  Availability of diagnostic codes in Electronic Health Records (EHRs) is
crucial for patient care as well as reimbursement purposes. However, entering
them in the EHR is tedious, and some clinical codes may be overlooked. Given an
in-complete list of clinical codes, we investigate the performance of ML
methods on predicting the complete ones, and assess the added predictive value
of including other clinical patient data in this task. We used the MIMIC-III
dataset and frame the task of completing the clinical codes as a recommendation
problem. We con-sider various autoencoder approaches plus two strong baselines;
item co-occurrence and Singular Value Decomposition (SVD). Inputs are 1) a
record's known clinical codes, 2) the codes plus variables. The
co-occurrence-based ap-proach performed slightly better (F1 score=0.26, Mean
Average Precision [MAP]=0.19) than the SVD (F1=0.24, MAP=0.18). However, the
adversarial autoencoder achieved the best performance when using the codes plus
variables (F1=0.32, MAP=0.25). Adversarial autoencoders performed best in terms
of F1 and were equal to vanilla and denoising autoencoders in term of MAP.
Using clinical variables in addition to the incomplete codes list, improves the
predictive performance of the models.
