scholarly journals Prediction of Autonomy Loss in Alzheimer’s Disease

Forecasting ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 26-35
Author(s):  
Anne-Sophie Nicolas ◽  
Michel Ducher ◽  
Laurent Bourguignon ◽  
Virginie Dauphinot ◽  
Pierre Krolak-Salmon
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Predictive Value ◽  
Cross Validation ◽  
External Validation ◽  
Cost Of Care ◽  
University Hospital ◽  
Functional Autonomy ◽  
Fold Cross Validation ◽  
Autonomy Loss

The evolution of functional autonomy loss leads to institutionalization of people affected by Alzheimer’s disease (AD), to an alteration of their quality of life and that of their caregivers. To predict loss of functional autonomy could optimize prevention strategies, aids and cost of care. The aim of this study was to develop and to cross-validate a model to predict loss of functional autonomy as assessed by Instrumental Activities of Daily Living (IADL) score. Outpatients with probable AD and with 2 or more visits to the Clinical and Research Memory Centre of the University Hospital were included. Four Tree-Augmented Naïve bayesian networks (6, 12, 18 and 24 months of follow-up) were built. Variables included in the model were demographic data, IADL score, MMSE score, comorbidities, drug prescription (psychotropics and AD-specific drugs). A 10-fold cross-validation was conducted to evaluate robustness of models. The study initially included 485 patients in the prospective cohort. The best performance after 10-fold cross-validation was obtained with the model able to predict loss of functional autonomy at 18 months (area under the curve of the receiving operator characteristic curve = 0.741, 27% of patients misclassified, positive predictive value = 77% and negative predictive value = 73%). The 13 variables used explain 41.6% of the evolution of functional autonomy at 18 months. A high-performing predictive model of AD evolution of functional autonomy was obtained. An external validation is needed to use the model in clinical routine so as to optimize the patient care.

scholarly journals ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

2021 ◽  
Vol 13 ◽  
Author(s):  
Shui-Hua Wang ◽  
Qinghua Zhou ◽  
Ming Yang ◽  
Yu-Dong Zhang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cross Validation ◽  
Data Augmentation ◽  
State Of The Art ◽  
Attention Network ◽  
Backbone Network ◽  
Novel Method ◽  
Precision And Accuracy ◽  
Fold Cross Validation

Aim: Alzheimer's disease is a neurodegenerative disease that causes 60–70% of all cases of dementia. This study is to provide a novel method that can identify AD more accurately.Methods: We first propose a VGG-inspired network (VIN) as the backbone network and investigate the use of attention mechanisms. We proposed an Alzheimer's Disease VGG-Inspired Attention Network (ADVIAN), where we integrate convolutional block attention modules on a VIN backbone. Also, 18-way data augmentation is proposed to avoid overfitting. Ten runs of 10-fold cross-validation are carried out to report the unbiased performance.Results: The sensitivity and specificity reach 97.65 ± 1.36 and 97.86 ± 1.55, respectively. Its precision and accuracy are 97.87 ± 1.53 and 97.76 ± 1.13, respectively. The F1 score, MCC, and FMI are obtained as 97.75 ± 1.13, 95.53 ± 2.27, and 97.76 ± 1.13, respectively. The AUC is 0.9852.Conclusion: The proposed ADVIAN gives better results than 11 state-of-the-art methods. Besides, experimental results demonstrate the effectiveness of 18-way data augmentation.

scholarly journals A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaker El-Sappagh ◽  
Jose M. Alonso ◽  
S. M. Riazul Islam ◽  
Ahmad M. Sultan ◽  
Kyung Sup Kwak
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cross Validation ◽  
Disease Risk ◽  
Binary Classification ◽  
Fuzzy Rule ◽  
Large Set ◽  
Detection Model ◽  
Multi Class Classification ◽  
Clinical Measures

AbstractAlzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.

Health Disparities in Cost of Care in Patients With Alzheimer’s Disease

2012 ◽  
Vol 28 (1) ◽  
pp. 84-92 ◽  
Author(s):  
Adrienne M. Gilligan ◽  
Daniel C. Malone ◽  
Terri L. Warholak ◽  
Edward P. Armstrong
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Health Disparities ◽  
Cost Of Care

scholarly journals Progress Report on the Canadian Multicentre Trial of Tetrahydroaminoacridine with Lecithin in Alzheimer's Disease

1989 ◽  
Vol 16 (S4) ◽  
pp. 543-546 ◽  
Author(s):  
S. Gauthier ◽  
R. Bouchard ◽  
Y. Bacher ◽  
P. Bailey ◽  
H. Bergman ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Rating Scale ◽  
Fixed Dose ◽  
Symptomatic Therapy ◽  
Muscarinic Agonists ◽  
Double Blind ◽  
Disability Rating Scale ◽  
Functional Autonomy ◽  
Disability Rating

