Text-to-Text Extraction and Verbalization of Biomedical Event Graphs

Biomedical events represent complex, graphical, and semantically rich interactions expressed in the scientific literature. Almost all contributions in the event realm orbit around semantic parsing, usually employing discriminative architectures and cumbersome multi-step pipelines limited to a small number of target interaction types. We present the first lightweight framework to solve both event extraction and event verbalization with a unified text-to-text approach, allowing us to fuse all the resources so far designed for different tasks. To this end, we present a new event graph linearization technique and release highly comprehensive event-text paired datasets, covering more than 150 event types from multiple biology subareas (English language). By streamlining parsing and generation to translations, we propose baseline transformer model results according to multiple biomedical text mining benchmarks and NLG metrics. Our extractive models achieve greater state-of-the-art performance than single-task competitors and show promising capabilities for the controlled generation of coherent natural language utterances from structured data

Enhancing Biomedical Scientific Reviews Summarization with Graph-based Factual Evidence Extracted from Papers

Combining structured knowledge and neural language models to tackle natural language processing tasks is a recent research trend that catalyzes community attention. This integration holds a lot of potential in document summarization, especially in the biomedical domain, where the jargon and the complex facts make the overarching information truly hard to interpret. In this context, graph construction via semantic parsing plays a crucial role in unambiguously capturing the most relevant parts of a document. However, current works are limited to extracting open-domain triples, failing to model real-world n-ary and nested biomedical interactions accurately. To alleviate this issue, we present EASumm, the first framework for biomedical abstractive summarization enhanced by event graph extraction (i.e., graphical representations of medical evidence learned from scientific text), relying on dual text-graph encoders. Extensive evaluations on the CDSR dataset corroborate the importance of explicit event structures, with better or comparable performance than previous state-of-the-art systems. Finally, we offer some hints to guide future research in the field

NLG-Metricverse: An End-to-End Library for Evaluating Natural Language Generation

Driven by deep learning breakthroughs, natural language generation (NLG) models have been at the center of steady progress in the last few years, with a ubiquitous task influence. However, since our ability to generate human-indistinguishable artificial text lags behind our capacity to assess it, it is paramount to develop and apply even better automatic evaluation metrics. To facilitate researchers to judge the effectiveness of their models broadly, we introduce NLG-Metricverse—an end-to-end open-source library for NLG evaluation based on Python. Our framework provides a living collection of NLG metrics in a unified and easy-to-use environment, supplying tools to efficiently apply, analyze, compare, and visualize them. This includes (i) the extensive support to heterogeneous automatic metrics with n-arity management, (ii) the meta-evaluation upon individual performance, metric-metric and metric-human correlations, (iii) graphical interpretations for helping humans better gain score intuitions, (iv) formal categorization and convenient documentation to accelerate metrics understanding. NLG-Metricverse aims to increase the comparability and replicability of NLG research, hopefully stimulating new contributions in the area

Autoimmune Hepatitis and Celiac Disease: Case Report Showing an Entero-Hepatic Link

Celiac disease is an autoimmune disorder primarily targeting the small bowel, although extraintestinal extensions have been reported. The autoimmune processes can affect the liver with manifestations such as primary biliary cirrhosis and autoimmune hepatitis. We describe a 61-year-old woman with celiac disease and an increased levels of aminotransferases. The persistence of increased levels of aminotransferases after 1 year of gluten-free diet and the positivity for an anti-nuclear and anti-double-strand DNA antibodies led to a misdiagnosis of systemic lupus erythematosus-related hepatitis. Based on these findings the patient was placed on steroids, which after a few months were stopped because of the onset of diabetes mellitus. Soon after steroid withdrawal, the patient had a marked increase in aminotransferases and γ-globulins, and a liver biopsy revealed chronic active hepatitis. A course of three months of steroids and azathioprine normalized both biochemical and clinical parameters. Currently the patient is symptom-free and doing well. In conclusion, a hypertransaminasemia persisting after a gluten-free diet should be interpreted as a sign of coexisting autoimmune liver disease. Any autoantibody positivity (in this case to ANA and anti-dsDNA) should be carefully considered in order to avoid misdiagnosis delaying appropriate clinical management

Preliminary Assessment of Radiolysis for the Cooling Water System in the Rotating Target of {SORGENTINA}-{RF}

