Multitask learning (MTL) using electronic health records allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however, it often suffers from negative transfer - impaired learning if tasks are not appropriately selected. We introduce a sequential subnetwork routing (SeqSNR) architecture that uses soft parameter sharing to find related tasks and encourage cross-learning between them.
Author(s): Roy, Subhrajit, Mincu, Diana, Loreaux, Eric, Mottram, Anne, Protsyuk, Ivan, Harris, Natalie, Xue, Yuan, Schrouff, Jessica, Montgomery, Hugh, Connell, Alistair, Tomasev, Nenad, Karthikesalingam, Alan, Seneviratne, Martin
DOI: 10.1093/jamia/ocab101