Comparing machine and deep learning models for pediatric anxiety classification using structured EHRs and area-based measures of health data.
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| Title: | Comparing machine and deep learning models for pediatric anxiety classification using structured EHRs and area-based measures of health data. |
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| Authors: | Lee EW; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America., Choo S; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America.; Department of Industrial Engineering, Kumoh National Institute of Technology, Gumi-si, Gyeongsangbuk-do, Republic of Korea., Maguire D; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America., Shivanna A; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America.; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States of America., Santel D; Information Technology Services Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America., Bhatnagar S; Information Technology Services Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America., Goethert I; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States of America., Patterson K; Information Technology Services Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America., Gholap J; Information Technology Services Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America., Hanson HA; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America., Chandrashekar M; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America., Ammerman RT; Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States of America.; Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio, United States of America., Pestian JP; Information Technology Services Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America.; Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio, United States of America., Glauser T; Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio, United States of America.; Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States of America., Brokamp C; Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio, United States of America.; Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States of America., Strawn JR; Department of Psychiatry & Behavioral Neuroscience, University of Cincinnati, Cincinnati, Ohio, United States of America., Kapadia AJ; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America., Agasthya G; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America.; Nuclear & Radiological Engineering and Medical Physics Program, Georgia Institute of Technology, Atlanta, Georgia, United States of America. |
| Source: | PloS one [PLoS One] 2026 May 12; Vol. 21 (5), pp. e0324673. Date of Electronic Publication: 2026 May 12 (Print Publication: 2026). |
| Publication Type: | Journal Article; Comparative Study |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0324673 |
