Medical datasets are typically affected by issues such as missing values, class imbalance, a heterogeneous feature types, and a high number of features versus a relatively small number of samples, preventing machine learning models from obtaining proper results in classification and regression tasks. This paper introduces AutoML-Med, an Automated Machine Learning tool specifically designed to address these challenges, minimizing user intervention and identifying the optimal combination of preprocessing techniques and predictive models. AutoML-Med's architecture incorporates Latin Hypercube Sampling (LHS) for exploring preprocessing methods, trains models using selected metrics, and utilizes Partial Rank Correlation Coefficient (PRCC) for fine-tuned optimization of the most influential preprocessing steps. Experimental results demonstrate AutoML-Med's effectiveness in two different clinical settings, achieving higher balanced accuracy and sensitivity, which are crucial for identifying at-risk patients, compared to other state-of-the-art tools. AutoML-Med's ability to improve prediction results, especially in medical datasets with sparse data and class imbalance, highlights its potential to streamline Machine Learning applications in healthcare.

In medical process mining, specific domain characteristics have to be dealt with: in particular, in medicine, a significant amount of expert knowledge is typically available; moreover, an interactive approach, letting medical users be involved in the work of process model discovery, is more acceptable than a completely automated strategy. To this end, in our recent work we have defined SIM (Semantic Interactive Miner), an innovative process mining tool able to: (i) support the interaction with medical experts, who can progressively merge parts of the initially mined model, obtaining a more generalized version; (ii) exploit pre-encoded domain knowledge, to move from a model where activities are reported at the ground level to a more user-interpretable high-level version. In this paper we illustrate the features of our tool by showing its application to the case study of neonatal resuscitation simulation: we use SIM to mine the process models produced by two different groups of students of a simulation course, aiming at verifying whether differently skilled young professionals produce different processes, which can finally be compared to the correct guideline.

Personalized financial advisory systems based on Case-Based Reasoning and on historical user activity are an emerging trend. In the present paper, we report the experience related to the development of a case-based recommendation module in a project called SMARTFASI, where the knowledge about past experiences is exploited, in order to suggest suitable asset investments to the final user. We present a solution aimed at personalizing the asset picking phase, by taking into consideration choices made by customers who have a financial and personal data profile "similar" to the current one. We discuss the notion of distance-based similarity adopted in our system and how to actually implement an asset recommendation strategy integrated with the other software modules of SMARTFASI. We finally discuss the impact such a strategy may have both from the point of view of private investors and professional users.

Logs recording the traces of execution of previous process instances can be exploited for different process management tasks, such as prediction and recommendation in operational support. Efficient retrieval of past traces can be very important to achieve such tasks, while building a model of the process from the log can support problem/anomaly detection and, more generally, process analysis. Trace retrieval is gaining attention in the Case Based Reasoning research community, but so far it has been faced in a completely separate way from the construction of a process model from the log, instead, we propose an approach aiming at integrating these two goals. The core notion of our proposal is the log-tree, which constitutes a "bridge" between the notions of (log) index and process model. We propose a mining algorithm to build it, and an algorithm exploiting it for trace retrieval. Future extensions of our initial contribution are also widely discussed.

This paper discusses the data mining approach followed in a project called TRAQUASwine, aimed at the definition of methods for data analytical assessment of the authenticity and protection, against fake versions, of some of the highest value Nebbiolo-based wines from Piedmont region in Italy. This is a big issue in the wine market, where commercial frauds related to such a kind of products are estimated to be worth millions of Euros. The objective is twofold: to show that the problem can be addressed without expensive and hyper-specialized wine analyses, and to demonstrate the actual usefulness of classification algorithms for data mining on the resulting chemical profiles. Following Wagstaff's proposal for practical exploitation of machine learning (and data mining) approaches, we describe how data have been collected and prepared for the production of different datasets, how suitable classification models have been identified and how the interpretation of the results suggests the emergence of an active role of classification techniques, based on standard chemical profiling, for the assesment of the authenticity of the wines target of the study.

