Showing papers by "Federica Mandreoli published in 2020"

Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia-Challenges, strengths, and opportunities in a global health emergency.

Abstract: Aims The aim of this study was to estimate a 48 hour prediction of moderate to severe respiratory failure, requiring mechanical ventilation, in hospitalized patients with COVID-19 pneumonia. Methods This was an observational prospective study that comprised consecutive patients with COVID-19 pneumonia admitted to hospital from 21 February to 6 April 2020. The patients' medical history, demographic, epidemiologic and clinical data were collected in an electronic patient chart. The dataset was used to train predictive models using an established machine learning framework leveraging a hybrid approach where clinical expertise is applied alongside a data-driven analysis. The study outcome was the onset of moderate to severe respiratory failure defined as PaO2/FiO2 ratio Results A total of 198 patients contributed to generate 1068 usable observations which allowed to build 3 predictive models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth "boosted mixed model" included 20 variables was selected from the model 3, achieved the best predictive performance (AUC = 0.84) without worsening the FN rate. Its clinical performance was applied in a narrative case report as an example. Conclusion This study developed a machine model with 84% prediction accuracy, which is able to assist clinicians in decision making process and contribute to develop new analytics to improve care at high technology readiness levels.

Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia - challenges, strengths, and opportunities in a global health emergency

Abstract: Background Machine learning can assist clinicians in forecasting patients with COVID-19 who develop respiratory failure requiring mechanical ventilation. This analysis aimed to determine a 48 hours prediction of moderate to severe respiratory failure, as assessed with PaO2/FiO2 < 150 mmHg, in hospitalized patients with COVID-19 pneumonia. Methods This was an observational study that comprised all consecutive adult patients with COVID-19 pneumonia admitted to the Infectious Diseases Clinic of the University Hospital of Modena, Italy from 21 February to 6 April 2020. COVID-19 was confirmed with PCR positive nasopharyngeal swabs while the presence of pneumonia was radiologically confirmed. Patients received standard of care according to national guidelines for clinical management of SARS-CoV-2 infection. The patients' full medical history, demographic and epidemiological features, clinical data, complete blood count, coagulation, inflammatory and biochemical markers were routinely collected and aggregated in a clinically-oriented logical framework in order to build different datasets. The dataset was used to train a learning framework relying on Microsoft LightGBM and leveraging a hybrid approach, where clinical expertise is applied alongside a data-driven analysis. Shapley Additive exPlanations (SHAP) values were used to quantify the positive or negative impact of each variable included in the model on the predicted outcome. The study outcome was the onset of moderate to severe respiratory failure defined as PaO2/FiO2 ratio < 150 mmHg ([≥] 13.3 kPa) in at least one of two consecutive arterial blood gas analyses in the following 48 hours. Results A total of 198 patients contributed to generate 1068 valuable observations which allowed to build 3 prediction models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth boosted mixed model which included 20 variables was selected from the model 3, achieved the best predictive performance (AUC=0.84). Its clinical performance was applied in a narrative case report as an example. Conclusion This study developed a machine learning algorithm, with a 84% prediction accuracy, which is potentially able to assist clinicians in decision making process with therapeutic implications.

Work datafication and digital work behavior analysis as a source of social good

Abstract: The digital transformation of organizations is boosting workplace networking and collaboration while making it “observable” with unprecedented timeliness and detail. However, the informational and managerial potential of work datafication is still largely unutilized in Human Resource Management (HRM) and its social benefits, both at the individual and the organizational level, remain largely unexplored. Our research focuses on the relationship between digitally tracked work behaviors and employee attitudes and, in so doing, it explores work datafication as a source of social good. As part of a wider research program, this paper presents some data analysis we performed on a collection of Enterprise Collaboration Software (ECS) data, in search for promising correlations between behavioral and relational (digital) work patterns and employee attitudes. To this end, we transformed the digital actions performed by 106 employees during a one year period into a graph representation to analyze data under two different points of view: the individual (behavioral) perspective, according to the user who performed the action and the action undertaken, and the social (relational) perspective, making explicit the interactions between users and the objects of their actions. Different employees' rankings are thus derived and correlated with their attitudes. We discuss the obtained results and their benefits in terms of perspective social good for both the company and the employee.

