All Outputs (4)

Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data using Deep Learning Models (2018)
Conference Proceeding
Alhassan, Z., McGough, S., Alshammari, R., Daghstani, T., Budgen, D., & Al Moubayed, N. (2018). Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data using Deep Learning Models. In V. Kůrková, Y. Manolopoulos, B. Hammer, L. Iliadis, & I. Maglogiannis (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, proceedings, part III (468-478). https://doi.org/10.1007/978-3-030-01424-7_46

Clinical data is usually observed and recorded at irregular intervals and includes: evaluations, treatments, vital sign and lab test results. These provide an invaluable source of information to help diagnose and understand medical conditions. In thi... Read More about Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data using Deep Learning Models.

Factors impeding the effective utilisation of an electronic patient report form during handover from an ambulance to an emergency department (2018)
Journal Article
Altuwaijri, E., Budgen, D., & Maxwell, S. (2019). Factors impeding the effective utilisation of an electronic patient report form during handover from an ambulance to an emergency department. Health Informatics Journal, 25(4), 1705-1721. https://doi.org/10.1177/1460458218797984

We investigated the reasons why the transition from paper to electronically formatted records during patient handover between ambulance crews and emergency department staff in a North East England Emergency Department has not always been viewed posit... Read More about Factors impeding the effective utilisation of an electronic patient report form during handover from an ambulance to an emergency department.

SSM: Scheduling Security Model for a Cloud Environment (2018)
Conference Proceeding
Sheikh, A., Munro, M., & Budgen, D. (2018). SSM: Scheduling Security Model for a Cloud Environment. In Proceedings of the 2018 2nd International Conference on Cloud and Big Data Computing (ICCBDC'18) (11-15). https://doi.org/10.1145/3264560.3264568

Scheduling in the cloud is a complex task due to the number and variety of resources available and the volatility of usage-patterns of resources considering that the resource setting is on the service provider. This complexity is compounded further w... Read More about SSM: Scheduling Security Model for a Cloud Environment.

Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data (2018)
Conference Proceeding
Alhassan, Z., McGough, A. S., Alshammari, R., Daghstani, T., Budgen, D., & Al Moubayed, N. (2018). Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data. In 17th IEEE International Conference on Machine Learning and Applications (ICMLA) ; proceedings (541-546). https://doi.org/10.1109/icmla.2018.00087

Clinical data, such as evaluations, treatments, vital sign and lab test results, are usually observed and recorded in hospital systems. Making use of such data to help physicians to evaluate the mortality risk of in-hospital patients provides an inva... Read More about Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data.