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README.md

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 # AgingNLP
 
-AgingNLP aims to develop aging-related NLP resources including NLP algorithms, knowledge bases, taxonomies, and lexicons for supporting the development and evaluation of various NLP applications. This is part of the collaboration effort between the Rochester Epidemiology Project and the Mayo NLP team. All related NLP resources can be accessed under the OHNLP consortium - aiming to create an interoperable, scalable, and usable NLP ecosystem.
+AgingNLP aims to develop aging-related NLP resources including NLP algorithms, knowledge bases, taxonomies, and lexicons for supporting the development and evaluation of various NLP applications. This is part of the collaboration effort between the Rochester Epidemiology Project and the Mayo NLP team. All related NLP resources can be accessed under the OHNLP consortium - aiming to create an interoperable, scalable, and usable NLP ecosystem.
+
+## Reference
+
+### Delirium
+Fu S, Lopes GS, Pagali SR, Thorsteinsdottir B, LeBrasseur NK, Wen A, Liu H, Rocca WA, Olson JE, St. Sauver J, Sohn S. Ascertainment of delirium status using natural language processing from electronic health records. The Journals of Gerontology: Series A. 2022 Mar 1;77(3):524-30.
+
+Sauver JS, Fu S, Sohn S, Weston S, Fan C, Olson J, Thorsteinsdottir B, LeBrasseur N, Pagali S, Rocca W, Liu H. Identification of delirium from real-world electronic health record clinical notes. Journal of Clinical and Translational Science. 2023 Jan;7(1):e187.
+
+Pagali S, Fu S, Lindroth H, Sohn S, Burton MC, Lapid M. Delirium occurrence and association with outcomes in hospitalized COVID-19 patients. International psychogeriatrics. 2021 Oct;33(10):1105-9.
+
+Pagali SR, Kumar R, Fu S, Sohn S, Yousufuddin M. Natural language processing CAM algorithm improves delirium detection compared with conventional methods. American Journal of Medical Quality. 2023 Jan 1;38(1):17-22.
+
+
+### Fall
+Fu S, Thorsteinsdottir B, Zhang X, Lopes GS, Pagali SR, LeBrasseur NK, Wen A, Liu H, Rocca WA, Olson JE, Sauver JS. A hybrid model to identify fall occurrence from electronic health records. International journal of medical informatics. 2022 Jun 1;162:104736.
+
+
+### Functional Status
+Fu S, Jia H, Vassilaki M, Keloth VK, Dang Y, Zhou Y, Garg M, Petersen RC, St Sauver J, Moon S, Wang L. FedFSA: Hybrid and federated framework for functional status ascertainment across institutions. Journal of Biomedical Informatics. 2024 Apr 1;152:104623.
+
+Fu S, Vassilaki M, Ibrahim OA, Petersen RC, Pagali S, St Sauver J, Moon S, Wang L, Fan JW, Liu H, Sohn S. Quality assessment of functional status documentation in EHRs across different healthcare institutions. Frontiers in Digital Health. 2022 Sep 27;4:958539.
+
+### Cognitive-behavioral Symptom
+Liwei Wang, Sunyang Fu, Sunghwan Sohn, Sungrim Moon, Hua Xu, Cui Tao, Jennifer St. Sauver, Ronald C. Peterson, Hongfang Liu, & J. Wilfred Fan. (2022). Development of a general purpose cognitive-behavioral symptom taxonomy. Zenodo. https://doi.org/10.5281/zenodo.7025711