{"type":"ScholarlyArticle","title":"Multi-TA: Multilevel Temporal Augmentation for Robust Septic Shock Early Prediction","authors":[{"name":"Hyunwoo Sohn","slug":"hyunwoo-sohn","given":"Hyunwoo","family":"Sohn","affiliation":"North Carolina State University"},{"name":"Kyungjin Park","slug":"kyungjin-park","given":"Kyungjin","family":"Park","affiliation":"Integral Ad Science"},{"name":"Baekkwan Park","slug":"baekkwan-park","given":"Baekkwan","family":"Park","affiliation":"University of Missouri"},{"name":"Min Chi","slug":"min-chi","given":"Min","family":"Chi","affiliation":"North Carolina State University"}],"abstract":"Early predicting the onset of a disease is critical to timely and accurate clinical decision-making, where a model determines whether a patient will develop the disease n hours later. While deep learning algorithms have demonstrated great success using multivariate irregular time-series data such as electronic health records (EHRs), they often lack temporal robustness due to data scarcity problems becoming more prominent at multilevel as n increases. At event-level, the decreasing number of available events per trajectory increases uncertainty in anticipating future disease behavior. At trajectory-level, the scarcity of patient trajectories limits diversity in the training population, hindering the model's generalization. This work introduces Multi-TA, a multilevel temporal augmentation framework that leverages BERT-based temporal EHRs representation learning and a unified data augmentation approach, effectively addressing data scarcity issues at both event and trajectory levels. Validated on two real-world EHRs for septic shock, Multi-TA outperforms mixup and GAN-based state-of-the-art models across eight prediction windows, demonstrating improved temporal robustness. Further, we provide in-depth analyses on data augmentation.","doi":"10.24963/ijcai.2024/667","url":"https://ijcai.org/proceedings/2024/667","pdf":"https://ijcai.org/proceedings/2024/0667.pdf","markdown":"https://ijcai.org/proceedings/2024/667.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/667","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":667,"pages":{"first":6035,"last":6043},"keywords":["Machine Learning: ML: Robustness","Natural Language Processing: NLP: Applications","Multidisciplinary Topics and Applications: MTA: Health and medicine","Machine Learning: ML: Time series and data streams"],"videos":[],"references":[{"position":1,"raw":"[Achenchabe et al., 2021] Y. 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