AgileChains: Agile Supply Chains through Smart Digital Twins

Abstract: Currently, production and logistics performance of a single organization are only partially dependent on the internal resources, but more and more often, they also depend on the interactions that happen across the so-called supply chain , that is, the interactions between the organization and its customers and suppliers. In particular, the production and logistics coordination between actors in the supply chain is often a difficult activity which draws significant resources. Also, such coordination requires continuous revisions and updates to be performed. In Industry 4.0, the digital twins paradigm is currently adopted to represent, simulate and test the behavior of one or more machines and production plants belonging to an organization. This paper introduces the AgileChains paradigm, extending the digital twin paradigm to supply chains and the dynamics of their participants. This extension also positively affects the reactivity and resilience of the internal processes in case the supply chain has to be reconfigured. We propose a novel conceptual framework that combines Service Oriented Architectures (SOA) with Cyber-Physical Systems (CPS), in order to create service oriented systems suited for exchanging data in a dynamic and adaptive way. In addition, we propose a novel data management mechanism capable of finding the right balance between the internal needs of each organization when handling their data and the need to securely and efficiently export data in the supply chain (cf. smart data movement ). Finally, we plan to define governance tools to model and manage the supply chain that treat agility as a first-class citizen. These tools will allow users to dynamically and predictively change the involved actors, as well as the nature of the exchanged data and the data exchange policies, focusing in particular on adverse, risk-prone events, so to minimize the risk and to optimize the supply chain performance both in terms of efficiency and effectiveness.

VarCopy: a Visual Exploratory Data Analysis Platform for Copy Number Variation Studies

Abstract: The study of such a complex phenomenon as cancer, which depends on several but unexplored and unclear factors, needs new ways to visualize, analyze and combine different data both on species characteristics and genes function. To this respect, we propose a novel platform, named VarCopy, supporting visual Exploratory Data Analysis (EDA) in the context of Copy Number Variation (CNV) data. The platform will be publicly available as a web application soon, and is, to our best knowledge, the first tool allowing visual, interactive exploration and analysis of the CNV landscape of multiple species, allowing the identification of new target genes that might be useful for biomedical research.

Towards Smart Manufacturing with Dynamic Dataspace Alignment

Abstract: The technological foundation of smart manufacturing consists of cyber-physical systems and the Internet-of-Things (IoT). Despite smart manufacturing has become a key paradigm to promote the integration of manufacturing processes using digital technologies, the manufacturing processes themselves are designed by human experts in a traditional way and have limited ability to adapt their behavior to exceptional circumstances. We leverage the fact that each IoT device in a smart factory can be coupled with a digital twin – that is, a software artefact that faithfully represents the physical system using real-time sensor data – to envision a software architecture to support adaptation of the manufacturing process when divergence from reference practices occur.

Data-driven vs knowledge-driven inference of health outcomes in the ageing population: a case study.

Abstract: Preventive, Predictive, Personalised and Participative (P4) medicine has the potential to not only vastly improve people’s quality of life, but also to significantly reduce healthcare costs and improve its efficiency. Our research focuses on age-related diseases and explores the opportunities offered by a data-driven approach to predict wellness states of ageing individuals, in contrast to the commonly adopted knowledge-driven approach that relies on easy-to-interpret metrics manually introduced by clinical experts. This is done by means of machine learning models applied on the My Smart Age with HIV (MySAwH) dataset, which is collected through a relatively new approach especially for older HIV patient cohorts. This includes Patient Related Outcomes values from mobile smartphone apps and activity traces from commercial-grade activity loggers. Our results show better predictive performance for the data-driven approach. We also show that a post hoc interpretation method applied to the predictive models can provide intelligible explanations that enable new forms of personalised and preventive medicine.

Predicting respiratory failure in patients with COVID-19 pneumonia: A case study from Northern Italy

Abstract: The Covid-19 crisis caught health care services around the world by surprise, putting unprecedented pressure on Intensive Care Units (ICU) To help clinical staff to manage the limited ICU capacity, we have developed a Machine Learning model to estimate the probability that a patient admitted to hospital with COVID-19 symptoms would develop severe respiratory failure and require Intensive Care within 48 hours of admission The model was trained on an initial cohort of 198 patients admitted to the Infectious Disease ward of Modena University Hospital, in Italy, at the peak of the epidemic, and subsequently refined as more patients were admitted Using the Light- GBM Decision Tree ensemble approach, we were able to achieve good accuracy (AUC = 0 84) despite a high rate of missing values Furthermore, we have been able to provide clinicians with explanations in the form of personalised ranked lists of features for each prediction, using only 20 out of more than 90 variables, using Shapley values to describe the importance of each feature Copyright © 2020 for this paper by its authors