A natural language processing pipeline to synthesize patient-generated notes toward improving remote care and chronic disease management: a cystic fibrosis case study. Hussain, S., Sezgin, E., Krivchenia, K., Luna, J., Rust, S., & Huang, Y. JAMIA Open, 4(3):ooab084, July, 2021. ![A natural language processing pipeline to synthesize patient-generated notes toward improving remote care and chronic disease management: a cystic fibrosis case study [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Objectives: Patient-generated health data (PGHD) are important for tracking and monitoring out of clinic health events and supporting shared clinical decisions. Unstructured text as PGHD (eg, medical diary notes and transcriptions) may encapsulate rich information through narratives which can be critical to better understand a patient’s condition. We propose a natural language processing (NLP) supported data synthesis pipeline for unstructured PGHD, focusing on children with special healthcare needs (CSHCN), and demonstrate it with a case study on cystic fibrosis (CF).
@article{hussain_natural_2021,
title = {A natural language processing pipeline to synthesize patient-generated notes toward improving remote care and chronic disease management: a cystic fibrosis case study},
volume = {4},
copyright = {https://creativecommons.org/licenses/by-nc/4.0/},
issn = {2574-2531},
shorttitle = {A natural language processing pipeline to synthesize patient-generated notes toward improving remote care and chronic disease management},
url = {https://academic.oup.com/jamiaopen/article/doi/10.1093/jamiaopen/ooab084/6377936},
doi = {10.1093/jamiaopen/ooab084},
abstract = {Objectives: Patient-generated health data (PGHD) are important for tracking and monitoring out of clinic health events and supporting shared clinical decisions. Unstructured text as PGHD (eg, medical diary notes and transcriptions) may encapsulate rich information through narratives which can be critical to better understand a patient’s condition. We propose a natural language processing (NLP) supported data synthesis pipeline for unstructured PGHD, focusing on children with special healthcare needs (CSHCN), and demonstrate it with a case study on cystic fibrosis (CF).},
language = {en},
number = {3},
urldate = {2026-07-15},
journal = {JAMIA Open},
author = {Hussain, Syed-Amad and Sezgin, Emre and Krivchenia, Katelyn and Luna, John and Rust, Steve and Huang, Yungui},
month = jul,
year = {2021},
pages = {ooab084},
}
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