METHODS AND SYSTEMS FOR PREDICTING CLINICAL TRIAL CRITERIA USING MACHINE LEARNING TECHNIQUES

• 1. 基于机器学习技术预测细菌保护性抗原蛋白的方法及系统 [P] . 中国专利： CN110060738B . 2021.10.22
• 2. 基于机器学习技术预测细菌保护性抗原蛋白的方法及系统 [P] . 中国专利： CN110060738A . 2019-07-26
• 3. IDENTIFYING IMPLIED CRITERIA IN CLINICAL TRIALS USING MACHINE LEARNING TECHNIQUES [P] . 美国专利： US2020251188A1 . 2020-08-06

  机译： 使用机器学习技术识别临床试验中的隐含标准

• 4. Methods and systems for recommending filters to apply to clinical trial search results using machine learning techniques [P] . US11145390B2 . 2021-10-12

  机译： 使用机器学习技术将过滤器适用于临床试验搜索结果的方法和系统

• 5. METHODS AND SYSTEMS FOR RECOMMEDING FILTERS TO APPLY TO CLINICAL TRIAL SEARCH RESULTS USING MACHINE LEARNING TECHNIQUES [P] . 美国专利： US2020258600A1 . 2020-08-13

  机译： 推荐使用机器学习技术过滤器应用于临床试验搜索结果的方法和系统

• 1. Performance of statistical and machine learning-based methods for predicting biogeographical patterns of fungal productivity in forest ecosystems [J] . Albert Morera, Juan Martínez de Aragón, José Antonio Bonet, 森林生态系统：英文版 . 2021,第002期
• 2. Performance of statistical and machine learning-based methods for predicting biogeographical patterns of fungal productivity in forest ecosystems [J] . Albert Morera, Juan Martínez de Aragón, José Antonio Bonet, 中国林学（英文版） . 2021,第002期
• 3. New evidence-based adaptive clinical trial methods for optimally integrating predictive biomarkers into oncology clinical development programs [J] . Robert A.Beckman, Cong Chen 癌症（英文版） . 2013,第005期
• 4. The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis [J] . Yue Hou, Qiuhan Li, Chen Zhang, 工程（英文） . 2021,第006期
• 5. The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis [J] . Yue Hou, Qiuhan Li, Chen Zhang, 工程（英文） . 2021,第006期
• 6. Semantic categorization of Chinese eligibility criteria in clinical trials using machine learning methods [J] . Hui Zong, Jinxuan Yang, Zeyu Zhang, BMC Medical Informatics and Decision Making . 2021,第1期
• 7. Predicting Diabetes Mellitus With Machine Learning Techniques Using Multi-Criteria Decision Making [J] . Abhinav Juneja, Sapna Juneja, Sehajpreet Kaur, International journal of information retrieval research . 2021,第2期
• 8. Clinical intelligence: New machine learning techniques for predicting clinical drug response [J] . Turki Turki, Wang Jason T. L. Computers in Biology and Medicine . 2019,第期
• 9. The Combination of Clinical, Dose-Related and Imaging Features Helps Predict Radiation-Induced Normal-Tissue Toxicity in Lung-cancer Patients -- An in-silico Trial Using Machine Learning Techniques [C] . Nalbantov G., Dekker A., De Ruysscher D., Machine Learning and Applications and Workshops (ICMLA), 2011 10th International Conference on . 2011
• 10. Predicting Individual Treatment Effect from Randomized Clinical Trial Data: A Nested Cross-Validation Evaluation Framework for Machine Learning Methods [D] . Liu, Yu. 2021
• 11. Semantic categorization of Chinese eligibility criteria in clinical trials using machine learning methods [O] . Hui Zong, Jinxuan Yang, Zeyu Zhang, 2021
• 12. Semantic categorization of Chinese eligibility criteria in clinical trials using machine learning methods [O] . Hui Zong, Jinxuan Yang, Zeyu Zhang, 2021

• 1. 机器学习支持下利用震后遥感影像的近实时震损预测提升方法研究 [C] . LU Xin-zheng ,陆新征 ,SUN Chu-jin . 第28全国结构工程学术会议 . 2019
• 2. 利用生理生化方法及RAPD标记技术预测西瓜杂种优势 [A] . 夏妍 . 2003