Application of Artificial Intelligence in Diabetes Education and Management: Present Status and Promising Prospect

Li, Juan; Huang, Jin; Zheng, Lanbo; Li, Xia

doi:10.3389/fpubh.2020.00173

Cited by 44 publications

(26 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…349 Although there are still no unified standards to guide the construction of candidate auxiliary intelligent prediction modelaided system for COVID-19, the realization of the clinical application is generally divided into two parts: system design and system implementation. [350][351][352][353] In the system design, there are five core steps (Fig. 5).…”

Section: Intelligent Prediction Model-aided Systemmentioning

confidence: 99%

Applications of laboratory findings in the prevention, diagnosis, treatment, and monitoring of COVID-19

Meng

Guo

Zhou

et al. 2021

Sig Transduct Target Ther

View full text Add to dashboard Cite

The worldwide pandemic of coronavirus disease 2019 (COVID-19) presents us with a serious public health crisis. To combat the virus and slow its spread, wider testing is essential. There is a need for more sensitive, specific, and convenient detection methods of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Advanced detection can greatly improve the ability and accuracy of the clinical diagnosis of COVID-19, which is conducive to the early suitable treatment and supports precise prophylaxis. In this article, we combine and present the latest laboratory diagnostic technologies and methods for SARS-CoV-2 to identify the technical characteristics, considerations, biosafety requirements, common problems with testing and interpretation of results, and coping strategies of commonly used testing methods. We highlight the gaps in current diagnostic capacity and propose potential solutions to provide cutting-edge technical support to achieve a more precise diagnosis, treatment, and prevention of COVID-19 and to overcome the difficulties with the normalization of epidemic prevention and control.

show abstract

Section: Intelligent Prediction Model-aided Systemmentioning

confidence: 99%

Applications of laboratory findings in the prevention, diagnosis, treatment, and monitoring of COVID-19

Meng

Guo

Zhou

et al. 2021

Sig Transduct Target Ther

View full text Add to dashboard Cite

show abstract

“…Recently, AI attracted considerable interest for its advantages in health and chronic disease management [33,34].…”

Section: Application Of Ai In the Management Of Chronic Diseasesmentioning

confidence: 99%

Application Prospect of Artificial Intelligence in Rehabilitation and Management of Myasthenia Gravis

Zhang

Dong

et al. 2021

BioMed Research International

View full text Add to dashboard Cite

Myasthenia gravis (MG) is a chronic autoimmune disease of the nervous system, which is still incurable. In recent years, with the progress of immunosuppressive and supportive treatment, the therapeutic effect of MG in the acute stage is satisfactory, and the mortality rate has been greatly reduced. However, there is still no consensus on how to conduct long-term management of stable MG, such as guiding patients to identify relapses, practice exercise, return to work and school, etc. In the international consensus guidance for management of myasthenia gravis published by the Myasthenia Gravis Foundation of America (MGFA) in 2020, for the first time, “the role of physical training/exercise in MG” was identified as the topic of discussion. Finally, due to a lack of high-quality evidence on physical training/exercise in patients with MG, the topic was excluded after the literature review. Therefore, this paper reviewed the current status of MG rehabilitation research and the difficulties faced by stable MG patients in self-management. It is suggested that we should take advantage of artificial intelligence (AI) and leverage it to develop the data-driven decision support platforms for MG management which can be used for adverse event monitoring, disease education, chronic management, and a wide variety of data collection and analysis.

show abstract

“…The use of Machine Learning (ML) has become more prominent in several areas of healthcare, such as diabetes, arthritis, cancer [6]- [8], with varying input formats ranging from tabular data in stored in relational databases to large scale image datasets [9]. Stemming from the involvement of data sensitivity in the medical domain is the necessity of gaining human trust towards ML application [10].…”

Section: Related Workmentioning

confidence: 99%

ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics

Kapcia

Eshkiki

Duell

et al. 2021

2021 IEEE 33rd International Conference on Tools With Artificial Intelligence (ICTAI)

View full text Add to dashboard Cite

The recent explosion of demand for Explainable AI (XAI) techniques has encouraged the development of various algorithms such as the Local Interpretable Model-Agnostic Explanations (LIME) and the SHapley Additive exPlanations ones (SHAP). Although these algorithms have been widely discussed by the AI community, their applications to wider domains are rare, potentially due to the lack of easy-to-use tools built around these methods. In this paper, we present ExMed, a tool that enables XAI data analytics for domain experts without requiring explicit programming skills. In particular, it supports data analytics with multiple feature attribution algorithms for explaining machine learning classifications and regressions. We illustrate its domain of applications on two real world medical case studies, with the first one analysing COVID-19 control measure effectiveness and the second one estimating lung cancer patient life expectancy from the artificial Simulacrum health dataset. We conclude that ExMed can provide researchers and domain experts with a tool that both concatenates flexibility and transferability of medical sub-domains and reveal deep insights from data.

show abstract

Application of Artificial Intelligence in Diabetes Education and Management: Present Status and Promising Prospect

Cited by 44 publications

References 50 publications

Applications of laboratory findings in the prevention, diagnosis, treatment, and monitoring of COVID-19

Applications of laboratory findings in the prevention, diagnosis, treatment, and monitoring of COVID-19

Application Prospect of Artificial Intelligence in Rehabilitation and Management of Myasthenia Gravis

ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics

Contact Info

Product

Resources

About