We explore the Covid-19 diffusion with an agent-based model of an Italian region with a population on a scale of 1:1000. We also simulate different vaccination strategies. From a decision support ...system perspective, we investigate the adoption of artificial intelligence techniques to provide suggestions about more effective policies. We adopt the widely used multi-agent programmable modeling environment NetLogo, adding genetic algorithms to evolve the best vaccination criteria. The results suggest a promising methodology for defining vaccine rates by population types over time. The results are encouraging towards a more extensive application of agent-oriented methods in public healthcare policies.
Agent-based approaches have been known to be appropriate as systems and methods in medical administration in recent years. The increased attention to processes led to the recent growth of Business ...Process Management discipline, which quite exclusively adopt discrete-event modeling and simulation. This paper proposes a medical agent-oriented decision support system to integrate the achievements from management science, agent-based modeling, and artificial intelligence. In particular, we performed a practical application concerning a hospital emergency department medical system. We adopt the widely used multi-agent programmable modeling environment NetLogo. First, we demonstrated the ability to perform a clear representation of healthcare processes where agents (i.e., patients and hospital staff) operate in a 3D environment. This model allows performing a traditional
what-if
scenario analysis. Second, we explore how performing intelligent management of patients by applying genetic algorithms to find the criteria for the selection process of the subjects in the admission procedure. The results are encouraging towards a more extensive application of agent-oriented methodologies in healthcare management.
Companies need to be able to demonstrate compliance with rules and regulations, especially start-ups who typically do not have the legal expertise to identify, assess and address legal risks of ...initial business ideas, nor do they have the resources to hire such expertise. Tools could help them identify and deal with legal risk at an early stage. Existing research in BPM focuses on compliance verification of a consolidated business model by checking the ability of a company to comply with the standards. The challenge is to apply a ‘continuous improvement’ by steering the business on values. Moreover, legal choices typically sit at the strategic level, and not only at the operational level. In this paper, we therefore propose an approach to handle legal risks as part of business model development. The approach makes use of Continuous Business Model Planning method, a value-driven modeling approach for strategic planning, and legal argumentation. The suitability and potential usefulness of the approach is illustrated by a study of the Kenyan court case Lipisha & BitPesa vs. Safaricom.
The growing number of next-generation applications offers a relevant opportunity for healthcare services, generating an urgent need for architectures for systems integration. Moreover, the huge ...amount of stored information related to events can be explored by adopting a process-oriented perspective. This paper discusses an Ambient Assisted Living healthcare architecture to manage hospital home-care services. The proposed solution relies on adopting an event manager to integrate sources ranging from personal devices to web-based applications. Data are processed on a federated cloud platform offering computing infrastructure and storage resources to improve scientific research. In a second step, a business process analysis of telehealth and telemedicine applications is considered. An initial study explored the business process flow to capture the main sequences of tasks, activities, events. This step paves the way for the integration of process mining techniques to compliance monitoring in an AAL architecture framework.
BACKGROUND Demographers are increasingly interested in connecting demographic behaviour and trends with 'soft' measures, i.e., complementary information on attitudes, values, feelings, and ...intentions. OBJECTIVE The aim of this paper is to demonstrate how computational linguistic techniques can be used to explore opinions and semantic orientations related to parenthood. METHODS In this article we scrutinize about three million filtered Italian tweets from 2014. First, we implement a methodological framework relying on Natural Language Processing techniques for text analysis, which is used to extract sentiments. We then run a supervised machine-learning experiment on the overall dataset, based on the annotated set of tweets from the previous stage. Consequently, we infer to what extent social media users report negative or positive affect on topics relevant to the fertility domain. RESULTS Parents express a generally positive attitude towards being and becoming parents, but they are also fearful, surprised, and sad. They also have quite negative sentiments about their children's future, politics, fertility, and parental behaviour. By exploiting geographical information from tweets we find a significant correlation between the prevalence of positive sentiments about parenthood and macro-regional indicators of both life satisfaction and fertility level. CONTRIBUTION We show how tweets can be used to represent soft measures such as attitudes, values, and feelings, and we establish how they relate to demographic features. Linguistic analysis of social media data provides a middle ground between qualitative studies and more standard quantitative approaches.
Background and Objective: The agent abstraction is a powerful one, developed decades ago to represent crucial aspects of artificial intelligence research. The meaning has transformed over the years ...and now there are different nuances across research communities. At its core, an agent is an autonomous computational entity capable of sensing, acting, and capturing interactions with other agents and its environment. This review examines how agent-based techniques have been implemented and evaluated in a specific and very important domain, i.e. healthcare research.
