- 这里是医疗组成员读过的论文的paper-list，内容总结见《论文总结》
- 分类如下：
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疾病诊断与预测； 1.1临床决策与智能诊断 1.2单类疾病概率预测 1.3多疾病概率预测 1.4再住院预测
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临床路径模式发现；
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医保欺诈检测分析；
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医疗保健与护理；
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数据处理方法； 5.1缺失值补全方法 5.2特征选择与提取 5.3数据整合分析
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精准医疗: 6.1 subtype
7.其他（非医保直接相关但是可以借鉴的文章）。
[1] Goodwin, Travis R., and Sanda M. Harabagiu. "Medical Question Answering for Clinical Decision Support." Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 2016.[pdf]
[对临床决策支持的医学问题回答]
[2] Kamadi, VSRP Varma, Appa Rao Allam, and Sita Mahalakshmi Thummala. "A computational intelligence technique for the effective diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree approach." Applied Soft Computing 49 (2016): 137-145.[pdf]
[利用PCA和改进决策树的智能诊断]
[1] Steinberg, Gregory B., et al. "Novel predictive models for metabolic syndrome risk: a" big data" analytic approach." The American journal of managed care 20.6 (2014): e221-8.[pdf]
[用于代谢综合征风险的新型预测模型：“大数据”分析方法]
[2] Weng, Stephen F., et al. "Can machine-learning improve cardiovascular risk prediction using routine clinical data?." PloS one 12.4 (2017): e0174944.[pdf]
[使用机器学习方法提高冠心病预测概]
[3] Choi, Edward, et al. "Using recurrent neural network models for early detection of heart failure onset." Journal of the American Medical Informatics Association (2016): ocw112.[pdf]
[针对心力衰竭的早期发现]
[4] Liu, Chuanren, et al. "Temporal phenotyping from longitudinal electronic health records: A graph based framework." Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015.[pdf]
[一种基于图的框架：从EHR中得到时序表型]
[*] Ng, Kenney, et al. "PARAMO: A PARAllel predictive MOdeling platform for healthcare analytic research using electronic health records." Journal of biomedical informatics 48 (2014): 160-170.[pdf]
[*] Ooi, Beng Chin, et al. "Contextual crowd intelligence." ACM SIGKDD Explorations Newsletter 16.1 (2014): 39-46.[pdf]
[*] Hwang, San-Yih, Chih-Ping Wei, and Wan-Shiou Yang. "Discovery of temporal patterns from process instances." Computers in industry 53.3 (2004): 345-364.[pdf]
[*] Yang, Wan-Shiou, and San-Yih Hwang. "A process-mining framework for the detection of healthcare fraud and abuse." Expert Systems with Applications 31.1 (2006): 56-68.[pdf]
[*] Cao, Ni, et al. "Predictive and preventive models for diabetes prevention using clinical information in electronic health record." Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on. IEEE, 2015. [pdf]
[*] Weng, Stephen F., et al. "Can machine-learning improve cardiovascular risk prediction using routine clinical data?." PloS one 12.4 (2017): e0174944.[pdf]
[1] Miotto, Riccardo, et al. "Deep patient: An unsupervised representation to predict the future of patients from the electronic health records." Scientific reports 6 (2016).[pdf]
[使用无监督深度学习方法预测疾病]
[2] Choi, Edward, Mohammad Taha Bahadori, and Jimeng Sun. "Doctor ai: Predicting clinical events via recurrent neural networks." arXiv preprint arXiv:1511.05942 (2015). [pdf]
[根据病人以往的诊断和用药情况来预测病人未来的诊断和用药情况以及下一次就医的时间]
[*] Ji, Xiang, et al. "Collaborative and trajectory prediction models of medical conditions by mining patients' Social Data." Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on. IEEE, 2015.[pdf]
