{"type":"ScholarlyArticle","title":"MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement","authors":[{"name":"Zifeng Wang","slug":"zifeng-wang","given":"Zifeng","family":"Wang","affiliation":"University of Illinois Urbana-Champaign"},{"name":"Chufan Gao","slug":"chufan-gao","given":"Chufan","family":"Gao","affiliation":"University of Illinois Urbana-Champaign"},{"name":"Cao Xiao","slug":"cao-xiao","given":"Cao","family":"Xiao","affiliation":"GE Healthcare"},{"name":"Jimeng Sun","slug":"jimeng-sun","given":"Jimeng","family":"Sun","affiliation":"University of Illinois Urbana-Champaign; Carle Illinois College of Medicine , University of Illinois Urbana-Champaign"}],"abstract":"Tabular data prediction has been employed in medical applications such as patient health risk prediction. However, existing methods usually revolve around the algorithm design while overlooking the significance of data engineering. Medical tabular datasets frequently exhibit significant heterogeneity across different sources, with limited sample sizes per source. As such, previous predictors are often trained on manually curated small datasets that struggle to generalize across different tabular datasets during inference. This paper proposes to scale medical tabular data predictors (MediTab) to various tabular inputs with varying features. The method uses a data engine that leverages large language models (LLMs) to consolidate tabular samples to overcome the barrier across tables with distinct schema. It also aligns out-domain data with the target task using a \"learn, annotate, and refinement'' pipeline. The expanded training data then enables the pre-trained MediTab to infer for arbitrary tabular input in the domain without fine-tuning, resulting in significant improvements over supervised baselines: it reaches an average ranking of 1.57 and 1.00 on 7 patient outcome prediction datasets and 3 trial outcome prediction datasets, respectively. In addition, MediTab exhibits impressive zero-shot performances: it outperforms supervised XGBoost models by 8.9% and 17.2% on average in two prediction tasks, respectively.","doi":"10.24963/ijcai.2024/670","url":"https://ijcai.org/proceedings/2024/670","pdf":"https://ijcai.org/proceedings/2024/0670.pdf","markdown":"https://ijcai.org/proceedings/2024/670.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/670","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":670,"pages":{"first":6062,"last":6070},"keywords":["Multidisciplinary Topics and Applications: MTA: Bioinformatics","Multidisciplinary Topics and Applications: MTA: Life sciences","Multidisciplinary Topics and Applications: MTA: Health and medicine"],"videos":[],"references":[{"position":1,"raw":"[Arik and Pfister, 2021] Sercan Ö Arik and Tomas Pfister. 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