Machine Learning-Based Method for Personalized and Cost-Effective Detection of Alzheimer's Disease

Escudero, Javier; Ifeachor, Emmanuel; Zajicek, John; Green, C.; Shearer, James; Pearson, Stephen

doi:10.1109/tbme.2012.2212278

Cited by 86 publications

(58 citation statements)

References 13 publications

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“…Using their framework, a classification accuracy of 89.0% was obtained for HC versus AD and delineated a set of brain regions which were in agreement with previous clinical studies [76]. Recently, Escudero [77] proposed a more conventional ML approach for the identification of biomarkers for AD. Using a subset of the ADNI dataset, a comparable classification accuracy of 84.9% for HC versus AD was obtained.…”

Section: Hybrid Mechanistic Models and MLsupporting

confidence: 77%

Bridging Paradigms: Hybrid Mechanistic-Discriminative Predictive Models

Doyle

Tsaneva-Atansaova

Harte

et al. 2013

IEEE Trans. Biomed. Eng.

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Many disease processes are extremely complex and characterized by multiple stochastic processes interacting simultaneously. Current analytical approaches have included mechanistic models and machine learning (ML), which are often treated as orthogonal viewpoints. However, to facilitate truly personalized medicine, new perspectives may be required. This paper reviews the use of both mechanistic models and ML in healthcare as well as emerging hybrid methods, which are an exciting and promising approach for biologically based, yet data-driven advanced intelligent systems.

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Section: Hybrid Mechanistic Models and MLsupporting

confidence: 77%

Bridging Paradigms: Hybrid Mechanistic-Discriminative Predictive Models

Doyle

Tsaneva-Atansaova

Harte

et al. 2013

IEEE Trans. Biomed. Eng.

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“…In general, these methodological approaches underlie the foundation for implementation of personalized medicine, which could potentially revolutionize the way we diagnose and treat disease. Although the conceptual framework of personalized biomarkers for disease detection has only been recently advanced, it is rapidly gaining traction (40-42). For example, a recent study leveraged massive parallel sequencing to develop personalized biomarkers capable of identifying translocations in solid tumors (43).…”

Section: Discussionmentioning

confidence: 99%

Contextualised urinary biomarker analysis facilitates diagnosis of paediatric obstructive sleep apnoea

et al. 2014

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Background: Intrinsic variance of the urine proteome limits the discriminative power of proteomic analysis and complicates potential biomarker detection in the context of pediatric sleep disorders. Methods and Results: Using a rigorous workflow for proteomic analysis of urine, we demonstrate that gender and diurnal effects constitute two important sources of variability in healthy children. In the context of disease, complex pathophysiological perturbations magnify these proteomic differences and therefore require contextualized biomarker analysis. Indeed, by performing biomarker discovery in a gender and diurnal-dependent manner, we identified ~30-fold more candidate biomarkers of pediatric obstructive sleep apnea (OSA), a highly prevalent condition in children characterized by repetitive episodes of intermittent hypoxia and hypercapnia, and sleep fragmentation in the context of recurrent upper airway obstructive events during sleep. Remarkably, biomarkers were highly specific for gender and sampling time since poor overlap (~3%) was observed in the proteins identified in boys and girls across morning and bedtime samples. Conclusions: Since no clinical basis to explain gender-specific effects in OSA or healthy children is apparent, we propose that implementation of contextualized biomarker strategies will be applicable to a broad range of human diseases, and may be specifically applicable to pediatric OSA.

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“…Today’s health care environment requires biomarker tests that are maximally informative but minimally expensive. Escudero et al [420] proposed an approach to minimize the number of biomarkers required for diagnosis. This personalized and iterative approach initially attempts to classify the patient by comparison of available variables with data from a pool of local diagnosed patients.…”

Section: Methods Papersmentioning

confidence: 99%

“…The PredictAD appears capable of beneficially combining data from different modalities and therefore may prove a useful adjunct for decision support in a clinical environment. The study of Escudero et al [420] addressed cost-effectiveness of biomarker-based diagnosis. They found that when the number of biomarkers was minimized, classification accuracies were comparable to methods that include all biomarkers.…”

Section: Studies Of the Adni Cohortmentioning

confidence: 99%

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Weiner

Veitch

Aisen

et al. 2015

Alzheimer's & Dementia

273

224

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The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer’s disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer’s Dis 2006;9(Suppl 3):151–3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [18F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding ...

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Machine Learning-Based Method for Personalized and Cost-Effective Detection of Alzheimer's Disease

Cited by 86 publications

References 13 publications

Bridging Paradigms: Hybrid Mechanistic-Discriminative Predictive Models

Bridging Paradigms: Hybrid Mechanistic-Discriminative Predictive Models

Contextualised urinary biomarker analysis facilitates diagnosis of paediatric obstructive sleep apnoea

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

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