A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer's Disease.
With the advances in machine learning for the diagnosis of Alzheimer's disease (AD), most studies have focused on either identifying the subject's status through classification algorithms or on predicting their cognitive scores through regression methods, neglecting the potential association between...
| Published in: | Frontiers in Aging Neuroscience Vol. 14; pp. 1 - 15 |
|---|---|
| Main Authors: | , , , , , , , , , , |
| Format: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Frontiers Media S.A.
5/6/2022
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156767481&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156767481 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 5/6/2022 vid: 14 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 156767481 156767481 156767481 10.3389/fnagi.2022.810873 156767481 ppf: 1 ppct: 14 formats: tig: atl: A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer's Disease. aug: au: Tabarestani, Solale Eslami, Mohammad Cabrerizo, Mercedes Curiel, Rosie E. Barreto, Armando Rishe, Naphtali Vaillancourt, David DeKosky, Steven T. Loewenstein, David A. Duara, Ranjan Adjouadi, Malek affil: Center for Advanced Technology and Education, Florida International University, Miami, FL, United States sug: subj: Alzheimer's Disease Pathology Disease Progression Risk Factors Risk Assessment Deep Learning Neural Networks (Computer) Prediction Models Human Male Female Aged Aged, 80 and Over Neuropsychological Tests Aged: 65+ years Aged, 80 & over Male Female ab: With the advances in machine learning for the diagnosis of Alzheimer's disease (AD), most studies have focused on either identifying the subject's status through classification algorithms or on predicting their cognitive scores through regression methods, neglecting the potential association between these two tasks. Motivated by the need to enhance the prospects for early diagnosis along with the ability to predict future disease states, this study proposes a deep neural network based on modality fusion, kernelization, and tensorization that perform multiclass classification and longitudinal regression simultaneously within a unified multitask framework. This relationship between multiclass classification and longitudinal regression is found to boost the efficacy of the final model in dealing with both tasks. Different multimodality scenarios are investigated, and complementary aspects of the multimodal features are exploited to simultaneously delineate the subject's label and predict related cognitive scores at future timepoints using baseline data. The main intent in this multitask framework is to consolidate the highest accuracy possible in terms of precision, sensitivity, F1 score, and area under the curve (AUC) in the multiclass classification task while maintaining the highest similarity in the MMSE score as measured through the correlation coefficient and the RMSE for all time points under the prediction task, with both tasks, run simultaneously under the same set of hyperparameters. The overall accuracy for multiclass classification of the proposed KTMnet method is 66.85 ± 3.77. The prediction results show an average RMSE of 2.32 ± 0.52 and a correlation of 0.71 ± 5.98 for predicting MMSE throughout the time points. These results are compared to state-of-the-art techniques reported in the literature. A discovery from the multitasking of this consolidated machine learning framework is that a set of hyperparameters that optimize the prediction results may not necessarily be the same as those that would optimize the multiclass classification. In other words, there is a breakpoint beyond which enhancing further the results of one process could lead to the downgrading in accuracy for the other. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|