ACBI: An Alternating Clustering and Bayesian Inference approach for optimizing medical intervention budget under chance constraints

Chen He; Benjamin Dalmas; Xiaolan Xie

doi:10.1109/CASE48305.2020.9216791

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ACBI: An Alternating Clustering and Bayesian Inference approach for optimizing medical intervention budget under chance constraints

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1
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Chen He

Function : Author

Centre Ingénierie et Santé

Benjamin Dalmas

Function : Author

Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes

Centre Ingénierie et Santé

Xiaolan Xie

Function : Author
PersonId : 866155

Centre Ingénierie et Santé

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Artificial Intelligence [cs.AI]

Benjamin DALMAS : Connect in order to contact the contributor

https://hal-emse.ccsd.cnrs.fr/emse-03128243

Submitted on : Tuesday, February 2, 2021-10:08:26 AM

Last modification on : Saturday, April 22, 2023-4:31:27 AM

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emse-03128243 , version 1 (02-02-2021)

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HAL Id : emse-03128243 , version 1
DOI : 10.1109/CASE48305.2020.9216791

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Chen He, Benjamin Dalmas, Xiaolan Xie. ACBI: An Alternating Clustering and Bayesian Inference approach for optimizing medical intervention budget under chance constraints. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), Aug 2020, Hong Kong, France. pp.55-60, ⟨10.1109/CASE48305.2020.9216791⟩. ⟨emse-03128243⟩

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ACBI: An Alternating Clustering and Bayesian Inference approach for optimizing medical intervention budget under chance constraints

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