ArticleNPJ digital medicine2024
Multimodal data fusion using sparse canonical correlation analysis and cooperative learning: a COVID-19 cohort study.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- A hierarchical clinical fusion transformer model for personalized opioid treatment: Development and validation in diabetic surgical patients.medRxiv : the preprint server for health sciences · 2026Article
- A multimodal radiomics model to predict disease-free survival in resected non-small cell lung cancer: integrating clinicopathology, dual-energy CT, and deep learning features.Journal of thoracic disease · 2026Article
- Explainable AI multiomics analysis reveals shared and divergent host responses in COVID-19 and influenza.NPJ digital medicine · 2026Article
- Exploring the genetic landscape of COVID-19 susceptibility and severity among patients in Türkiye.BMC medical genomics · 2025Article
- Fair Multi-modal Canonical Correlation Analysis: A Neuroimaging Study of Alzheimer's Disease.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
- Update of
Authors and funding
14 authors.
Funding
Abstract
Through technological innovations, patient cohorts can be examined from multiple views with high-dimensional, multiscale biomedical data to classify clinical phenotypes and predict outcomes. Here, we aim to present our approach for analyzing multimodal data using unsupervised and supervised sparse linear methods in a COVID-19 patient cohort. This prospective cohort study of 149 adult patients was conducted in a tertiary care academic center. First, we used sparse canonical correlation analysis (CCA) to identify and quantify relationships across different data modalities, including viral genome sequencing, imaging, clinical data, and laboratory results. Then, we used cooperative learning to predict the clinical outcome of COVID-19 patients: Intensive care unit admission. We show that serum biomarkers representing severe disease and acute phase response correlate with original and wavelet radiomics features in the LLL frequency channel (cor(Xu
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.