Predicting diabetes second-line therapy initiation in the Australian population via time span-guided neural attention network.

Icon for Public Library of Science Icon for PubMed Central Related Articles

Predicting diabetes second-line therapy initiation in the Australian population via time span-guided neural attention network.

PLoS One. 2019;14(10):e0211844

Authors: Fiorini S, Hajati F, Barla A, Girosi F

INTRODUCTION: The first line of treatment for people with Diabetes mellitus is metformin. However, over the course of the disease metformin may fail to achieve appropriate glycemic control, and a second-line therapy may become necessary. In this paper we introduce Tangle, a time span-guided neural attention model that can accurately and timely predict the upcoming need for a second-line diabetes therapy from administrative data in the Australian adult population. The method is suitable for designing automatic therapy review recommendations for patients and their providers without the need to collect clinical measures.
DATA: We analyzed seven years of de-identified records (2008-2014) of the 10% publicly available linked sample of Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) electronic databases of Australia.
METHODS: By design, Tangle inherits the representational power of pre-trained word embedding, such as GloVe, to encode sequences of claims with the related MBS codes. Moreover, the proposed attention mechanism natively exploits the information hidden in the time span between two successive claims (measured in number of days). We compared the proposed method against state-of-the-art sequence classification methods.
RESULTS: Tangle outperforms state-of-the-art recurrent neural networks, including attention-based models. In particular, when the proposed time span-guided attention strategy is coupled with pre-trained embedding methods, the model performance reaches an Area Under the ROC Curve of 90%, an improvement of almost 10 percentage points over an attentionless recurrent architecture.
IMPLEMENTATION: Tangle is implemented in Python using Keras and it is hosted on GitHub at

PMID: 31626666 [PubMed – indexed for MEDLINE]

Source link

Related posts

Opinion: An NHS fit for 2030 – Delivering value to citizens


More perspectives from our readers on the COVID-19 health emergency


Crowdsourced Natural Language or Speech Training – Use Cases and Explanation


This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Accept Read More

Privacy & Cookies Policy


COVID-19 (Coronavirus) is a new illness that is having a major effect on all businesses globally LIVE COVID-19 STATISTICS FOR World