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A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction

Annette Spooner, Emily Chen, Arcot Sowmya, Perminder Sachdev, Nicole A. Kochan, Julian Trollor, Henry Brodaty
2020 Scientific Reports  
methods when modelling high-dimensional, heterogeneous, clinical data.  ...  This work compares the performance and stability of ten machine learning algorithms, combined with eight feature selection methods, capable of performing survival analysis of high-dimensional, heterogeneous  ...  As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.  ... 
doi:10.1038/s41598-020-77220-w pmid:33230128 fatcat:vjo33snfzfajbmaehcrrpte3yy

Trace Ratio Linear Discriminant Analysis for Medical Diagnosis: A Case Study of Dementia

Mingbo Zhao, Rosa H. M. Chan, Peng Tang, Tommy W. S. Chow, Savio W. H. Wong
2013 IEEE Signal Processing Letters  
This novel method can be integrated with advanced missing value imputation method and utilized for the analysis of the nonlinear datasets in many real-world medical diagnosis problems.  ...  In this paper, we introduce trace ratio linear discriminant analysis (TR-LDA) for dementia diagnosis. An improved ITR algorithm (iITR) is developed to solve the TR-LDA problem.  ...  Besser for database support. The NACC database was supported by NIA Grant UO1 AG016976.  ... 
doi:10.1109/lsp.2013.2250281 pmid:24077217 pmcid:PMC3784002 fatcat:gj2y3vnrxjdn7n2wwopejazb6q

Dementia Patient Segmentation Using EMR Data Visualization: A Design Study

Hyoji Ha, Jihye Lee, Hyunwoo Han, Sungyun Bae, Sangjoon Son, Changhyung Hong, Hyunjung Shin, Kyungwon Lee
2019 International Journal of Environmental Research and Public Health  
It facilitates the investigation of disease patterns and efficient treatment via collaboration with data scientists. (2) Methods: In this study, we present multidimensional visual tools for the analysis  ...  based on EMR data via visual analysis.  ...  For these reasons, the medical community has called for a need to develop a simplified model for dementia diagnosis to reduce expenses, based on patient data analysis.  ... 
doi:10.3390/ijerph16183438 pmid:31527556 pmcid:PMC6765847 fatcat:hpd2h2k4wbedlelwccghjylqcq

Automated identification of dementia using medical imaging: a survey from a pattern classification perspective

Chuanchuan Zheng, Yong Xia, Yongsheng Pan, Jinhu Chen
2015 Brain Informatics  
, and four categories of classifiers, including the linear discriminant analysis, Bayes classifiers, support vector machines, and artificial neural networks.  ...  We also compare the reported performance of many recently published dementia identification algorithms.  ...  [54] used a high-dimensional template to wrap original data and employed a watershed method to get the robust features.  ... 
doi:10.1007/s40708-015-0027-x pmid:27747596 pmcid:PMC4883162 fatcat:yefc226j4za75afkkkolg4m5n4

Cooperative Correlational and Discriminative Ensemble Classifier Learning for Early Dementia Diagnosis Using Morphological Brain Multiplexes

Rory Raeper, Anna Lisowska, Islem Rekik
2018 IEEE Access  
One potential factor for their reduced accuracies was the addition of principle component analysis (PCA) as neither method can directly handle high-dimensional data.  ...  In particular, for ECFS method, we applied PCA to the deep convolutional multiplexes as it was unable to handle high dimensional data.  ... 
doi:10.1109/access.2018.2863657 fatcat:cuqkferfzbgn7itr4eelg5mkgm

Individual Subject Classification of Mixed Dementia from Pure Subcortical Vascular Dementia Based on Subcortical Shape Analysis

Hee Jin Kim, Jeonghun Kim, Hanna Cho, Byoung Seok Ye, Cindy W. Yoon, Young Noh, Geon Ha Kim, Jae Hong Lee, Jae Seung Kim, Yearn Seong Choe, Kyung-Han Lee, Chang-Hun Kim (+5 others)
2013 PLoS ONE  
Identifying mixed dementia from SVaD is important because potential amyloid-targeted therapies may be effective for treatment in mixed dementia.  ...  An incremental learning method using hippocampal and amygdalar shape distinguishes mixed dementia from pure SVaD.  ...  Our classification method provides a useful tool for classifying mixed dementia from pure SVaD with relatively high accuracy, which has clinical implications for diagnosis and treatment.  ... 
doi:10.1371/journal.pone.0075602 pmid:24130724 pmcid:PMC3794958 fatcat:phb2ezzezjaurm5qcwn63pons4

