Learning from Major Accidents: a Machine Learning Approach

A B S T R A C T Learning from past mistakes is crucial to prevent the reoccurrence of accidents involving dangerous sub-stances. Nevertheless, historical accident data are rarely used by the industry, and their full potential is largely unexpressed. In this setting, this study set out to take advantage of improvements in data sci-ence and Machine Learning to exploit accident data and build a predictive model for severity prediction. The proposed method makes use of classification algorithms to map the features of an accident to the corresponding severity category (i.e., the number of people that are killed and injured). Data extracted from existing databases is used to train the model. The method has been applied to a case study, where three classification models - i.e., Wide, Deep Neural Network, and Wide&Deep - have been trained and evaluated on the Major Hazard Incident Data Service database (MHIDAS). The results indicate that the Wide&Deep model offers the best performance.(c) 2022 The Authors. Published by Elsevier Ltd.This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/

Prevalence of hepatic steatosis in patients with type 2 diabetes and response to glucose-lowering treatments. A multicenter retrospective study in Italian specialist care

Type 2 diabetes (T2D) is a risk factor for metabolic dysfunction-associated fatty liver disease (MAFLD), which is becoming the commonest cause of chronic liver disease worldwide. We estimated MAFLD prevalence among patients with T2D using the hepatic steatosis index (HSI) and validated it against liver ultrasound. We also examined whether glucose-lowering medications (GLM) beneficially affected HSI

Similar effectiveness of dapagliflozin and GLP-1 receptor agonists concerning combined endpoints in routine clinical practice: A multicentre retrospective study

Aims According to cardiovascular outcome trials, some sodium-glucose contransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) are recommended for secondary cardiovascular prevention in type 2 diabetes (T2D). In this real-world study, we compared the simultaneous reductions in HbA1c, body weight and systolic blood pressure after initiation of dapagliflozin or GLP-1RA as second or a more advanced line of therapy. Materials and methods DARWIN-T2D was a retrospective multi-centre study conducted at diabetes specialist clinics in Italy that compared T2D patients who initiated dapagliflozin or GLP-1RA (exenatide once weekly or liraglutide). Data were collected at baseline and at the first follow-up visit after 3 to 12 months. The primary endpoint was the proportion of patients achieving a simultaneous reduction in HbA1c, body weight and systolic blood pressure. To reduce confounding, we used multivariable adjustment (MVA) or propensity score matching (PSM). Results Totals of 473 patients initiating dapagliflozin and 336 patients initiating GLP-1RA were included. The two groups differed in age, diabetes duration, HbA1c, weight and concomitant medications. The median follow-up was 6 months in both groups. Using MVA or PSM, the primary endpoint was observed in 30% to 32% of patients, with no difference between groups. Simultaneous reduction of HbA1c, BP and SBP by specific threshold, as well as achievement of final goals, did not differ between groups. GLP-1RA reduced HbA1c by 0.3% more than the reduction achieved with dapagliflozin. Conclusion In routine specialist care, initiation of dapagliflozin can be as effective as initiation of a GLP-1RA for attainment of combined risk factor goals

Clinical Features, Cardiovascular Risk Profile, and Therapeutic Trajectories of Patients with Type 2 Diabetes Candidate for Oral Semaglutide Therapy in the Italian Specialist Care

Introduction: This study aimed to address therapeutic inertia in the management of type 2 diabetes (T2D) by investigating the potential of early treatment with oral semaglutide. Methods: A cross-sectional survey was conducted between October 2021 and April 2022 among specialists treating individuals with T2D. A scientific committee designed a data collection form covering demographics, cardiovascular risk, glucose control metrics, ongoing therapies, and physician judgments on treatment appropriateness. Participants completed anonymous patient questionnaires reflecting routine clinical encounters. The preferred therapeutic regimen for each patient was also identified. Results: The analysis was conducted on 4449 patients initiating oral semaglutide. The population had a relatively short disease duration (42%  60% of patients, and more often than sitagliptin or empagliflozin. Conclusion: The study supports the potential of early implementation of oral semaglutide as a strategy to overcome therapeutic inertia and enhance T2D management

Data Analytics for Chemical Process Risk Assessement: Learning Lessons from Past Events towards Accident Prediction

Il potenziale per la generazione di dati è cresciuto esponenzialmente al giorno d’oggi. In questo contesto, la disciplina di machine learning è suggerita. Questo lavoro suggerisce un approccio per analizzare dati eterogenei riguardanti incidenti passati avvenuti nelle industrie di processo ed estrarre importanti informazioni per supportare il processo decisionale relativo alla sicurezza. Lo strumento di machine learning utilizzato è la libreria open source TensorFlow. Diversi modelli vengono costruiti attraverso il suo uso: un modello lineare, un modello di deep learning basato sulle reti neurali ed una combinazione dei due. Questi, sulla base di input specifici, sarebbero in grado di fare predizioni sul numero di persone morte o ferite. Per raggiungere questo obiettivo, due fonti di dati sono state utilizzate: il database MHIDAS e un nuovo database, costruito considerando eventi indesiderati avvenuti in impianti di ammoniaca. Diverse simulazioni sono state eseguite usando MHIDAS per individuare il modello che meglio riesca a prevedere le conseguenze degli incidenti sull’uomo. Questo è stato poi usato per effettuare le simulazioni con il database di incidenti in impianti di ammoniaca. Un buon modello per la predizione degli incidenti deve essere in grado di prevedere eventi rari. Tale condizione viene raggiunta se il valore della grandezza statistica "recall" è alto. Per questo motivo, i risultati delle simulazioni sono stati analizzati considerando il valore dell’area sotto la curva precision-recall. Da questo, è possibile capire se il valore di recall può essere di interesse. I risultati ottenuti hanno dimostrato un andamento comune. Un caso rappresentativo, in cui i risultati riportavano un buon valore di area sotto la curva precision-recall ma una bassa recall, è stato considerato e il metodo per migliorare il valore di recall è stato indicato. In questo modo, il modello può essere calibrato e diventare di uso pratico

