智能诊疗，个性化干预，机器学习，因果推理 

[1].Chen, Y.W.,. Xu, L.q, and B. Yi, Early recognition of risk of critical adverse events based on deep neural decision gradient boosting. Frontiers in Public Health, 2023. 10 https://doi.org/10.3389/fpubh.2022.1065707.【SCI,IF:6.461,2区,论文基于深度神经决策梯度提升网络预测危重症不良事件风险】   

[2].Chen, Y.W., Liu J . Polynomial dendritic neural networks[J]. Neural Computing and Applications, 34, 11571–11588 (2022). 【SCI, IF:5.606,2区,论文提出多项式树突神经网络可解释性对危重症进行风险预测】   

[3].Chen, Y.W., Zhong,K.H., Zhu.Y.Z.T., and Sun.Q.L.,Two-stage hemoglobin prediction based on prior causality. Frontiers in Public Health, 2022. 10: p. 12 https://doi.org/10.3389/fpubh.2022.1079389. 【SCI, IF:6.461,2区,基于先验因果性两阶段预测围术期患者血红蛋白浓度】   

[4].Chen, Y.W., Zhu, Y., K. Zhong, Z. Yang, Y. Li, X. Shu, D. Wang, P. Deng, X. Bai, J. Gu, K. Lu, J. Zhang, L. Zhao, T. Zhu, K. Wei, and B. Yi, Optimization of anesthetic decision-making in ERAS using Bayesian network. Frontiers in Medicine 2022. 9【SCI, IF:5.058,2区,基于贝叶斯网络优化麻醉医疗决策干预】   

[5].Chen, Y.W , Qin X , Zhang L , et al. A Novel Method of Heart Failure Prediction Based on DPCNN-XGBOOST Model[J]. Computers, Materials and Continua, 2020, 65(1):495-510.【SCI, IF:3.860,2区,基于深度金字塔卷积神经网络和XGBOOST预测围术期心衰风险】   

[6].Chen, Y.W., Zhang J , Qin X L . Interpretable instance disease prediction based on causal feature selection and effect analysis[J]. BMC Medical Informatics and Decision Making, 2022, 22(1):1-14. 【SCI, IF:3.298,基于因果特征选择和效应分析可解释性预测疾病】   

[7].Chen, Y.W. and B. Qi, Representation learning in intraoperative vital signs for heart failure risk prediction. BMC medical informatics and decision making, 2019. 19(1): p. 260-260. 【SCI, IF:3.298,基于表示学习利用术中监护数据预测心衰风险】   

[8].Chen, Y.W, Li YJ, Deng P, Yang ZY, Zhong KH, Zhang LG, Chen Y, Zhi HY, Hu XY, Gu JT, Ning JL, Lu KZ, Zhang J, Xia ZY, Qin XL, Yi B. Learning to predict in-hospital mortality risk in the intensive care unit with attention-based temporal convolution network. BMC Anesthesiol. 2022 Apr 23;22(1):119.【SCI, IF:2.376,基于时序注意力机制卷积网络预测危重症住院死亡】   

[9].Chen, Y.W., Zhang, J., Wang, P. , Hu, Z.Y., and Zhong, K.H., Convolutional-de-convolutional neural networks for recognition of surgical workflow. Frontiers in Computational Neuroscience, 2022. 16: p. 【SCI, IF:3.387,基于卷积与反卷积智能识别手术流程】   

[10].Li YJ, Zhong KH, Bai XH, Tang X, Li P, Yang ZY, Zhi HY, Li XJ, Chen Y, Deng P, Qin XL, Gu JT, Ning JL, Lu KZ, Zhang J, Xia ZY, Chen, Y.W, Yi B. A Simple and Quick Screening Method for Intrapulmonary Vascular Dilation in Cirrhotic Patients Based on Machine Learning. J Clin Transl Hepatol. 2021 Oct 28;9(5):682-689. doi: 10.14218/JCTH.2020.00184. 【SCI, IF:5.065,2区,基于机器学习设计一种快速便捷肺内血管扩张的的识别方法】   

