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For: Huang Y, Talwar A, Chatterjee S, Aparasu RR. Application of machine learning in predicting hospital readmissions: a scoping review of the literature. BMC Med Res Methodol 2021;21:96. [PMID: 33952192 DOI: 10.1186/s12874-021-01284-z] [Cited by in Crossref: 1] [Cited by in F6Publishing: 7] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Michailidis P, Dimitriadou A, Papadimitriou T, Gogas P. Forecasting Hospital Readmissions with Machine Learning. Healthcare 2022;10:981. [DOI: 10.3390/healthcare10060981] [Reference Citation Analysis]
2 Lin S, Shah S, Sattler A, Smith M. Predicting Avoidable Health Care Utilization: Practical Considerations for Artificial Intelligence/Machine Learning Models in Population Health. Mayo Clinic Proceedings 2022;97:653-7. [DOI: 10.1016/j.mayocp.2021.11.039] [Reference Citation Analysis]
3 Huepenbecker SP, Meyer LA. Our dual responsibility of improving quality and questioning the metrics: Reflections on 30-day readmission rate as a quality indicator. Gynecologic Oncology 2022;165:1-3. [DOI: 10.1016/j.ygyno.2022.03.001] [Reference Citation Analysis]
4 Plummer NR, Lone NI. Reducing hospital re-admission after intensive care: from risk-factors to interventions. Anaesthesia 2022;77:380-3. [PMID: 35226965 DOI: 10.1111/anae.15666] [Reference Citation Analysis]
5 Symum H, Zayas-Castro J. Identifying Children at Readmission Risk: At-Admission versus Traditional At-Discharge Readmission Prediction Model. Healthcare (Basel) 2021;9:1334. [PMID: 34683014 DOI: 10.3390/healthcare9101334] [Reference Citation Analysis]
6 Squires A, Ma C, Miner S, Feldman P, Jacobs EA, Jones SA. Assessing the influence of patient language preference on 30 day hospital readmission risk from home health care: A retrospective analysis. Int J Nurs Stud 2021;125:104093. [PMID: 34710627 DOI: 10.1016/j.ijnurstu.2021.104093] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Bacchi S, Gilbert T, Gluck S, Cheng J, Tan Y, Chim I, Jannes J, Kleinig T, Koblar S. Daily estimates of individual discharge likelihood with deep learning natural language processing in general medicine: a prospective and external validation study. Intern Emerg Med 2021. [PMID: 34333736 DOI: 10.1007/s11739-021-02816-7] [Cited by in F6Publishing: 1] [Reference Citation Analysis]