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

PRAESIIDIUM


Personalised risk assessment for prediabetes using physics-informed machine learning

NextGen


Next generation tools for genome-centric multimodal data integration in personalized cardiovascular medicine.

SPEARHEAD


Prediction models for antimicrobial resistance

VAP


Early identification of ventilator associated pneumonia

K-CLIER


A clinical application of single-cell RNA sequencing data

GUIDE


NLP for type 1 diabetes management

 

Selected Publications

  • L. Multerer, S. Toniolo, S. Mitrović, M.C. Palumbo, A. Ravoni, P. Tieri, M. Forgione, L. Azzimonti “A Computationally Efficient Deep Learning-Based Surrogate Model of Prediabetes Progression.” 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Lisbon, Portugal, 2024, pp. 6951-6958. (link)

  • C. Bonesana, D. Malpetti, S. Mitrović, F. Mangili, L. Azzimonti: “Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework.” The 2nd IEEE International Conference on Federated Learning Technologies and Applications (FLTA24).

  • D. Legouis, A. Rinaldi, D. Malpetti, G. Arnoux, T. Verissimo, A. Faivre, F. Mangili, A. Rinaldi, L. Ruinelli, J. Pugin, S. Moll, L. Clivio, M. Bolis, S. de Seigneux, L. Azzimonti, P.E. Cipp`a: “A transfer learning framework to elucidate the clinical relevance of altered proximal tubule cell states in kidney disease.” iScience, 27(3), 109271, 2024. (link)

  • A. D’Agostino, C. Garbazza, D. Malpetti, L. Azzimonti, F. Mangili, H.C. Stein, R. del Giudice, A. Cicolin, F. Cirignotta, M. Manconi: “Optimal risk and diagnosis assessment strategies in perinatal depression: A machine learning approach from the life-ON study cohort.” Psychiatry Research, vol. 332, 2024. (link)

  • G. Delfanti, F. Cortesi, A. Perini, G. Antonini, L. Azzimonti, C. de Lalla, C. Garavaglia, M.L. Squadrito, M. Fedeli, M. Consonni, S. Sesana, F. Re, H., Shen, P. Dellabona, G. Casorati: “TCR-engineered iNKT cells induce robust antitumor response by dual targeting cancer and suppressive myeloid cells.” Science immunology, 7(74), eabn6563, 2022. (link)

  • 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)

Contacts

Dr. Laura Azzimonti

Senior Researcher and Lecturer at IDSIA and Group Leader at SIB, Personal website

Dr. Francesca Mangili

Senior Researcher at IDSIA, Personal website