Abstract
Health problems are very common among pregnant women, and seemingly normal pregnant women may experience physiological disorders, which can lead to perinatal complications, greatly endangering the health of pregnant women and their newborns. Timely identification, provision of relevant resources, and timely response are the key to preventing serious complications and mortality in delivery women. The current predictive models used in medicine have an imbalance between interpretability and accuracy. In addition, there is a lack of utilization of knowledge in the field of medical expert domain knowledge, which is a waste. The method proposed in this article combines deep learning with regular decision trees to ensure high accuracy while improving its interpretability. In addition, adding expert domain knowledge and providing additional useful information can improve model performance.
J. Wang and G. Wu—Contributed equally to this work.
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Acknowledgments
This work is supported in part by the National Natural Science Funds for Distinguished Young Scholar under Grant 62325307, in part by the National Nature Science Foundation of China under Grants 62072315, 62073225, 62176164, and 62203134, in part by the Shenzhen Science and Technology Program under grants KCXFZ20230731094001003, JCYJ20210324093808021, and JCYJ2022053110281 7040, in part by the Scientific Instrument Developing Project of Shenzhen University under Grant 2023YQ019.
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Wang, J. et al. (2024). Explainable Deep Learning with Human Feedback for Perioperative Complications Prediction. In: Huang, DS., Zhang, X., Pan, Y. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14863. Springer, Singapore. https://doi.org/10.1007/978-981-97-5581-3_17
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