SynthesisFrontiers in computational neuroscience2019
The Application of Unsupervised Clustering Methods to Alzheimer's Disease.
Synthesis in Frontiers in computational neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed.
- Stratification of Alzheimer's disease patients using knowledge-guided unsupervised latent factor clustering with electronic health record data.Communications medicine · 2026Article
- Next generation preventive neurology: how artificial intelligence and machine learning are reshaping Alzheimer's disease research.Behavioral and brain functions : BBF · 2026Review
- Enhancing Anesthetic Depth Assessment via Unsupervised Machine Learning in Processed Electroencephalography Analysis: Novel Methodological Study.JMIR medical informatics · 2026Article
- Machine learning clustering of carotid body tumor patients by clinical features and surgical approaches.European journal of medical research · 2025Article
- A formal explanation space for the simultaneous clustering of neurologic diseases based on their signs and symptoms.BMC medical informatics and decision making · 2025Article
- Modeling Missing at Random Neuropsychological Test Scores Using a Mixture of Binomial Product Experts.Psychometrika · 2025Article
- Using Medication Dispensation Data to Identify Clusters with Similar Prescribing Patterns in Older Adults Living with Dementia.Drugs & aging · 2025Article
- Effective assistance timing using characteristics of paretic ground reaction force in ankle robotic gait training for individuals with hemiparesis after stroke: a cross-sectional study.Journal of neuroengineering and rehabilitation · 2025Article
- A comparative analysis of unsupervised machine-learning methods in PSG-related phenotyping.Journal of sleep research · 2025Article
- Article
- Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.Schizophrenia bulletin · 2025Review
- Medications and cognitive decline in Alzheimer's disease: Cohort cluster analysis of 15,428 patients.Journal of Alzheimer's disease : JAD · 2025Article
- Multi-omic biomarker panel in pancreatic cyst fluid and serum predicts patients at a high risk of pancreatic cancer development.Scientific reports · 2025Article
- IMCKDE algorithm: an improvement in a clustering technique based on kernel density estimation.Journal of applied statistics · 2025Article
- Interpretable deep clustering survival machines for Alzheimer's disease subtype discovery.Medical image analysis · 2024Article
- Identification of clear cell renal cell carcinoma subtypes by integrating radiomics and transcriptomics.Heliyon · 2024Article
- Medical calculators derived synthetic cohorts: a novel method for generating synthetic patient data.Scientific reports · 2024Article
- Identifying clusters of objective functional impairment in patients with degenerative lumbar spinal disease using unsupervised learning.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2024Article
- Predicting buoyant jet characteristics: a machine learning approach.Chemical product and process modeling · 2024Article
- ELMAS: a one-year dataset of hourly electrical load profiles from 424 French industrial and tertiary sectors.Scientific data · 2023Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Clustering is a powerful machine learning tool for detecting structures in datasets. In the medical field, clustering has been proven to be a powerful tool for discovering patterns and structure in labeled and unlabeled datasets. Unlike supervised methods, clustering is an unsupervised method that works on datasets in which there is no outcome (target) variable nor is anything known about the relationship between the observations, that is, unlabeled data. In this paper, we focus on studying and reviewing clustering methods that have been applied to datasets of neurological diseases, especially Alzheimer's disease (AD). The aim is to provide insights into which clustering technique is more suitable for partitioning patients of AD based on their similarity. This is important as clustering algorithms can find patterns across patients that are difficult for medical practitioners to find. We further discuss the implications of the use of clustering algorithms in the treatment of AD. We found that clustering analysis can point to several features that underlie the conversion from early-stage AD to advanced AD. Furthermore, future work can apply semi-clustering algorithms on AD datasets, which will enhance clusters by including additional information.
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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.