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| Park S, Yoo J, Lee Y, et al. (2024) Quantifying emergency department nursing workload at the task level using NASA-TLX: An exploratory descriptive study. International Emergency Nursing. 74: 101424 |
| Song W, Latham NK, Liu L, et al. (2024) Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach. Journal of the American Geriatrics Society |
| Song W, Zhang L, Liu L, et al. (2022) Predicting Hospitalization of COVID-19 Positive Patients Using Clinician-guided Machine Learning Methods. Journal of the American Medical Informatics Association : Jamia |
| Rossetti SC, Dykes PC, Knaplund C, et al. (2021) The Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) Clinical Decision Support Early Warning System: Protocol for a Cluster Randomized Pragmatic Clinical Trial. Jmir Research Protocols. 10: e30238 |
| Fu LH, Knaplund C, Cato K, et al. (2021) Utilizing timestamps of longitudinal electronic health record data to classify clinical deterioration events. Journal of the American Medical Informatics Association : Jamia |
| Schnock KO, Kang MJ, Rossetti SC, et al. (2021) Identifying nursing documentation patterns associated with patient deterioration and recovery from deterioration in critical and acute care settings. International Journal of Medical Informatics. 153: 104525 |
| Kang MJ, Rossetti SC, Knaplund C, et al. (2021) Nursing Documentation Variation Across Different Medical Facilities Within an Integrated Healthcare System. Computers, Informatics, Nursing : Cin |
| Zhu X, Plasek JM, Tang C, et al. (2021) Embedding, aligning and reconstructing clinical notes to explore sepsis. Bmc Research Notes. 14: 136 |
| Rossetti SC, Knaplund C, Albers D, et al. (2021) Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework. Journal of the American Medical Informatics Association : Jamia |
| Song W, Kang MJ, Zhang L, et al. (2021) Predicting pressure injury using nursing assessment phenotypes and machine learning methods. Journal of the American Medical Informatics Association : Jamia |