[11].Huang ,W, Chen, Y.W, Wang, P., Liu, X., and Liu, S., An Interpretable Temporal Convolutional Network Model for Acute Kidney Injury Prediction in the Intensive Care Unit,  IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021, pp. 3021-3028. 【BIBM会议，基于时序卷积网络的可解释性急性肾损伤重症预测】   

[12].Li YJ, Zhang LG, Zhi HY, Zhong KH, He WQ, Chen Y, Yang ZY, Chen L, Bai XH, Qin XL, Li DF,Wang DD, Gu JT, Ning JL, Lu KZ, Zhang J, Xia ZY, Chen YW, Yi B. A better method for the dynamic, precise estimating of blood/haemoglobin loss based on deep learning of artificial intelligence. Annals of Translational Medicine,2020;8(19):1219. doi: 10.21037/atm-20-1806.   

[13].Baolian Qi, Xiaolin Qin, Jia Liu, Yang Xu, Chen, Y.W. A Deep Architecture for Surgical Workflow Recognition with Edge Information, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, 2019, pp. 1358-1364. 18-21 Nov. 2019    

[14].Sun L., Cao Q., Chen Y.W., Zheng Y. H., Wu Z.b., Mixed noise removal for hyperspectral images based on global tensor low-rankness and non-local SVD-aided group sparsity, IEEE Transactions on Geoscience and Remote Sensing, 2023. DOI: 10.1109/TGRS.2023.3257851.   

专利：   

陈芋文；张矩；钟坤华；孙启龙；林小光；刘江。一种基于强化学习的监护预警方法及系统  CN202011217940.8  【提出一种基于强化学习的危重症预警方法】 2022年授权   

陈芋文；鲁开智；张矩；钟坤华；祁宝莲；孙启龙；李亚晴。一种基于跨模态深度学习的围术期危重症事件预测方法 ZL201910223568.2 【提出一种跨模态的危重症预测方法】2022年授权   

陈芋文;唐鹏;钟坤华;祁宝莲;孙启龙;汪鹏;王飞。一种半监督手术视频流程识别方法 201910142716.8 【提出一种半监督的手术流程智能识别的方法】 2022年授权   

钟坤华；陈芋文；张矩；孙启龙。基于贝叶斯网络和效用体系的围手术期危重不良事件干预决策方法 CN201910806510.0  【提出一种基于因果贝叶斯网络的围术期医疗决策方法】 2021年授权   

钟坤华；易斌；陈芋文；张力戈；李雨捷；支鸿羽；杨智勇；鲁开智；张矩。一种基于特征工程的纱布浸血估算模型构建方法 ZL 202010324238.5 【提出一种血红蛋白的估计方法】 2021年授权   

国家重点研发计划课题：重症和手术监护危重事件预测模型与智能干预推理决策（主持人：陈芋文，240万，编号：2018YFC0116704，2018.8-2022.8）   

中国科学院人才项目：基于人工智能算法的危重症风险预测研究（主持人：陈芋文，80万，编号：2020377，2020.1-2023.12）   

重庆市自然科学基金面上项目：基于可解释性DQN算法改进的围术期心血管危重不良事件早期预警研究（主持人：陈芋文，10万，编号：CSTB2022NSCQ-MSX0894，2022.8-2024.7）   

国家重点研发计划课题：云端融合的多模态数据交互意图理解（子课题：陈芋文，135万，2016YFB1001404， 2016.6-2020.6）    

军委科技委项目：野战医疗所自然人机交互智能手术器械辅助管理机器人研发（主持人：陈芋文，12万，编号：Y81Z110）   

重庆市民生工程重点项目：基于人工智能算法的术后心血管危重不良事件早期预警研究（主持人：陈芋文，10万，编号：E0316006）   

中科院STS项目：内蒙古复杂地形条件下数值预报模拟关键技术研发及应用（240万，参与）   

中科院人才项目：西南地区复杂地形条件下数值预报模拟关键技术应用研究（75万，参与）   

横向项目：高分辨率数值预报系统运维（454万，参与），重庆市超算服务平台（3200万，参与）