[1] Zheng, Bichen, et al. "Predictive modeling of hospital readmissions using metaheuristics and data mining." Expert Systems with Applications 42.20 (2015): 7110-7120.[pdf]
[使用元启发式和数据挖掘的再入院预测模型]
[2] Davis, Darcy A., et al. "Time to CARE: a collaborative engine for practical disease prediction." Data Mining and Knowledge Discovery 20.3 (2010): 388-415.[pdf]
[医疗领域中协同过滤技术的应用]
[*] Davis, Darcy A., et al. "Predicting individual disease risk based on medical history." Proceedings of the 17th ACM conference on Information and knowledge management. ACM, 2008.[pdf]
[*] Dentino, Brian, Darcy Davis, and Nitesh V. Chawla. "HealthCareND: leveraging EHR and CARE for prospective healthcare." Proceedings of the 1st ACM International Health Informatics Symposium. ACM, 2010.[pdf]
[*] Chawla, Nitesh V., and Darcy A. Davis. "Bringing big data to personalized healthcare: a patient-centered framework." Journal of general internal medicine 28.3 (2013): 660-665.[pdf]
[*] Feldman, Keith, Darcy Davis, and Nitesh V. Chawla. "Scaling and contextualizing personalized healthcare: a case study of disease prediction algorithm integration." Journal of biomedical informatics 57 (2015): 377-385.[pdf]
[1] Huang, Zhengxing, Xudong Lu, and Huilong Duan. "Latent treatment pattern discovery for clinical processes." Journal of medical systems 37.2 (2013): 9915.[pdf]
[发现临床路径中潜在治疗模式]
[2] Huang, Zhengxing, Xudong Lu, and Huilong Duan. "On mining clinical pathway patterns from medical behaviors." Artificial intelligence in medicine 56.1 (2012): 35-50.[pdf]
[从医学行为中挖掘临床路径模式]
[1] Archenaa, J., and EA Mary Anita. "A survey of big data analytics in healthcare and government." Procedia Computer Science 50 (2015): 408-413.[pdf]
[2] Dua, Prerna, and Sonali Bais. "Supervised learning methods for fraud detection in healthcare insurance." Machine Learning in Healthcare Informatics. Springer Berlin Heidelberg, 2014. 261-285.[pdf]
[监督式学习方法 - 医疗保健保险的欺诈检测]
[3] Joudaki, Hossein, et al. "Using data mining to detect health care fraud and abuse: a review of literature." Global journal of health science 7.1 (2014): 194.[pdf]
[使用数据挖掘检测医疗保健欺诈和滥用：文献回顾]
[2] Olsen, Peder A., Ramesh Natarajan, and Sholom M. Weiss. "Graphical Models for Identifying Fraud and Waste in Healthcare Claims." Proceedings of the 2014 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2014.[pdf]
[3] Chandola, Varun, Sreenivas R. Sukumar, and Jack C. Schryver. "Knowledge discovery from massive healthcare claims data." Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2013.[pdf]
[4] Weiss, Sholom M., et al. "Managing healthcare costs by peer-group modeling." Applied Intelligence 43.4 (2015): 752-759.[pdf]
[5] Rawte, Vipula, and G. Anuradha. "Fraud detection in health insurance using data mining techniques." Communication, Information & Computing Technology (ICCICT), 2015 International Conference on. IEEE, 2015.[pdf]
[6] Liu, Juan, et al. "Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data." AAAI. 2015.[pdf]
[7] Shadmi, Efrat, et al. "Predicting 30-day readmissions with preadmission electronic health record data." Medical care 53.3 (2015): 283-289.[pdf]
