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Carlo Ricciardi

Researcher at University of Naples Federico II

Publications -  72
Citations -  1249

Carlo Ricciardi is an academic researcher from University of Naples Federico II. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 15, co-authored 50 publications receiving 613 citations. Previous affiliations of Carlo Ricciardi include Reykjavík University.

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Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa.

TL;DR: ML analysis using MRI-derived TA features could be a feasible tool in the identification of placental tissue abnormalities underlying PAS in patients with placenta previa, thus expanding the application field of artificial intelligence to medical images.
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Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach.

TL;DR: Whether a radiomic machine learning approach employing texture-analysis features extracted from primary tumor lesions (PTLs) is able to predict tumor grade and nodal status (NS) in patients with oropharyngeal and oral cavity squamous-cell carcinoma is investigated.
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Using gait analysis’ parameters to classify Parkinsonism: A data mining approach

TL;DR: The novelty of the study is the use of a data mining approach on the spatial and temporal parameters of gait analysis in order to classify patients affected by typical (PD) and atypical Parkinsonism (PSP) based on gait patterns.
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Lean Six Sigma in healthcare: Fast track surgery for patients undergoing prosthetic hip replacement surgery

TL;DR: It is found that multiple variables could influence the length of hospital stay (LOS) for inpatient treatment, thereby increasing patient management costs due to longer periods of hospitalisation and using LSS as the correct methodology to analyse a clinical pathway.
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Linear discriminant analysis and principal component analysis to predict coronary artery disease

TL;DR: The use of data mining techniques to analyse a population of 10,265 people who were evaluated by the Department of Advanced Biomedical Sciences for myocardial ischaemia and principal component analysis is used as an algorithm for feature reduction.