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Interpreting PET scans by structured patient data: a data mining case study in dementia research

Jana Schmidt, Andreas Hapfelmeier, Marianne Mueller, Robert Perneczky, Alexander Kurz, Alexander Drzezga, Stefan Kramer
2009 Knowledge and Information Systems  
We believe that explaining medical images in terms of other variables (patient records, demographic information, etc.) is a challenging new and rewarding area for data mining research.  ...  It comprises 10 GB of image data from 454 PET scans, and 42 variables from psychological and demographical data organized in 11 relations of a relational database.  ...  Acknowledgments This work has been supported in part by DFG-grants (Deutsche Forschungsgemeinschaft) Project Numbers: DR 445/3-1, DR 445/4-1 (Drzezga).  ... 
doi:10.1007/s10115-009-0234-y fatcat:zbsd6uwyozal7lzakwbxrjjmru

Interpreting PET Scans by Structured Patient Data: A Data Mining Case Study in Dementia Research

Andreas Hapfelmeier, Jana Schmidt, Marianne Mueller, Stefan Kramer, Robert Perneczky, Alexander Kurz, Alexander Drzezga
2008 2008 Eighth IEEE International Conference on Data Mining  
We believe that explaining medical images in terms of other variables (patient records, demographic information, etc.) is a challenging new and rewarding area for data mining research.  ...  It comprises 10 GB of image data from 454 PET scans, and 42 variables from psychological and demographical data organized in 11 relations of a relational database.  ...  Acknowledgments This work has been supported in part by DFG-grants (Deutsche Forschungsgemeinschaft) Project Numbers: DR 445/3-1, DR 445/4-1 (Drzezga).  ... 
doi:10.1109/icdm.2008.128 dblp:conf/icdm/HapfelmeierSMKPKD08 fatcat:yeleumaxmjambcjbi3yzikoudq

Inference from Structured and Unstructured Electronic Medical Data for Dementia Detection [chapter]

Joseph Bullard, Rohan Murde, Qi Yu, Cecilia Ovesdotter Alm, Rubén Proaño
2015 Operations Research and Computing: Algorithms and Software for Analytics  
Whereas prior work on this problem has focused on structured data (e.g. test results) alone, this study integrates structured and unstructured (e.g. clinical notes) from the Alzheimer's Disease Neuroimaging  ...  Prediction based on unstructured data alone performs with similar accuracy compared to structured data, and integration of the two provides performance improvements over either in isolation.  ...  Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense  ... 
doi:10.1287/ics.2015.0018 fatcat:jlulmjw57ngfvmcuh76getk5ha

Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review

Sayantan Kumar, Inez Oh, Suzanne Schindler, Albert M Lai, Philip R O Payne, Aditi Gupta
2021 JAMIA Open  
Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression.  ...  Results There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR.  ...  In this SLR, our main research question is: How are machine learning algorithms being applied by researchers for studying progression of AD dementia using clinical EHR data?  ... 
doi:10.1093/jamiaopen/ooab052 pmid:34350389 pmcid:PMC8327375 fatcat:pbmn7vqnpfg4bnfeadtuxzylwq

Alzheimer's Disease Classification Using Cluster-based Labelling for Graph Neural Network on Tau PET Imaging and Heterogeneous Data [article]

Niamh McCombe, Jake Bamrah, Jose M Sanchez-Bornot, David P Finn, Paula L McClean, KongFatt Wong-Lin
2022 medRxiv   pre-print
This study addresses these limitations by applying cluster-based labelling on heterogeneous data, including tau PET neuroimaging data, before applying GNN for classification.  ...  A post-hoc analysis supported the re-labelling of these cases given their similar brain tau PET levels, lying between those of the remaining AD and non-AD cases.  ...  this study to further improve the interpretability and accessibility of ML research in computer-aided diagnostic systems.  ... 
doi:10.1101/2022.03.03.22271873 fatcat:4tszwvvrvzh3dhf2ua3ltublfa

A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining

Mahsa Mansourian, Sadaf Khademi, Hamid Reza Marateb
2021 Diagnostics  
We further discussed how various performance indices are essential based on the biostatistical and data mining perspective.  ...  We provided the list of the critical issues to consider in such studies.  ...  However, they analyzed papers published by 2017, and the data mining validation frameworks and methods focused on in our study were not covered in their study.  ... 
doi:10.3390/diagnostics11030393 pmid:33669114 pmcid:PMC7996506 fatcat:zynohu6szjc2rd4kwnd4jc4nje

Big Data for Health

Javier Andreu-Perez, Carmen C. Y. Poon, Robert D. Merrifield, Stephen T. C. Wong, Guang-Zhong Yang
2015 IEEE journal of biomedical and health informatics  
of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging  ...  This paper provides an overview of recent developments in big data in the context of biomedical and health informatics.  ...  [131] IMG 1414 subjects Network Analysis Resting state of neural fMRI data [132] IMG EHR 228 * patients Machine Learning PET scans and patient records [133] HI (SN and ENV) 465 million  ... 
doi:10.1109/jbhi.2015.2450362 pmid:26173222 fatcat:wdmtokdztffmjavdosqcjxiyxm

Machine Learning Approaches for the Neuroimaging Study of Alzheimer's Disease

Jieping Ye, Teresa Wu, Jing Li, Kewei Chen
2011 Computer  
To separate AD patients from norma l control ( NC) subject s, researchers in the MKL study cited above applied the technique to fuse structural MRI data based on tensor factorization, structural MRI data  ...  Machine Learning Approaches for the Neuroimaging Study of Alzheimer's Disease A lzheimer's disease (AD) is the most common type of dementia, accounting for 60-80 percent of age-related dementia cases.  ... 
doi:10.1109/mc.2011.117 fatcat:avbndwwjonag5d6cufaxrdlsae

