Intelligible Models for HealthCare: Predicting the Probability of 6-Month Unfavorable Outcome in Patients with Ischemic Stroke-Reference-Cited by-同舟云学术

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Intelligible Models for HealthCare: Predicting the Probability of 6-Month Unfavorable Outcome in Patients with Ischemic Stroke

Published:2021-08-26 Issue: Volume: Page:
ISSN:1539-2791
Container-title:Neuroinformatics
language:en
Short-container-title:Neuroinform

Author:

Feng Xiaobing,Hua Yingrong,Zou Jianjun,Jia Shuopeng,Ji Jiatong,Xing Yan,Zhou Junshan,Liao Jun^ORCID

Funder

Double First Class University Plan

National Natural Science Foundation of China

Jiangsu Provincial Key Research and Development Program

Publisher

Springer Science and Business Media LLC

Subject

Information Systems,General Neuroscience,Software

Link

https://link.springer.com/content/pdf/10.1007/s12021-021-09535-6.pdf

Reference49 articles.

1. Basu Roy, S., Teredesai, A., Zolfaghar, K., Liu, R., Hazel, D., Newman, S., et al. (2015). Dynamic hierarchical classification for patient risk-of-readmission. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1691–1700).

2. Belard, A., Buchman, T., Forsberg, J., Potter, B. K., Dente, C. J., Kirk, A., & Elster, E. (2017). Precision diagnosis: A view of the clinical decision support systems (CDSS) landscape through the lens of critical care. Journal of Clinical Monitoring and Computing, 31(2), 261–271.

3. Blum, A. L., & Langley, P. (1997). Selection of relevant features and examples in machine learning. Artificial Intelligence, 97(1), 245–271. https://doi.org/10.1016/S0004-3702(97)00063-5.

4. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

5. Cappellari, M., Turcato, G., Forlivesi, S., Micheletti, N., Tomelleri, G., Bonetti, B., Merlino, G., Eleopra, R., Russo, M., L’Erario, R., Adami, A., Gentile, C., Gaudenzi, A., Bruno, S., & Bovi, P. (2018). Introduction of direct oral anticoagulant within 7 days of stroke onset: A nomogram to predict the probability of 3-month modified Rankin scale score > 2. Journal of Thrombosis and Thrombolysis, 46(3), 292–298.

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1. Machine learning is an effective method to predict the 3-month prognosis of patients with acute ischemic stroke;Frontiers in Neurology;2024-06-12

2. Systematic Review of Machine Learning Applied to the Secondary Prevention of Ischemic Stroke;Journal of Medical Systems;2024-01-02

3. Effects of heavy metal exposure on hypertension: A machine learning modeling approach;Chemosphere;2023-10

4. Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis;Frontiers in Neurology;2023-06-13

5. A Review of Interpretable ML in Healthcare: Taxonomy, Applications, Challenges, and Future Directions;Symmetry;2021-12-17

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