FedIO: Bridge Inner- and Outer-hospital Information for Perioperative Complications Prognostic Prediction via Federated Learning

Abstract

Perioperative complications are associated with increased patient morbidity and mortality, and result in substantial healthcare resource utilization. With the aim to facilitate medical decision-making and improve health outcomes, machine learning methods are used to train prediction models to inform healthcare professionals and patients about the risks, which require both inner- and outer-hospital information, e.g., daily performance and clinical tests. For sake of data security and privacy, the Hospital Information System (HIS) is usually isolated from the public Internet and the raw patient samples are forbidden to transfer directly, which limits the integration of inner-and outer-hospital information. In this paper, we propose a learning framework named FedIO which bridges Inner- and Outer-hospital information via vertical Federated Learning for perioperative complications prognostic prediction. Instead of transmitting data into one cloud center, FedIO leverages the locally kept data to train a prediction model, during which only the intermediate parameters are transmitted and integrated. Extensive experiments are conducted on real-world datasets and the results manifest that combining inner- and outer-hospital knowledge is better than either of them, and FedIO shows the same-level performance as the cloud-based methods but without sharing raw data.

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