Multi-Sorted Inverse Frequent Itemsets Mining: On-Going Research

Inverse frequent itemset mining (IFM) consists of generating artificial transactional databases reflecting patterns of real ones, in particular, satisfying given frequency constraints on the itemsets. An extension of IFM called many-sorted IFM, is introduced where the schemes for the datasets to be generated are those typical of Big Tables, as required in emerging big data applications, e.g., social network analytics

Inverse Tree-OLAP: Definition, Complexity and First Solution

Count constraint is a data dependency that requires the results of given count operations on a relation to be within a certain range. By means of count constraints a new decisional problem, called the Inverse OLAP, has been recently introduced: given a flat fact table, does there exist an instance satisfying a set of given count constraints? This paper focus on a special case of Inverse OLAP, called Inverse Tree-OLAP, for which the flat fact table key is modeled by a Dimensional Fact Model (DFM) with a tree structure

Comparison of methods for logic-query implementation

AbstractA logic query Q is a triple < G, LP, D, where G is the query goal, LP is a logic program without function symbols, and D is a set of facts, possibly stored as tuples of a relational database. The answers of Q are all facts that can be inferred from LP ∪ D and unify with G. A logic query is bound if some argument of the query goal is a constant; it is canonical strongly linear (a CSL query) if LP contains exactly one recursive rule and this rule is linear, i.e., only one recursive predicate occurs in its body. In this paper, the problem of finding the answers of a bound CSL query is studied with the aim of comparing for efficiency some well-known methods for implementing logic queries: the eager method, the counting method, and the magic-set method. It is shown that the above methods can be expressed as algorithms for finding particular paths in a directed graph associated to the query. Within this graphical formalism, a worst-case complexity analysis of the three methods is performed. It turns out that the counting method has the best upper bound for noncyclic queries. On the other hand, since the counting method is not safe if queries are cyclic, the method is extended to safely implement this kind of queries as well

Machine Learning Methods for Generating High Dimensional Discrete Datasets

The development of platforms and techniques for emerging Big Data and Machine Learning applications requires the availability of real-life datasets. A possible solution is to synthesize datasets that reflect patterns of real ones using a two-step approach: first, a real dataset X is analyzed to derive relevant patterns Z and, then, to use such patterns for reconstructing a new dataset X\u27 that preserves the main characteristics of X. This survey explores two possible approaches: (1) Constraint-based generation and (2) probabilistic generative modeling. The former is devised using inverse mining (IFM) techniques, and consists of generating a dataset satisfying given support constraints on the itemsets of an input set, that are typically the frequent ones. By contrast, for the latter approach, recent developments in probabilistic generative modeling (PGM) are explored that model the generation as a sampling process from a parametric distribution, typically encoded as neural network. The two approaches are compared by providing an overview of their instantiations for the case of discrete data and discussing their pros and cons

Grid-VirtuE: a layered architecture for grid virtual enterprises

A grid virtual enterprise is a community of independent enterprises concerned with a particular sector of the economy. Its members (nodes) are small or medium size enterprises (SME) engaged in bilateral transactions. An important principle of a grid virtual enterprise is the lack of any global "guiding force", with each member of the community making its own independent decisions. In this paper we describe Grid-VirtuE, a three-layer architecture for grid virtual enterprises. The top layer of the architecture, representing its ultimate purpose, is an environment in which grid virtual enterprises can be modeled and implemented. This layer is supported by middleware infrastructure for grids, providing a host of grid services, such as node-to-node communication, bilateral transactions, and data collection. The bottom layer is essentially a distributed data warehouse for storing, sharing and analyzing the large amounts of data generated by the grid. Among other functionalities, the warehouse handles the dissemination of data among the members of the grid; it confronts issues of data magnitude with an aging mechanism that aggregates old data at a lower level of detail; and it incorporates privacy-preserving features that retain the confidentiality of individual members. Warehouse information is also used for data and process mining, aimed at analyzing the behavior of the enterprise, and subsequently inducing evolutionary changes that will improve its performance.A grid virtual enterprise is a community of independent enterprises concerned with a particular sector of the economy. Its members (nodes) are small or medium size enterprises (SME) engaged in bilateral transactions. An important principle of a grid virtual enterprise is the lack of any global "guiding force", with each member of the community making its own independent decisions. In this paper we describe Grid-VirtuE, a three-layer architecture for grid virtual enterprises. The top layer of the architecture, representing its ultimate purpose, is an environment in which grid virtual enterprises can be modeled and implemented. This layer is supported by middleware infrastructure for grids, providing a host of grid services, such as node-to-node communication, bilateral transactions, and data collection. The bottom layer is essentially a distributed data warehouse for storing, sharing and analyzing the large amounts of data generated by the grid. Among other functionalities, the warehouse handles the dissemination of data among the members of the grid; it confronts issues of data magnitude with an aging mechanism that aggregates old data at a lower level of detail; and it incorporates privacy-preserving features that retain the confidentiality of individual members. Warehouse information is also used for data and process mining, aimed at analyzing the behavior of the enterprise, and subsequently inducing evolutionary changes that will improve its performance.Monograph's chapter

