EDDAMAP: efficient data-dependent approach for monitoring asymptomatic patient

BMC Medical Informatics and Decision Making

Daniel Adu-Gyamfi¹  Fengli Zhang²  Albert Kofi Kwansah Ansah³ 

[1] Department of Computer Science and Informatics, University of Energy and Natural Resources, P O Box 214, Sunyani, Ghana;School of Information and Software Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, China;School of Information and Software Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, China;School of Information and Software Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, China;Department of Computer Science and Engineering, University of Mines and Technology, P O Box 237, Tarkwa, Ghana;

关键词: Asymptomatic patient;    Data-dependent technique;    Health decision-support system;    Pandemic;    Patient monitoring system;    Place of interests;    Preventive intervention;    Public health;    Stay place;    Trajectory data mining;   

DOI  :  10.1186/s12911-020-01258-z

来源: Springer

【 摘 要 】

BackgroundA pandemic affects healthcare delivery and consequently leads to socioeconomic complications. During a pandemic, a community where there lives an asymptomatic patient (AP) becomes a potential endemic zone. Assuming we want to monitor the travel and/or activity of an AP in a community where there is a pandemic. Presently, most monitoring algorithms are relatively less efficient to find a suitable solution as they overlook the continuous mobility instances and activities of the AP over time. Conversely, this paper proposes an EDDAMAP as a compelling data-dependent technique and/or algorithm towards efficient continuous monitoring of the travel and/or activity of an AP.MethodsIn this paper, it is assumed that an AP is infected with a contagious disease in which the EDDAMAP technique exploits a GPS-enabled mobile device by tagging it to the AP along with its travel within a community. The technique further examines the Spatio-temporal trajectory of the AP to infer its spatial time-bounded activity. The technique aims to learn the travels of the AP and correlates them to its activities to derive some classes of point of interests (POIs) in a location. Further, the technique explores the natural occurring POIs via modelling to identify some regular stay places (SP) and present them as endemic zones. The technique adopts concurrent object feature localization and recognition, branch and bound formalism and graph theory to cater for the worst error-guaranteed approximation to obtain a valid and efficient query solution and also experiments with a real-world GeoLife dataset to confirm its performance.ResultsThe EDDAMAP technique proofs a compelling technique towards efficient monitoring of an AP in case of a pandemic.ConclusionsThe EDDAMAP technique will promote the discovery of endemic zones and hence some public healthcare facilities can rely on it to facilitate the design of patient monitoring system applications to curtail a global pandemic.

【 授权许可】

CC BY