scholarly journals Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Ji Hwan Park ◽  
Han Eol Cho ◽  
Jong Hun Kim ◽  
Melanie M. Wall ◽  
Yaakov Stern ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Health Data ◽  
Administrative Health Data

scholarly journals Machine Learning Prediction of Incidence of Alzheimer’s Disease Using Large-Scale Administrative Health Data

2019 ◽  
Author(s):  
Ji Hwan Park ◽  
Han Eol Cho ◽  
Jong Hun Kim ◽  
Melanie Wall ◽  
Yaakov Stern ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Risk Prediction ◽  
Clinical Features ◽  
Large Scale ◽  
Health Data ◽  
Learning Models ◽  
Administrative Health Data ◽  
History Of ◽  
Machine Learning Models

AbstractNationwide population-based cohort provides a new opportunity to build a completely automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N=40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness, and socio-demographics. To define incident AD two operational definitions were considered: “definite AD” with diagnostic codes and dementia medication (n=614) and “probable AD” with only diagnosis (n=2,026). We trained and validated a random forest, support vector machine, and logistic regression to predict incident AD in 1,2,3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age, and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

scholarly journals Alzheimer's Disease Risk Assessment Using Large-Scale Machine Learning Methods

PLoS ONE ◽  
2013 ◽  
Vol 8 (11) ◽  
pp. e77949 ◽  
Author(s):  
Ramon Casanova ◽  
Fang-Chi Hsu ◽  
Kaycee M. Sink ◽  
Stephen R. Rapp ◽  
Jeff D. Williamson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Risk Assessment ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Disease Risk ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Machine Learning Driven Profiling of Cerebrospinal Fluid Core Biomarkers in Alzheimer’s Disease and Other Neurological Disorders

2021 ◽  
Vol 15 ◽  
Author(s):  
Giovanni Bellomo ◽  
Antonio Indaco ◽  
Davide Chiasserini ◽  
Emanuela Maderna ◽  
Federico Paolini Paoletti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurological Disorders ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Neurological Diseases ◽  
Gaussian Mixture ◽  
Control Group ◽  
T Tau

Amyloid-beta (Aβ) 42/40 ratio, tau phosphorylated at threonine-181 (p-tau), and total-tau (t-tau) are considered core biomarkers for the diagnosis of Alzheimer’s disease (AD). The use of fully automated biomarker assays has been shown to reduce the intra- and inter-laboratory variability, which is a critical factor when defining cut-off values. The calculation of cut-off values is often influenced by the composition of AD and control groups. Indeed, the clinically defined AD group may include patients affected by other forms of dementia, while the control group is often very heterogeneous due to the inclusion of subjects diagnosed with other neurological diseases (OND). In this context, unsupervised machine learning approaches may overcome these issues providing unbiased cut-off values and data-driven patient stratification according to the sole distribution of biomarkers. In this work, we took advantage of the reproducibility of automated determination of the CSF core AD biomarkers to compare two large cohorts of patients diagnosed with different neurological disorders and enrolled in two centers with established expertise in AD biomarkers. We applied an unsupervised Gaussian mixture model clustering algorithm and found that our large series of patients could be classified in six clusters according to their CSF biomarker profile, some presenting a typical AD-like profile and some a non-AD profile. By considering the frequencies of clinically defined OND and AD subjects in clusters, we subsequently computed cluster-based cut-off values for Aβ42/Aβ40, p-tau, and t-tau. This approach promises to be useful for large-scale biomarker studies aimed at providing efficient biochemical phenotyping of neurological diseases.