The ménage à trois of healthcare: the actors in after-AI era under patient consent

Introduction: Artificial intelligence has become an increasingly powerful technological instrument in recent years, revolutionizing many sectors, including public health. Its use in this field will inevitably change clinical practice, the patient-caregiver relationship and the concept of the diagnosis and treatment pathway, affecting the balance between the patient’s right to self-determination and health, and thus leading to an evolution of the concept of informed consent. The aim was to characterize the guidelines for the use of artificial intelligence, its areas of application and the relevant legislation, to propose guiding principles for the design of optimal informed consent for its use. Materials and methods: A classic review by keywords on the main search engines was conducted. An analysis of the guidelines and regulations issued by scientific authorities and legal bodies on the use of artificial intelligence in public health was carried out. Results: The current areas of application of this technology were highlighted, divided into sectors, its impact on them, as well as a summary of current guidelines and legislation. Discussion: The ethical implications of artificial intelligence in the health care system were assessed, particularly regarding the therapeutic alliance between doctor and patient, and the balance between the right to self-determination and health. Finally, given the evolution of informed consent in relation to the use of this new technology, seven guiding principles were proposed to guarantee the right to the most informed consent or dissent

Fasting glucose and body mass index as predictors of activity in breast cancer patients treated with everolimus-exemestane: the EverExt study

Evidence on everolimus in breast cancer has placed hyperglycemia among the most common high grade adverse events. Anthropometrics and biomarkers of glucose metabolism were investigated in a observational study of 102 postmenopausal, HR + HER2- metastatic breast cancer patients treated with everolimus-exemestane in first and subsequent lines. Best overall response (BR) and clinical benefit rate (CBR) were assessed across subgroups defined upon fasting glucose (FG) and body mass index (BMI). Survival was estimated by Kaplan-Meier method and log-rank test. Survival predictors were tested in Cox models. Median follow up was 12.4 months (1.0-41.0). The overall cohort showed increasing levels of FG and decreasing BMI (p &lt; 0.001). Lower FG fasting glucose at BR was more commonly associated with C/PR or SD compared with PD (p &lt; 0.001). We also observed a somewhat higher BMI associated with better response (p = 0.052). More patients in the lowest FG category achieved clinical benefit compared to the highest (p &lt; 0.001), while no relevant differences emerged for BMI. Fasting glucose at re-assessment was also predictive of PFS (p = 0.037), as confirmed in models including BMI and line of therapy (p = 0.049). Treatment discontinuation was significantly associated with changes in FG (p = 0.014). Further research is warranted to corroborate these findings and clarify the underlying mechanisms

Role of gonadotropin-releasing hormone analogues in metastatic male breast cancer: Results from a pooled analysis

Background: Male breast cancer is a rare malignancy. Despite the lack of prospectively generated data from trials in either the adjuvant or metastatic setting, patients are commonly treated with hormone therapies. Much controversy exists over the use of gonadotropin-releasing hormone analogues in metastatic male breast cancer patients. We conducted this study to provide more concrete ground on the use of gonadotropin-releasing hormone analogues in this setting. Methods: We herein present results from a pooled analysis including 60 metastatic male breast cancer patients treated with either an aromatase inhibitor or cyproterone acetate as a monotherapy (23 patients) or combined with a gonadotropin-releasing hormone analogue (37 patients). Results: Overall response rate was 43.5 % in patients treated with monotherapy and 51.3 % with combination therapy (p = 0.6). Survival outcomes favored combination therapy in terms of median progression-free survival (11.6 months versus 6 months; p = 0.05), 1-year progression-free survival rate (43.2 % versus 21.7 %; p = 0.05), median overall survival (29.7 months versus 22 months; p = 0.05), and 2-year survival rate (64.9 % versus 43.5 %; p = 0.05). Conclusions: In metastatic male breast cancer patients, the combined use of gonadotropin-releasing hormone analogues and aromatase inhibitors or antiandrogens seems to be associated with greater efficacy, particularly in terms of survival outcomes, compared with monotherapy. Collectively, these results encourage considering these agents in the metastatic setting

The T.O.S.CA. Project: Research, Education and Care

Despite recent and exponential improvements in diagnostic- therapeutic pathways, an existing “GAP” has been revealed between the “real world care” and the “optimal care” of patients with chronic heart failure (CHF). We present the T.O.S.CA. Project (Trattamento Ormonale dello Scompenso CArdiaco), an Italian multicenter initiative involving different health care professionals and services aiming to explore the CHF “metabolic pathophysiological model” and to improve the quality of care of HF patients through research and continuing medical education