A Mobile‐Based Question‐Answering and Early Warning System for Assisting Diabetes Management

Abstract

With increasing demand for preventive management of chronic diseases in real time by using the Internet, interest in developing a convenient device on health management and monitoring has intensified. Unlike other chronic diseases, diabetes particularly type 2 is a lifelong chronic disease and usually requires daily health management by patients themselves. This study is to develop a mobilebased diabetes question-answering (Q&A) and early warning system named Dia-AID, assisting diabetes patients and populations at high risk. The Dia-AID system consists of three modules: a large-scale multilanguage diabetes frequently asked question repository, a multimode fusion Q&A framework, and a health data management module. A list of services including risk assessment and health early warning is provided to users for health condition monitoring. Using the diabetes frequently asked question repository as data, experiments are conducted on answer ranking and answer selection aspects. Results show that two essential methods in the system outperform baseline methods on both aspects.

A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches

“…Figure 15 demonstrates how Accuracy, Precision, Recall, and F1 are employed to evaluate the different methods used in the question answering systems, where KNN is the K-Nearest Neighbor method, GaussianNB is Gaussian Naive Bayes method, RF is Random Forest method, SVM is Support Vector Machine method, PPN is Perceptron method, and Dia-AID is the method proposed in [75].…”

Diabetes and conversational agents: the AIDA project case study

“…The KB was built during a co-design workshop, where a scientific board drafted the content guided by three facilitators and two computational linguists. Unlike previous work [ 33 , 35 ], AIDAs’ KB was built manually, in order to assure that both questions and answers were scientifically sound as well as linguistically correct.…”

“…In the domain of diabetes, some studies on educational tools to facilitate pathology self-management have been carried out [30][31][32][33]. However, most of them concern either type 1 or type 2 diabetes patients, while our focus is on both: as will be detailed in Sect.…”

Natural Language Processing for Smart Healthcare

“…Through knowledge engineering, accurate and complete medical knowledge bases can be established to promote the popularization of medical knowledge among the population [90]- [93], [101]- [104], [186]. Specifically, people can easily access medical knowledge through question answering systems [187], [188], information retrieval systems [83], [85], and machine translation systems [1], [134], [189], [190], facilitating the popularization and education of medical knowledge. In addition, text generation techniques, such as question generation and text summarization, can also be used in medical education to generate medical case-based questions [191] and construct simplified summaries [65].…”