| Autor: |
Ahmet Gorkem Er, Daisy Yi Ding, Berrin Er, Mertcan Uzun, Mehmet Cakmak, Christoph Sadee, Gamze Durhan, Mustafa Nasuh Ozmen, Mine Durusu Tanriover, Arzu Topeli, Yesim Aydin Son, Robert Tibshirani, Serhat Unal, Olivier Gevaert |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
npj Digital Medicine, Vol 7, Iss 1, Pp 1-11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2398-6352 |
| DOI: |
10.1038/s41746-024-01128-2 |
| Popis: |
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 1, Zv 1) = 0.596, p value |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full text from SpringerLink