Discovering hospital admission patterns using models learnt from electronic hospital records

Ognjen Arandjelovic*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

Motivation: 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. Results: 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. Availability and implementation: Full Matlab source code is freely available for download at: http://ognjen-arandjelovic.t15.org/data/dprog.zip.
Original languageEnglish
Pages (from-to)3970-3976
Number of pages7
JournalBioinformatics
Volume31
Issue number24
Early online date3 Sept 2015
DOIs
Publication statusPublished - 15 Dec 2015

Fingerprint

Dive into the research topics of 'Discovering hospital admission patterns using models learnt from electronic hospital records'. Together they form a unique fingerprint.

Cite this