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1. (Co-Author)Provost, S. with Tighe, D., Ho, M., Puglia, F., McMahon, J., 2023. Risk Adjusted Cumulative Sum chart methodology to monitor of free flap failure rates in the QOMS national audit. British Journal of Oral and Maxillofacial Surgery VOLUME 61, ISSUE 10, E5.

2. (Co-Author)Provost, S. with Tighe, D., Sasson, I., Ho, M., 2023. Technical appendix-Validating risk-adjustment models used in QOMS. British Journal of Oral and Maxillofacial Surgery (QOMS).

3. (Co-Author)Provost, S. with Tighe, D., McMahon, J., Schilling, C., Ho, M., and Freitas, A., 2022. Machine learning methods applied to risk adjustment of cumulative sum chart methodology to audit free flap outcomes after head and neck surgery. British Journal of Oral and Maxillofacial Surgery, 60(10), pp.1353-1361.

4. (Co-Author)Provost, S. with Tighe, D., Tekeli, K., Gouk, T., Smith, J., Ho, M., Moody, A., and Walsh, S., Freitas, A., 2023. Machine learning methods applied to audit of surgical margins after curative surgery for facial (non-melanoma) skin cancer. British Journal of Oral and Maxillofacial Surgery, 61(1), pp.94-100.

Medium Blogs

1. Provost, S., Introduction to Automated Machine Learning with Auto-Sklearn ⚙️.

2. Provost, S., Diversity and a novel Deep Learning model called Deep Forest: Applying an Important Concept to a Promising Framework.

3. Provost, S., Classification of Sleep-Wake states with the use of a novel Deep-Learning approach.

4. Provost, S., Michele, L., Classification of Sleep-Wake states with the use of a novel Deep-Learning approach.

5. Provost, S., Michele, L., Computer Science Students take a step in the sleep-medicine field with Actigraphy.

6. More can be seen here.