Michela Carlotta Massi
Polytechnic University of Milan
Survival analysisMachine learningInternal medicineBenchmark (computing)Logistic regressionArtificial intelligenceGenerative modelDomain (software engineering)Range (mathematics)Observational studyProstate cancerUnstructured dataPopulationToxicityComputer scienceProbabilistic logicMedicineCohortCluster analysisSelection (genetic algorithm)
Publications 8
#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...
May 7, 2021 in ICLR (International Conference on Learning Representations)
#1Laura Manduchi (ETH Zurich)H-Index: 1
#2Ricards Marcinkevics (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 3
Last. Julia E. Vogt (ETH Zurich)H-Index: 9
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#1Nicola Rares Franco (Ghent University Hospital)H-Index: 1
#1Nicola Rares Franco (Ghent University Hospital)
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...
#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...
1 Citations
#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...
#1Michela Carlotta Massi (Polytechnic University of Milan)H-Index: 2
#2Francesca Gasperoni (University of Cambridge)H-Index: 1
Last. Tiziana RancatiH-Index: 27
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Background: REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk factors....
3 CitationsSource
#1Michela Carlotta Massi (Polytechnic University of Milan)H-Index: 2
#2Francesca Ieva (Polytechnic University of Milan)H-Index: 14
Last. Emanuele Lettieri (Polytechnic University of Milan)H-Index: 19
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BACKGROUND The healthcare sector is an interesting target for fraudsters. The availability of a great amount of data makes it possible to tackle this issue with the adoption of data mining techniques, making the auditing process more efficient and effective. This research has the objective of developing a novel data mining model devoted to fraud detection among hospitals using Hospital Discharge Charts (HDC) in Administrative Databases. In particular, it is focused on the DRG upcoding practice, ...
2 CitationsSource
Last. Catharine M L WestH-Index: 77
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