[8] Choi, Edward, et al. "Constructing disease network and temporal progression model via context-sensitive hawkes process." Data Mining (ICDM), 2015 IEEE International Conference on. IEEE, 2015.[pdf]
[9} Viceconti, Marco, Peter Hunter, and Rod Hose. "Big data, big knowledge: big data for personalized healthcare." IEEE journal of biomedical and health informatics 19.4 (2015): 1209-1215.[pdf]
[10] Liu, Peng, et al. "Healthcare data mining: Prediction inpatient length of stay." Intelligent Systems, 2006 3rd International IEEE Conference on. IEEE, 2006.[pdf]
[11] Lin, Chin-Ho, et al. "Temporal event tracing on big healthcare data analytics." Big Data Applications and Use Cases. Springer International Publishing, 2016. 95-108.[pdf]
[12] Getchius, Jeffrey M. "Healthcare fraud detection with machine learning." U.S. Patent Application No. 13/689,168.[pdf]
[13] Xu, Hongteng, et al. "Patient Flow Prediction via Discriminative Learning of Mutually-Correcting Processes." IEEE Transactions on Knowledge and Data Engineering 29.1 (2017): 157-171.[pdf]
[14] Branting, L. Karl, et al. "Graph analytics for healthcare fraud risk estimation." Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on. IEEE, 2016.[pdf]
[15] Cui, Haoyi, et al. "Healthcare Fraud Detection Based on Trustworthiness of Doctors." Trustcom/BigDataSE/I SPA, 2016 IEEE. IEEE, 2016. [pdf]
[*] Alkhatib, Mohammad, Amir Talaei-Khoei, and Amir Ghapanchi. "Analysis of Research in Healthcare Data Analytics." arXiv preprint arXiv:1606.01354 (2016).[pdf]
[*] Christy, A., G. Meera Gandhi, and S. Vaithyasubramanian. "Cluster Based Outlier Detection Algorithm for Healthcare Data." Procedia Computer Science 50 (2015): 209-215.[pdf]
[*] Choi, Edward, et al. "Constructing disease network and temporal progression model via context-sensitive hawkes process." Data Mining (ICDM), 2015 IEEE International Conference on. IEEE, 2015.[pdf]
[*] Hrovat, Goran, et al. "Contrasting temporal trend discovery for large healthcare databases." Computer methods and programs in biomedicine 113.1 (2014): 251-257.[pdf]
[*] Branting, L. Karl, et al. "Graph analytics for healthcare fraud risk estimation." Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on. IEEE, 2016.[pdf]
[*] Yang, Hui, et al. "Healthcare intelligence: turning data into knowledge." IEEE Intelligent Systems 29.3 (2014): 54-68.[pdf]
[*] Lu, Hsin-Min, Chih-Ping Wei, and Fei-Yuan Hsiao. "Modeling healthcare data using multiple-channel latent Dirichlet allocation." Journal of biomedical informatics 60 (2016): 210-223.[pdf]
[1] Ling, Zheng Jye, et al. "GEMINI: an integrative healthcare analytics system." Proceedings of the VLDB Endowment 7.13 (2014): 1766-1771.[pdf]
[一个综合医疗保健分析系统]
[2] Xiaohan Li, Shu Wu, Liang Wang. "Blood Pressure Prediction via Recurrent Models with Contextual Layer." WWW 2017, April 3–7, Perth, Australia(2017).[pdf](Propose a novel model named recurrent models with contextual layer.):star::star::star::star:
[血压序列预测]
[1] Hung, Yu-Shiang, et al. "Web usage mining for analysing elder self-care behavior patterns." Expert Systems with Applications 40.2 (2013): 775-783.[pdf]
[使用数据挖掘来分析老年人自我保健行为模式]
[2] Dipanwita Dasgupta, Keith Feldman, Disha Waghray, W.A. Mikels-Carrasco, Patty Willaert,Debra A. Raybold, Nitesh V. Chawla."An Integrated Care Framework for Successful Aging." IEEE-EMBS International Conference on Biomedical & Health Informatics,440-443, 2014.