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

João Maroco, Dina Silva, Ana Rodrigues, Manuela Guerreiro, Isabel Santana, Alexandre de Mendonça
2011 BMC Research Notes  
Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia.  ...  Dementia and cognitive impairment associated with aging are a major medical and social concern.  ...  of the cerebrospinal fluid, and Positron Emission Tomography (PET) scan [5] .  ... 
doi:10.1186/1756-0500-4-299 pmid:21849043 pmcid:PMC3180705 fatcat:lumtrdenjzhxraw4qe7jmwo27m

Mining brain region connectivity for alzheimer's disease study via sparse inverse covariance estimation

Liang Sun, Rinkal Patel, Jun Liu, Kewei Chen, Teresa Wu, Jing Li, Eric Reiman, Jieping Ye
2009 Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09  
Effective diagnosis of Alzheimer's disease (AD), the most common type of dementia in elderly patients, is of primary importance in biomedical research.  ...  We apply the proposed algorithm to a collection of FDG-PET images from 232 NC, MCI, and AD subjects.  ...  Acknowledgments This research is sponsored in part by the Arizona Alzheimer's Consortium and by NSF IIS-0612069 and IIS-0812551.  ... 
doi:10.1145/1557019.1557162 dblp:conf/kdd/SunPLCWLRY09 fatcat:75rmharn7ja2hlevldojqlosgm

Radiogenomics for Precision Medicine With A Big Data Analytics Perspective

Andreas S. Panayides, Marios Pattichis, Stephanos Leandrou, Costas Pitris, Anastasia Constantinidou, Constantinos S. Pattichis
2019 IEEE journal of biomedical and health informatics  
More specifically, our goal is to highlight the fundamental challenges in emerging fields of radiomics and radiogenomics by reviewing the case studies of Cancer and Alzheimer's disease, describe the computational  ...  The objective of this paper is to provide insights with respect to the state-of-the-art research in precision medicine.  ...  CASE STUDY I: CONNECTING CANCER PHENOTYPES TO GENOTYPES In response to the ever increasing scientific community demands for heterogeneous multi-level data sharing and driven by notable research discoveries  ... 
doi:10.1109/jbhi.2018.2879381 pmid:30596591 fatcat:rqmjhmdmr5h3rdaody264ogs24

Predicting future amyloid biomarkers in dementia patients with machine learning to improve clinical trial patient selection

Fabian H. Reith, Elizabeth C. Mormino, Greg Zaharchuk
2021 Alzheimer s & Dementia Translational Research & Clinical Interventions  
Using 2577 PET scans from 1224 unique individuals, we showed that the GBDT with deep image features was significantly more accurate than the other approaches, reaching a root mean squared error of 0.0339  ...  In Alzheimer's disease, asymptomatic patients may have amyloid deposition, but predicting their progression rate remains a substantial challenge with implications for clinical trial enrollment.  ...  Elisabeth Mormino: study design, data interpretation. Greg Zaharchuk: funding, study conceptualization, study design, data collection, data interpretation, manuscript drafting, and editing.  ... 
doi:10.1002/trc2.12212 pmid:34692985 pmcid:PMC8515556 fatcat:jyqvc2mqgjepvle3oyrbrwzili

A Machine Learning Approach towards Detecting Dementia based on its Modifiable Risk Factors

Reem Bin-Hezam, Tomas E.
2019 International Journal of Advanced Computer Science and Applications  
Fortunately, dementia can be delayed or possibly prevented by changes in lifestyle as dictated through known modifiable risk factors.  ...  A binary classification (dementia vs. nondementia) yielded approximately 92% accuracy, while the full multi-class prediction performance yielded to a 77% accuracy using logistic regression, followed by  ...  Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) ( This publication has emanated from research supported in part by a research grant from  ... 
doi:10.14569/ijacsa.2019.0100820 fatcat:4uxrffbw7zcclm6x6l2mhg4sum

Identifying degenerative effects of repetitive head trauma with neuroimaging: a clinically-oriented review

Breton M. Asken, Gil D. Rabinovici
2021 Acta Neuropathologica Communications  
We provide a clinically-oriented review of neuroimaging data from repetitive head trauma cohorts based on structural MRI, FDG-PET, Aβ-PET, and tau-PET.  ...  We supplement the review with two patient reports of neuropathology-confirmed, clinically impaired adults with prior repetitive head trauma who underwent structural MRI, FDG-PET, Aβ-PET, and tau-PET in  ...  La Joie for lending interpretations of the PET scans.  ... 
doi:10.1186/s40478-021-01197-4 pmid:34022959 pmcid:PMC8141132 fatcat:kfpmvznihfdvdjy2qo54d3cl2y

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  
In this review paper, we summarized the automated dementia identification algorithms in the literature from a pattern classification perspective.  ...  We also compare the reported performance of many recently published dementia identification algorithms.  ...  Perspective Due to the advances in medical imaging, it is now possible to sequentially capture two separate yet complementary information of a patient study in a single scan, i.e., PET/CT [110] .  ... 
doi:10.1007/s40708-015-0027-x pmid:27747596 pmcid:PMC4883162 fatcat:yefc226j4za75afkkkolg4m5n4
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