Discovering hospital admission patterns using models learnt from electronic hospital records.

Abstract

Electronic medical records, nowadays routinely collected in many developed countries, open a new avenue for medical knowledge acquisition. In this article, this vast amount of information is used to develop a novel model for hospital admission type prediction. I introduce a novel model for hospital admission-type prediction based on the representation of a patient's medical history in the form of a binary history vector. This representation is motivated using empirical evidence from previous work and validated using a large data corpus of medical records from a local hospital. The proposed model allows exploration, visualization and patient-specific prognosis making in an intuitive and readily understood manner. Its power is demonstrated using a large, real-world data corpus collected by a local hospital on which it is shown to outperform previous state-of-the-art in the literature, achieving over 82% accuracy in the prediction of the first future diagnosis. The model was vastly superior for long-term prognosis as well, outperforming previous work in 82% of the cases, while producing comparable performance in the remaining 18% of the cases. Full Matlab source code is freely available for download at: http://ognjen-arandjelovic.t15.org/data/dprog.zip.