Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review
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Publication:1730516
DOI10.1007/S10479-016-2393-ZzbMath1434.90028OpenAlexW2563185281MaRDI QIDQ1730516
Publication date: 6 March 2019
Published in: Annals of Operations Research (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/11343/283170
Cites Work
- Outpatient appointment scheduling given individual day-dependent no-show predictions
- Non-homogeneous Markov models for sequential pattern mining of healthcare data
- A hybrid data mining/simulation approach for modelling outpatient no-shows in clinic scheduling
- Combining Data Mining and Discrete Event Simulation for a value-added view of a hospital emergency department
Related Items (5)
Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis ⋮ The interconnectedness of the economic content in the speeches of the US presidents ⋮ Conversational commerce: entering the next stage of AI-powered digital assistants ⋮ Integration of text-mining and telemedicine appointment optimization ⋮ Central European journal of operations research (CJOR) ``operations research applied to health services (ORAHS) in Europe: general trends and ORAHS 2020 conference in Vienna, Austria
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