Use of a machine learning framework to predict substance use disorder treatment success
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Abstract
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DOI: 10.1371/journal.pone.0175383
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References listed on IDEAS
- Evans, Elizabeth & Li, Libo & Hser, Yih-Ing, 2009. "Client and program factors associated with dropout from court mandated drug treatment," Evaluation and Program Planning, Elsevier, vol. 32(3), pages 204-212, August.
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Cited by:
- Franck Jaotombo & Vanessa Pauly & Guillaume Fond & Veronica Orleans & Pascal Auquier & Badih Ghattas & Laurent Boyer, 2023. "Machine-learning prediction for hospital length of stay using a French medico-administrative database," Post-Print hal-04325691, HAL.
- Vinícius Serafini Roglio & Eduardo Nunes Borges & Francisco Diego Rabelo-da-Ponte & Felipe Ornell & Juliana Nichterwitz Scherer & Jaqueline Bohrer Schuch & Ives Cavalcante Passos & Breno Sanvicente-Vi, 2020. "Prediction of attempted suicide in men and women with crack-cocaine use disorder in Brazil," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-19, May.
- Alexander Engels & Katrin C Reber & Ivonne Lindlbauer & Kilian Rapp & Gisela Büchele & Jochen Klenk & Andreas Meid & Clemens Becker & Hans-Helmut König, 2020. "Osteoporotic hip fracture prediction from risk factors available in administrative claims data – A machine learning approach," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-14, May.
- Sheelu Sagar & Rohit Rastogi & Vikas Garg & Ishwar V. Basavaraddi, 2022. "Impact of Meditation on Quality of Life of Employees," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 11(1), pages 1-16, January.
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