  Existing Clinical Decision Support Systems (CDSSs) largely depend on the
availability of structured patient data and Electronic Health Records (EHRs) to
aid caregivers. However, in case of hospitals in developing countries,
structured patient data formats are not widely adopted, where medical
professionals still rely on clinical notes in the form of unstructured text.
Such unstructured clinical notes recorded by medical personnel can also be a
potential source of rich patient-specific information which can be leveraged to
build CDSSs, even for hospitals in developing countries. If such unstructured
clinical text can be used, the manual and time-consuming process of EHR
generation will no longer be required, with huge person-hours and cost savings.
In this paper, we propose a generic ICD9 disease group prediction CDSS built on
unstructured physician notes modeled using hybrid word embeddings. These word
embeddings are used to train a deep neural network for effectively predicting
ICD9 disease groups. Experimental evaluation showed that the proposed approach
outperformed the state-of-the-art disease group prediction model built on
structured EHRs by 15% in terms of AUROC and 40% in terms of AUPRC, thus
proving our hypothesis and eliminating dependency on availability of structured
patient data.