Data Analytics for Chemical Process Risk Assessement: a Representative Case Study to Support Safe Handling of Hazardous Substances

Potential of data generation has exponentially increased nowadays. Through the World Wide Web, for instance, information of any kind are collected and stored in databases. In the industrial sector, a huge amount of data is set to be collected by the so-called industry 4.0. Also the Seveso III Directive, which contains the rules for preventing and fighting major accidents in establishments handling dangerous substances, advances the need of monitoring and analyzing data in order to improve the safety management system of the plants. In this context, the discipline of machine learning is suggested. It consists in methods through which computers automatically retrieve knowledge from data. On the basis of this, they are able to support or take decisions. However, data are still not exploited as they should be and opportunities to learn are lost. It is necessary to improve the use of such data and increase our knowledge. This work suggests an approach to analyze heterogeneous data about past accidents in process industries and extract important information to support safety-related decision making. The knowledge retrieved should help improving the evaluation of the risk picture, by predicting the consequence on humans. The machine learning tool used to analyze the data is the open-source library TensorFlow. Through its use, different models are built - a linear model, a deep neural network model and a combination of the two. The models, on the basis of specific inputs, may be able to make predictions about the number of people killed or number of people injured. The tuning of the model's parameters is carried out using the past accident data contained in the MHIDAS database as training data set. To evaluate the performance of the model another data set is needed. For this reason, a new database has been built. Ammonia plants, which fall under the Seveso III Directive, have been taken into consideration. Accidents occurred in these establishments - or in similar sections of other plants - have been take into account, investigating public accident databases, books, articles and journals. Their data have been collected and registered in a common database using MHIDAS keywords. A set of simulations have been performed not only to validate the models, but also to identify their limitations. A good model for accident prediction needs to be able to predict rare events - \emph{i.e.} the ones with the highest number of people killed or injured. This condition is obtained if the value of the statistic metric "recall" is high. For this reason, the results returned by the simulations have been analyzed considering the value of the area under the curve precision recall as a priority. From this, it is possible to understand if the value of recall can be of interest or not. The results obtained have shown a common trend and a type of model that, in general, have had better prediction skills than the other ones

MG53 marks poor beta cell performance and predicts onset of type 2 diabetes in subjects with different degrees of glucose tolerance

Aim: MG53 is a myokine modulating insulin signaling in several tissues; its relationship to glucose tolerance or risk of developing type 2 diabetes mellitus (T2DM) is unknown. This observational, prospective study aimed at evaluating the relationship between MG53 and glucose tolerance, testing whether its circulating levels may be associated with disease progression in a cohort at high risk of T2DM. Methods: Five hundred and fifteen subjects who underwent a deep characterization of their glucose tolerance in the years 2003-2005 participated in this study. MG53 levels were measured at baseline. Glucose tolerance status was available over a follow-up of 15 ± 2 years for 283 of them; their vital status as of December 2020 was also retrieved. Results: MG53 levels were significantly lower in subjects with normal glucose tolerance than in subjects with impaired glucose regulation (IGR) or T2DM. Individuals in the highest MG53 levels quartile had more frequently 1h-post load glucose ≥ 155 mg/dL (54% vs 39%; p = 0.015), worse proportional control of β-cell function (p < 0.05-0.01), as determined by mathematical modeling, and worse Disposition Index (DI) (0.0155 ± 0.0081 vs 0.0277 ± 0.0030; p < 0.0001). At follow-up, baseline MG53 levels were higher in progressors than in non-progressors (120.1 ± 76.7 vs 72.7 ± 63.2 pg/ml; p = 0.001; ROC curve area for incident diabetes of 0.704). In a multivariable regression with classic risk factors for T2DM and DI, MG53 remained independently associated with progression with T2DM. Conclusion: MG53 may be a novel biomarker of glucose dysregulation associated with β-cell dysfunction, likely improving our ability to identify, among high-risk subjects, those more likely to develop T2DM

Sodium-glucose cotransporter 2 inhibitors antagonize lipotoxicity in human myeloid angiogenic cells and ADP-dependent activation in human platelets: potential relevance to prevention of cardiovascular events.

The clear evidence of cardiovascular benefits in cardiovascular outcome trials of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in type 2 diabetes might suggest an effect on atherosclerotic plaque vulnerability and/or thrombosis, in which myeloid angiogenic cells (MAC) and platelets (PLT) are implicated. We tested the effects of SGLT2i on inflammation and oxidant stress in a model of stearic acid (SA)-induced lipotoxicity in MAC and on PLT activation. The possible involvement of the Na+/H+ exchanger (NHE) was also explored.info:eu-repo/semantics/publishe