Language Models for Hierarchical Classification of Radiology Reports with Attention Mechanisms, BERT and GPT-4

Radiology reports are a valuable source of textual information used to improve clinical care and support research. In recent years, deep learning techniques have been shown to be effective in classifying radiology reports. This article investigates the use of deep learning techniques with attention mechanisms to achieve better performance in the classification of radiology reports.We focus on various Natural Language Processing approaches, such as LSTM with Attention, BERT, and GPT-4, evaluated on a chest tomography report dataset regarding neoplastic diseases collected from an Italian hospital. In particular, we compare the results with a previous machine learning system, showing that models based on attention mechanisms can achieve higher performance. The Attention Mechanism allows us to identify the most relevant bits of text used by the model to make its predictions. We show that our model achieves state-of-the-art results on the hierarchical classification of radiology reports. Moreover, we evaluate the performance of GPT-4 on the classification of these reports in a zero-shot setup through prompt engineering, showing interesting results even with a small context and a non-English language. Our findings suggest that deep learning techniques with attention mechanisms may be successful in the classification of radiology reports even in non-English languages for which it is not possible to leverage on large text corpus

Recurrent Neural Networks for Daily Estimation of COVID-19 Prognosis with Uncertainty Handling

Most ML-based applications for COVID-19 assess the general conditions of a patient trained and tested on cohorts of patients collected over a short period of time and are capable of providing an alarm a few days in advance, helping clinicians in emergency situations, monitor hospitalised patients and identify potentially critical situations at an early stage. However, the pandemic continues to evolve due to new variants, treatments, and vaccines; considering datasets over short periods could not capture this aspect. In addition, these applications often avoid dealing with the uncertainty associated with the prediction provided by machine learning models, potentially causing costly mistakes. In this work, we present a system based on Recurrent Neural Networks (RNN) for the daily estimate of the prognosis of COVID-19 patients that is built and tested using data collected over a long period of time. Our system achieves high predictive performance and uses an algorithm to effectively determine and discard those patients for whom RNN cannot predict the prognosis with sufficient confidence

Ein neuer Papyrus des Flavius Johannes, comes consistorianus

Edition eines Papyrus aus der Leipziger Sammlung, der einen Prochreiavertrag aus dem Jahr 466 n. Chr. enthält und neues Licht auf die Person des comes consistorianus Flavius Johannes wirft. Das Dossier dieses wichtigen oxyrhynchitischen Grundbesitzers wird im zweiten Teil des Beitrages diskutiert und mit den Belegen vorgestellt.An edition of a papyrus of the Collection of Leipzig which contains a prochreia agreement from the year 466 CE. The text sheds light on the comes consistorianus Flavius Ioannes who is already known from several other documents. His dossier is discussed in the second part of this paper

Experimental material compatibility analysis for innovative CO2 blends to be adopted as working fluid in high-efficiency CSP power plants.

