The number of service visits of Alzheimer’s disease (AD) patients is different from each other and their visit time intervals are non-uniform. Although the literature has revealed many approaches in disease progression modeling, they fail to leverage these time-relevant part of patients’ medical records in predicting disease’s future status. This paper investigates how to predict the AD progression for a patient’s next medical visit through leveraging heterogeneous medical data. Data provided by the National Alzheimer’s Coordinating Center includes 5432 patients with probable AD from August 31, 2005 to May 25, 2017. Long short-term memory recurrent neural networks (RNN) are adopted. The approach relies on an enhanced “many-to-one” RNN architecture to support the shift of time steps. Hence, the approach can deal with patients’ various numbers of visits and uneven time intervals. The results show that the proposed approach can be utilized to predict patients’ AD progressions on their next visits with over 99% accuracy, significantly outperforming classic baseline methods. This study confirms that RNN can effectively solve the AD progression prediction problem by fully leveraging the inherent temporal and medical patterns derived from patients’ historical visits. More promisingly, the approach can be customarily applied to other chronic disease progression problems.
Nowadays, the investigation of the Wireless Sensor Network (WSN) has materialized its functional area ubiquitously such as environmental engineering, industrial and business applications, military, feedstock and habitat, agriculture sector, seismic detection, intelligent buildings, smart grids, and predictive maintenance, etc. Although some challenges still exist in the wireless sensor network, in spite of the shortcoming, it has been gaining significant attention among researchers and technologists due to its versatility and robustness. WSN is subject to a high potential technology that has been successfully implemented and tested in real-time scenarios, as well as deployed practically in various applications. In this paper, we have carried out an extensive survey in real-time applications of wireless sensor network deployment in a practical scenario such as the real-time intelligent monitoring of temperature, criminal activity in borders and surveillance on traffic monitoring, vehicular behavior on roads, water level and pressure, and remote monitoring of patients. The application of the Wireless Sensor Network in the assorted field of research areas has been widely deliberated. WSN is found to be the most effective solution in remote areas which are not yet explored due to its perilous nature and unreachable places. Here, in this study, we have cited the recent and updated research on the ubiquitous usage of WSN in diverse fields in an extensive and comprehensive approach.
The accurate diagnosis of Alzheimer's disease (AD) and its early stage, e.g., mild cognitive impairment (MCI), is essential for timely treatment or possible intervention to slow down AD progression. Recent studies have demonstrated that multiple neuroimaging and biological measures contain complementary information for diagnosis and prognosis. Therefore, information fusion strategies with multi-modal neuroimaging data, such as voxel-based measures extracted from structural MRI (VBM-MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET), have shown their effectiveness for AD diagnosis. However, most existing methods are proposed to simply integrate the multi-modal data, but do not make full use of structure information across the different modalities. In this paper, we propose a novel multi-modal neuroimaging feature selection method with consistent metric constraint (MFCC) for AD analysis. First, the similarity is calculated for each modality (i.e. VBM-MRI or FDG-PET) individually by random forest strategy, which can extract pairwise similarity measures for multiple modalities. Then the group sparsity regularization term and the sample similarity constraint regularization term are used to constrain the objective function to conduct feature selection from multiple modalities. Finally, the multi-kernel support vector machine (MK-SVM) is used to fuse the features selected from different models for final classification. The experimental results on the *
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