A Smart IoT-Aware System For Crisis Scenario Management

In most dangerous events, involving many people in large buildings, rescue workers need to intervene in a timely and targeted manner in order to help most number of people and secure the environments without wasting resources. This work presents an Internet of Things(IoT)-based framework, aiming at monitoring environmental parameters in order to alert rescuers when they exceed some alarm thresholds. A hardware infrastructure driven by a software layer adds flexibility and adaptability to the Complex Event Processing engine and to a rule engine-based reflective middleware that manages and analyzes raw data in conjunction with a knowledge base modeling the application domain

A Smart IoT-Aware System For Crisis Scenario Management

In most dangerous events, involving many people in large buildings, rescue workers need to intervene in a timely and targeted manner in order to help most number of people and secure the environments without wasting resources. This work presents an Internet of Things(IoT)-based framework, aiming at monitoring environmental parameters in order to alert rescuers when they exceed some alarm thresholds. A hardware infrastructure driven by a software layer adds flexibility and adaptability to the Complex Event Processing engine and to a rule engine-based reflective middleware that manages and analyzes raw data in conjunction with a knowledge base modeling the application domain

Pulmonary Artery Catheter Monitoring in Patients with Cardiogenic Shock: Time for a Reappraisal?

Cardiogenic shock represents one of the most dramatic scenarios to deal with in intensive cardiology care and is burdened by substantial short-term mortality. An integrated approach, including timely diagnosis and phenotyping, along with a well-established shock team and management protocol, may improve survival. The use of the Swan-Ganz catheter could play a pivotal role in various phases of cardiogenic shock management, encompassing diagnosis and haemodynamic characterisation to treatment selection, titration and weaning. Moreover, it is essential in the evaluation of patients who might be candidates for long-term heart-replacement strategies. This review provides a historical background on the use of the Swan-Ganz catheter in the intensive care unit and an analysis of the available evidence in terms of potential prognostic implications in this setting

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions.ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models’ prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR.ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Proarrhythmic effect of bipolar epicardial left ventricular stimulation in CRT resolved maintaining biventricular pacing with unipolar‐cathodical configuration: A peculiar case report

Abstract Background The effect of cardiac resynchronization therapy (CRT) on the risk of ventricular arrhythmias is controversial. Several studies reported a decreased risk, but some studies reported a potential proarrhythmic effect of epicardial left ventricular pacing resolved upon discontinuation of biventricular pacing (BiVp). Case Summary A 67‐year‐old woman with a history of heart failure due to nonischemic cardiomyopathy and left bundle branch block was hospitalized for CRT device implantation. Unpredictably, as soon as the leads have been connected to the generator, an electrical storm (ES) occurred with relapsing self‐resolving polymorphic ventricular tachycardia (PVT) triggered by ventricular extra beats with short‐long‐short sequences. The ES was resolved without interrupting BiVp switching to unipolar left ventricular (LV) pacing. This allowed to keep CRT active with extreme clinical benefit for the patient and to demonstrate that the cause of the PVT was the anodic capture of bipolar LV stimulation. Reverse electrical remodeling was also demonstrated after 3 months of effective BiVp. Discussion Proarrhythmic effect of CRT is a rare but significant complication of CRT, and it may compel to discontinuation of the BiVp. The reversal of the physiological transmural activation sequence of epicardial LV pacing and subsequent prolonging of corrected QT interval have been speculated as the most probable explanation, but our case highlights the possibility that the anodic capture may play a relevant role in PVT genesis

A Cellular Bonding and Adaptive Load Balancing Based Multi-Sim Gateway for Mobile Ad Hoc and Sensor Networks

<p>As it is well known, the QoS(quality of service) provided by mobile Internet access point devices is far from the QoS level offered by the common ADSL modem-router due to several reasons: in fact, mobile Internet access networks are not designed to support real-time data traffic because of several drawbacks concerning the wireless medium such as resource sharing, traffic congestion, radio link coverage etc., which impact directly such parameters as delay, jitter, and packet loss rate that are strictly connected to the quality of user experience. The main scope of the present paper is to introduce a dual USIM HSPA gateway for ad hoc and sensors networks thanks to which it will be possible to guarantee a QoS suitable for a series of network-centric application such as real-time communications and monitoring, video surveillance, real-time sensor networks, telemedicine, vehicular and mobile sensor networks and so on. The main idea is to exploit multiple radio access networks in order to enhance the available end-to-end bandwidth and the perceived quality of experience. The scope has been reached by combining multiple radio access with dynamic load balancing and the VPN (virtual private network) bond technique.</p

A Cellular Bonding and Adaptive Load Balancing Based Multi-Sim Gateway for Mobile Ad Hoc and Sensor Networks

<p>As it is well known, the QoS(quality of service) provided by mobile Internet access point devices is far from the QoS level offered by the common ADSL modem-router due to several reasons: in fact, mobile Internet access networks are not designed to support real-time data traffic because of several drawbacks concerning the wireless medium such as resource sharing, traffic congestion, radio link coverage etc., which impact directly such parameters as delay, jitter, and packet loss rate that are strictly connected to the quality of user experience. The main scope of the present paper is to introduce a dual USIM HSPA gateway for ad hoc and sensors networks thanks to which it will be possible to guarantee a QoS suitable for a series of network-centric application such as real-time communications and monitoring, video surveillance, real-time sensor networks, telemedicine, vehicular and mobile sensor networks and so on. The main idea is to exploit multiple radio access networks in order to enhance the available end-to-end bandwidth and the perceived quality of experience. The scope has been reached by combining multiple radio access with dynamic load balancing and the VPN (virtual private network) bond technique.</p