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Articles / References
 

Xiao, C., Ma, T., Dieng, A. B., Blei, D. M., & Wang, F. (2018, April 9). Readmission prediction via deep contextual embedding of clinical concepts. PLOS ONE. https://doi.org/10.1371/journal.pone.0195024 

 

Jamei, M., Nisnevich, A., Wetchler, E., Sudat, S., & Liu, E. (2017, July 14). Predicting all-cause risk of 30-day hospital readmission using Artificial Neural Networks. PLOS ONE.  https://doi.org/10.1371/journal.pone.0181173 

 

Golas, S. B., Shibahara, T., Agboola, S., Otaki, H., Sato, J., Nakae, T., Hisamitsu, T., Kojima, G., Felsted, J., Kakarmath, S., Kvedar, J., & Jethwani, K. (2018, June 22). A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: A retrospective analysis of electronic medical records data - BMC Medical Informatics and Decision making. BioMed Central. https://doi.org/10.1186/s12911-018-0620-z 

 

Eckert, C., Nieves-Robbins, N., Spieker, E., Louwers, T., Hazel, D., Marquardt, J., Solveson, K., Zahid, A., Ahmad, M., Barnhill, R., McKelvey, T. G., Marshall, R., Shry, E., & Teredesai, A. (2019, May 8). Development and prospective validation of a machine learning-based risk of readmission model in a large military hospital. Applied Clinical Informatics. https://doi.org/10.1055/s-0039-1688553  

 

Cho, S.-Y., Kim, S.-H., Kang, S.-H., Lee, K. J., Choi, D., Kang, S., Park, S. J., Kim, T., Yoon, C.-H., Youn, T.-J., & Chae, I.-H. (2021, April 26). Pre-existing and machine learning-based models for Cardiovascular Risk Prediction. Nature News. https://doi.org/10.1038/s41598-021-88257-w 

 

Akella, A., & Kaushik, V. (2020, January 1). Machine learning algorithms for predicting coronary artery disease: Efforts toward an open source solution. bioRxiv. https://doi.org/10.1101/2020.02.13.948414 

2021-2022 Academy of Science Research Portfolio

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