ArXiv Preprint
As hospitals move towards automating and integrating their computing systems,
more fine-grained hospital operations data are becoming available. These data
include hospital architectural drawings, logs of interactions between patients
and healthcare professionals, prescription data, procedures data, and data on
patient admission, discharge, and transfers. This has opened up many
fascinating avenues for healthcare-related prediction tasks for improving
patient care. However, in order to leverage off-the-shelf machine learning
software for these tasks, one needs to learn structured representations of
entities involved from heterogeneous, dynamic data streams. Here, we propose
DECENT, an auto-encoding heterogeneous co-evolving dynamic neural network, for
learning heterogeneous dynamic embeddings of patients, doctors, rooms, and
medications from diverse data streams. These embeddings capture similarities
among doctors, rooms, patients, and medications based on static attributes and
dynamic interactions. DECENT enables several applications in healthcare
prediction, such as predicting mortality risk and case severity of patients,
adverse events (e.g., transfer back into an intensive care unit), and future
healthcare-associated infections. The results of using the learned patient
embeddings in predictive modeling show that DECENT has a gain of up to 48.1% on
the mortality risk prediction task, 12.6% on the case severity prediction task,
6.4% on the medical intensive care unit transfer task, and 3.8% on the
Clostridioides difficile (C.diff) Infection (CDI) prediction task over the
state-of-the-art baselines. In addition, case studies on the learned doctor,
medication, and room embeddings show that our approach learns meaningful and
interpretable embeddings.
Hankyu Jang, Sulyun Lee, D. M. Hasibul Hasan, Philip M. Polgreen, Sriram V. Pemmaraju, Bijaya Adhikari
2023-03-21
