ACHIM: Adaptive Clinical Latent Hierarchy Construction and Information Fusion Model for Healthcare Knowledge Representation

Utilize electronic health records (EHR) to forecast the likelihood of a patient succumbing under the current clinical condition. This assists healthcare professionals in identifying clinical emergencies promptly, enabling timely intervention to alter the patient's critical state. Existing healthcare prediction models are typically based on clinical features of EHR data to learn a patient's clinical representation, but they frequently disregard structural information in features. To address this issue, we propose Adaptive Clinical latent Hierarchy construction and Information fusion Model (ACHIM), which adaptively constructs a clinical potential level without prior knowledge and aggregates the structural information from the learned into the original data to obtain a compact and informative representation of the human state. Our experimental results on real-world datasets demonstrate that our model can extract fine-grained representations of patient characteristics from sparse data and significantly improve the performance of death prediction tasks performed on EHR datasets.