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Identification of asthma control factor in clinical notes using a hybrid deep learning model.
Agnikula Kshatriya, Bhavani Singh; Sagheb, Elham; Wi, Chung-Il; Yoon, Jungwon; Seol, Hee Yun; Juhn, Young; Sohn, Sunghwan.
Affiliation
  • Agnikula Kshatriya BS; Department of Artificial Intelligence and Informatics, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
  • Sagheb E; Department of Artificial Intelligence and Informatics, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
  • Wi CI; Precision Population Science Lab, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, USA.
  • Yoon J; Department of Pediatrics, Myongji Hospital, Goyang, South Korea.
  • Seol HY; Pusan National University, Yangsan Hospital, Yangsan, South Korea.
  • Juhn Y; Precision Population Science Lab, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, USA.
  • Sohn S; Department of Artificial Intelligence and Informatics, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA. sohn.sunghwan@mayo.edu.
BMC Med Inform Decis Mak ; 21(Suppl 7): 272, 2021 11 09.
Article in En | MEDLINE | ID: mdl-34753481
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Asthma / Deep Learning Type of study: Diagnostic_studies / Guideline / Prognostic_studies Limits: Humans Language: En Journal: BMC Med Inform Decis Mak Journal subject: INFORMATICA MEDICA Year: 2021 Type: Article Affiliation country: United States
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Asthma / Deep Learning Type of study: Diagnostic_studies / Guideline / Prognostic_studies Limits: Humans Language: En Journal: BMC Med Inform Decis Mak Journal subject: INFORMATICA MEDICA Year: 2021 Type: Article Affiliation country: United States