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Next-Day Medical Activities Recommendation Model with Double Attention Mechanism Using Generative Adversarial Network
Open accessAbstract
Medical activities recommendation is a key aspect of an intelligent healthcare system, which can assist doctors with little clinical experience in clinical decision making. Medical activities recommendation can be seen as a kind of temporal set prediction. Previous studies about them are based on Recurrent Neural Network (RNN), which does not incorporate personalized medical history or differentiate between the impact of medical activities. To address the above-given issues, this paper proposes a Next-Day Medical Activities Recommendation (NDMARec) model. Specifically, our model firstly proposes an inpatient day embedding method based on soft-attention which balances the impact of different medical activities to get a joint representation of medical activities that occurred within the same day. Then, a fusion module is designed to combine features of inpatient day and medical history to achieve personalization. These features are learned by the self-attention mechanism that solves the long-term dependency problem of RNNs. Last, adversarial training is introduced to improve the generalization ability of our model. Extensive experiments on a real dataset from a hospital are conducted to show that NDMARec outperformed both classical and state-of-the-art methods.
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“Synthetic data generation using generative models can create realistic, diverse data sets that mimic real-world multimodal medical scenarios, thus expanding the training data set without compromising privacy or ethical standards [149-151].”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [150]
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Cited by
Sorted by citations
Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook.
AlSaad, R., Abd-alrazaq, A., Boughorbel, S., Ahmed, A., Renault, M.-A., Damseh, R., & Sheikh, J. (2024) · Journal of medical Internet research · doi:10.2196/59505 · Cited by 349
Metadata
- Authors
- Li, W., Yang, J., & Min, X.
- Affiliations
- Northeastern University (CN)
- Corresponding author
- Li, Wei
- Venue
- Journal of Healthcare Engineering
- Publisher
- Wiley
- Published
- 2022-11-07
- Volume
- 2022
- Pages
- 1-14
- ISSN
- 2040-2295 (ISSN-L)
- 2040-2309
- PMC
- PMC9663225
- OpenAlex
- W4308393531
- Cited by
- 2
- References
- 36
- Citations by year
- 2026: 1 · 2024: 1
- FWCI
- 0.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Healthcare Engineering (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Basic Research Program of China (2021YFC2701003, N2016006)
- Fundamental Research Funds for the Central Universities (2021YFC2701003, N2016006)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- soft attention, self-attention, personalized recommendation, generative adversarial networks, adversarial training, clinical decision support, hospital data
- MeSH terms
- Computer Simulation, Delivery of Health Care, Humans, Neural Networks, Computer
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