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Date of Award
Industrial and Manufacturing Engineering
Heterogeneous data including patient demographics, clinical notes, vitals, lab test
results, and visit metadata accumulate in the Electronic Health Record (EHR) during the
course of a patient encounter. Having advanced notification of a patient’s care pathway
can facilitate patient flow leading to increased patient satisfaction, more effective resource
utilization, and lower operating cost. The objective of this study is to use EHR data to
predict emergency department (ED) disposition decision.
The role of distributed representation, including pre-trained word embeddings and
embeddings learned jointly with the network parameters during training, are identified as
critical components to successful deep learning models. Based on empirical results a
prototype clinician interface for interpreting prediction results is developed and pipelines
for dynamically predicting ED disposition decision throughout the course of the patient
encounter in real time are detailed. The data processing pipelines for heterogeneous
models are contrasted between the areas of traditional classification methods vs. deep
neural network-based classification.
Vucenovic, Alexander, "Deep Learning Methods For Prediction Of Emergency Department Disposition Decision" (2020). Wayne State University Theses. 782.