| Linear discriminant analysis and principal component analysis to predict coronary artery disease C Ricciardi, AS Valente, K Edmund, V Cantoni, R Green, A Fiorillo, ... Health informatics journal 26 (3), 2181-2192, 2020 | 94 | 2020 |
| Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa V Romeo, C Ricciardi, R Cuocolo, A Stanzione, F Verde, L Sarno, ... Magnetic resonance imaging 64, 71-76, 2019 | 94 | 2019 |
| Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach V ROMEO, R CUOCOLO, C RICCIARDI, L UGGA, S COCOZZA, F VERDE, ... Anticancer Research 40 (1), 271-280, 2020 | 93 | 2020 |
| Lean Six Sigma in healthcare: fast track surgery for patients undergoing prosthetic hip replacement surgery G Improta, G Balato, C Ricciardi, MA Russo, I Santalucia, M Triassi, ... The TQM Journal 31 (4), 526-540, 2019 | 87 | 2019 |
| Using gait analysis’ parameters to classify Parkinsonism: A data mining approach C Ricciardi, M Amboni, C De Santis, G Improta, G Volpe, L Iuppariello, ... Computer methods and programs in biomedicine 180, 105033, 2019 | 76 | 2019 |
| Agile Six Sigma in Healthcare: Case Study at Santobono Pediatric Hospital G Improta, G Guizzi, C Ricciardi, V Giordano, AM Ponsiglione, ... International Journal of Environmental Research and Public Health 17 (3), 1052, 2020 | 73 | 2020 |
| Toward predicting motion sickness using virtual reality and a moving platform assessing brain, muscles, and heart signals M Recenti, C Ricciardi, R Aubonnet, I Picone, D Jacob, HÁR Svansson, ... Frontiers in Bioengineering and Biotechnology 9, 635661, 2021 | 67 | 2021 |
| A Six Sigma DMAIC methodology as a support tool for Health Technology Assessment of two antibiotics AM Ponsiglione, C Ricciardi, G Improta, GDA Orabona, A Sorrentino, ... Mathematical Biosciences and Engineering 18 (4), 3469-3490, 2021 | 66 | 2021 |
| Fast track surgery for knee replacement surgery: a lean six sigma approach C Ricciardi, G Balato, M Romano, I Santalucia, M Cesarelli, G Improta The TQM Journal 32 (3), 461-474, 2020 | 61 | 2020 |
| Machine learning to predict mortality after rehabilitation among patients with severe stroke D Scrutinio, C Ricciardi, L Donisi, E Losavio, P Battista, P Guida, ... Scientific reports 10 (1), 20127, 2020 | 59 | 2020 |
| Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions C Ricciardi, KJ Edmunds, M Recenti, S Sigurdsson, V Gudnason, ... Scientific reports 10 (1), 2863, 2020 | 57 | 2020 |
| The application of six sigma to reduce the pre-operative length of hospital stay at the hospital Antonio Cardarelli G Improta, C Ricciardi, A Borrelli, A D’alessandro, C Verdoliva, ... International Journal of Lean Six Sigma 11 (3), 555-576, 2020 | 55 | 2020 |
| Classifying the type of delivery from cardiotocographic signals: A machine learning approach C Ricciardi, G Improta, F Amato, G Cesarelli, M Romano Computer Methods and Programs in Biomedicine 196, 105712, 2020 | 54 | 2020 |
| Application of data mining in a cohort of Italian subjects undergoing myocardial perfusion imaging at an academic medical center C Ricciardi, V Cantoni, G Improta, L Iuppariello, I Latessa, M Cesarelli, ... Computer Methods and Programs in Biomedicine 189, 105343, 2020 | 51 | 2020 |
| Efficacy of machine learning in predicting the kind of delivery by cardiotocography G Improta, C Ricciardi, F Amato, G D’Addio, M Cesarelli, M Romano XV Mediterranean Conference on Medical and Biological Engineering and …, 2020 | 47 | 2020 |
| Implementation and validation of a new method to model voluntary departures from emergency departments C Ricciardi, AM Ponsiglione, G Converso, I Santalucia, M Triassi, ... Mathematical Biosciences and Engineering 18 (1), 253-273, 2021 | 46 | 2021 |
| Lean Six Sigma approach to reduce LOS through a diagnostic-therapeutic-assistance path at AORNA Cardarelli C Ricciardi, A Fiorillo, AS Valente, A Borrelli, C Verdoliva, M Triassi, ... The TQM Journal 31 (5), 657-672, 2019 | 46 | 2019 |
| Machine learning can detect the presence of Mild cognitive impairment in patients affected by Parkinson’s Disease C Ricciardi, M Amboni, C De Santis, G Ricciardelli, G Improta, G D’Addio, ... 2020 IEEE International Symposium on Medical Measurements and Applications …, 2020 | 43 | 2020 |
| Distinguishing functional from non-functional pituitary macroadenomas with a machine learning analysis R Carlo, C Renato, C Giuseppe, U Lorenzo, I Giovanni, S Domenico, ... XV Mediterranean Conference on Medical and Biological Engineering and …, 2020 | 43 | 2020 |
| MRI radiomics for the prediction of fuhrman grade in clear cell renal cell carcinoma: A machine learning exploratory study A Stanzione, C Ricciardi, R Cuocolo, V Romeo, J Petrone, M Sarnataro, ... Journal of digital imaging 33, 879-887, 2020 | 42 | 2020 |
Giovanni ImprotaRTD B (SSD ING-INF/06) - Dip. di Sanità Pubblica - Università degli Studi di Napoli "Federico II"在 unina.it 的电子邮件经过验证