Methods: We survey key areas of agent-based research in healthcare, e.g. individual and collective behaviours, communicable and non-communicable diseases, and social epidemiology. We propose a systematic search and critical review of relevant recent works, introduced by an exploratory network analysis.
Results: Network analysis enables to devise out 5 main research clusters, the most active authors, and 4 main research topics.
Conclusions: Our findings support discussion of some future directions for increasing the value of agent-based approaches in healthcare.
The use of irony and sarcasm has been proven to be a pervasive phenomenon in social media posing a challenge to sentiment analysis systems. Such devices, in fact, can influence and twist the polarity ...of an utterance in different ways. A new dataset of over 10,000 tweets including a high variety of figurative language types, manually annotated with sentiment scores, has been released in the context of the task 11 of SemEval-2015. In this paper, we propose an analysis of the tweets in the dataset to investigate the open research issue of how separated figurative linguistic phenomena irony and sarcasm are, with a special focus on the role of features related to the multi-faceted affective information expressed in such texts. We considered for our analysis tweets tagged with #irony and #sarcasm, and also the tag #not, which has not been studied in depth before. A distribution and correlation analysis over a set of features, including a wide variety of psycholinguistic and emotional features, suggests arguments for the separation between irony and sarcasm. The outcome is a novel set of sentiment, structural and psycholinguistic features evaluated in binary classification experiments. We report about classification experiments carried out on a previously used corpus for #irony vs #sarcasm. We outperform in terms of F-measure the state-of-the-art results on this dataset. Overall, our results confirm the difficulty of the task, but introduce new data-driven arguments for the separation between #irony and #sarcasm. Interestingly, #not emerges as a distinct phenomenon.
Abstract Cooperation without third-party enforcement is particularly puzzling in illicit online markets given the anonymity of online exchanges in the ‘dark web’ and the asymmetry of information ...between buyers and sellers. Most of the literature investigates the effects of reputation systems on sales. Less is known about the role of (semi)institutionalized solutions to trust problems, such as the escrow service, which deposits payments for online purchases with the market platform and releases them only upon confirmation of the item delivery by a customer. We study the effect of such a trust intermediary on sales in a cryptomarket for illegal drugs. Using a large dataset of illegal online transactions, we estimate two sets of fixed effects models predicting the sellers’ choice to offer the trust intermediary and examine the effects of such a choice on sales. Our results indicate that the trust intermediary reduces online drug sales. We explain this finding by showing suggestive evidence that escrow may crowd out traders’ trust and reciprocity. Our findings have implications for theories of the role of institutions in online markets and offer policy recommendations for law enforcement agencies.
The influence of training, posture, nutrition or psychological attitudes on an athlete's career is well described in literature. An additional factor of success that is widely recognized as crucial ...is the network of matches that an athlete plays during a season. The hypothesis is that the quality of a player's opponents affects her long-term ranking and performance. Even though the relevance of these factors is widely recognized as important, a quantitative characterization is missing. In this paper, we try to fill this gap combining network analysis and machine learning to estimate the contribution of the network of matches in predicting an athlete's success. We consider all the official games played by the Italian table tennis players between 2011 and 2016. We observe that the matches network shows scale-free behavior, typical of several real-world systems, and that different structural properties are positively correlated with the athletes' performance (Spearman
, p-value
). Using these findings, we implement three different tasks, such as talent identification, performance and ranking prediction. Results shows consistently that machine learning approaches are able to predict players' success and that the topological features play an effective role in increasing their predictive power.
The interpretation of any legal norm typically requires consideration of relationships between parts within the same piece of legislation. This work describes a general framework for the development ...of a system to identify and classify implicit inter-relationships between parts of a legal text. In particular, our approach demonstrates the usefulness of co-occurrence networks of terms, in a practical experimental setting based on an EU Regulation. First, a manual annotation task identify instances of different kinds of implicit links in the norm. In addition to a typical NLP pipeline, our framework includes a technique from Information Architecture, i.e. card sorting. Second, we construct co-occurrence networks of the law terms to derive graph metrics. Third, binary classification experiments identify the existence (and the type) of inter-relationships by using a Bag-of-Ngrams model integrated with network analysis features. The results demonstrate how the adoption of co-occurrence network features improves the identification of links, for all the classifiers here considered. This is encouraging toward a wider adoption of this kind of network analysis technique in legal informatics.
•To provide a general framework in addressing the identification of inter-relationships in a legal document.•As a novelty, the adoption of card sorting in the norm types identification process.•To explore the role of network metrics in a graph-based supervised learning NLP classification task.
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