ArticleExpert systems with applications2019
A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual.
Article in Expert systems with applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data-driven decision making in patient management: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.Scientific reports · 2021Trial
- Artificial neural networks fighting real neural decline: a systematic review of AI in Alzheimer's research.Artificial intelligence review · 2026Article
- Non-Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Recent Advances in the Application of Artificial Intelligence in Alzheimer's Disease.Current Alzheimer research · 2025Review
- Auto-branch multi-task learning for simultaneous prediction of multiple correlated traits associated with Alzheimer's disease.Frontiers in genetics · 2025Article
- A mini review of transforming dementia care in China with data-driven insights: overcoming diagnostic and time-delayed barriers.Frontiers in aging neuroscience · 2025Review
- Autoencoder imputation of missing heterogeneous data for Alzheimer's disease classification.Healthcare technology letters · 2024Article
- Distance-based novelty detection model for identifying individuals at risk of developing Alzheimer's disease.Frontiers in aging neuroscience · 2024Article
- Explainable Machine Learning Techniques to Predict Muscle Injuries in Professional Soccer Players through Biomechanical Analysis.Sensors (Basel, Switzerland) · 2023Article
- Explainable Machine Learning with Pairwise Interactions for Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease Utilizing Multi-Modalities Data.Brain sciences · 2023Article
- Machine Learning-Based Routine Laboratory Tests Predict One-Year Cognitive and Functional Decline in a Population Aged 75+ Years.Brain sciences · 2023Article
- Article
- Identification of Outcome-Oriented Progression Subtypes from Mild Cognitive Impairment to Alzheimer's Disease Using Electronic Health Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Improvement of a prediction model for heart failure survival through explainable artificial intelligence.Frontiers in cardiovascular medicine · 2023Article
- Multimodal attention-based deep learning for Alzheimer's disease diagnosis.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Prediction of the disease course in Friedreich ataxia.Scientific reports · 2022Article
- A Scoring System for Predicting Neoadjuvant Chemotherapy Response in Primary High-Grade Bone Sarcomas: A Multicenter Study.Orthopaedic surgery · 2022Article
- An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI.Sensors (Basel, Switzerland) · 2022Article
- A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
12 authors.
Funding
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.,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.