Questa tesi approfondisce il contesto della transizione energetica, concentrandosi sulle energie rinnovabili, in particolare sull'energia solare utilizzata nella tecnologia CSP. Nel corso del tempo, la tecnologia CSP è stata perfezionata per ridurre la complessità delle turbomacchine e migliorarne l'efficienza. Nonostante questi progressi, il costo dell'elettricità prodotta da questa tecnologia rimane non competitivo sul mercato. Un approccio innovativo per ridurre i costi coinvolge l'uso di miscele di anidride carbonica supercritica (sCO2) come fluido di lavoro, invece di vapore d'acqua o anidride carbonica supercritica pura. Gli obiettivi principali di questa ricerca sono affrontare la sfida della compatibilità dei materiali metallici, come leghe di Fe e Ni, con miscele di CO2 ad alta temperatura, focalizzandosi sulla resistenza alla corrosione ad alta temperatura, e esplorare nuove miscele efficienti e termicamente stabili da mescolare con la CO2. Mentre la corrosione sCO2 ad alta temperatura è ampiamente discussa in letteratura, le ricerche sulle miscele di sCO2 sono limitate. Nel corso dello studio, diverse potenziali miscele di CO2, tra cui C6F6, C4F10, TiCl4, SiCl4, SO2 e Novec4710 come dopanti, sono state sottoposte a una valutazione preliminare dell'efficienza mediante il software Aspen Plus per valutarne l'applicazione potenziale come fluidi di lavoro in cicli di potenza. Successivamente, l'apparato sperimentale utilizzato per le analisi metallografiche è stato perfezionato nel corso degli anni e nella sua versione finale include un cilindro smontabile realizzato in Inconel 625 per valutare la cinetica di corrosione del materiale, consentendo l'estrazione dei campioni durante il test. Questo cilindro è riempito con la miscela sCO2 e un supporto contenente dischetti forati di vari materiali, riscaldato in un forno a muffola. Questo approccio consente lo studio della stabilità termica del fluido e della corrosione ad alta temperatura delle leghe. Le temperature selezionate per lo studio mirano a identificare i materiali più adatti per diverse parti di un impianto CSP pilota, che possono variare da 120°C a 550°C. La caratterizzazione degli strati di ossido è stata condotta utilizzando la microscopia ottica, la tecnica SEM-EDS e l'analisi metallografica. Riguardo a due miscele, quelle contenenti SiCl4 e SO2, sono stati identificati materiali e rivestimenti promettenti dopo un test di 2000 ore. Questa ricerca fa parte dei progetti europei H2020 Sarabeus e Desolination, fornendo un contributo iniziale alla comprensione e all'ottimizzazione dei materiali utilizzati nella nuova tecnologia CSP.This thesis delves into the context of the energy transition, focusing on renewable energy, specifically solar energy used in the Concentrated Solar Power (CSP) technology. Over time, CSP technology has undergone improvements to reduce the complexity of turbomachinery and enhance efficiency. Despite these advances, the cost of electricity produced by this technology remains non-competitive in the market. An innovative approach to cost reduction involves using supercritical carbon dioxide (sCO2) mixtures as the working fluid, instead of water vapor or pure supercritical CO2. The primary aims of this research are to address the challenge of the compatibility of metallic materials, such as Fe and Ni-based alloys, with high-temperature CO2 mixtures, with a focus on high-temperature corrosion resistance and to explore new, efficient and thermally stable mixtures to blend with CO2. While high-temperature sCO2 corrosion is extensively discussed in the literature, research on sCO2 mixtures are limited. First, in the study, various potential CO2 mixtures, including C6F6, C4F10, TiCl4, SiCl4, SO2 and Novec4710 as dopants, underwent a preliminary efficiency evaluation using Aspen Plus software to assess their potential application as working fluids in power cycles. Then, the experimental apparatus used for metallographic analyses has been refined over the years and in its final version includes a disassemblable cylinder made of Inconel 625 to evaluate the corrosion kinetic of the material as it allows to extract the samples during the test. This cylinder is filled with the sCO2 blend and a support containing perforated disks of various materials, heated in a muffle furnace. This approach enables the study of the thermal stability of the fluid and high-temperature corrosion of alloys. The selected temperatures for the study aim to identify the most suitable materials for different parts of a pilot CSP plant, which may range from 120°C to 550°C. Characterization of oxide layers was conducted using optical microscopy, SEM-EDS technique and metallographic analysis. Regarding two mixtures, those containing SiCl4 and SO2, promising materials and coatings have been identified after a test of 2000h. This research is part of the H2020 European projects Sarabeus and Desolination, providing an initial contribution to understanding and optimizing materials used in the new CSP technology

Attention Mechanism e Interpretabilità del Deep Learning per il Natural Language Processing in Ambito Biomedico

Attention Mechanism e Interpretabilità del Deep Learning per il Natural Language Processing in Ambito Biomedic

Deep learning for classification of radiology reports with a hierarchical schema

Radiological reports are a valuable source of textual information, which can be exploited to improve clinical care and to support research. Such information can be extracted and put into a structured form using machine learning techniques. Some of them rely not only on the classification labels but also on the manual annotation of relevant snippets, which is a time consuming job and requires domain experts. In this paper, we apply deep learning techniques and in particular Long Short Term Memory (LSTM) networks to perform such a task relying only on the classification labels. We focus on the classification of chest computed tomography reports in Italian according to a classification schema proposed for this task by the radiologists of Spedali Civili di Brescia. Each report is classified according to such schema using a combination of neural network classifiers. The resulting system is a novel classification system, which we compare to a previous system based on standard machine learning techniques which used annotations of relevant snippets