Abstract
This research is an interdisciplinary work between data scientists, innovation management researchers and experts from Swedish academia and a hygiene and health company. Based on this collaboration, we have developed a novel package for automatic idea detection with the motivation of controlling and preventing 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 called 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.
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References
Barata, R., Leite, M., Pacheco, R., Sampaio, M.O., Ascensão, J.T., Bizarro, P.: Active learning for imbalanced data under cold start. In: Proceedings of the Second ACM International Conference on AI in Finance, pp. 1–9 (2021)
Chen, Y., Mani, S.: Active learning for unbalanced data in the challenge with multiple models and biasing. In: Active Learning and Experimental Design Workshop in Conjunction with AISTATS 2010, pp. 113–126. JMLR Workshop and Conference Proceedings (2011)
Christensen, K., et al.: Mining online community data: the nature of ideas in online communities. Food Qual. Prefer. 62, 246–256 (2017)
Christensen, K., et al.: How good are ideas identified by an automatic idea detection system? Creativity Innov. Manage. 27(1), 23–31 (2018)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Gissin, D., Shalev-Shwartz, S.: Discriminative active learning. arXiv preprint arXiv:1907.06347 (2019)
Goff, D.A., Kullar, R., Newland, J.G.: Review of twitter for infectious diseases clinicians: useful or a waste of time? Clin. Infect. Dis. 60(10), 1533–1540 (2015)
Gould, D., Lindström, H., Purssell, E., Wigglesworth, N.: Electronic hand hygiene monitoring: accuracy, impact on the Hawthorne effect and efficiency. J. Infect. Prev. 21(4), 136–143 (2020)
Guo, L., Rohde, J.A., Wu, H.D.: Who is responsible for Twitter’s echo chamber problem? evidence from 2016 US election networks. Inf. Commun. Soc. 23(2), 234–251 (2020)
Irgang, L., Holmén, M., Gama, F., Svedberg, P.: Facilitation activities for change response: a qualitative study on infection prevention and control professionals during a pandemic in Brazil. J. Health Organ. Manage. 35, 886–903 (2021)
Kesselheim, A.S., Xu, S., Avorn, J.: Clinicians’ contributions to the development of coronary artery stents: a qualitative study of transformative device innovation. PLoS ONE 9(2), e88664 (2014)
Kharazian, Z., Rahat, M., Fatemizadeh, E., Nasrabadi, A.M.: Increasing safety at smart elderly homes by human fall detection from video using transfer learning approaches. In: 30th European Safety and Reliability Conference (ESREL2020) & 15th Probabilistic Safety Assessment and Management Conference (PSAM15), Venice, Italy, 1–5 November 2020 (2020)
Lewis, D.D.: A sequential algorithm for training text classifiers: corrigendum and additional data. In: ACM Sigir Forum, vol. 29, pp. 13–19. ACM New York, NY, USA (1995)
Lotfinejad, N., Peters, A., Tartari, E., Fankhauser-Rodriguez, C., Pires, D., Pittet, D.: Hand hygiene in health care: 20 years of ongoing advances and perspectives. Lancet. Infect. Dis 21(8), e209–e221 (2021)
Malte, A., Ratadiya, P.: Evolution of transfer learning in natural language processing. arXiv preprint arXiv:1910.07370 (2019)
Martischang, R., et al.: Enhancing engagement beyond the conference walls: analysis of twitter use at# icpic2019 infection prevention and control conference. Antimicrob. Resist. Infect. Control 10(1), 1–10 (2021)
Nguyen, D.Q., Vu, T., Nguyen, A.T.: Bertweet: a pre-trained language model for English tweets. arXiv preprint arXiv:2005.10200 (2020)
Rahat, M., Mashhadi, P.S., Nowaczyk, S., Rognvaldsson, T., Taheri, A., Abbasi, A.: Domain adaptation in predicting turbocharger failures using vehicle’s sensor measurements. In: PHM Society European Conference, vol. 7, pp. 432–439 (2022)
Settles, B.: Active learning literature survey (2009)
Smith, S.W., Sfekas, A.: How much do physician-entrepreneurs contribute to new medical devices? Med. Care 51, 461–467 (2013)
Thune, T., Mina, A.: Hospitals as innovators in the health-care system: a literature review and research agenda. Res. Policy 45(8), 1545–1557 (2016)
Weigel, T., Goffin, K.: Creating innovation capabilities: mölnlycke health care’s journey. Res. Technol. Manage. 58(4), 28–35 (2015)
Acknowledgment
The work was supported by research grants from KK-Foundation, Scania CV AB and the Vinnova program for Strategic Vehicle Research and Innovation (FFI).
We would like to express our utmost gratitude to Håkan Lindström and Peter Blomström and their team for their valuable help and professional support during this project. Also, we would like to thank our annotation team for helping us to provide this labeled data.
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Kharazian, Z. et al. (2023). AID4HAI: Automatic Idea Detection for Healthcare-Associated Infections from Twitter, a Framework Based on Active Learning and Transfer Learning. In: Crémilleux, B., Hess, S., Nijssen, S. (eds) Advances in Intelligent Data Analysis XXI. IDA 2023. Lecture Notes in Computer Science, vol 13876. Springer, Cham. https://doi.org/10.1007/978-3-031-30047-9_16
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