scholarly journals A Prediction Model for Mild Cognitive Impairment Using Random Forests

2015 ◽  
Vol 6 (12) ◽  
Author(s):  
Haewon Byeon
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Prediction Model ◽  
Random Forests

scholarly journals Identification of and a Prediction Model for Cognitive Trajectories in Patients With Mild Cognitive Impairment Due to Alzheimer’s Disease

2020 ◽  
Author(s):  
Sang Won Seo ◽  
Seung Joo Kim ◽  
Sook-Young Woo ◽  
Young Ju Kim ◽  
Yeshin Kim ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Prediction Model ◽  
Multinomial Logistic Regression ◽  
Standardized Uptake Value ◽  
Disease Assessment ◽  
Risk Scores ◽  
Clinical Biomarkers

Abstract Background: Few studies have investigated cognitive trajectories or developed a prediction model for amyloid beta-positive (Aβ+) mild cognitive impairment (MCI) patients. We aimed to identify distinct cognitive trajectories in Aβ+ MCI patients based on longitudinal Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-cog) 13 scores. Furthermore, we aimed to develop and visualize a prediction model to predict trajectory groups using the demographic, genetic, and clinical biomarkers of Aβ+ MCI patients.Methods: We performed a retrospective analysis of the data in 238 Aβ+ MCI patients from the Alzheimer’s Disease Neuroimaging Initiative who underwent at least three rounds of annual neuropsychological testing to identify cognitive trajectories. A group-based trajectory model (GBTM) was used to classify distinct groups based on ADAS-cog 13 scores. The prediction model was estimated using multinomial logistic regression and visualized using a bar-based method for risk prediction. Results: Three distinct classes, namely slow decliners (18.5%), intermediate decliners (42.9%), and fast decliners (38.7%), were suggested. Intermediate decliners were associated with higher age (≥70 years) (odds ratio [OR] 2.72, 95% confidence interval [CI] 1.78-6.28), higher AV45 standardized uptake value ratios (SUVRs)*10 (OR 1.69, 95% CI 1.22-2.34), and lower fluorodeoxyglucose (FDG) SUVR*10 (OR 0.65, 95% CI 0.46-0.93) than slow decliners. Fast decliners were associated with higher age (≥70 years) (OR 3.76, 95% CI 1.40-10.10), greater likelihood of being an apolipoprotein E 4 carrier (OR 4.2, 95% CI 1.53-11.58), higher AV45 positron emission tomography SUVR*10 (OR 2.14, 95% CI 1.50-3.05), and lower FDG SUVR*10 (OR 0.31, 95% CI 0.20-0.48) than slow decliners. The predicted probability of being classified to a trajectory group according to the risk scores of predictors was visualized.Conclusions: Our GBTM analysis yielded novel insights into the heterogeneous cognitive trajectories among Aβ+ MCI patients, which further facilitates the effective stratification of Aβ+ MCI patients in Aβ-targeted clinical trials.

scholarly journals Prognosis prediction model for conversion from mild cognitive impairment to Alzheimer’s disease created by integrative analysis of multi-omics data

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Daichi Shigemizu ◽  
Shintaro Akiyama ◽  
Sayuri Higaki ◽  
Taiki Sugimoto ◽  
Takashi Sakurai ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
High Risk ◽  
Prediction Model ◽  
Target Genes ◽  
Meta Analysis ◽  
Hub Genes ◽  
Blood Samples ◽  
Prognosis Prediction ◽  
Prognosis Prediction Model

Abstract Background Mild cognitive impairment (MCI) is a precursor to Alzheimer’s disease (AD), but not all MCI patients develop AD. Biomarkers for early detection of individuals at high risk for MCI-to-AD conversion are urgently required. Methods We used blood-based microRNA expression profiles and genomic data of 197 Japanese MCI patients to construct a prognosis prediction model based on a Cox proportional hazard model. We examined the biological significance of our findings with single nucleotide polymorphism-microRNA pairs (miR-eQTLs) by focusing on the target genes of the miRNAs. We investigated functional modules from the target genes with the occurrence of hub genes though a large-scale protein-protein interaction network analysis. We further examined the expression of the genes in 610 blood samples (271 ADs, 248 MCIs, and 91 cognitively normal elderly subjects [CNs]). Results The final prediction model, composed of 24 miR-eQTLs and three clinical factors (age, sex, and APOE4 alleles), successfully classified MCI patients into low and high risk of MCI-to-AD conversion (log-rank test P = 3.44 × 10−4 and achieved a concordance index of 0.702 on an independent test set. Four important hub genes associated with AD pathogenesis (SHC1, FOXO1, GSK3B, and PTEN) were identified in a network-based meta-analysis of miR-eQTL target genes. RNA-seq data from 610 blood samples showed statistically significant differences in PTEN expression between MCI and AD and in SHC1 expression between CN and AD (PTEN, P = 0.023; SHC1, P = 0.049). Conclusions Our proposed model was demonstrated to be effective in MCI-to-AD conversion prediction. A network-based meta-analysis of miR-eQTL target genes identified important hub genes associated with AD pathogenesis. Accurate prediction of MCI-to-AD conversion would enable earlier intervention for MCI patients at high risk, potentially reducing conversion to AD.