The SORGENTINA-RF project aims at developing a 14 MeV fusion neutron source featuring an emission rate in the order of 5-7 x 10(13) s(-1). The plant relies on a metallic water-cooled rotating target and a deuterium (50%) and tritium (50%) ion beam. Beyond the main focus of medical radioisotope production, the source may represent a multi-purpose neutron facility by implementing a series of neutron-based techniques. Among the different engineering and technological issues to be addressed, the production of incondensable gases and corrosion product into the rotating target deserves a dedicated investigation. In this study, a preliminary analysis is carried out, considering the general layout of the target and the present choice of the target material

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Progetto e sviluppo di un sistema software per lo studio e la ricerca di malattie rare

Quando si riflette sul concetto di rarità in ambito sanitario, si tende a pensare a un’eventualità remota e riservata a pochi individui. Nel momento in cui questo fenomeno irrompe nella quotidianità di una persona, si realizza come la parola “rara” sia in realtà usata frequentemente in maniera impropria. L’impatto a una diagnosi di malattia rara può dar luogo a una reazione a tratti devastante e inconcepibile, specie sul piano psicologico. La successiva presa di coscienza e la necessità di ricevere informazioni da una società molto spesso non troppo preparata, spingono il soggetto a una ricerca individuale per l’ottenimento di risposte sulla gestione della sua patologia. Allo stato attuale, la reperibilità di questi dati è tutt’altro che semplice a causa delle scarse fonti e della loro disgregazione. Le ultime indagini riconoscono il settore informatico come il principale strumento per l’accesso a tutte le richieste che un malato raro può presentare, tuttavia i progetti oggi disponibili sono incompleti. L’obiettivo di questa tesi è progettare un sistema software capace di supportare un individuo affetto da malattia rara, durante tutto il suo percorso, e realizzare una rete di pazienti in grado di velocizzare il raggiungimento di una soluzione ai quesiti posti quotidianamente da tali persone. Ricordando come per la maggior parte di queste patologie non esista ancora una cura efficace, un’ulteriore esigenza alla quale l’elaborato fa fronte è l’implementazione di apposite funzionalità dedicate ai medici e ai ricercatori. Per affrontare la scarsa attenzione spesso constatata verso i trattamenti di queste malattie, un’ultima caratteristica dell’applicazione riguarda la flessibilità nella gestione di tutti i disturbi (anche quelli meno noti). Partendo da un’iniziale analisi dei requisiti, la tesi affronta lo sviluppo della base di dati, una possibile implementazione cloud, la disamina dei dataset disponibili e la descrizione delle problematiche giuridiche

A New Unsupervised Methodology of Descriptive Text Mining for Knowledge Graph Learning

Rare diseases pose particular challenges to patients, families, caregivers, clinicians and researchers. Currently, more than 6000 rare diseases are described (but up to 7000 are estimated) and more than 350 million people live with them (5\% of the world population). Due to the scarce availability of information and their disintegration, in recent years we are witnessing strong growth of patient communities on social platforms such as Facebook. The work presented in this thesis is intended to extract knowledge from the large availability of unstructured text generated by the users over time, in order to represent it in an organized way and to make logical reasoning above. Starting from the awareness of the need to integrate different methodologies in complex domains, the research shows a combined use of Text Mining and Semantic Web techniques, taking Esophageal Achalasia as a case study. In particular, an ontology is created to extend ORDO and introduce a patient-centered vision into the world of linked data. The significance of this development is that it potentially constitutes the basis of a project that can allow rapid access to many high-value information (in topics such as symptomatology, epidemiology, diagnosis, treatments, drugs, nutrition, lifestyle), responding to patients' questions and providing them with an additional tool for decision making, minimizing costs through the automatic retrieval of these data and increasing the productivity of investigators

Graph-Enhanced Biomedical Abstractive Summarization Via Factual Evidence Extraction

Infusing structured semantic representations into language models is a rising research trend underpinning many natural language processing tasks that require understanding and reasoning capabilities. Decoupling factual non-ambiguous concept units from the lexical surface holds great potential in abstractive summarization, especially in the biomedical domain, where fact selection and rephrasing are made more difficult by specialized jargon and hard factuality constraints. Nevertheless, current graph-augmented contributions rely on extractive binary relations, failing to model real-world n-ary and nested biomedical interactions mentioned in the text. To alleviate this issue, we present EASumm, the first framework for biomedical abstractive summarization empowered by event extraction, namely graph-based representations of relevant medical evidence derived from the source scientific document. By relying on dual text-graph encoders, we prove the promising role of explicit event structures, achieving better or comparable performance than previous state-of-the-art models on the CDSR dataset. We conduct extensive ablation studies, including a wide experimentation of graph representation learning techniques. Finally, we offer some hints to guide future research in the field