A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual

Abstract

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R ² = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.

Keywords: Alzheimer’s disease; cognitive impairment; decision support system; dementia; diagnosis support; machine learning.

Fig. 1.

Overview of the model development and validation procedure.

Fig. 2.

UML activity diagram of the computer-based clinical decision support system for predicting AD severity of an individual.

Fig. 3.

A) Performance profile across different subset sizes evaluated using the RFE-RF technique. Dark blue dot: the subset of features with the best performance B) Resampling performance of the best subset of features across different folds.

Fig. 4.

KRR model predictions of medical diagnosis (CDRSB) of individual patients for 5 modality types: a) CFA, b) MRI, c) PET, d) CSF, and e) Age. Blue dots: observed values of CDRSB; red dots: predicted values of CDRSB; vertical lines: differences between observed and predicted values of the outcome. Models’ predictions for each set of considered markers were obtained using an (unseen) testing set partitioned from the original data (10%). CFA: functional and cognitive assessments; MRI: magnetic resonance imaging; PET: positron emission tomography; CSF: cerebrospinal fluid biomarkers.

Fig. 5.

SVM model predictions of medical diagnosis of individual patients for 5 data types: a) CFA, b) MRI, c) PET, d) CSF, and e) Age. The vertical axis values and corresponding horizontal lines refer to the target CDRSB class, i.e., ‘Normal’ (green) = 0 (CDRSB = 0), ‘QCI’ (yellow) = 1 (0.5 ≤ CDRSB ≤ 4.0), and ‘Mild/Moderate’ (red) = 2 (4.5 ≤ CDRSB ≤ 15.5). Circles: predicted CDRSB class. CFA: functional and cognitive assessments; MRI: magnetic resonance imaging; PET: positron emission tomography; CSF: cerebrospinal fluid biomarkers.

Fig. 6.

Graphical user interface of the computer-based clinical decision support system for predicting severity of dementia of an individual patient. A) Patient information panel; B) AD severity measurement scale with AD severity score (black line) and its confidence interval (gray range); C) Measurement scales for the selected cognitive/functional assessments (FAQ, ADAS13, MoCA, MMSE). To allow quick interpretation, the AD severity measurement scale is divided into 5 classes based on the CDRSB score, i.e., ‘Normal’ (CDRSB = 0), ‘QCI’ (0.5 ≤ CDRSB ≤ 4.0), ‘AD Mild’ (4.0 ≤ CDRSB ≤ 9), ‘AD Moderate’ (9.5 ≤ CDRSB ≤ 15.5)., and ‘AD severe’ (16 ≤ CDRSB ≤ 18).”