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Inpatient length of stay (LoS) is an important managerial metric which ifknown in advance can be used to efficiently plan admissions, allocate resourcesand improve care. Using historical patient data and machine learningtechniques, LoS prediction models can be developed. Ethically, these models cannot be used for patient discharge in lieu of unit heads but are of utmostnecessity for hospital management systems in charge of effective hospitalplanning. Therefore, the design of the prediction system should be adapted towork in a true hospital setting. In this study, we predict early hospital LoSat the granular level of admission units by applying domain adaptation toleverage information learned from a potential source domain. Time-varying datafrom 110,079 and 60,492 patient stays to 8 and 9 intensive care units wererespectively extracted from eICU-CRD and MIMIC-IV. These were fed into aLong-Short Term Memory and a Fully connected network to train a source domainmodel, the weights of which were transferred either partially or fully toinitiate training in target domains. Shapley Additive exPlanations (SHAP)algorithms were used to study the effect of weight transfer on modelexplanability. Compared to the benchmark, the proposed weight transfer modelshowed statistically significant gains in prediction accuracy (between 1% and5%) as well as computation time (up to 2hrs) for some target domains. Theproposed method thus provides an adapted clinical decision support system forhospital management that can ease processes of data access via ethicalcommittee, computation infrastructures and time.

Length of Stay prediction for Hospital Management using Domain Adaptation