Patient Discharge Classification Using Machine Learning Techniques

Author:

Gramaje Anthony,Thabtah Fadi,Abdelhamid Neda,Ray Sayan Kumar

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Statistics, Probability and Uncertainty,Computer Science Applications,Business, Management and Accounting (miscellaneous)

Reference30 articles.

1. Abdelhamid N, Ayesh A, Thabtah F (2013) Phishing detection using associative classification data mining. In: Proceedings of the ICAI’13—the 2013 international conference on artificial intelligence, USA, pp 491–499

2. Abdelhamid N, Ayesh A, Thabtah F (2012) An experimental study of three different rule ranking formulas in associative classification mining. In: Proceedings of the 7th IEEE international conference for internet technology and secured transactions (ICITST-2012), UK, pp 795–800

3. Baek H, Cho M, Kim S, Hwang H, Song M, Yoo S (2018) Analysis of length of hospital stay using electronic health records: a statistical and data mining approach. PLoS ONE 13(4):1–20

4. Barnes S, Hamrock E, Toerper M, Siddiqui S, Levin S (2015) Real-time prediction of inpatient length of stay for discharge prioritization. J Am Med Inform Assoc 23(e1):e2–e10. https://doi.org/10.1093/jamia/ocv106

5. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

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