[健康老龄化的综合护理框架]
[*] Zhou, Jiayu, et al. "From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records." Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014.[pdf]
[从微观到宏观：数据驱动通过纵向医疗电子记录致密化来检测表型][补全缺失值]
[1] Alelyani, Salem, Jiliang Tang, and Huan Liu. "Feature Selection for Clustering: A Review." Data Clustering: Algorithms and Applications 29 (2013): 110-121.[pdf]
[特征选择的综述]
[1] Das, Sanmay. "Filters, wrappers and a boosting-based hybrid for feature selection." ICML. Vol. 1. 2001.[pdf]
[结合过滤器，包装器的特征选择方法]
[2] Pölsterl, Sebastian, et al. "Survival analysis for high-dimensional, heterogeneous medical data: Exploring feature extraction as an alternative to feature selection." Artificial Intelligence in Medicine 72 (2016): 1-11.
[高维医学数据上的生存分析]
[3] Nezhad, Milad Zafar, et al. "SAFS: A deep feature selection approach for precision medicine." Bioinformatics and Biomedicine (BIBM), 2016 IEEE International Conference on. IEEE, 2016.[pdf]
[1] Ho, Joyce C., Joydeep Ghosh, and Jimeng Sun. "Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization." Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014.[pdf]
[Marble通过稀疏非负偏差因子分解从电子健康记录进行高通量表型分析]
##6.1 subtype
[1] Saria, Suchi, and A. Goldenberg. "Subtyping: What It is and Its Role in Precision Medicine." IEEE Intelligent Systems 30.4(2015):70-75.[pdf]
[subtype介绍与综述]
[1] Lovro Šubelj,Štefan Furlan,Marko Bajec. “An expert system for detecting automobile insurance fraud using social network analysis”.Expert Systems with Applications,38(1):1039-1052, 2011.[pdf]
[一个使用社交网络分析检测汽车保险欺诈的专家系统]
[2] Kuck, Jonathan, et al. "Query-based outlier detection in heterogeneous information networks." Advances in database technology: proceedings. International Conference on Extending Database Technology. Vol. 2015. NIH Public Access, 2015.[pdf]
[异构信息网络中基于查询的离群值检测方法]
[*] Derczynski, Leon, and Kalina Bontcheva. "Spatio-temporal grounding of claims made on the web, in PHEME." Proceedings of the 10th Joint ISO-ACL SIGSEM Workshop on Interoperable Semantic Annotation, ISA. Vol. 14. 2014.[pdf]
[*] Liu, Chuanren, et al. "Temporal skeletonization on sequential data: patterns, categorization, and visualization." IEEE Transactions on Knowledge and Data Engineering 28.1 (2016): 211-223.[pdf]
[*] Zhu, Hengshu, et al. "Discovery of ranking fraud for mobile apps." IEEE Transactions on knowledge and data engineering 27.1 (2015): 74-87.[pdf]
[*] Liu, Bin, et al. "A general geographical probabilistic factor model for point of interest recommendation." IEEE Transactions on Knowledge and Data Engineering 27.5 (2015): 1167-1179.[pdf]
[*] Sun, Jing, et al. "Multi-source Information Fusion for Personalized Restaurant Recommendation." Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2015.[pdf]
[*] Hu, Tianming, et al. "High-dimensional clustering: a clique-based hypergraph partitioning framework." Knowledge and information systems 39.1 (2014): 61-88.[pdf]
[*] Tan, Chang, et al. "Object-oriented travel package recommendation." ACM Transactions on Intelligent Systems and Technology (TIST) 5.3 (2014): 43.[pdf]
[*] Zhu, Hengshu, et al. "Mining mobile user preferences for personalized context-aware recommendation." ACM Transactions on Intelligent Systems and Technology (TIST) 5.4 (2015): 58.[pdf]
[*] Huai, Baoxing, et al. "Toward personalized context recognition for mobile users: A semisupervised bayesian HMM approach." ACM Transactions on Knowledge Discovery from Data (TKDD) 9.2 (2014): 10.[pdf]
[*] Liu, Chuanren, Kai Zhang, and Hui Xiong. "Sequential Pattern Analysis with Right Granularity." Data Mining Workshop (ICDMW), 2014 IEEE International Conference on. IEEE, 2014.[pdf]
[*] Strack, Beata, et al. "Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records." BioMed research international 2014 (2014).[pdf]