Role of EEG as Biomarker in the Early Detection and Classification of Dementia

Noor Kamal Al-Qazzaz, Sawal Hamid Bin MD. Ali, Siti Anom Ahmad, Kalaivani Chellappan, Md. Shabiul Islam, Javier Escudero
2014 The Scientific World Journal  
This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.  ...  The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression  ...  These methods are well-established methods for dimensionality reduction. PCA is a widely used method to avoid the redundancy because of high-dimensional data [125] [126] [127] .  ... 
doi:10.1155/2014/906038 pmid:25093211 pmcid:PMC4100295 fatcat:l5qz7chnzzgizaoxjpvshuyqr4

Three-dimensional stereotactic surface projection in the statistical analysis of single photon emission computed tomography data for distinguishing between Alzheimer's disease and depression

Eiji Kirino
2017 World Journal of Psychiatry  
Of these, Factor 1 could be interpreted as favouring a tendency for AD, Factor 2 as favouring a tendency for pseudo-dementia, and Factor 3 as favouring a depressive tendency.  ...  Factor analysis identified three significant factors.  ...  In the analysis of the score for each factor for each type, Type A (true AD) had high scores for Factor 1 (tendency for AD), type B (pseudo-dementia) had high scores for Factor 2 (tendency for pseudo-dementia  ... 
doi:10.5498/wjp.v7.i2.121 pmid:28713690 pmcid:PMC5491477 fatcat:jyqwu35tpvdlrjpx6hk327owqi

Multi-Method Analysis of Medical Records and MRI Images for Early Diagnosis of Dementia and Alzheimer's Disease Based on Deep Learning and Hybrid Methods

Badiea Abdulkarem Mohammed, Ebrahim Mohammed Senan, Taha H. Rassem, Nasrin M. Makbol, Adwan Alownie Alanazi, Zeyad Ghaleb Al-Mekhlafi, Tariq S. Almurayziq, Fuad A. Ghaleb
2021 Electronics  
For the OASIS dataset, we balanced the dataset, replaced the missing values, and applied the t-Distributed Stochastic Neighbour Embedding algorithm (t-SNE) to represent the high-dimensional data in the  ...  All of the machine learning algorithms, namely, Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbours (KNN), achieved high performance for diagnosing dementia.  ...  Acknowledgments: We would like to acknowledge the Scientific Research Deanship at the University of Ha'il, Saudi Arabia, for funding this research.  ... 
doi:10.3390/electronics10222860 fatcat:2vgkbev6ifdp7adweaoesanr3a

Guidelines for reporting methodological challenges and evaluating potential bias in dementia research

Jennifer Weuve, Cécile Proust-Lima, Melinda C. Power, Alden L. Gross, Scott M. Hofer, Rodolphe Thiébaut, Geneviève Chêne, M. Maria Glymour, Carole Dufouil
2015 Alzheimer's & Dementia  
months, years, or even decades; (4) time-varying measurements; and (5) high-dimensional data.  ...  We present findings from a large scientific working group on research methods for clinical and population studies of dementia, which identified five categories of methodological challenges as follows:  ...  High-dimensional data analysis generates multiple statistical comparisons/tests potentially addressed by various statistical corrections (family-wise error and false discovery).  ... 
doi:10.1016/j.jalz.2015.06.1885 pmid:26397878 pmcid:PMC4655106 fatcat:ptlg2oi6jfa6bpfxouc4zi2yby

Full exploitation of high dimensionality in brain imaging: The JPND working group statement and findings

