The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.
Recommender systems in the healthcare domain: state-of-the-art and research issues
Open accessAbstract
Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future.
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Cites
Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
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Content-based Recommender Systems: State of the Art and Trends
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Gurwitz, J. H., Field, T. S., Harrold, L. R., Rothschild, J., Debellis, K., Seger, A. C., Cadoret, C., Fish, L. S., Garber, L., Kelleher, M., et al. (2003) · JAMA · doi:10.1001/jama.289.9.1107 · Cited by 1,676
Evaluating Recommendation Systems
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Recommender Systems: An Introduction
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Cited by
Large language models in medical and healthcare fields: applications, advances, and challenges
Wang, D., & Zhang, S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10921-0 · Cited by 246
BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions
Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102285 · Cited by 125
Kim, M., Kim, S., Kim, J., Song, T.-J., & Kim, Y. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2023.103160 · Cited by 53
Get To The Point! Problem-Based Curated Data Views To Augment Care For Critically Ill Patients
Zhang, M., Ehrmann, D., Mazwi, M., Eytan, D., Ghassemi, M., & Chevalier, F. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501887 · Cited by 16
Metadata
- Authors
- Tran, T. N. T., Felfernig, A., Trattner, C., & Holzinger, A.
- Venue
- Journal of Intelligent Information Systems
- Published
- 2020-12-17
- Volume
- 57
- Issue
- 1
- Pages
- 171-201
- ISSN
- 0925-9902 (ISSN-L)
- 1573-7675
- OpenAlex
- W3111523098
- Cited by
- 303
- References
- 73
- FWCI
- 29.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Tran, T. N. T., Felfernig, A., Trattner, C., & Holzinger, A. (2021). Recommender systems in the healthcare domain: state-of-the-art and research issues. Journal of Intelligent Information Systems. https://doi.org/10.1007/s10844-020-00633-6
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
