Mobile personal health records for cardiovascular patients

Ouhbi, Sofía; Idri, Ali; Fernández-Alemán, José Luis; Toval, Ambrosio

doi:10.1109/icocs.2015.7483226

Cited by 14 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From another point of view, the training time and the epoch numbers were low, which means that the model can be used on a mobile platform or wearable device [7] which require rapidity [8]. In fact, knowing the importance of the mobile personal health records in health management [28], [29], the deployment of our model on a mobile platform is another direction to explore. Toward that aim, we used TensorFlow as it provides an end-to-end support which goes till the deployment of the models on mobile apps, cloud server and others [30].…”

Section: Discussionmentioning

confidence: 99%

Predicting blood glucose using an LSTM Neural Network

Idriss¹,

Idri

Abnane

et al. 2019

Annals of Computer Science and Information Systems

Self Cite

View full text Add to dashboard Cite

Diabetes self-management relies on the blood glucose prediction as it allows taking suitable actions to prevent low or high blood glucose level. In this paper, we propose a deep learning neural network (NN) model for blood glucose prediction. It is a sequential one using a Long-Short-Term Memory (LSTM) layer with two fully connected layers. Several experiments were carried out over data of 10 diabetic patients to decide on the model's parameters in order to identify the best variant of it. The performance of the proposed LSTM NN measured in terms of root mean square error (RMSE) was compared with the ones of an existing LSTM and an autoregressive (AR) models. The results show that our LSTM NN is significantly more accurate; in fact, it outperforms the existing LSTM model for all patients and outperforms the AR model in 9 over 10 patients, besides, the performance differences were assessed by the Wilcoxon statistical test. Furthermore, the mean of the RMSE of our model was 12.38 mg/dl while it was 28.84 mg/dl and 50.69 mg/dl for AR and the existing LSTM respectively.

show abstract

Section: Discussionmentioning

confidence: 99%

Predicting blood glucose using an LSTM Neural Network

Idriss¹,

Idri

Abnane

et al. 2019

Annals of Computer Science and Information Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…It has been foreseen that ICT will facilitate the shifting center of care from physicians to patients and to self-management of diseases. Current digital solutions such as home nursing [3], health monitoring systems [4], mobile applications [5], electronic medical records (EMR) [6], electronic health records (EHR) [7], personal health records (PHRs) [8] and mobile PHRs (mPHRs) [9], can support this argument.…”

Section: Introductionmentioning

confidence: 99%

“…PHRs can stand-alone on personal computers [17] or can be a part of a system administered by healthcare organizations [8]. Besides PHRs and EHRs, mPHRs allow users to access and coordinate their life long health information through their smartphones and enable them to distribute their personal health data (PHD) when necessary [9,18]. The system combining EHR and mPHR is considered a powerful tool that can facilitate seamless communication between patients and medical professionals leading to faster and informed decisions, especially in cases of patients with chronic conditions [19].…”

Section: Introductionmentioning

confidence: 99%

Personal health data: A systematic mapping study

Karampela

Ouhbi

Isomursu

2018

International Journal of Medical Informatics

View full text Add to dashboard Cite

Sustainability requirements for connected health applications

Ouhbi

Fernández-Alemán

Toval

et al. 2017

J Software Evolu Process

View full text Add to dashboard Cite

Connected health applications are efficient tools to improve health care services. The aim of this paper is to define a reusable requirements repository (catalog) for sustainable connected health applications based on the main related software engineering standards, e‐health technology standards, and literature. The catalog contains requirements which cover the individual, social, environmental, and technical dimensions of sustainability. The requirements presented in this paper can be very useful to connected health application stakeholders and can be adapted to specific m‐health applications. Moreover, the requirements catalog can be used to generate a checklist of requirements to evaluate and audit connected health applications. An application example is presented to show that the evaluation of connected health applications using the generated checklist is realistic.

show abstract

Mobile personal health records for cardiovascular patients

Cited by 14 publications

References 26 publications

Predicting blood glucose using an LSTM Neural Network

Predicting blood glucose using an LSTM Neural Network

Personal health data: A systematic mapping study

Sustainability requirements for connected health applications

Contact Info

Product

Resources

About