Contextualized biomedical language processing enhances ICU survival prediction
Rui Chen, Yu Cai, Sitong Zhang, Zirong Huo, Mingming Song, Wenqing Li, Dongyan Yang, Seungyong Hwang, Ling Bai, Fuxin Han, Xi Zhang · iScience · 2025
= 58,615) during external validation. Excluding text features or replacing free-text ICD entries with coded formats reduced performance (AUROC from 0.983 to 0.946-0.947), highlighting the importance of contextual embeddings. As a secondary task, cerebrospinal fluid culture prediction achieved AUROC = 0.853. Overall, integrating contextualized biomedical language representations significantly improves multimodal learning and ICU survival prediction.