友情链接

• Dlib组主页
• 深度学习资源
• MOOC研究资源

健康大数据资源

简介

Tutorial

• Opportunities and obstacles for deep learning in biology and medicine
• review_Big EHR Healthcare Data Analytics challange and app
• Deep Learning Models for Health Care- Challenges and Solutions

EMR event sequence

• Nguyen, P., Tran, T., Wickramasinghe, N., & Venkatesh, S. (2016). Deepr: a convolutional net for medical records.
• Wang, F., Lee, N., Hu, J., Sun, J., Ebadollahi, S., & Laine, A. F. (2013). A framework for mining signatures from event sequences and its applications in healthcare data. IEEE Transactions on Pattern Analysis & Machine Intelligence, 35(2), 272-285.
• Choi, E., Bahadori, M. T., Song, L., Stewart, W. F., & Sun, J. (2016). Gram: graph-based attention model for healthcare representation learning.
• Zhou, J., Wang, F., Hu, J., & Ye, J. (2014). From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp.135-144). ACM.
• Choi, E., Bahadori, M. T., Searles, E., Coffey, C., Thompson, M., & Bost, J., et al. (2016). Multi-layer Representation Learning for Medical Concepts. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp.1495-1504). ACM.
• Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Retain: interpretable predictive model in healthcare using reverse time attention mechanism.
• Pham, T., Tran, T., Phung, D., & Venkatesh, S. (2016). DeepCare: A Deep Dynamic Memory Model for Predictive Medicine. PAKDD.
• Choi, E., Bahadori, M. T., & Sun, J. (2015). Doctor ai: predicting clinical events via recurrent neural networks. Computer Science.
• Liu, C., Wang, F., Hu, J., & Xiong, H. (2015). Temporal Phenotyping from Longitudinal Electronic Health Records: A Graph Based Framework. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp.705-714). ACM.

EMR text

• Prakash, A., Zhao, S., Hasan, S. A., Datla, V., Lee, K., & Qadir, A., et al. (2016). Condensed memory networks for clinical diagnostic inferencing.
• Grnarova, P., Schmidt, F., Hyland, S. L., & Eickhoff, C. (2016). Neural document embeddings for intensive care patient mortality prediction.

EMR time series：

• Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2016). Recurrent neural networks for multivariate time series with missing values.
• Ghassemi, M., Pimentel, M. A. F., Naumann, T., Brennan, T., Clifton, D. A., & Szolovits, P., et al. (2015). A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data. AAAI (pp.446-453).
• Lipton, Z. C., Kale, D. C., & Wetzel, R. (2016). Directly modeling missing data in sequences with rnns: improved classification of clinical time series.
• Mccarthy, A., & Williams, C. K. I. (2016). Predicting patient state-of-health using sliding window and recurrent classifiers.
• Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2016). Recurrent neural networks for multivariate time series with missing values.

EMR 异构

• Zhao, J. (2015). Temporal weighting of clinical events in electronic health records for pharmacovigilance. IEEE International Conference on Bioinformatics and Biomedicine (pp.375-381). IEEE.
• Henriksson, A., Zhao, J., Bostrom, H., & Dalianis, H. (2015). Modeling electronic health records in ensembles of semantic spaces for adverse drug event detection. IEEE International Conference on Bioinformatics and Biomedicine (pp.343-350). IEEE.
• Karlsson, I., & Bostrom, H. (2016). Predicting Adverse Drug Events Using Heterogeneous Event Sequences. IEEE International Conference on Healthcare Informatics (pp.356-362). IEEE Computer Society.
• Huddar, V., Desiraju, B. K., Rajan, V., Bhattacharya, S., Roy, S., & Reddy, C. K. (2016). Predicting complications in critical care using heterogeneous clinical data. 1-1.