• Predicting disease severity in multiple sclerosis using multimodal data and machine learning 

      Andorra, Magi; Freire, Ana; Zubizarreta, Irati; de Rosbo, Nicole Kerlero; Bos, Steffan Daniel; Rinas, Melanie; Høgestøl, Einar August; de Rodez Benavent, Sigrid Aune; Berge, Tone; Brune, Synne; Ivaldi, Federico; Cellerino, Maria; Pardini, Matteo; Vila, Gemma; Pulido-Valdeolivas, Irene; Martinez-Lapiscina, Elena H.; Llufriu, Sara; Saiz, Albert; Blanco, Yolanda; Martinez-Heras, Eloy; Solana, Elisabeth; Bäcker-Koduah, Priscilla; Behrens, Janina; Kuchling, Joseph; Asseyer, Susanna; Scheel, Michael; Chien, Claudia; Zimmermann, Hanna; Motamedi, Seyedamirhosein; Kauer-Bonin, Josef; Brandt, Alex; Saez-Rodriguez, Julio; Alexopoulos, Leonidas G.; Paul, Friedemann; Harbo, Hanne-Cathrin Flinstad; Shams, Hengameh; Oksenberg, Jorge; Uccelli, Antonio; Baeza-Yates, Ricardo; Villoslada, Pablo (Peer reviewed; Journal article, 2023)
      Background Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity. Methods We have analysed a prospective ...