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


Autoři: Samuele Fiorini aff001;  Farshid Hajati aff002;  Annalisa Barla aff005;  Federico Girosi aff002
Působiště autorů: Iren S.p.A, Genoa, Italy aff001;  School of Information Technology and Engineering, MIT Sydney, Sydney, New South Wales, Australia aff002;  Translational Health Research Institute, Western Sydney University, Penrith, New South Wales, Australia aff003;  Capital Markets CRC, Sydney, New South Wales, Australia aff004;  Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy aff005;  Digital Health CRC, Sydney, New South Wales, Australia aff006
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0211844

Souhrn

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 https://github.com/samuelefiorini/tangle.

Klíčová slova:

Attention – Deep learning – Diabetes mellitus – Insulin – Machine learning – Medicare – Sequence databases – Word embedding


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Článek vyšel v časopise

PLOS One


2019 Číslo 10