Artificial Intelligence in Surgical Risk Prediction
Author:
Affiliation:
1. Department of General Surgery, School of Medicine, University Hospital of Heraklion, University of Crete, 71500 Heraklion, Greece
2. Laboratory of Biostatistics, School of Medicine, University of Crete, 71003 Heraklion, Greece
Abstract
Publisher
MDPI AG
Subject
General Medicine
Link
https://www.mdpi.com/2077-0383/12/12/4016/pdf
Reference10 articles.
1. Steyerberg, E.W. (2019). Clinical Prediction Models, Springer International Publishing.
2. Cho, J., Park, J., Jeong, E., Shin, J., Ahn, S., Park, M.G., Park, R.W., and Park, Y. (2021). Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality. J. Pers. Med., 11.
3. Choi, B., Oh, A.R., Lee, S.-H., Lee, D.Y., Lee, J.-H., Yang, K., Kim, H.Y., Park, R.W., and Park, J. (2022). Prediction Model for 30-Day Mortality after Non-Cardiac Surgery Using Machine-Learning Techniques Based on Preoperative Evaluation of Electronic Medical Records. J. Clin. Med., 11.
4. Kinoshita, M., Ueda, D., Matsumoto, T., Shinkawa, H., Yamamoto, A., Shiba, M., Okada, T., Tani, N., Tanaka, S., and Kimura, K. (2023). Deep Learning Model Based on Contrast-Enhanced Computed Tomography Imaging to Predict Postoperative Early Recurrence after the Curative Resection of a Solitary Hepatocellular Carcinoma. Cancers, 15.
5. Liu, C.-Y., Cheng, C.-Y., Yang, S.-Y., Chai, J.-W., Chen, W.-H., and Chang, P.-Y. (2023). Mortality Evaluation and Life Expectancy Prediction of Patients with Hepatocellular Carcinoma with Data Mining. Healthcare, 11.
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