output
20192024
most citedOn Classifying Sepsis Heterogeneity in the ICU: Insight Using Machine Learning

78 citations

6 papers

math.NA2024★ 7 cited

Two new calibration techniques of lumped-parameter mathematical models for the cardiovascular system

Andrea Tonini, Francesco Regazzoni, Matteo Salvador +7

Cardiocirculatory mathematical models serve as valuable tools for investigating physiological and pathological conditions of the circulatory system. To investigate the clinical con…

math.NA2023★ 9 cited

A mathematical model to assess the effects of COVID-19 on the cardiocirculatory system

Andrea Tonini, Christian Vergara, Francesco Regazzoni +4

Impaired cardiac function has been described as a frequent complication of COVID-19-related pneumonia. To investigate possible underlying mechanisms, we represented the cardiovascu…

math.NA2020★ 19 cited

A non-intrusive data-driven ROM framework for hemodynamics problems

M. Girfoglio, L. Scandurra, F. Ballarin +7

Reduced order modeling (ROM) techniques are numerical methods that approximate the solution of parametric partial differential equation (PDE) by properly combining the high-fidelit…

q-bio.OT2020

Forcing Seasonality of influenza-like epidemics with daily Solar resonance

F. Nicastro, G. Sironi, E. Antonello +9

Seasonality of acute viral respiratory diseases is a well-known and yet not fully understood phenomenon. Several models have been proposed to explain the regularity of yearly recur…

q-bio.TO2020★ 7 cited

Massive viral replication and cytopathic effects in early COVID-19 pneumonia

L. Schifanella, J. L. Anderson, M. Galli +7

SARS-CoV-2 is the cause of COVID-19 acute respiratory illness that like its predecessors, MERS and SARS, can be severe and fatal 1-4. By April of 2020, COVID-19 infections had beco…

q-bio.QM2019★ 78 cited

On Classifying Sepsis Heterogeneity in the ICU: Insight Using Machine Learning

Zina Ibrahim, Honghan Wu, Ahmed Hamoud +3

Current machine learning models aiming to predict sepsis from Electronic Health Records (EHR) do not account for the heterogeneity of the condition, despite its emerging importance…