AI@RISC-19-ICU. RIsk Stratification in COVID-19 patients in the ICU
The aim of the research is the design and development of Artificial Intelligence and Machine Learning methods (ML) for the prediction of complications and risk stratification of COVID 19 Intensive Care Units (ICUs) using heterogeneous longitudinal Electronic Health Record (EHR) data. In particular, the study will be performed as part of the "Collaborative international ICU registry for critically ill COVID-19 patients - RISC-19-ICU" (https://sites.google.com/view/risc-19-icu).
The Visual Robotics and Artificial Intelligence (VRAI) group from the Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy supervised by Prof. Emanuele Frontoni will be responsible to design and develop AI and ML methods, AI data architectures and learning-testing procedures.
The idea behind the project is to better prevent and treat the complication that is about to appear in the patient affected by COVID19 by developing a decision support system that allows computing:
risk profiles of individual patients from which a different intensity of care can be deduced, with consequent modification of the control time according to the needs; this approach would produce a shortening of the waiting time and an increase in the appropriateness of care.
prediction of risk of short-term complications which will activate personalized prevention systems directly addressed to the patient: from targeted recalls to targeted motivational and training activities
Objectives (AI/ML Section)
The analysis of EHR data using statistical and Machine Learning methodologies aims to:
develop innovative solutions and predictions whether a patient belongs to a risk class related to specific COVID-19 complications over a short period of time.
the extraction of clinical features of EHR (e.g. specific drugs) are related to the development of the complication.
The developing a clinical decision support system, integrated in an electronic record, that allows the risk profile of morbidity and resource consumption of the patients according to their clinical features would allow more appropriate and intensive use of resources for those who need them most.
Publication 1: Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients
Background: Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care. Methods: We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry. We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary out- come the increase or decrease in patients’ Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the study cohort. Results: The final study population consisted of 675 patients. The XGBoost model correctly predicted a decrease in SOFA score in 320/385 (83%) critically ill COVID-19 patients, and an increase in the score in 210/290 (72%) patients. The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model (0.86 vs. 0.69, P < 0.01 [paired t-test with 95% confidence interval]). Conclusions: The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems (CDSSs) aimed at optimizing available resources.
Publication 2: Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World
As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This article provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas, where IoT can contribute are discussed, namely: 1) tracking and tracing; 2) remote patient monitoring (RPM) by wearable IoT (WIoT); 3) personal digital twins (PDTs); and 4) real-life use case: ICT/IoT solution in South Korea. Second, the role and novel applications of AI are explained, namely: 1) diagnosis and prognosis; 2) risk prediction; 3) vaccine and drug development; 4) research data set; 5) early warnings and alerts; 6) social control and fake news detection; and 7) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including: 1) crowd surveillance; 2) public announcements; 3) screening and diagnosis; and 4) essential supply delivery. Finally, we discuss how distributed ledger technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19
Publication 3: A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign
The current ML approaches do not fully focus to answer a still unresolved and topical challenge, namely the prediction of priorities of COVID-19 vaccine administration. Thus, our task includes some additional methodological challenges mainly related to avoiding unwanted bias while handling categorical and ordinal data with a highly imbalanced nature. Hence, the main contribution of this study is to propose a machine learning algorithm, namely Hierarchical Priority Classification eXtreme Gradient Boosting for priority classification for COVID-19 vaccine administration using the Italian Federation of General Practitioners dataset that contains Electronic Health Record data of 17k patients. We measured the effectiveness of the proposed methodology for classifying all the priority classes while demonstrating a significant improvement with respect to the state of the art. The proposed ML approach, which is integrated into a clinical decision support system, is currently supporting General Pracitioners in assigning COVID-19 vaccine administration priorities to their assistants.
The RISC-19-ICU board includes Dr. M. P. Hilty, P. D. Wendel Garcia, MSc, Prof. Dr. R. Schuepbach, Dr. J. Montomoli, Dr. Ph. Guerci, Prof. Dr. T. Fumeaux. The aim of the registry is to collect real-time data of COVID patients admitted to non-ICU or ICU wards. The registry was launched on the 13th of March and it includes already 75 centers from 14 countries collecting data.
Contact: Luca Romeo (firstname.lastname@example.org)