Michela Carlotta Massi
Polytechnic University of Milan
AutoencoderSurvival analysisDeep learningMachine learningCancerData miningBasis (linear algebra)Internal medicineSample size determinationBenchmark (computing)OncologyHealth informaticsFeature selectionLogistic regressionSingle-nucleotide polymorphismArtificial intelligenceRadiogenomicsGenerative modelDomain (software engineering)Set (abstract data type)Coding (social sciences)Prospective cohort studyRange (mathematics)RankingAuditk-means clusteringObservational studyPublic hospitalExternal beam radiotherapyInferenceNocturiaProstate cancerTask (project management)InterpretabilityUnstructured dataPopulationToxicityComputer scienceProbabilistic logicCategorical variableDuality (mathematics)Receiver operating characteristicBinary classificationComputational biologyMedicineCohortCluster analysisFeature (computer vision)Anomaly detectionDatabaseUrinary systemOutlierSelection (genetic algorithm)Pattern searchGastroenterology
7Publications
2H-index
4Citations
Publications 6
Newest
#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...
Source
#1Michela Carlotta Massi (Polytechnic University of Milan)H-Index: 2
#2Francesca IevaH-Index: 15
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...
#1Michela Carlotta Massi (Polytechnic University of Milan)H-Index: 2
Last. Paolo ZuninoH-Index: 27
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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 (Medical Research Council)
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....
2 CitationsSource
#1Michela Carlotta Massi (Polytechnic University of Milan)H-Index: 2
#2Francesca Ieva (Polytechnic University of Milan)H-Index: 15
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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