ReviewJournal of medical systems2018
Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors.
Review in Journal of medical systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed.
- Machine-Learning Approach to Identify Tissue Inhibitors of Metalloproteinases (TIMP) and Clinical Variables Predicting Executive Phenotypes in HIV-infected Adults.Open forum infectious diseases · 2026Article
- Towards Early Prediction of Amyotrophic Lateral Sclerosis Empowered by Machine Learning and Clinical Big Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Multimodal MRI biomarkers and machine learning in neuromyelitis optica spectrum disorder: a review.Frontiers in neurology · 2026Review
- Nanomedicine-Driven Modulation of the Gut-Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer's and Parkinson's Disease.International journal of molecular sciences · 2025Review
- A ferroptosis-related risk model for non-coding RNA AC002331.1 in colon cancer: construction via competing endogenous RNA network analysis.Discover oncology · 2025Article
- Exploration of Risk Factors for Cardiovascular Disease in Patients with Rheumatoid Arthritis: A Retrospective Study.Inflammation · 2025Article
- Article
- Development and interpretation of a machine learning predictive model for early cognitive impairment in hypertension associated with environmental factors.Frontiers in cardiovascular medicine · 2025Article
- A Multivariable Prediction Model for Mild Cognitive Impairment and Dementia: Algorithm Development and Validation.JMIR medical informatics · 2024Article
- Assessment of the Daily Living Activities of Older People (2004-2023): A Bibliometric and Visual Analysis.Healthcare (Basel, Switzerland) · 2024Article
- Prediction Model for Cognitive Impairment among Disabled Older Adults: A Development and Validation Study.Healthcare (Basel, Switzerland) · 2024Article
- Data-driven clustering approach to identify novel clusters of high cognitive impairment risk among Chinese community-dwelling elderly people with normal cognition: A national cohort study.Journal of global health · 2024Article
- Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities.Frontiers in pharmacology · 2024Article
- Prediction of in-hospital mortality risk for patients with acute ST-elevation myocardial infarction after primary PCI based on predictors selected by GRACE score and two feature selection methods.Frontiers in cardiovascular medicine · 2024Article
- Artificial intelligence approaches for early detection of neurocognitive disorders among older adults.Frontiers in computational neuroscience · 2024Article
- A risk prediction model based on machine learning for postoperative cognitive dysfunction in elderly patients with non-cardiac surgery.Aging clinical and experimental research · 2023Article
- A dual-task-embedded virtual reality system for intelligent quantitative assessment of cognitive processing speed.Frontiers in human neuroscience · 2023Article
- Using machine learning to understand determinants of IUD use in India: Analyses of the National Family Health Surveys (NFHS-4).SSM - population health · 2022Article
- Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with Natural Language Processing capabilities.Journal of ambient intelligence and humanized computing · 2022Article
- Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Machine learning and data mining approaches are being successfully applied to different fields of life sciences for the past 20 years. Medicine is one of the most suitable application domains for these techniques since they help model diagnostic information based on causal and/or statistical data and therefore reveal hidden dependencies between symptoms and illnesses. In this paper we give a detailed overview of the recent machine learning research and its applications for predicting cognitive diseases, especially the Alzheimer's disease, mild cognitive impairment and the Parkinson's disease. We survey different state-of-the-art methodological approaches, data sources and public data, and provide their comparative analysis. We conclude by identifying the open problems within the field that include an early detection of the cognitive diseases and inclusion of machine learning tools into diagnostic practice and therapy planning.
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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 OpenQuestion graph.