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Predicting emergency department visits and hospitalizations for patients with heart failure in home healthcare using a time series risk model.

, , , , , , , , , , , and . J. Am. Medical Informatics Assoc., 30 (10): 1622-1633 (September 2023)

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Product rating prediction using centrality measures in social networks., and . Sarnoff Symposium, page 94-98. IEEE, (2015)Annotation and extraction of age and temporally-related events from clinical histories., , , and . BMC Medical Informatics Decis. Mak., 20-S (11): 338 (2020)A Preliminary Characterization of Canonicalized and Non-Canonicalized Section Headers Across Variable Clinical Note Types., , , and . AMIA, AMIA, (2020)Uncovering hidden trends: identifying time trajectories in risk factors documented in clinical notes and predicting hospitalizations and emergency department visits during home health care., , , , , , , , , and 2 other author(s). J. Am. Medical Informatics Assoc., 30 (11): 1801-1810 (October 2023)Towards Automatic Bot Detection in Twitter for Health-related Tasks., , , and . CoRR, (2019)Product rating prediction using trust relationships in social networks., and . CCNC, page 115-118. IEEE, (2016)Social trust model for rating prediction in recommender systems: Effects of similarity, centrality, and social ties., and . Online Soc. Networks Media, (2018)Predicting emergency department visits and hospitalizations for patients with heart failure in home healthcare using a time series risk model., , , , , , , , , and 2 other author(s). J. Am. Medical Informatics Assoc., 30 (10): 1622-1633 (September 2023)Relation Detection to Identify Stroke Assertions from Clinical Notes Using Natural Language Processing., , , , , , and . MedInfo, volume 310 of Studies in Health Technology and Informatics, page 619-623. IOS Press, (2023)Effects of User Interactions on Online Social Recommender Systems.. ICDE, page 1444-1448. IEEE Computer Society, (2017)