scholarly journals Is the Random Forest Algorithm Suitable for Predicting Parkinson’s Disease with Mild Cognitive Impairment out of Parkinson’s Disease with Normal Cognition?

2020 ◽  
Vol 17 (7) ◽  
pp. 2594 ◽  
Author(s):  
Haewon Byeon
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Random Forest ◽  
Decision Tree ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Early Stage ◽  
Normal Cognition

Because it is possible to delay the progression of dementia if it is detected and treated in an early stage, identifying mild cognitive impairment (MCI) is an important primary goal of dementia treatment. The objectives of this study were to develop a random forest-based Parkinson’s disease with mild cognitive impairment (PD-MCI) prediction model considering health behaviors, environmental factors, medical history, physical functions, depression, and cognitive functions using the Parkinson’s Dementia Clinical Epidemiology Data (a national survey conducted by the Korea Centers for Disease Control and Prevention) and to compare the prediction accuracy of our model with those of decision tree and multiple logistic regression models. We analyzed 96 subjects (PD-MCI = 45; Parkinson’s disease with normal cognition (PD-NC) = 51 subjects). The prediction accuracy of the model was calculated using the overall accuracy, sensitivity, and specificity. Based on the random forest analysis, the major risk factors of PD-MCI were, in descending order of magnitude, Clinical Dementia Rating (CDR) sum of boxes, Untitled Parkinson’s Disease Rating (UPDRS) motor score, the Korean Mini Mental State Examination (K-MMSE) total score, and the K- Korean Montreal Cognitive Assessment (K-MoCA) total score. The random forest method achieved a higher sensitivity than the decision tree model. Thus, it is advisable to develop a protocol to easily identify early stage PDD based on the PD-MCI prediction model developed in this study, in order to establish individualized monitoring to track high-risk groups.

scholarly journals P1-124: A multidomain prediction model for mild cognitive impairment to Alzheimer's disease conversion

2012 ◽  
Vol 8 (4S_Part_4) ◽  
pp. P149-P149
Author(s):  
Sei Lee ◽  
John Boscardin ◽  
Irena Stijacic Cenzer ◽  
Deborah Barnes
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Prediction Model

scholarly journals Using Naturalistic Driving Data to Predict Mild Cognitive Impairment and Dementia: Preliminary Findings from the Longitudinal Research on Aging Drivers (LongROAD) Study

Geriatrics ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 45
Author(s):  
Xuan Di ◽  
Rongye Shi ◽  
Carolyn DiGuiseppi ◽  
David W. Eby ◽  
Linda L. Hill ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Random Forests ◽  
Longitudinal Research ◽  
Demographic Characteristics ◽  
Machine Learning Techniques ◽  
Driving Behaviors ◽  
Naturalistic Driving ◽  
Race Ethnicity ◽  
Aging Drivers

Emerging evidence suggests that atypical changes in driving behaviors may be early signals of mild cognitive impairment (MCI) and dementia. This study aims to assess the utility of naturalistic driving data and machine learning techniques in predicting incident MCI and dementia in older adults. Monthly driving data captured by in-vehicle recording devices for up to 45 months from 2977 participants of the Longitudinal Research on Aging Drivers study were processed to generate 29 variables measuring driving behaviors, space and performance. Incident MCI and dementia cases (n = 64) were ascertained from medical record reviews and annual interviews. Random forests were used to classify the participant MCI/dementia status during the follow-up. The F1 score of random forests in discriminating MCI/dementia status was 29% based on demographic characteristics (age, sex, race/ethnicity and education) only, 66% based on driving variables only, and 88% based on demographic characteristics and driving variables. Feature importance analysis revealed that age was most predictive of MCI and dementia, followed by the percentage of trips traveled within 15 miles of home, race/ethnicity, minutes per trip chain (i.e., length of trips starting and ending at home), minutes per trip, and number of hard braking events with deceleration rates ≥ 0.35 g. If validated, the algorithms developed in this study could provide a novel tool for early detection and management of MCI and dementia in older drivers.

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