Hieab H.H. Adams, Gennady V. Roshchupkin, Charles DeCarli, Barbara Franke, Hans J. Grabe, Mohamad Habes, Neda Jahanshad, Sarah E. Medland, Wiro Niessen, Claudia L. Satizabal, Reinhold Schmidt, Sudha Seshadri (+5 others)
2019 Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring  
The combination of multiple types of high-throughput data for complex analyses, however, has been limited by analytical and logistic resources to handle high-dimensional data sets.  ...  For instance, high-dimensional features, such as voxels and vertices, which are common in neuroimaging, remain difficult to harmonize.  ...  Future directions: In this report, we propose a framework for analyzing high-dimensional data. Still, this is only an initial step.  ... 
doi:10.1016/j.dadm.2019.02.003 pmid:30976649 pmcid:PMC6441785 fatcat:gcfwt7hjijcedl2m5duecwx5gy

Usefulness of 3-dimensional stereotactic surface projection FDG PET images for the diagnosis of dementia

Jahae Kim, Sang-Geon Cho, Minchul Song, Sae-Ryung Kang, Seong Young Kwon, Kang-Ho Choi, Seong-Min Choi, Byeong-Chae Kim, Ho-Chun Song
2016 Medicine  
of dementia compared with the visual method alone, except for AD/MCI specificity and FTD sensitivity.  ...  The addition of 3D-SSP images to visual analysis helped to discriminate different types of dementia in FDG PET scans, by correcting misdiagnoses and enhancing diagnostic confidence in the correct diagnosis  ...  Acknowledgments The authors thank statistical advice for this manuscript from our statistician (Professor Myung Hwan Na, Department of statistics, Chonnam National University, Korea).  ... 
doi:10.1097/md.0000000000005622 pmid:27930593 pmcid:PMC5266065 fatcat:42mykwde45dspfrg44ci43nnha

Modeling Large Sparse Data for Feature Selection: Hospital Admission Predictions of the Dementia Patients using Primary Care Electronic Health Records

Gavin Tsang, Shang-Ming Zhou, Xianghua Xie
2020 IEEE Journal of Translational Engineering in Health and Medicine  
Such data however, provides a challenging problem space for traditional statistics and machine learning due to high dimensionality and sparse data elements.  ...  The identification of at risk individuals allows for preventative measures to alleviate said strain. Electronic health records provide opportunity for big data analysis to address such applications.  ...  To address such challenges, this article proposes a novel methodology for predicting hospital admission for individuals with dementia whilst simultaneously performing feature reduction on a sparse, high-dimensional  ... 
doi:10.1109/jtehm.2020.3040236 pmid:33354439 pmcid:PMC7737850 fatcat:yke3szmmzff6hg54ngwu6p7koa

Dementia: Continuum or distinct entity?

Glenn D. Walters
2010 Psychology and Aging  
Subjecting these data to taxometric analysis using mean above minus below a cut (MAMBAC), maximum covariance (MAXCOV), and latent mode factor analysis (L-Mode) produced results more consistent with dementia  ...  as a dimensional (lying along a continuum) than categorical (representing a distinct entity) construct.  ...  Latent mode factor analysis (L-Mode) data curve for the four dementia indicators (dark solid line) in comparison to simulated taxonic (lighter solid line) and dimensional data (lighter dotted line) with  ... 
doi:10.1037/a0018167 pmid:20677881 pmcid:PMC2943994 fatcat:qyx3rgw4vzcatbcacr4s3uqdri

Re-assessing the dimensional structure of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE): empirical evidence for a shortened Brazilian version

Michael Reichenheim, Maria Angélica dos Santos Sanchez, Roberto Alves Lourenço
2015 BMC Geriatrics  
A Confirmatory Factor Analysis (CFA) first tested the originally proposed one-dimensional structure comprised of 26 items.  ...  Methods: The sample comprised 652 elderly and their informants, either attending a geriatric service of a public university clinic or enrolled in a health care provider database in Rio de Janeiro, Brazil  ...  As for the dimensional structure of the instrument, most authors suggested a one-dimensional solution, through studies using different statistical methods [8-12, 23, 24] .  ... 
doi:10.1186/s12877-015-0098-9 pmid:26227264 pmcid:PMC4521482 fatcat:kpat6t6jw5bojgzcbbamzmlh4u
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