Outputs (4)

Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records (2021)
Journal Article
Alhassan, Z., Watson, M., Budgen, D., Alshammari, R., Alessa, A., & Al Moubayed, N. (2021). Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records. JMIR Medical Informatics, 9(5), Article e25237. https://doi.org/10.2196/25237

Background: Predicting the risk of glycated hemoglobin (HbA1c) elevation can help identify patients with the potential for developing serious chronic health problems such as diabetes. Early preventive interventions based upon advanced predictive mode... Read More about Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records.

Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction (2019)
Presentation / Conference Contribution
Alhassan, Z., Budgen, D., Alessa, A., Alshammari, R., Daghstani, T., & Al Moubayed, N. (2019, September). Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction. Presented at 28th International Conference on Artificial Neural Networks (ICANN2019), Munich, Germany

A pioneering study is presented demonstrating that the presence of high glycated haemoglobin (HbA1c) levels in a patient’s blood can be reliably predicted from routinely collected clinical data. This paves the way for performing early detection of Ty... Read More about Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction.

Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data using Deep Learning Models (2018)
Presentation / Conference Contribution
Alhassan, Z., McGough, S., Alshammari, R., Daghstani, T., Budgen, D., & Al Moubayed, N. (2018, October). Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data using Deep Learning Models. Presented at 27th International Conference on Artificial Neural Networks (ICANN)., Rhodes, Greece

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.

Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data (2018)
Presentation / Conference Contribution
Alhassan, Z., McGough, A. S., Alshammari, R., Daghstani, T., Budgen, D., & Al Moubayed, N. (2018, December). Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data. Presented at IEEE 17th International Conference on Machine Learning and Applications (ICMLA 2018)., Orlando, Fl, USA

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.