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AID4HAI: Automatic Idea Detection for Healthcare-Associated Infections from Twitter, A Framework based on Active Learning and Transfer Learning
Halmstad University, School of Information Technology. Stockholm University, Stockholm, Sweden.ORCID iD: 0000-0002-8430-1606
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0003-2590-6661
Halmstad University, School of Business, Innovation and Sustainability.ORCID iD: 0000-0003-1390-1820
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-0051-0954
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2023 (English)In: Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings / [ed] Crémilleux, B.; Hess, S.; Nijssen, S., Cham: Springer, 2023, Vol. 13876, p. 195-207Conference paper, Published paper (Refereed)
Abstract [en]

This research is an interdisciplinary work between data scientists, innovation management researchers, and experts from a Swedish hygiene and health company. Based on this collaboration, we have developed a novel package for automatic idea detection to control and prevent healthcare-associated infections (HAI). The principal idea of this study is to use machine learning methods to extract informative ideas from social media to assist healthcare professionals in reducing the rate of HAI. Therefore, the proposed package offers a corpus of data collected from Twitter, associated expert-created labels, and software implementation of an annotation framework based on the Active Learning paradigm. We employed Transfer Learning and built a two-step deep neural network model that incrementally extracts the semantic representation of the collected text data using the BERTweet language model in the first step and classifies these representations as informative or non-informative using a multi-layer perception (MLP) in the second step. The package is AID4HAI (Automatic Idea Detection for controlling and preventing Healthcare-Associated Infections) and is made fully available (software code and the collected data) through a public GitHub repository (https://github.com/XaraKar/AID4HAI). We believe that sharing our ideas and releasing these ready-to-use tools contributes to the development of the field and inspires future research.

Place, publisher, year, edition, pages
Cham: Springer, 2023. Vol. 13876, p. 195-207
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13876
Keywords [en]
automatic idea detection, healthcare-associated infections, human-in-the-loop, active learning, feedback loops, supervised machine learning, natural language processing
National Category
Computer Systems Health Care Service and Management, Health Policy and Services and Health Economy
Research subject
Health Innovation, Information driven care
Identifiers
URN: urn:nbn:se:hh:diva-50007DOI: 10.1007/978-3-031-30047-9_16ISI: 000999877600016Scopus ID: 2-s2.0-85152539906ISBN: 978-3-031-30046-2 (print)ISBN: 978-3-031-30047-9 (electronic)OAI: oai:DiVA.org:hh-50007DiVA, id: diva2:1738537
Conference
Symposium on Intelligent Data Analysis (IDA 2023), Louvain-la-Neuve, Belgium, 12-14 April, 2023
Projects
AID project
Funder
Knowledge Foundation, 220023Vinnova
Note

Funding: KK-Foundation, Scania CV AB and the Vinnova program for Strategic Vehicle Research and Innovation (FFI).

Available from: 2023-02-22 Created: 2023-02-22 Last updated: 2023-08-11Bibliographically approved

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Rahat, MahmoudGama, FábioSheikholharam Mashhadi, PeymanNowaczyk, Sławomir

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Kharazian, ZahraRahat, MahmoudGama, FábioSheikholharam Mashhadi, PeymanNowaczyk, SławomirMagnússon, Sindri
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