Research
The first AI system that we have developed uses data of radiological exams and lab tests of more than 2000 (anonymised) patients hospitalised at the city hospital of Brescia (ASST Spedali Civili di Brescia), one of the Italian hospitals with more Covid-19 patients. From these clinical data we have created and engineered a data set that is used by a collection of machine learning algorithms to build a predictive model of the patient prognostic risk (e.g., the risk of death). The first experimental results are quite encouraging and show that our AI system has a good predictive accuracy.
Alfonso Emilio Gerevini, Roberto Maroldi, Matteo Olivato, Luca Putelli, Ivan Serina
Prognosis Prediction in Covid-19 Patients from Lab Tests and X-ray Data through Randomized Decision Trees
Abstract:
AI and Machine Learning can offer powerful tools to help in the fight against Covid-19. In this paper we present a study and a concrete tool based on machine learning to predict the prognosis of hospitalised patients with Covid-19. In particular we address the task of predicting the risk of death of a patient at different times of the hospitalisation, on the base of some demographic information, chest X-ray scores and several laboratory findings. Our machine learning models use ensembles of decision trees trained and tested using data from more than 2000 patients. An experimental evaluation of the models shows good performance in solving the addressed task.
Mattia Chiari, Alfonso E. Gerevini, Roberto Maroldi, Matteo Olivato, Luca Putelli, Ivan Serina
Length of Stay Prediction for Northern Italy COVID-19 Patients based on Lab Tests and X-Ray Data
Abstract:
The recent spread of COVID-19 put a strain on hospitals all over the world. In this paper we address the problem of hospital overloads and present a tool based on machine learning to predict the length of stay of hospitalised patients affected by COVID-19. This tool was developed using Random Forests and Extra Trees regression algorithms and was trained and tested on the data from more than 1000 hospitalised patients from Northern Italy. These data contain demographics, several laboratory test results and a score that evaluates the severity of the pulmonary conditions. The experimental results show good performance for the length of stay prediction and, in particular, for identifying which patients will stay in hospital for a long period of time.