The paper proposes a methodology based on Natural Language Processing (NLP) and Sentiment Analysis (SA) to get insights into sentiments and opinions toward COVID-19 vaccination in Italy. The studied dataset consists of vaccine-related tweets published in Italy from January 2021 to February 2022. In the considered period, 353,217 tweets have been analyzed, obtained after filtering 1,602,940 tweets with the word “vaccin”. A main novelty of the approach is the categorization of opinion holders in four classes, Common users, Media, Medicine, Politics, obtained by applying NLP tools, enhanced with large-scale domain-specific lexicons, on the short bios published by users themselves. Feature-based sentiment analysis is enriched with an Italian sentiment lexicon containing polarized words, expressing semantic orientation, and intensive words which give cues to identify the tone of voice of each user category. The results of the analysis highlighted an overall negative sentiment along all the considered periods, especially for the Common users, and a different attitude of opinion holders towards specific important events, such as deaths after vaccination, occurring in some days of the examined 14 months.

Detecting diseases at early stage can help to overcome and treat them accurately and a Clinical Decision Support System (CDS) can greatly help in identifying diseases and methods of treatment. In this paper, we propose a CDS framework able to integrate heterogeneous health data from different sources, such as laboratory test results, basic information of patients, health records and social media data. Using the data so collected, innovative machine learning and deep learning approaches can be employed. A neural network model for predicting patients’ future health information, is proposed. The approach employs word embedding to model the semantic relations of hospital admissions, symptoms and diagnosis, and it introduces a mechanism to measure the relationships of different diagnosis in terms of symptoms similarity to exploit for the prediction task. Several clinical decision support systems, including diagnostic decision support systems for inferring patient diagnosis, have been proposed in the literature. However, these methods typically focus on a single patient and apply manually or automatically constructed decision rules to produce a diagnosis. Even worst, they consider only a single medical condition, whereas it is not uncommon that a patient has more than one medical condition, at the same time, because of the complications of the first disease. The novelty of the proposed approach is the combination of supervised and unsupervised artificial intelligence methods allowing to combine and integrate several and heterogeneous data sources related to a multitude of patients and concerning different medical conditions. Furthermore, with respect to previous approaches, the diagnosis prediction problem is formulated to predict the exact diagnosis in terms of semantic meaning. In fact, instead of referring to the diagnosis in terms of the International Classification of Diseases (ICD-9) codes, as the related literature does, we consider the diagnosis semantics by exploiting Natural Language Processing. Experimental results, performed on a real-world EHR dataset, show that the proposed approach is effective and accurate and provides clinically meaningful interpretations.

The aim of the paper is to exploit Twitter to enhance the comprehension of COVID-19 diffusion and people reactions. To this purpose, the objectives are to identify the key terms and features used in the tweets, and the interest in the COVID-19 topics. To address those goals the paper proposes an approach that combines peak detection and clustering techniques. Space-time features are extracted from the tweets and modeled as time series. After that, peaks are detected and the textual features are clustered based on the co-occurrence in the tweets. Each cluster obtained is then associated to a topic. Results, performed over a real-world dataset of tweets related to COVID-19 in US, show that the approach is able to detect several relevant topics, of varying importance and character. A case study about the correlation of Twitter data with COVID-19 confirmed cases has been presented, also evaluating the feasibility of exploiting Twitter for the outbreak diffusion prediction. Results highlight a high correlation between tweets and real COVID-19 data, proving that Twitter can be considered a reliable indicator of the epidemic spreading and that data generated by user activity on social media is becoming an invaluable source for capturing and understanding epidemics outbreaks.