Artif Intell Med 2018 04 23;85:1-6. Epub 2018 Feb 23.
Department of Systems Management Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea. Electronic address:
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J Biomed Inform 2017 05 12;69:218-229. Epub 2017 Apr 12.
Center for Pattern Recognition and Data Analytics, Deakin University Geelong, Australia.
Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. Read More
Stud Health Technol Inform 2017 ;245:1277
Pfizer Investment Co. Ltd., Beijing, China.
In healthcare, applying deep learning models to electronic health records (EHRs) has drawn considerable attention. This sequential nature of EHR data make them wellmatched for the power of Recurrent Neural Network (RNN). In this poster, we propose "Deep Diabetologist" - using RNNs for EHR sequential data modeling to provide personalized hypoglycemic medication prediction for diabetic patients. Read More
J Diabetes Sci Technol 2008 Sep;2(5):792-801
Department of Bioengineering, University of Toledo, Toledo, Ohio 43606-3390, USA.
Background: A major difficulty in the management of diabetes is the optimization of insulin therapies to avoid occurrences of hypoglycemia and hyperglycemia. Many factors impact glucose fluctuations in diabetes patients, such as insulin dosage, nutritional intake, daily activities and lifestyle (e.g. Read More
J Med Assoc Thai 2001 Mar;84(3):332-8
Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
In the present study we developed and assessed the performance of a simple prediction rule and a neural network model to predict beta-cell reserve in young adults with diabetes. Eighty three young adults with diabetes were included in the study. All were less than 40 years old and without apparent secondary causes of diabetes. Read More