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Survival analysis is a crucial tool in healthcare, allowing us to understand and predict time-to-event occurrences using statistical and machine-learning techniques. As deep learning gains traction in this domain, a specific challenge emerges: neural network-based survival models often produce discrete-time outputs, with the number of discretization points being much fewer than the unique time points in the dataset, leading to potentially inaccurate survival functions. To this end, our study explores post-processing techniques for survival functions. Specifically, interpolation and smoothing can act as effective regularization, enhancing performance metrics integrated over time, such as the Integrated Brier Score and the Cumulative Area-Under-the-Curve. We employed various regularization techniques on diverse real-world healthcare datasets to validate this claim. Empirical results suggest a significant performance improvement when using these post-processing techniques, underscoring their potential as a robust enhancement for neural network-based survival models. These findings suggest that integrating the strengths of neural networks with the non-discrete nature of survival tasks can yield more accurate and reliable survival predictions in clinical scenarios.

Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-world observational data used to train and validate these models are almost always affected by biases that can strongly impact the outcomes validity: two examples are values missing not-at-random and selection bias. Addressing them is a key element in achieving transportability and in studying the causal relationships that are critical in clinical decision making, going beyond simpler statistical approaches based on probabilistic association. In this context, we propose a novel approach that combines selection diagrams, missingness graphs, causal discovery and prior knowledge into a single graphical model to estimate the cardiovascular risk of adolescent and young females who survived breast cancer. We learn this model from data comprising two different cohorts of patients. The resulting causal network model is validated by expert clinicians in terms of risk assessment, accuracy and explainability, and provides a prognostic model that outperforms competing machine learning methods.

Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task. In principle, machine learning and deep learning models are flexible and expressive enough to capture the dynamics of clinical risk assessment. However, in this setting we are limited to observational data with quality issues, missing values, small sample size and high dimensionality: we cannot reliably learn such models from limited observational data with these sources of bias. Instead, we choose to learn a causal Bayesian network to mitigate the issues above and to leverage the prior knowledge on endometrial cancer available from clinicians and physicians. We introduce a causal discovery algorithm for causal Bayesian networks based on bootstrap resampling, as opposed to the single imputation used in related works. Moreover, we include a context variable to evaluate whether selection bias results in learning spurious associations. Finally, we discuss the strengths and limitations of our findings in light of the presence of missing data that may be missing-not-at-random, which is common in real-world clinical settings.

Much information is conveyed within tables, which can be semantically annotated by humans or (semi)automatic approaches. Nevertheless, many applications cannot take full advantage of semantic annotations because of the low quality. A few methodologies exist for the quality assessment of semantic annotation of tabular data, but they do not automatically assess the quality as a multidimensional concept through different quality dimensions. The quality dimensions are implemented in STILTool 2, a web application to automate the quality assessment of the annotations. The evaluation is carried out by comparing the quality of semantic annotations with gold standards. The work presented here has been applied to at least three use cases. The results show that our approach can give us hints about the quality issues and how to address them.

This paper introduces the RASH Framework, i.e., a set of specifications and tools for writing academic articles in RASH, a simplified version of HTML. RASH focuses strictly on writing the content of the paper leaving all the issues about its validation, visualisation, conversion, and data extraction to the tools developed within the framework.

A new model for aggregating multiple criteria evaluations for relevance assessment is proposed. An Information Retrieval context is considered, where relevance is modeled as a multidimensional property of documents. The usefulness and effectiveness of such a model are demonstrated by means of a case study on personalized Information Retrieval with multi-criteria relevance. The following criteria are considered to estimate document relevance: aboutness, coverage, appropriateness, and reliability. The originality of this approach lies in the aggregation of the considered criteria in a prioritized way, by considering the existence of a prioritization relationship over the criteria. Such a prioritization is modeled by making the weights associated to a criterion dependent upon the satisfaction of the higher-priority criteria. This way, it is possible to take into account the fact that the weight of a less important criterion should be proportional to the satisfaction degree of the more important criterion. Experimental evaluations are also reported.

In this paper a user evaluation is proposed to assess the effectiveness of systems based on multidimensional relevance assessment. First of all, we introduce our approach to multidimensional modeling and aggregation, and the criteria used for the experiments. Then, we describe how the user evaluation has been performed, and ̄nally, we discuss the results obtained.

In this paper a new model is proposed for aggregating multiple criteria evaluations for relevance assessment based on a refinement of the “min” (“and”) operator. The peculiarity of such an operator which also distinguishes it from the traditional “min” aggregation operator is that the extent to which the least satisfied criterion plays a role in determining the overall satisfaction degree depends both on its satisfaction degree and on its importance for the user. If it is not important at all, its satisfaction degree is not considered, while if it is the most important criterion for the user, only its satisfaction degree is considered (like with the traditional “min” operator). The usefulness and effectiveness of such a model are demonstrated by means of a case study on personalized Information Retrieval with multicriteria relevance. Some preliminary experimental results are also reported

In this paper, a new model for aggregating multiple criteria evaluations for relevance assessment is proposed. An information retrieval context is considered, where relevance is modelled as a multidimensional property of documents. In the paper, the proposed aggregation operator is applied to define amodel for personalized Information Retrieval (IR), in which four criteria are considered in order to assess document relevance:aboutness, coverage, appropriateness and reliability.The originality of this approach lies in the aggregation of the considered criteria in a prioritized way, by considering the existence of a prioritization relationship over the criteria. Such a prioritization is modeled by making the weights associated with a criterion dependent upon the satisfaction of the higher-priority criteria. This way, it is possible to take into account the fact that the weight of a less important criterion should be proportional to the satisfaction degree of the more important criterion.In the paper, some preliminary experimental results are also reported.