Multi-Modal Traumatic Brain Injury Prognosis via Structure-Aware Field-Wise Learning

Traumatic brain injury (TBI) remains a growing significant public health problem and prognosis of outcome is difficult due to the multitude of factors that underlie the heterogeneity of TBI. Prognosis aims to forecast the likely development of the disease and significantly affects patient's recovery and healthcare. Traditionally, TBI prognosis relies on the physician's insights and their empirical knowledge which makes it infeasible for large-scale implementation. Existing methods utilize a single modality (i.e., either clinical data or Computed Tomography scan images) for TBI prognosis, leaving crucial information from multi-modal data largely underexplored. To address this concern, we explore a Multi-modal Structure-aware Field-wise learning (MSF) method that is capable of mining complex correlations between multi-modal data and TBI outcomes for prognosis on a real-world dataset. Specifically, we develop a High-Level Structure-Aware (HSA) module to capture the structure information of the multilayered clinical data. Experimental results on the publicly available TRACK-TBI dataset demonstrate the viability and effectiveness of our proposed method, by achieving the top-3 accuracy of 96.07% and 98.13% for 3-month and 6-month predictions after injury, respectively.