NICE: Neural in-hospital cost estimation from medical records

Abstract

Estimating in-hospital costs from medical records is an important task with many applications such as accountable care. Existing methods for this task usually rely on manual feature engineering which needs massive domain knowledge, and do not exploit the textual information in medical records, e.g., diagnosis and operation texts. In this paper, we propose a neural in-hospital cost estimation (NICE) approach to estimate the in-hospital costs of patients from their admission records. Our approach can exploit the heterogeneous information in records, such as patient features, diagnosis/operation texts, and the diagnosis/operation IDs, via a multi-view learning framework. In addition, since different words, diagnoses and operations have different importance for cost estimation, we propose a hierarchical attention network to select important words, diagnoses and operations for learning informative record representations. Extensive experiments on a real-world medical dataset validate the effectiveness of our approach.