@inproceedings{87e5d60d8c2e4192bc4c8865f574d9b7,
title = "Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure",
keywords = "Electronic Health Records (EHRs), heart failure, length of stay, predictive modeling, topic modeling",
author = "Ankita Agarwal and Tanvi Banerjee and Romine, {William L.} and Krishnaprasad Thirunarayan and Lingwei Chen and Mia Cajita",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Digital Health, ICDH 2023 ; Conference date: 02-07-2023 Through 08-07-2023",
year = "2023",
doi = "10.1109/ICDH60066.2023.00038",
language = "English",
series = "Proceedings - 2023 IEEE International Conference on Digital Health, ICDH 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "208--216",
editor = "Chang, {Carl K.} and Chang, {Rong N.} and Jing Fan and Fox, {Geoffrey C.} and Zhi Jin and Graziano Pravadelli and Hossain Shahriar",
booktitle = "Proceedings - 2023 IEEE International Conference on Digital Health, ICDH 2023",
}