Francesca Ieva
Polytechnic University of Milan
Nonparametric statisticsStatisticsCovariateMachine learningData miningInternal medicineArtificial intelligenceHealth careMultivariate statisticsIntensive care medicinePattern recognitionData scienceHeart failureContext (language use)MathematicsComputer scienceMyocardial infarctionMedical emergencyMedicineCluster analysis
152Publications
14H-index
644Citations
Publications 148
Newest
#1Michela Carlotta Massi (Polytechnic University of Milan)H-Index: 2
#2Francesca Ieva (Polytechnic University of Milan)H-Index: 14
EEG technology finds applications in several domains. Currently, most EEG systems require subjects to wear several electrodes on the scalp to be effective. However, several channels might include noisy information, redundant signals, induce longer preparation times and increase computational times of any automated system for EEG decoding. One way to reduce the signal-to-noise ratio and improve classification accuracy is to combine channel selection with feature extraction, but EEG signals are kn...
#1Guido Costa (Humanitas University)H-Index: 12
#2Lara Cavinato (Polytechnic University of Milan)H-Index: 2
Last. Luca Di Tommaso (Humanitas University)H-Index: 29
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Non-invasive diagnosis of chemotherapy-associated liver injuries (CALI) is still an unmet need. The present study aims to elucidate the contribution of radiomics to the diagnosis of sinusoidal dilatation (SinDil), nodular regenerative hyperplasia (NRH), and non-alcoholic steatohepatitis (NASH). Patients undergoing hepatectomy for colorectal metastases after chemotherapy (January 2018-February 2020) were retrospectively analyzed. Radiomic features were extracted from a standardized volume of non-...
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#1Chiara Masci (Polytechnic University of Milan)H-Index: 5
#2Francesca Ieva (Polytechnic University of Milan)H-Index: 14
Last. Anna Maria Paganoni (Polytechnic University of Milan)H-Index: 18
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This paper proposes an innovative statistical method to measure the impact of the class/school on student achievements in multiple subjects. We propose a semiparametric model for a bivariate response variable with random coefficients, that are assumed to follow a discrete distribution with an unknown number of support points, together with an Expectation-Maximization algorithm—called BSPEM algorithm—to estimate its parameters. In the case study, we apply the BSPEM algorithm to data about Italian...
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#1Luca Viganò (Humanitas University)H-Index: 36
#2Martina Sollini (Humanitas University)H-Index: 22
Last. Guido Torzilli (Humanitas University)H-Index: 55
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#1Nicola Rares Franco (Ghent University Hospital)
#1Nicola Rares Franco (Ghent University Hospital)H-Index: 1
Last. Tiziana RancatiH-Index: 27
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AIM To identify the effect of single nucleotide polymorphism (SNP) interactions on the risk of toxicity following radiotherapy (RT) for prostate cancer (PCa) and propose a new method for polygenic risk score incorporating SNP-SNP interactions (PRSi). MATERIALS AND METHODS Analysis included the REQUITE PCa cohort that received external beam RT and was followed for 2 years. Late toxicity endpoints were: rectal bleeding, urinary frequency, haematuria, nocturia, decreased urinary stream. Among 43 li...
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#1Michela Carlotta Massi (Polytechnic University of Milan)H-Index: 2
#2Francesca IevaH-Index: 14
Last. Anna Maria PaganoniH-Index: 18
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Class imbalance is a common issue in many domain applications of learning algorithms. Oftentimes, in the same domains it is much more relevant to correctly classify and profile minority class observations. This need can be addressed by Feature Selection (FS), that offers several further advantages, s.a. decreasing computational costs, aiding inference and interpretability. However, traditional FS techniques may become sub-optimal in the presence of strongly imbalanced data. To achieve FS advanta...
#1Marta Spreafico (Polytechnic University of Milan)H-Index: 1
#2Francesca Ieva (University of Milano-Bicocca)H-Index: 14
In clinical practice, it is often the case where the association between the occurrence of events and time-to-event outcomes is of interest; thus, it can be modeled within the framework of recurrent events. The purpose of our study is to enrich the information available for modeling survival with relevant dynamic features, properly taking into account their possibly time-varying nature, as well as to provide a new setting for quantifying the association between time-varying processes and time-to...
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#1Massimo Pellagatti (Polytechnic University of Milan)H-Index: 1
#2Chiara Masci (Polytechnic University of Milan)H-Index: 5
Last. Anna Maria Paganoni (Polytechnic University of Milan)H-Index: 18
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1 CitationsSource
#1Michela Carlotta Massi (Polytechnic University of Milan)H-Index: 2
Last. Paolo ZuninoH-Index: 28
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Logistic Regression (LR) is a widely used statistical method in empirical binary classification studies. However, real-life scenarios oftentimes share complexities that prevent from the use of the as-is LR model, and instead highlight the need to include high-order interactions to capture data variability. This becomes even more challenging because of: (i) datasets growing wider, with more and more variables; (ii) studies being typically conducted in strongly imbalanced settings; (iii) samples g...
Adherence to medication is the process by which patients take their drugs as prescribed, and represents an issue in pharmacoepidemiological studies. Poor adherence is often associated with adverse health conditions and outcomes, especially in case of chronic diseases such as heart failure (HF). This turns out in an increased request for health care services, and in a greater burden for the health care system. In recent years, there has been a substantial growth in pharmacotherapy research, aimed...
1 CitationsSource