Personalized prediction of drug efficacy for diabetes treatment via patient-level sequential modeling with neural networks.

Authors:

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
http://dx.doi.org/10.1016/j.artmed.2018.02.004DOI Listing

Still can't find the full text of the article?

Sign up to send a request to the authors directly.
April 2018
16 Reads

Publication Analysis

Top Keywords

previous records
12
sequential modeling
8
patient-level sequential
8
current prescription
8
neural networks
8
sequential dependencies
8
prescription efficacy
8
personalized prediction
8
records
6
efficacies sequential
4
prescriptions efficacies
4
records inputs
4
inputs predict
4
render personalized
4
embedded records
4
dependencies embedded
4
sequence previous
4
records prescriptions
4
multiple records
4
patient associated
4

Similar Publications