Alzheimer's Disease Brain Areas: The Machine Learning Support for Blind Localization

2016 ◽  
Vol 13 (5) ◽  
pp. 498-508 ◽  
Author(s):  
V. Vigneron ◽  
A. Kodewitz ◽  
A. M. Tome ◽  
S. Lelandais ◽  
E. Lang
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Learning Support ◽  
Brain Areas

Dehydroepiandrosterone (DHEA) and its Sulphate (DHEAS) in Alzheimer’s Disease

2020 ◽  
Vol 17 (2) ◽  
pp. 141-157 ◽  
Author(s):  
Dubravka S. Strac ◽  
Marcela Konjevod ◽  
Matea N. Perkovic ◽  
Lucija Tudor ◽  
Gordana N. Erjavec ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Therapeutic Potential ◽  
Relevant Literature ◽  
Comprehensive Overview ◽  
Brain Functions ◽  
Specific Effects ◽  
And Behavior

Background: Neurosteroids Dehydroepiandrosterone (DHEA) and Dehydroepiandrosterone Sulphate (DHEAS) are involved in many important brain functions, including neuronal plasticity and survival, cognition and behavior, demonstrating preventive and therapeutic potential in different neuropsychiatric and neurodegenerative disorders, including Alzheimer’s disease. Objective: The aim of the article was to provide a comprehensive overview of the literature on the involvement of DHEA and DHEAS in Alzheimer’s disease. Method: PubMed and MEDLINE databases were searched for relevant literature. The articles were selected considering their titles and abstracts. In the selected full texts, lists of references were searched manually for additional articles. Results: We performed a systematic review of the studies investigating the role of DHEA and DHEAS in various in vitro and animal models, as well as in patients with Alzheimer’s disease, and provided a comprehensive discussion on their potential preventive and therapeutic applications. Conclusion: Despite mixed results, the findings of various preclinical studies are generally supportive of the involvement of DHEA and DHEAS in the pathophysiology of Alzheimer’s disease, showing some promise for potential benefits of these neurosteroids in the prevention and treatment. However, so far small clinical trials brought little evidence to support their therapy in AD. Therefore, large-scale human studies are needed to elucidate the specific effects of DHEA and DHEAS and their mechanisms of action, prior to their applications in clinical practice.

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease

2020 ◽  
Vol 16 (1s) ◽  
pp. 1-35 ◽  
Author(s):  
M. Tanveer ◽  
B. Richhariya ◽  
R. U. Khan ◽  
A. H. Rashid ◽  
P. Khanna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Machine Learning Techniques ◽  
Learning Techniques

Structural Changes in Thalamic Nuclei Across Prodromal and Clinical Alzheimer’s Disease

2021 ◽  
pp. 1-11
Author(s):  
Adam S. Bernstein ◽  
Steven Z. Rapcsak ◽  
Michael Hornberger ◽  
Manojkumar Saranathan ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Structural Changes ◽  
Thalamic Nuclei ◽  
Medial Temporal Lobes ◽  
Cognitive Changes ◽  
Temporal Lobes ◽  
Medial Geniculate ◽  
Healthy Control

Background: Increasing evidence suggests that thalamic nuclei may atrophy in Alzheimer’s disease (AD). We hypothesized that there will be significant atrophy of limbic thalamic nuclei associated with declining memory and cognition across the AD continuum. Objective: The objective of this work was to characterize volume differences in thalamic nuclei in subjects with early and late mild cognitive impairment (MCI) as well as AD when compared to healthy control (HC) subjects using a novel MRI-based thalamic segmentation technique (THOMAS). Methods: MPRAGE data from the ADNI database were used in this study (n = 540). Healthy control (n = 125), early MCI (n = 212), late MCI (n = 114), and AD subjects (n = 89) were selected, and their MRI data were parcellated to determine the volumes of 11 thalamic nuclei for each subject. Volumes across the different clinical subgroups were compared using ANCOVA. Results: There were significant differences in thalamic nuclei volumes between HC, late MCI, and AD subjects. The anteroventral, mediodorsal, pulvinar, medial geniculate, and centromedian nuclei were significantly smaller in subjects with late MCI and AD when compared to HC subjects. Furthermore, the mediodorsal, pulvinar, and medial geniculate nuclei were significantly smaller in early MCI when compared to HC subjects. Conclusion: This work highlights nucleus specific atrophy within the thalamus in subjects with early and late MCI and AD. This is consistent with the hypothesis that memory and cognitive changes in AD are mediated by damage to a large-scale integrated neural network that extends beyond the medial temporal lobes.

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