Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes
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- Bohui Liang & Ayten Turkcan & Mehmet Erkan Ceyhan & Keith Stuart, 2015. "Improvement of chemotherapy patient flow and scheduling in an outpatient oncology clinic," International Journal of Production Research, Taylor & Francis Journals, vol. 53(24), pages 7177-7190, December.
- Yan-Ling Zheng & Li-Ping Zhang & Xue-Liang Zhang & Kai Wang & Yu-Jian Zheng, 2015. "Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang, China," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-13, March.
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Keywords
patient-reported outcomes; symptom complexity levels; forecasting model; predictive analytics; ARIMA; clinic scheduling; staff allocation;All these keywords.
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