Supporting decision making in domains in which the observed phenomenon dynamics have to be dealt with, can greatly benefit of retrieval of past cases, provided that proper representation and retrieval techniques are implemented. In particular, when the parameters of interest take the form of time series, dimensionality reduction and flexible retrieval have to be addresses to this end. Classical methodological solutions proposed to cope with these issues, typically based on mathematical transforms, are characterized by strong limitations, such as a difficult interpretation of retrieval results for end users, reduced flexibility and interactivity, or inefficiency. In this paper, we describe a novel framework, in which time-series features are summarized by means of Temporal Abstractions, and then retrieved resorting to abstraction similarity. Our approach grants for interpretability of the output results, and understandability of the (user-guided) retrieval process. In particular, multilevel abstraction mechanisms and proper indexing techniques are provided, for flexible query issuing, and efficient and interactive query answering. Experimental results have shown the efficiency of our approach in a scalability test, and its superiority with respect to the use of a classical mathematical technique in flexibility, user friendliness, and also quality of results.

Studying the interactions between arbuscular mycorrhizal fungi (AMFs) and their symbiotic endo bacteria has potentially strong impacts on the development of new biotechnology applications. The analysis of genomic data and syntenies is a key technique for acquiring information about phylogenetic relationships and metabolic functions of such organisms. In this paper we describe a case-based retrieval tool, which supports customized comparative genomics searches, and which is part of a modular architecture meant to support in-silico genome sequence analysis, being developed within the project BIOBITS. From a methodological viewpoint, the tool takes advantage of an abstraction technique similar to Temporal Abstractions, thus allowing to neglect un-relevant details. Retrieval is made flexible by the use of such multi-level abstractions, and efficient by the use of proper taxonomical index structures. Moreover, end-users are allowed to progressively relax or refine their queries, in an interactive way. A case study taken from the application domain is used to illustrate the approach.

CBR systems designers and developers' research can benefit from the availability of existing platforms, able to provide software design and implementation assistance. The JColibri platform, realized and maintained by the University of Madrid, is one of the most well known among such tools. In this work, we describe a couple of extensions we have provided to the core JColibri open source software. In particular, our extensions are meant to optimize case retrieval performances, in data-rich applications. Specifically, we focused our attention on treating (i) large case bases, in which retrieval time may become unacceptable, and (ii) cases with high-dimensional features - namely time series features - on which proper case representation and retrieval solutions need to be studied. The implemented code has been preliminarly tested, and it is now ready to be integrated with the JColibri code, and made available to the CBR research community. Additional extensions, always dealing with retrieval optimization, are foreseen as our future work.

In the hemodialysis domain, we are implementing a case‐based, closed‐loop architecture aimed at configuring temporal abstractions (TA), which will be applied to time series data. The advantage of a case‐based approach is the one of “quickly” obtaining a suitable TA parameter configuration, simply by looking at the most similar already configured case, where configured cases are indexed by means of contextual information. The retrieved configuration, together with the time series data, is then used as an input to a TA processing module, able to provide a set of qualitative states, trends, and significant combinations of both as an output. TA processing results can finally be evaluated, possibly leading to a (human‐supervized) reorganization/revision of the case base content, to ameliorate future TA configuration sessions—thus closing the loop. The work is being integrated with RHENE, a system for case‐based retrieval in hemodialysis, able to work both on raw time series data and on preprocessed (by means of TA) ones.

In this work we propose a case-based architecture tackling the problem of configuring and processing temporal abstractions (trends and qualitative states) produced from raw time series data. The parameter configuration is a critical problem in many temporal abstraction processes; in several application domains (especially in medical ones), contextual knowledge plays a fundamental role in the time series interpretation. Since defining the right configuration for each possible contextual situation may be impractical, we propose to adopt a case-based approach, where the suitable configuration can be obtained by looking at the most similar already configured case, with respect to the current situation. Configured cases are indexed by means of contextual information. The obtained configuration can then be used as input to a temporal abstraction module, providing a set of qualitative states, trends and suitable combination of both as a result. Cases can then be exploited in the processing of such results as well, by providing an evaluation of the whole abstraction processing, possibly leading to the revision of the case base. The approach is illustrated by means of an example taken from a medical application, concerning the monitoring and evaluation of patients undergoing hemodialysis treatment