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Multi-task sparse screening for predicting future clinical scores using longitudinal cortical thickness measures

Jie Zhang, Yanshuai Tu, Qingyang Li, Richard J. Caselli, Paul M. Thompson, Jieping Ye, Yalin Wang
2018 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)  
Thus far, there are very few methods that can accurately predict future clinical scores using longitudinal cortical thickness measures.  ...  In this paper, we propose an unsupervised dictionary learning algorithm, termed Multi-task Sparse Screening (MSS) that produces improved results over previous methods within this problem domain.  ...  MULTI-TASK SPARSE SCREENING Let X ∈ ℝ d×n represents the cortical thickness features and y ∈ ℝ n as a vector of n observations (responses) which are responses of the clinical scores (e.g., MMSE and ADAScog  ... 
doi:10.1109/isbi.2018.8363835 pmid:30023040 pmcid:PMC6047361 fatcat:wz5xcwlh5bfepfpzkt6tg3ts3e

Modeling disease progression via fused sparse group lasso

Jiayu Zhou, Jun Liu, Vaibhav A. Narayan, Jieping Ye
2012 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12  
In this paper, we develop novel multi-task learning techniques to predict the disease progression measured by cognitive scores and select biomarkers predictive of the progression.  ...  In multi-task learning, the prediction of cognitive scores at each time point is considered as a task, and multiple prediction tasks at different time points are performed simultaneously to capture the  ...  In this paper, we propose novel multi-task learning formulations for predicting the disease progression measured by the clinical scores (ADAS-Cog and MMSE).  ... 
doi:10.1145/2339530.2339702 pmid:25309808 pmcid:PMC4191837 dblp:conf/kdd/ZhouLNY12 fatcat:quicxpfhpzbvvosjmiqsbsudzq

A multi-task learning formulation for predicting disease progression

Jiayu Zhou, Lei Yuan, Jun Liu, Jieping Ye
2011 Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11  
In this paper, we propose a multi-task learning formulation for predicting the disease progression measured by the cognitive scores and selecting markers predictive of the progression.  ...  We have performed extensive evaluations using various types of data at the baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database for predicting the future MMSE and ADAS-Cog scores  ...  In this paper, we propose a multi-task learning formulation for predicting the progression of the disease measured by the clinical scores at multiple time points and simultaneously selecting markers predictive  ... 
doi:10.1145/2020408.2020549 dblp:conf/kdd/ZhouYLY11 fatcat:x77374hvwzdf5dbjzlkcif7nua

Modeling and predicting AD progression by regression analysis of sequential clinical data

Qing Xie, Su Wang, Jia Zhu, Xiangliang Zhang
2016 Neurocomputing  
The actual clinical scores are utilized in progress to make the prediction more pertinent and reliable.  ...  Different from the conventional approaches using only initial or static diagnosis data to model the disease progression for different durations, we design a score-involved approach and make use of the  ...  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:10.1016/j.neucom.2015.07.145 fatcat:lpfwwvwz4vcaldmkidba62qxcm

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer's Disease

Younghoon Seo, Hyemin Jang, Hyejoo Lee
2022 Life  
Clinical trials for Alzheimer's disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants.  ...  Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.  ...  AD; MCI to AD prediction RF; combining cortical thickness and volumetric measures 225 NC, 165 MCI, 185 AD AD vs.  ... 
doi:10.3390/life12020275 pmid:35207561 pmcid:PMC8879055 fatcat:46ccay3fhvhtrhxlq2g734sqzy

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

Michael W. Weiner, Dallas P. Veitch, Paul S. Aisen, Laurel A. Beckett, Nigel J. Cairns, Jesse Cedarbaum, Robert C. Green, Danielle Harvey, Clifford R. Jack, William Jagust, Johan Luthman, John C. Morris (+7 others)
2015 Alzheimer's & Dementia  
Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline.  ...  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  ...  funding for  ... 
doi:10.1016/j.jalz.2014.11.001 pmid:26073027 pmcid:PMC5469297 fatcat:2k7ag6astffy5gphqxf5lodkdq

