Learning Diagnosis from Electronic Health Records

Ioana Barbantan, Rodica Potolea

2016

Abstract

In the attempt to build a complete solution for a medical assistive decision support system we proposed a complex flow that integrates a sequence of modules which target the different data engineering tasks. This solution can analyse any type of unstructured medical documents which are processed by applying specific NLP steps followed by semantic analysis which leads to the medical concepts identification, thus imposing a structure on the input documents. The data collection, document pre-processing, concept extraction, and correlation are modules that have been researched by us in our previous works and for which we proposed original solutions. Using the collected and structured representation of the medical records, informed decisions regarding the health status of the patients can be made. The current paper focuses on the prediction module that joins all the components in a logical flow and is completed with the suggested diagnosis classification for the patient. The accuracy rate of 81.25%, obtained on the medical documents supports the strength of our proposed strategy.

Paper Citation

in Harvard Style

Barbantan I. and Potolea R. (2016). Learning Diagnosis from Electronic Health Records . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 344-351. DOI: 10.5220/0006069503440351

in Bibtex Style

@conference{kdir16,
author={Ioana Barbantan and Rodica Potolea},
title={Learning Diagnosis from Electronic Health Records},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},
year={2016},
pages={344-351},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006069503440351},
isbn={978-989-758-203-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - Learning Diagnosis from Electronic Health Records
SN - 978-989-758-203-5
AU - Barbantan I.
AU - Potolea R.
PY - 2016
SP - 344
EP - 351
DO - 10.5220/0006069503440351