Alhassan, Zakhriya and McGough, Stephen and Alshammari, Riyad and Daghstani, Tahini and Budgen, David and Al Moubayed, Noura (2018) 'Type-2 diabetes mellitus diagnosis from time series clinical data using deep learning models.', in Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, proceedings, part III. , pp. 468-478. Lecture notes in computer science., III (11141).
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.
|Item Type:||Book chapter|
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1007/978-3-030-01424-7_46|
|Publisher statement:||This is a post-peer-review, pre-copyedit version of an article published in Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, proceedings, part III. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-01424-7_46|
|Date accepted:||12 July 2018|
|Date deposited:||13 August 2018|
|Date of first online publication:||27 September 2018|
|Date first made open access:||27 September 2019|
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