  Despite the remarkable progress in the development of predictive models for
healthcare, applying these algorithms on a large scale has been challenging.
Algorithms trained on a particular task, based on specific data formats
available in a set of medical records, tend to not generalize well to other
tasks or databases in which the data fields may differ. To address this
challenge, we propose General Healthcare Predictive Framework (GenHPF), which
is applicable to any EHR with minimal preprocessing for multiple prediction
tasks. GenHPF resolves heterogeneity in medical codes and schemas by converting
EHRs into a hierarchical textual representation while incorporating as many
features as possible. To evaluate the efficacy of GenHPF, we conduct multi-task
learning experiments with single-source and multi-source settings, on three
publicly available EHR datasets with different schemas for 12 clinically
meaningful prediction tasks. Our framework significantly outperforms baseline
models that utilize domain knowledge in multi-source learning, improving
average AUROC by 1.2%P in pooled learning and 2.6%P in transfer learning while
also showing comparable results when trained on a single EHR dataset.
Furthermore, we demonstrate that self-supervised pretraining using multi-source
datasets is effective when combined with GenHPF, resulting in a 0.6%P AUROC
improvement compared to models without pretraining. By eliminating the need for
preprocessing and feature engineering, we believe that this work offers a solid
framework for multi-task and multi-source learning that can be leveraged to
speed up the scaling and usage of predictive algorithms in healthcare.
