Machine learning and dynamic user interfaces in a context aware nurse application environment

Abstract

The increasing usage of smartphones in daily life has received considerable attention in academic and industry driven research to be utilized in the health sector. There has been development of a variety of health-related smartphone applications. Currently, however, there are few to none applications based on nurses' historical or behavioral preferences. Mobile application development for the health care sector requires extensive attention to security, reliability, and accuracy. In nursing applications, the users are often required to navigate in hospital environments, select patients to support, read the patient history and set action points to assist the patient during their shift. Finally, they have to report their performance on patient related activities and other relevant information before they leave for the day. In a working day, a nurse often visits different locations such as the patient's room, different laboratories, and offices for filling reports. There is still a limited capability to access context relevant information on a smartphone with minimal recourse such as Wi-Fi triangulation. The Wi-Fi triangulation signals fluctuate significantly for indoor location positioning. Therefore, providing relevant location based services to a mobile subscriber has become challenging. This paper addresses this gap by applying machine learning and behavior analysis to anticipate the potential location of the nurse and provide the required services. The application concept was already presented at the IMCOM 2015 conference. This paper focuses on the process to ascertain a user's context, the process of analyzing and predicting user behavior, and finally, the process of displaying the information through a dynamically generated UI.

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

“…The technique can enhance development and decision development for wellspecified problems with the plentiful quality of information. It is best implemented to resolve intricacies in classification problems for which a vast number of data and numerous variables exist however a model or formula reciting is not identified (Ham et al 2017;Camastra et al 2013).…”

Location based services: ongoing evolution and research agenda

“…These rapid advances in context acquisition, representation and adaptation have triggered many context-aware LBS, such as for navigation and wayfinding (Raubal and Panov 2009), location recommendations and mobile guides (Huang 2016), mobile learning (i.e. learning experiences delivered via mobile devices) (Gómez et al 2014), healthcare (Ham, Dirin, and Laine 2017), and entertainment (Lee et al 2017).…”

CTASS: an intelligent framework for personalized travel behaviour advice to cardiac patients

“…Recently, smartphone apps which require minimal user intervention to provide rich information on patient physical activity (Ham et al 2016), have increased their use in cardiology. Smartphone-based cardiac health provision is delivered in two ways: 1) outpatient and inpatient continuous multi-parametric sign monitoring, and 2) prevention of cardiac events through risk factor management apps (weight management, physical inactivity, blood glucose control, smoking cessation) (Nguyen and Silva 2016).…”