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

Qing Xie, Su Wang, Jia Zhu, Xiangliang Zhang
<span title="">2016</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bby322qx6ndsje4ypr56c7nnly" style="color: black;">Neurocomputing</a> </i> &nbsp;
Alzheimer's Disease (AD) is currently attracting much attention in elders' care. As the increasing availability of massive clinical diagnosis data, especially the medical images of brain scan, it is highly significant to precisely identify and predict the potential AD's progression based on the knowledge in the diagnosis data. In this paper, we follow a novel sequential learning framework to model the disease progression for AD patients' care. Different from the conventional approaches using
more &raquo; ... y initial or static diagnosis data to model the disease progression for different durations, we design a score-involved approach and make use of the sequential diagnosis information in different disease stages to jointly simulate the disease progression. The actual clinical scores are utilized in progress to make the prediction more pertinent and reliable. We examined our approach by extensive experiments on the clinical data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI). The results indicate that the proposed approach is more effective to simulate and predict the disease progression compared with the existing methods. * Corresponding author. * * Data used in preparation of this article were obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). 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. A complete listing of ADNI investigators can be found at:
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neucom.2015.07.145">doi:10.1016/j.neucom.2015.07.145</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lpfwwvwz4vcaldmkidba62qxcm">fatcat:lpfwwvwz4vcaldmkidba62qxcm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200310172522/https://repository.kaust.edu.sa/bitstream/handle/10754/600281/1-s2.0-S0925231216001193-main.pdf;jsessionid=42B673645CA078706B286A79108E63DD?sequence=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5a/c6/5ac6b5fb621f1c2970b9ed5b23a8e7ad52585f75.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neucom.2015.07.145"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>