Interpretability and Personalization Aspects in the Development of Clinical Risk Assessment Models
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-49062-0_62
Reference9 articles.
1. Valente, F., Henriques, J., Paredes, S., et al.: A new approach for interpretability and reliability in clinical risk prediction: Acute coronary syndrome scenario, Artificial Intelligence in Medicine 7 (2021). https://doi.org/10.1016/j.artmed.2021.102113
2. Linardatos, P., et al.: Explainable AI: A review of machine learning interpretability methods. Entropy 23, 1 (2020). https://doi.org/10.3390/e23010018
3. Abedin, B.: Managing the tension between opposing effects of explainability of artificial intelligence: a contingency theory perspective. Internet Res. 3(32), 425–453 (2022). https://doi.org/10.1108/INTR-05-2020-0300
4. WHO. Cardiovascular diseases (CVDs) 2022. https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). Last accessed February 2022
5. Araújo, P., et al.: TIMI, PURSUIT, and GRACE risk scores: sustained prognostic value and interaction with revascularization in NSTE-ACS. Eur. Heart J. 26, 865–872 (2005). https://doi.org/10.1093/eurheartj/ehi187
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