ArticleMedical image analysis2024
Interpretable deep clustering survival machines for Alzheimer's disease subtype discovery.
Article in Medical image analysis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Coupled cross-sectional and longitudinal non-negative matrix factorization reveals dominant brain aging trajectories in 48,949 individuals.Nature communications · 2026Article
- Staging and Pseudotime Inference of Alzheimer's Disease Progression using Multimodal Imaging Data.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Expert-Driven Survival Machines: Improving Stratification and Interpretability in Multiple Clinical Cohorts.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2026Article
- The gut-brain axis in Alzheimer's disease: traditional Chinese medicine constituents as modulators of gut-brain homeostasis.Frontiers in microbiology · 2026Review
- Advanced topic modeling with large language models: analyzing social media content from dementia caregivers.Innovation in aging · 2025Article
- IRIS: Interpretable Risk Clustering Intelligence for Survival Analysis.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2025Article
- Magnetic Resonance Imaging and Cerebrospinal Fluid Biomarker Clustering Defines Biological Subtypes of Alzheimer's Disease.Biomedicines · 2025Article
- Multi-Modal Deep Clustering Survival Machines for Alzheimer's Disease Subtype Discovery.... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision · 2025Article
- Integrating Social Determinants of Health in a Multi-Modal Deep Clustering Survival Model for Injury-Risk in Alzheimer's and Related Dementia Patients.Proceedings of machine learning research · 2025Article
- Determining the Importance of Clinical Modalities for NeuroDegenerative Disorders and Risk of Patient Injury Using Machine Learning and Survival Analysis.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disorder that has impacted millions of people worldwide. The neuroanatomical heterogeneity of AD has made it challenging to fully understand the disease mechanism. Identifying AD subtypes during the prodromal stage and determining their genetic basis would be immensely valuable for drug discovery and subsequent clinical treatment. Previous studies that clustered subgroups typically used unsupervised learning techniques, neglecting the survival information and potentially limiting the insights gained. To address this problem, we propose an interpretable survival analysis method called Deep Clustering Survival Machines (DCSM), which combines both discriminative and generative mechanisms. Similar to mixture models, we assume that the timing information of survival data can be generatively described by a mixture of parametric distributions, referred to as expert distributions. We learn the weights of these expert distributions for individual instances in a discriminative manner by leveraging their features. This allows us to characterize the survival information of each instance through a weighted combination of the learned expert distributions. We demonstrate the superiority of the DCSM method by applying this approach to cluster patients with mild cognitive impairment (MCI) into subgroups with different risks of converting to AD. Conventional clustering measurements for survival analysis along with genetic association studies successfully validate the effectiveness of the proposed method and characterize our clustering findings.
Indexed as
Identifiers
What OpenQuestion holds
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.