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:

Patients with type 2 diabetes mellitus are generally under continuous long-term medical treatment based on anti-diabetic drugs to achieve the desired glucose level. Thus, each patient is associated with a sequence of multiple records for prescriptions and their efficacies. Sequential dependencies are embedded in these records as personal factors so that previous records affect the efficacy of the current prescription for each patient. In this study, we present a patient-level sequential modeling approach utilizing the sequential dependencies to render a personalized prediction of the prescription efficacy. The prediction models are implemented using recurrent neural networks that use the sequence of all the previous records as inputs to predict the prescription efficacy at the time the current prescription is provided for each patient. Through this approach, each patient's historical records are effectively incorporated into the prediction. The experimental results of both the regression and classification analyses on real-world data demonstrate improved prediction accuracy, particularly for those patients having multiple previous records.

Download full-text PDF

Source Listing
April 2018
9 Reads

Publication Analysis

Top Keywords

previous records
sequential modeling
patient-level sequential
current prescription
neural networks
sequential dependencies
prescription efficacy
personalized prediction
efficacies sequential
prescriptions efficacies
records inputs
inputs predict
render personalized
embedded records
dependencies embedded
sequence previous
records prescriptions
multiple records
patient associated

Similar Publications

Predicting healthcare trajectories from medical records: A deep learning approach.

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

View Article
May 2017

Deep Diabetologist: Learning to Prescribe Hypoglycemic Medications with Recurrent Neural Networks.

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

View Article
June 2018

Development of a neural network for prediction of glucose concentration in type 1 diabetes patients.

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

View Article
September 2008

A simple prediction rule and a neural network model to predict pancreatic beta-cell reserve in young adults with diabetes mellitus.

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

View Article
March 2001