  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.
