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Authors: Veena Bansal 1 ; Abhishek Poddar 2 and R. Ghosh-Roy 3

Affiliations: 1 Indian Institute of Technology Bhilai, India ; 2 Indian Institute of Technology Kanpur, India ; 3 IBM UK Limited, United Kingdom

ISBN: 978-989-758-298-1

Keyword(s): Healthcare, Big Data, Unstructured Data, Tertiary Healthcare.

Related Ontology Subjects/Areas/Topics: Accessibility and Usability ; Adaptive and Adaptable User Interfaces ; Enterprise Information Systems ; HCI on Enterprise Information Systems ; Human-Computer Interaction ; Interaction Techniques and Devices

Abstract: Health is an individual’s most precious asset and healthcare is one of the vehicles for preserving it. The Indian government’s spend on healthcare system is relatively low (1.2% of GDP). Consequently, Secondary and Tertiary government healthcare centers in India (that are presumed to be of above average ratings) are always rowded. In Tertiary healthcare centers, like AIIMS, patients are often unable to articulate correctly their problems to the healthcare center’s Reception staff for these patients to be directed to the correct healthcare department. In this paper, we propose a system based on Big Data and Machine Learning to direct the patient to the most relevant department .We have implemented and tested parts of this system wherein a patient enters his symptoms and/or provisional diagnosis; the system suggests a department based on this user input. Our system suggests the correct department 68.05% of the time. Our system presently makes its suggestions using gradient boosting algo rithm that has been trained using two information repositories- symptoms and disease data, functional description of each medical department. It is our informed assumption that, once we have incorporated medicine information and diagnostics imaging data to train the system and the complete medical history of the patient, performance of the system will improve significantly. (More)

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Paper citation in several formats:
Bansal, V.; Poddar, A. and Ghosh-Roy, R. (2018). Identifying a Medical Department based on Unstructured Data - A Big Data Application in Healthcare.In Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-298-1, pages 475-482. DOI: 10.5220/0006773904750482

@conference{iceis18,
author={Veena Bansal. and Abhishek Poddar. and R. Ghosh{-}Roy.},
title={Identifying a Medical Department based on Unstructured Data - A Big Data Application in Healthcare},
booktitle={Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2018},
pages={475-482},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006773904750482},
isbn={978-989-758-298-1},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Identifying a Medical Department based on Unstructured Data - A Big Data Application in Healthcare
SN - 978-989-758-298-1
AU - Bansal, V.
AU - Poddar, A.
AU - Ghosh-Roy, R.
PY - 2018
SP - 475
EP - 482
DO - 10.5220/0006773904750482

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