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      Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers


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          The manual diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.


          Our models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).


          Experimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD.

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          The online version of this article (doi:10.1186/s12859-016-1456-0) contains supplementary material, which is available to authorized users.

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          Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine.

          The clinical performance of a laboratory test can be described in terms of diagnostic accuracy, or the ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy refers to the quality of the information provided by the classification device and should be distinguished from the usefulness, or actual practical value, of the information. Receiver-operating characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. Furthermore, ROC plots occupy a central or unifying position in the process of assessing and using diagnostic tools. Once the plot is generated, a user can readily go on to many other activities such as performing quantitative ROC analysis and comparisons of tests, using likelihood ratio to revise the probability of disease in individual subjects, selecting decision thresholds, using logistic-regression analysis, using discriminant-function analysis, or incorporating the tool into a clinical strategy by using decision analysis.
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            Trends in the incidence and prevalence of Alzheimer's disease, dementia, and cognitive impairment in the United States.

            Declines in heart disease and stroke mortality rates are conventionally attributed to reductions in cigarette smoking, recognition and treatment of hypertension and diabetes, effective medications to improve serum lipid levels and to reduce clot formation, and general lifestyle improvements. Recent evidence implicates these and other cerebrovascular factors in the development of a substantial proportion of dementia cases. Analyses were undertaken to determine whether corresponding declines in age-specific prevalence and incidence rates for dementia and cognitive impairment have occurred in recent years. Data spanning 1 or 2 decades were examined from community-based epidemiological studies in Minnesota, Illinois, and Indiana, and from the Health and Retirement Study, which is a national survey. Although some decline was observed in the Minnesota cohort, no statistically significant trends were apparent in the community studies. A significant reduction in cognitive impairment measured by neuropsychological testing was identified in the national survey. Cautious optimism appears justified. Copyright © 2011 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.
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              Neuroimaging markers for the prediction and early diagnosis of Alzheimer's disease dementia.

              Alzheimer's disease (AD) is a progressive age-related neurodegenerative disease. At the time of clinical manifestation of dementia, significant irreversible brain damage is already present, rendering the diagnosis of AD at early stages of the disease an urgent prerequisite for therapeutic treatment to halt, or at least slow, disease progression. In this review, we discuss various neuroimaging measures that are proving to have potential value as biomarkers of AD pathology for the detection and prediction of AD before the onset of dementia. Recent studies that have identified AD-like structural and functional brain changes in elderly people who are cognitively within the normal range or who have mild cognitive impairment (MCI) are discussed. A dynamic sequence model of changes that occur in neuroimaging markers during the different disease stages is presented and the predictive value of multimodal neuroimaging for AD dementia is considered. Published by Elsevier Ltd.

                Author and article information

                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                14 January 2017
                14 January 2017
                : 18
                [1 ]Intelligent Health Research Group, School of Information Technology, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500 Malaysia
                [2 ]Jeffrey Cheah School of Medicine and Health Sciences, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500 Malaysia
                [3 ]Centre for Medical Informatics, Usher Institute for Population Health Sciences & Informatics, The University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG UK
                © The Author(s) 2017

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Funded by: Malaysian Ministry of Education Fundamental Research Grant Scheme (FRGS)
                Award ID: FRGS/2/2014/ICT07/ MUSM/03/1
                Award Recipient :
                Research Article
                Custom metadata
                © The Author(s) 2017

                Bioinformatics & Computational biology
                alzheimer’s disease,neurolinguistics,clinical diagnostics,prediction,machine learning


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