Publication detailsAlhassan, Zakhriya, McGough, Stephen, Alshammari, Riyad, Daghstani, Tahini, Budgen, David & Al Moubayed, Noura (2018), Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data using Deep Learning Models, in Kůrková, Věra, Manolopoulos, Yannis, Hammer, Barbara, Iliadis, Lazaros & Maglogiannis, Ilias eds, Lecture Notes in Computer Science 1141: International Conference on Artificial Neural Networks (ICANN). Rhodes, Greece, Springer, 468-478.
- Publication type: Conference Paper
- ISSN/ISBN: 0941-0643, 1433-3058, 9783030014230, 9783030014247
- DOI: 10.1007/978-3-030-01424-7_46
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
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 this work, we introduce the largest patient records dataset in diabetes research: King Abdullah International Research Centre Diabetes (KAIMRCD) which includes over 14k patient data. KAIMRCD contains detailed information about the patient’s visit and have been labelled against T2DM by clinicians. The data is processed as time series and then investigated using temporal predictive Deep Learning models with the goal of diagnosing Type 2 Diabetes Mellitus (T2DM). Long Short-Term Memory (LSTM) and Gated-Recurrent Unit (GRU) are trained on KAIMRCD and are demonstrated here to outperform classical machine learning approaches in the literature with over 97% accuracy.