ABSTRACT:Since the discovery of a significant depletion of acetylcholine in discrete areas of the brain of patients affected by Alzheimer's disease, attempts at symptomatic therapy have concentrated on acetylcholine supplementation, an approach that is based upon the efficacy of dopaminergic supplementation therapy for Parkinson's disease. Choline, then lecithin, used orally, failed to improve symptoms but the hypothesis that long-term choline supplementation might stabilize the course of Alzheimer's disease remains to be tested. Nerve growth factor may also offer that possibility. Bethanechol administered intracerebroventricularly did not help when a fixed dose was used but individual titration of more selective muscarinic agonists may prove more effective. In this article we report that tetrahydroaminoacridine (THA), given together with highly concentrated lecithin, appears to bring improvement in cognition and in functional autonomy using the Mini Mental State and the Rapid Disability Rating Scale-2 respectively, without change in behavior as reflected by the Behave-AD. Double-blind cross-over studies are in progress to establish its efficacy. Improvement in study design and means of assessment of cognition, functional autonomy and behavior have been made possible by these drug trials.

scholarly journals CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas

2021 ◽  
Vol 11 ◽  
Author(s):  
Tiansong Xie ◽  
Xuanyi Wang ◽  
Zehua Zhang ◽  
Zhengrong Zhou
Keyword(s):  
Predictive Value ◽  
Cross Validation ◽  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Cystic Neoplasm ◽  
Cystic Neoplasms ◽  
Serous Cystadenomas ◽  
Radiological Model ◽  
Fold Cross Validation

ObjectivesTo investigate the value of CT-based radiomics analysis in preoperatively discriminating pancreatic mucinous cystic neoplasms (MCN) and atypical serous cystadenomas (ASCN).MethodsA total of 103 MCN and 113 ASCN patients who underwent surgery were retrospectively enrolled. A total of 764 radiomics features were extracted from preoperative CT images. The optimal features were selected by Mann-Whitney U test and minimum redundancy and maximum relevance method. The radiomics score (Rad-score) was then built using random forest algorithm. Radiological/clinical features were also assessed for each patient. Multivariable logistic regression was used to construct a radiological model. The performance of the Rad-score and the radiological model was evaluated using 10-fold cross-validation for area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy.ResultsTen screened optimal features were identified and the Rad-score was then built based on them. The radiological model was built based on four radiological/clinical factors. In the 10-fold cross-validation, the Rad-score was proved to be robust and reliable (average AUC: 0.784, sensitivity: 0.847, specificity: 0.745, PPV: 0.767, NPV: 0.849, accuracy: 0.793). The radiological model performed slightly less well in classification (average AUC: average AUC: 0.734 sensitivity: 0.748, specificity: 0.705, PPV: 0.732, NPV: 0.798, accuracy: 0.728.ConclusionsThe CT-based radiomics analysis provided promising performance for preoperatively discriminating MCN from ASCN and showed good potential in improving diagnostic power, which may serve as a novel tool for guiding clinical decision-making for these patients.

Brazilian Version of Addenbrooke’s Cognitive Examination—Revised in the Differential Diagnosis of Alzheimer’S Disease and Behavioral Variant Frontotemporal Dementia

2021 ◽  
Author(s):  
Viviane Amaral-Carvalho ◽  
Thais Bento Lima-Silva ◽  
Luciano Inácio Mariano ◽  
Leonardo Cruz de Souza ◽  
Henrique Cerqueira Guimarães ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Diagnostic Accuracy ◽  
Frontotemporal Dementia ◽  
Predictive Value ◽  
Univariate Analysis ◽  
Behavioral Variant Frontotemporal Dementia ◽  
Present Sample ◽  
Addenbrooke’S Cognitive Examination

Abstract Introduction Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are frequent causes of dementia and, therefore, instruments for differential diagnosis between these two conditions are of great relevance. Objective To investigate the diagnostic accuracy of Addenbrooke’s Cognitive Examination-Revised (ACE-R) for differentiating AD from bvFTD in a Brazilian sample. Methods The ACE-R was administered to 102 patients who had been diagnosed with mild dementia due to probable AD, 37 with mild bvFTD and 161 cognitively healthy controls, matched according to age and education. Additionally, all subjects were assessed using the Mattis Dementia Rating Scale and the Neuropsychiatric Inventory. The performance of patients and controls was compared by using univariate analysis, and ROC curves were calculated to investigate the accuracy of ACE-R for differentiating AD from bvFTD and for differentiating AD and bvFTD from controls. The verbal fluency plus language to orientation plus name and address delayed recall memory (VLOM) ratio was also calculated. Results The optimum cutoff scores for ACE-R were <80 for AD, <79 for bvFTD, and <80 for dementia (AD + bvFTD), with area under the receiver operating characteristic curves (ROC) (AUC) >0.85. For the differential diagnosis between AD and bvFTD, a VLOM ratio of 3.05 showed an AUC of 0.816 (Cohen’s d = 1.151; p < .001), with 86.5% sensitivity, 71.4% specificity, 72.7% positive predictive value, and 85.7% negative predictive value. Conclusions The Brazilian ACE-R achieved a good diagnostic accuracy for differentiating AD from bvFTD patients and for differentiating AD and bvFTD from the controls in the present sample.

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