  Inpatient length of stay (LoS) is an important managerial metric which if
known in advance can be used to efficiently plan admissions, allocate resources
and improve care. Using historical patient data and machine learning
techniques, LoS prediction models can be developed. Ethically, these models can
not be used for patient discharge in lieu of unit heads but are of utmost
necessity for hospital management systems in charge of effective hospital
planning. Therefore, the design of the prediction system should be adapted to
work in a true hospital setting. In this study, we predict early hospital LoS
at the granular level of admission units by applying domain adaptation to
leverage information learned from a potential source domain. Time-varying data
from 110,079 and 60,492 patient stays to 8 and 9 intensive care units were
respectively extracted from eICU-CRD and MIMIC-IV. These were fed into a
Long-Short Term Memory and a Fully connected network to train a source domain
model, the weights of which were transferred either partially or fully to
initiate training in target domains. Shapley Additive exPlanations (SHAP)
algorithms were used to study the effect of weight transfer on model
explanability. Compared to the benchmark, the proposed weight transfer model
showed statistically significant gains in prediction accuracy (between 1% and
5%) as well as computation time (up to 2hrs) for some target domains. The
proposed method thus provides an adapted clinical decision support system for
hospital management that can ease processes of data access via ethical
committee, computation infrastructures and time.