Machine learning suggests polygenic contribution to cognitive dysfunction in amyotrophic lateral sclerosis (ALS) [article]

Katerina Placek, Michael Benatar, Joanne Wuu, Evadnie Rampersaud, Laura Hennessy, Vivianna M Van Deerlin, Murray Grossman, David Irwin, Lauren Elman, Leo McCluskey, Colin Quinn, Volkan Granit (+17 others)
2019 medRxiv   pre-print
We demonstrate that a polygenic risk score derived from sCCA relates to longitudinal cognitive decline in the same cohort, and also to in vivo cortical thinning (N=80) and post mortem burden of TDP-43  ...  in 330 ALS patients from the multicenter Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) consortium.  ...  We used voxel-wise in vivo measures of reduced cortical thickness (in mm 3 ) to quantify cortical neurodegeneration.  ... 
doi:10.1101/2019.12.23.19014407 fatcat:xxf6px3bz5hgpgrkzh7iscygmm

Accelerated cortical thinning within structural brain networks is associated with irritability in youth

Robert J. Jirsaraie, Antonia N. Kaczkurkin, Sage Rush, Kayla Piiwia, Azeez Adebimpe, Danielle S. Bassett, Josiane Bourque, Monica E. Calkins, Matthew Cieslak, Rastko Ciric, Philip A. Cook, Diego Davila (+11 others)
2019 Neuropsychopharmacology  
Irritability at follow-up was assessed using the Affective Reactivity Index, and cortical thickness was quantified using Advanced Normalization Tools software.  ...  The False Discovery Rate (q < 0.05) was used to correct for multiple comparisons.  ...  Future studies should incorporate repeated measurements of irritability and data from multi-modal imaging. Ultimately, such findings could allow for targeted interventions in youth at risk.  ... 
doi:10.1038/s41386-019-0508-3 pmid:31476764 pmcid:PMC6897907 fatcat:7culqwk52fcgdkighmdtw77ow4

The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Michael W. Weiner, Dallas P. Veitch, Paul S. Aisen, Laurel A. Beckett, Nigel J. Cairns, Robert C. Green, Danielle Harvey, Clifford R. Jack, William Jagust, Enchi Liu, John C. Morris, Ronald C. Petersen (+8 others)
2013 Alzheimer's & Dementia  
imaging and clinical data using a single regression model with sparse multi-task learning and found that this method was an improvement on multi-variate regression when used to predict decline in AVLT  ...  Methods to predict future clinical decline have appeared, sometimes in conjunction with classifiers -'multi-tasking' is a recent area of interest in methods development.  ... 
doi:10.1016/j.jalz.2013.05.1769 pmid:23932184 pmcid:PMC4108198 fatcat:epydl5poq5evrobxws3qerdolu

The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Michael W. Weiner, Dallas P. Veitch, Paul S. Aisen, Laurel A. Beckett, Nigel J. Cairns, Robert C. Green, Danielle Harvey, Clifford R. Jack, William Jagust, Enchi Liu, John C. Morris, Ronald C. Petersen (+7 others)
2012 Alzheimer's & Dementia  
imaging and clinical data using a single regression model with sparse multi-task learning and found that this method was an improvement on multi-variate regression when used to predict decline in AVLT  ...  Methods to predict future clinical decline have appeared, sometimes in conjunction with classifiers -'multi-tasking' is a recent area of interest in methods development.  ... 
doi:10.1016/j.jalz.2011.09.172 pmid:22047634 pmcid:PMC3329969 fatcat:kkpjb6lv6zbe3lkv7nllgxdnga

Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder

L Q Uddin, D R Dajani, W Voorhies, H Bednarz, R K Kana
2017 Translational Psychiatry  
The identification of brain-based biomarkers for ASD and ADHD could potentially assist in objective diagnosis, monitoring of treatment response and prediction of outcomes for children with these neurodevelopmental  ...  We summarize empirical work that has laid the foundation for using neuroimaging to objectively quantify brain structure and function in ways that are beginning to be used in biomarker development, noting  ...  A study investigating a wide range of classifiers reports that a random tree classifier using combined cortical thickness and functional connectivity measures resulted in improved classification and prediction  ... 
doi:10.1038/tp.2017.164 pmid:28892073 pmcid:PMC5611731 fatcat:7xciopdz3bdrza7xicssj63tde

Quantifying individual differences in brain morphometry underlying symptom severity in Autism Spectrum Disorders

Emmanuel Peng Kiat Pua, Gareth Ball, Chris Adamson, Stephen Bowden, Marc L. Seal
2019 Scientific Reports  
Subject-specific structural morphometry features significantly predicted individual variation in ASD symptom severity (19 cortical thickness features, p = 0.01, n = 5000 permutations; 10 surface area features  ...  Intrapair Euclidean distance of MRI brain morphometry and symptom severity measures (Social Responsiveness Scale) were entered into a regularised machine learning pipeline for feature selection, with rigorous  ...  The instrument is commonly used for both screening and as a tool to aid clinical diagnosis.  ... 
doi:10.1038/s41598-019-45774-z pmid:31289283 pmcid:PMC6617442 fatcat:xvx3aeqi5nhwljwmkvygojgbja

Clinical Use of Quantitative Computed Tomography and Peripheral Quantitative Computed Tomography in the Management of Osteoporosis in Adults: The 2007 ISCD Official Positions

Klaus Engelke, Judith E. Adams, Gabriele Armbrecht, Peter Augat, Cesar E. Bogado, Mary L. Bouxsein, Dieter Felsenberg, Masako Ito, Sven Prevrhal, Didier B. Hans, E. Michael Lewiecki
2008 Journal of clinical densitometry  
The International Society for Clinical Densitometry (ISCD) has developed Official Positions for the clinical use of dual-energy X-ray absorptiometry (DXA) and non-DXA technologies.  ...  While only DXA can be used for diagnostic classification according to criteria established by the World Health Organization, DXA and some other technologies may predict fracture risk and be used to monitor  ...  Newer spiral protocols, in particular for scans of the hip, may be more successful for measuring cortical thickness because thinner slice thicknesses (1e3 mm) are used.  ... 
doi:10.1016/j.jocd.2007.12.010 pmid:18442757 fatcat:prtie2qhafefpm5is7u7za3p7a

FORMULA:FactORizedMUlti-taskLeArning for task discovery in personalized medical models [chapter]

Jianpeng Xu, Jiayu Zhou, Pang-Ning Tan
2015 Proceedings of the 2015 SIAM International Conference on Data Mining  
To address these challenges, we propose a novel approach called FactORized MUlti-task LeArning model (Formula), which learns the personalized model of each patient via a sparse multi-task learning method  ...  The proposed approach delivered superior predictive performance while the personalized models offered many useful medical insights.  ...  We consider using these features to build models for predicting the ADAS cognitive scores or MMSE scores on each data set.  ... 
doi:10.1137/1.9781611974010.56 dblp:conf/sdm/XuZT15 fatcat:iqgqg5ngjnehfctukucm57exmu

Editorial: Predictive Intelligence in Biomedical and Health Informatics

E. Adeli, S. H. Rekik, S. H. Park, D. Shen
2020 IEEE journal of biomedical and health informatics  
MRI volumetric and cortical thickness measurements were used for brain morphology, and cerebrospinal fluid (CSF) biomarkers (t-tau, p-tau and amyloidbeta) were used as a proxy for characterizing AD pathology  ...  They proposed a multi-task recurrent neural network with attention mechanisms to predict patients' hospital mortality, using reconstruction of patient physiological time series as an auxiliary task.  ... 
doi:10.1109/jbhi.2019.2962852 fatcat:mp2kymg7yjb7ziukia7kjgjula
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