Prediction of individual COVID-19 diagnosis using baseline demographics and lab data
Abstract The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false findings. Here, we developed a machine learning-based framework for predicting indivi...
Saved in:
| Main Authors: | Jimmy Zhang, Tomi Jun, Jordi Frank, Sharon Nirenberg, Patricia Kovatch, Kuan-lin Huang |
|---|---|
| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2021
|
| Subjects: | |
| Online Access: | https://doaj.org/article/a6d017c9b7b44b3f80c3aba8f956ec04 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Inter-individual differences in baseline dynamic functional connectivity predict cognitive aftereffects of tDCS
by: Monika Pupíková, et al.
Published: (2021) -
Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis
by: Jun Miyoshi, et al.
Published: (2021) -
The COVID-19 lab score: an accurate dynamic tool to predict in-hospital outcomes in COVID-19 patients
by: Pablo Jose Antunez Muiños, et al.
Published: (2021) -
Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system
by: Elza Rechtman, et al.
Published: (2020) -
Deep learning to predict the lab-of-origin of engineered DNA
by: Alec A. K. Nielsen, et al.
Published: (2018)