Name
Title | ||
|---|---|---|
Prediction of ICU admission for COVID-19 patients: a Machine Learning approach based on Complete Blood Count data |
Abstract | ||
|---|---|---|
In this article we discuss the development of prognostic Machine Learning (ML) models for COVID-19 progression: specifically, we address the task of predicting intensive care unit (ICU) admission in the next 5 days. We developed three ML models on the basis of 4995 Complete Blood Count (CBC) tests. We propose three ML models that differ in terms of interpretability: two fully interpretable models ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CBMS52027.2021.00065 | 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS) |
Keywords | DocType | ISBN |
COVID-19,Hospitals,Machine learning,Predictive models,Data models,Task analysis,Surges | Conference | 978-1-6654-4121-6 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lorenzo Famiglini | 1 | 1 | 0.36 |
| Giorgio Bini | 2 | 1 | 0.36 |
| Anna Carobene | 3 | 1 | 0.36 |
| Andrea Campagner | 4 | 11 | 1.10 |
| Federico Cabitza | 5 | 399 | 52.88 |