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

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          Abstract

          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.

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          Most cited references5

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          Machine Learning and Data Mining Methods in Diabetes Research

          The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.
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            Visualizing data using t-SNE

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              An introduction to variable and feature selection

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draft
                Role: Data curation
                Role: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2019
                18 October 2019
                : 14
                : 10
                : e0211844
                Affiliations
                [1 ] Iren S.p.A, Genoa, Italy
                [2 ] School of Information Technology and Engineering, MIT Sydney, Sydney, New South Wales, Australia
                [3 ] Translational Health Research Institute, Western Sydney University, Penrith, New South Wales, Australia
                [4 ] Capital Markets CRC, Sydney, New South Wales, Australia
                [5 ] Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy
                [6 ] Digital Health CRC, Sydney, New South Wales, Australia
                Northwestern Polytechnical University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-9162-5767
                http://orcid.org/0000-0002-8573-5297
                Article
                PONE-D-19-02020
                10.1371/journal.pone.0211844
                6799900
                31626666
                a34d2cf0-c598-41a1-a4df-10d68703a81f
                © 2019 Fiorini et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 January 2019
                : 18 September 2019
                Page count
                Figures: 6, Tables: 3, Pages: 17
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100007366, Fondazione Italiana Sclerosi Multipla;
                Award ID: 2015/R/03
                Award Recipient :
                This research is funded by Multiple Sclerosis Italian Foundation (cod. 2015/R/03) with the following URL: https://www.aism.it (the recipient of the award is SF), and Capital Markets Cooperative Research Centre (CMCRC) Limited with the following URL: https://www.cmcrc.com and Australian Institute of Health and Welfare (AIHW) with the following URL: https://www.aihw.gov.au (the recipient of the award is FH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Computer and Information Sciences
                Information Technology
                Natural Language Processing
                Word Embedding
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Deep Learning
                Social Sciences
                Political Science
                Public Policy
                Medicare
                Research and Analysis Methods
                Database and Informatics Methods
                Biological Databases
                Sequence Databases
                Research and Analysis Methods
                Database and Informatics Methods
                Bioinformatics
                Sequence Analysis
                Sequence Databases
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Attention
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Attention
                Social Sciences
                Psychology
                Cognitive Psychology
                Attention
                Medicine and Health Sciences
                Endocrinology
                Diabetic Endocrinology
                Insulin
                Biology and Life Sciences
                Biochemistry
                Hormones
                Insulin
                Custom metadata
                Data cannot be shared publicly because they may contain potentially sensitive and identifying information (data access restriction are imposed by Australian Government). Data are available from the Australian Department of Health Institutional Data Access / Ethics Committee (contact via data.release@ 123456health.gov.au ) for researchers who meet the criteria for access to confidential data.

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