeneous data are integrated through selecting, cleaning, modeling, and evaluating the collected raw data and then the context is created. The structured input data such as a sensor data are normalized by transforming the time domain data to the frequency domain information. Using a deep neural network, the normalized data are applied to create an ambient context. A deep neural network is composed of the following three layers: input layers with treated and untreated data; hidden layers where connection strength is trained as a weight; and output layers of trained results. In the deep neural network layers, the control of the weight of training data enables repeated learning to create an ambient context pattern. Using an ontology inference engine, unstructured/structured data, including individual health data and environmental information, and their context is presented as ontology metadata. In the knowledge base, hidden association relationships are discovered through mining. To inform the individual health conditions exposed to the individual environmental contexts, a health risk assessment model is developed with a set of the ambient context pattern learned with metadata and a deep neural network. The Minkowski distance formula, which defines a normalized geometrical distance between two nodes, is used to measure the similarity between the patients with chronical disease and the individual user based on the context. In the proposed model, the risk is represented as a similarity-based index. The risk assessment model can be implemented into the individual risk alert/prevention system. The model may significantly impact the healthcare industry as well as ambient intelligence research, thus contributing to improve the quality of human life of the future society.
Author(s) Name:  Kyungyong Chung, Hyun Yoo & Do-Eun Choe
Journal name:  Journal of Ambient Intelligence and Humanized Computing
Publisher name:  Springer
Volume Information:   volume 11, pages 1387–1395 (2020)
Paper Link:   https://link.springer.com/article/10.1007/s12652-018-1033-7