Background Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling. Objective We aimed to develop a machine learning–based score—the Piacenza score—for 30-day mortality prediction in patients with COVID-19 pneumonia. Methods The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients’ medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naïve Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO2/FiO2 ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naïve Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naïve Bayes algorithm with 14 features chosen a priori. Results The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naïve Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively. Conclusions Our findings demonstrated that a customizable machine learning–based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia.

BACKGROUND Several models have been developed to predict mortality in patients with Covid-19 pneumonia, but only few have demonstrated enough discriminatory capacity. Machine-learning algorithms represent a novel approach for data-driven prediction of clinical outcomes with advantages over statistical modelling. OBJECTIVE To developed the Piacenza score, a Machine-learning based score, to predict 30-day mortality in patients with Covid-19 pneumonia METHODS The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital (Italy) from February to November 2020. The patients’ medical history, demographic and clinical data were collected in an electronic health records. The overall patient dataset was randomly splitted into derivation and test cohort. The score was obtained through the Naïve Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm six features were identified: age; mean corpuscular haemoglobin concentration; PaO2/FiO2 ratio; temperature; previous stroke; gender. The Brier index was used to evaluate the ability of ML to stratify and predict observed outcomes. A user-friendly web site available at (https://covid.7hc.tech.) was designed and developed to enable a fast and easy use of the tool by the final user (i.e., the physician). Regarding the customization properties to the Piacenza score, we added a personalized version of the algorithm inside the website, which enables an optimized computation of the mortality risk score for a single patient, when some variables used by the Piacenza score are not available. In this case, the Naïve Bayes classifier is re-trained over the same derivation cohort but using a different set of patient’s characteristics. We also compared the Piacenza score with the 4C score and with a Naïve Bayes algorithm with 14 features chosen a-priori. RESULTS The Piacenza score showed an AUC of 0.78(95% CI 0.74-0.84 Brier-score 0.19) in the internal validation cohort and 0.79(95% CI 0.68-0.89, Brier-score 0.16) in the external validation cohort showing a comparable accuracy respect to the 4C score and to the Naïve Bayes model with a-priori chosen features, which achieved an AUC of 0.78(95% CI 0.73-0.83, Brier-score 0.26) and 0.80(95% CI 0.75-0.86, Brier-score 0.17) respectively. CONCLUSIONS A personalized Machine-learning based score with a purely data driven features selection is feasible and effective to predict mortality in patients with COVID-19 pneumonia.

BackgroundSeveral models have been developed to predict mortality in patients with COVID-19 pneumonia, but only few have demonstrated enough discriminatory capacity. Machine-learning(ML) algorithms represent a novel approach for data-driven prediction of clinical outcomes with advantages over statistical modelling. We developed the Piacenza score, a ML-based score, to predict 30-day mortality in patients with COVID-19 pneumonia.Methods852 patients (mean age 70years, 70%males) were enrolled from February to November 2020. The dataset was randomly splitted into derivation and test. The Piacenza score was obtained through the Naïve Bayes classifier and externally validated on 86 patients. Using a forward-search algorithm the following six features were identified: age; mean corpuscular haemoglobin concentration; PaO2 /FiO2 ratio; temperature; previous stroke; gender. In case one or more of the features are not available for a patient, the model can be re-trained using only the provided features.We also compared the Piacenza score with the 4C score and with a Naïve Bayes algorithm with 14 variables chosen a-priori.ResultsThe Piacenza score showed an AUC of 0.78(95% CI 0.74-0.84, Brier-score 0.19) in the internal validation cohort and 0.79(95% CI 0.68-0.89, Brier-score 0.16) in the external validation cohort showing a comparable accuracy respect to the 4C score and to the Naïve Bayes model with a-priori chosen features, which achieved an AUC of 0.78(95% CI 0.73-0.83, Brier-score 0.26) and 0.80(95% CI 0.75-0.86, Brier-score 0.17) respectively.ConclusionA personalized ML-based score with a purely data driven features selection is feasible and effective to predict mortality in patients with COVID-19 pneumonia.

Service robotics is a fast-developing sector, requiring embedded intelligence into robotic platforms to interact with the humans and the surrounding environment. One of the main challenges in the field is robust and versatile manipulation in everyday life activities. An appealing opportunity is to exploit compliant end-effectors to address the manipulation of deformable objects. However, the intrinsic compliance of such grippers results in increased difficulties in grasping control. Within the described context, this work addresses the problem of optimizing the grasping of deformable objects making use of a compliant, under-actuated, sensorless robotic hand. The main aim of the paper is, therefore, finding the best position and joint configuration for the mentioned robotic hand to grasp an unforeseen deformable object based on collected RGB image and partial point cloud. Due to the complex grasping dynamics, learning-from-simulations approaches (e.g., Reinforcement Learning) are not effective in the faced context. Thus, trial-and-error-based methodologies have to be exploited. In order to save resources, a samples-efficient approach has to be employed. Indeed, a Bayesian approach to address the optimization of the grasping strategy is proposed, enhancing it with transfer learning capabilities to exploit the acquired knowledge to grasp (partially) new objects. A PAL Robotics TIAGo (a mobile manipulator with a 7-degrees-of-freedom arm and an anthropomorphic underactuated compliant hand) has been used as a test platform, executing a pouring task while manipulating plastic (i.e., deformable) bottles. The sampling efficiency of the data-driven learning is shown, compared to an evenly spaced grid sampling of the input space. In addition, the generalization capability of the optimized model is tested (exploiting transfer learning) on a set of plastic bottles and other liquid containers, achieving a success rate of the 88%.

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