Adaptative Clinical Decision Support System using Machine Learning and Authoring Tools

Jon Kerexeta, Jordi Torres, Naiara Muro, Naiara Muro, Naiara Muro, Kristin Rebescher, Nekane Larburu, Nekane Larburu

2020

Abstract

Clinical Decision Support Systems (CDSS) offer the potential to improve quality of clinical care and patients’ outcomes while reducing medical errors and economic costs. The development of these systems results difficult since (i) generating the knowledge base that CDSS use to evaluate clinical data requires technical and clinical knowledge, and (ii) usually the reasoning process of CDSS is difficult to understand for clinicians leading to a low adherence to the recommendations provided by these systems. Hereafter, to address these issues, we propose a web-based platform, named Knowledge Generation Tool (KGT), which (i) enables clinicians to take an active role in the creation of the CDSSs in a simple way, and (ii) clinicians’ involvement can turn in an improvement of the model predictor capabilities, while their comprehension of the reasoning process of the CDSS is increased. The KGT consist on three main modules: DT building, which implements machine learning methods to extract automatically decision trees (DTs) from clinical data frames; an authoring tool (AT), which enables the clinicians to modify the DT with their expert knowledge, and the DT testing, which allows to test any DT, being able to test objectively any modification made by clinician’s expert knowledge.

Paper Citation

in Harvard Style

Kerexeta J., Torres J., Muro N., Rebescher K. and Larburu N. (2020). Adaptative Clinical Decision Support System using Machine Learning and Authoring Tools. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF; ISBN 978-989-758-398-8, SciTePress, pages 95-105. DOI: 10.5220/0008952200950105

in Bibtex Style

@conference{healthinf20,
author={Jon Kerexeta and Jordi Torres and Naiara Muro and Kristin Rebescher and Nekane Larburu},
title={Adaptative Clinical Decision Support System using Machine Learning and Authoring Tools},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF},
year={2020},
pages={95-105},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008952200950105},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF
TI - Adaptative Clinical Decision Support System using Machine Learning and Authoring Tools
SN - 978-989-758-398-8
AU - Kerexeta J.
AU - Torres J.
AU - Muro N.
AU - Rebescher K.
AU - Larburu N.
PY - 2020
SP - 95
EP - 105
DO - 10.5220/0008952200950105
PB - SciTePress