The following publications are possibly variants of this publication:
- A Dynamic Bayesian Network model for the simulation of Amyotrophic Lateral Sclerosis progressionAlessandro Zandonà, Rosario Vasta, Adriano Chiò, Barbara Di Camillo. bmcbi, 20-S(4), 2019. [doi]
- Dealing with Data Scarcity in Rare Diseases: Dynamic Bayesian Networks and Transfer Learning to Develop Prognostic Models of Amyotrophic Lateral SclerosisEnrico Longato, Erica Tavazzi, Adriano Chiò, Gabriele Mora, Giovanni Sparacino, Barbara Di Camillo. aime 2023: 140-150 [doi]
- Baseline Machine Learning Approaches To Predict Amyotrophic Lateral Sclerosis Disease ProgressionIsotta Trescato, Alessandro Guazzo, Enrico Longato, Enidia Hazizaj, Chiara Roversi, Erica Tavazzi, Martina Vettoretti, Barbara Di Camillo. clef 2022: 1277-1293 [doi]
- Leveraging process mining for modeling progression trajectories in amyotrophic lateral sclerosisErica Tavazzi, Roberto Gatta, Mauro Vallati, Stefano Cotti Piccinelli, Massimiliano Filosto, Alessandro Padovani, Maurizio Castellano, Barbara Di Camillo. midm, 22-S(6):346, November 2022. [doi]
- Deep convolutional neural network for survival estimation of Amyotrophic Lateral Sclerosis patientsEnrico Grisan, Alessandro Zandonà, Barbara Di Camillo. esann 2019: [doi]