Dataset Information

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.

SUBMITTER: Bucholc M

PROVIDER: S-EPMC6688646 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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

Bucholc Magda M Ding Xuemei X Wang Haiying H Glass David H DH Wang Hui H Prasad Girijesh G Maguire Liam P LP Bjourson Anthony J AJ McClean Paula L PL Todd Stephen S Finn David P DP Wong-Lin KongFatt K

Expert systems with applications 20190410

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 ident ...[more]

PMID: 31402810

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Project description:Glucocorticoids are widely used for a variety of diseases, but the prevention of glucocorticoid-induced osteoporosis is sometimes neglected. Therefore, the effectiveness of a computerized clinical decision support system (CDSS) to improve the performance rate of preventive care for glucocorticoid-induced osteoporosis was evaluated. We conducted a prospective cohort study of outpatients who used glucocorticoids for three months or longer and who met the indication for preventive care based on a guideline. The CDSS recommended bisphosphonate (BP) prescription and bone mineral density (BMD) testing based on the risk of osteoporosis. The observation period was one year (phase 1: October 2017-September 2018) before implementation and the following one year (phase 2: October 2018-September 2019) after implementation of the CDSS. Potential alerts were collected without displaying them during phase 1, and the alerts were displayed during phase 2. We measured BP prescriptions and BMD testing for long-term prescription of glucocorticoids. A total of 938 patients (phase 1, 457 patients; phase 2, 481 patients) were included, and the baseline characteristics were similar between the phases. The median age was 71 years, and men accounted for 51%. The primary disease for prescription of glucocorticoids was rheumatic disease (28%), followed by hematologic diseases (18%). The prevalence of patients who needed an alert for BP prescription (67% vs. 63%, P = 0.24) and the acceptance rate of BP prescription (16% vs. 19%, P = 0.33) were similar between the phases. The number of patients who had orders for BMD testing was significantly increased (4% vs. 24%, P < 0.001) after CDSS implementation. The number of patients who needed an alert for BMD testing was significantly decreased from 93% in phase 1 to 87% in phase 2 (P = 0.004). In conclusion, the CDSS significantly increased BMD testing in patients with a higher risk of glucocorticoid-induced osteoporosis, but did not increase BP prescription.

Project description:BackgroundThe use of computerized clinical decision support systems (CCDSSs) may improve chronic disease management, which requires recurrent visits to multiple health professionals, ongoing disease and treatment monitoring, and patient behavior modification. The objective of this review was to determine if CCDSSs improve the processes of chronic care (such as diagnosis, treatment, and monitoring of disease) and associated patient outcomes (such as effects on biomarkers and clinical exacerbations).MethodsWe conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for potentially eligible articles published up to January 2010. We included randomized controlled trials that compared the use of CCDSSs to usual practice or non-CCDSS controls. Trials were eligible if at least one component of the CCDSS was designed to support chronic disease management. We considered studies 'positive' if they showed a statistically significant improvement in at least 50% of relevant outcomes.ResultsOf 55 included trials, 87% (n = 48) measured system impact on the process of care and 52% (n = 25) of those demonstrated statistically significant improvements. Sixty-five percent (36/55) of trials measured impact on, typically, non-major (surrogate) patient outcomes, and 31% (n = 11) of those demonstrated benefits. Factors of interest to decision makers, such as cost, user satisfaction, system interface and feature sets, unique design and deployment characteristics, and effects on user workflow were rarely investigated or reported.ConclusionsA small majority (just over half) of CCDSSs improved care processes in chronic disease management and some improved patient health. Policy makers, healthcare administrators, and practitioners should be aware that the evidence of CCDSS effectiveness is limited, especially with respect to the small number and size of studies measuring patient outcomes.

Dataset Information

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

Publications

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

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