Machine Learning for Personalised and Predictive Medicine

We apply machine learning techniques to a wide variety of data, ranging from clinical to OMICS data. Our main aim is to support clinical and translational research by extracting knowledge from data and developing prediction models.

Key Projects


Selected Publications

  • P. Berjano, F. Langella, L. Ventriglia, D. Compagnone, P. Barletta, D. Huber, F. Mangili, G. Licandro, F. Galbusera, A. Cina, T. Bassani, C. Lamartina, L. Scaramuzzo, R. Bassani, M. Brayda-Bruno, J.H. Villafañe, L. Monti, L. Azzimonti: “The Influence of Baseline Clinical Status and Surgical Strategy on Early Good to Excellent Result in Spinal Lumbar Arthrodesis: A Machine Learning Approach.” J. Pers. Med., vol. 11(12):1377, 2021. (link)

  • F. Bini, A. Pica, L. Azzimonti, A. Giusti, L. Ruinelli, F. Marinozzi, P. Trimboli: “Artificial intelligence in thyroid field. A comprehensive review.” Cancers, vol. 13(19), 2021. (link)

  • G. Halasz, M. Sperti; M. Villani, U. Michelucci, P. Agostoni, A. Biagi, L. Rossi, A. Botti, C. Mari, M. Maccarini, F. Pura, L. Roveda, A. Nardecchia, E. Mottola, M. Nolli, E. Salvioni, M. Mapelli, M. A. Deriu, D. Piga, M. Piepoli: “Predicting outcomes in the Machine Learning era: The Piacenza score a purely data driven approach for mortality prediction in COVID-19 Pneumonia”, Journal of Medical Internet Research, Vol. 23, No. 5, 2021.

  • Pedrero-Martin Y., Falla D., Martinez-Calderon J., Liew B.X.W., Scutari M., Luque-Suarez A.: “Self-Efficacy Beliefs Mediate the Association Between Pain Intensity and Pain Interference in Acute/Subacute Whiplash-Associated Disorders.” European Spine Journal 30, pp. 1689-1698, 2021.

  • Liew B. X. W., Scutari M., Peolsson A., Peterson G., Ludvigsson M. L., and Falla D.: “Investigating the Causal Mechanisms of Symptom Recovery in Chronic Whiplash Associated Disorders Using Bayesian Networks.” Clin. J. Pain. 35(8):647-655, 2019.

  • F. Gorini, L. Azzimonti, G. Delfanti, L. Scarfó, C. Scielzo, M.T. Bertilaccio, P. Ranghetti, A. Gulino, C. Doglioni, A. Di Napoli, M. Capri, C. Franceschi, F. Calligaris-Cappio, P. Ghia, M. Bellone, P. Dellabona, G. Casorati, C. de Lalla: “Invariant NKT cells contribute to Chronic Lymphocytic Leukemia surveillance and prognosis”, Blood, vol. 129, no. 26, 3440-3451, 2017. (link)

  • B. Guerciotti, C. Vergara, L. Azzimonti, L. Forzenigo, A. Buora, P. Biondetti, M. Domanin: “Computational study of the fluid-dynamics in carotids before and after endarterectomy”, Journal of Biomechanics, vol. 49, 26–38, 2016. (link)

  • L. Azzimonti, L.M. Sangalli, P. Secchi, M. Domanin, F. Nobile: “Blood flow velocity field estimation via spatial regression with PDE penalization”, Journal of the American Statistical Association, Theory and Methods Section, vol. 110, no. 511, 1057–1071, 2015. (link)

  • C. de Lalla, A. Rinaldi, D. Montagna, L. Azzimonti, M.E. Bernardo, L.M. Sangalli, A.M. Paganoni, R. Maccario, A. Di Cesare-Merlone, M. Zecca, F. Locatelli, P. Dellabona, G. Casorati: “Invariant Natural Killer T-cell reconstitution in pediatric leukemia patients given HLA-haploidentical stem cell transplantation defines distinct CD4+ and CD4- subset dynamics and associates with the remission state”, The Journal of Immunology, vol. 186, no. 7, 4490–4499, 2011. (link)


Dr. Laura Azzimonti

Senior Researcher and Lecturer at IDSIA with affiliation to MeDiTech

Personal website