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Behind the scenes: A medical natural language processing project.
Wu, Joy T; Dernoncourt, Franck; Gehrmann, Sebastian; Tyler, Patrick D; Moseley, Edward T; Carlson, Eric T; Grant, David W; Li, Yeran; Welt, Jonathan; Celi, Leo Anthony.
Afiliación
  • Wu JT; Harvard T.H. Chan School of Public Health, Cambridge, MA, USA; Medical Sieve Radiology, IBM Almaden Research Center, San Jose, CA, USA. Electronic address: joytywu@gmail.com.
  • Dernoncourt F; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Adobe Research, San Jose, CA, USA.
  • Gehrmann S; Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA, USA.
  • Tyler PD; Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
  • Moseley ET; University of Massachusetts, Boston, MA, USA.
  • Carlson ET; Philips Research North America, Cambridge, MA, USA.
  • Grant DW; Department of Surgery, Division of Plastic and Reconstructive Surgery, Washington University School of Medicine, St. Louis, MO, USA.
  • Li Y; Harvard T.H. Chan School of Public Health, Cambridge, MA, USA.
  • Welt J; Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA.
  • Celi LA; Massachusetts Institute of Technology, Cambridge, MA, USA.
Int J Med Inform ; 112: 68-73, 2018 04.
Article en En | MEDLINE | ID: mdl-29500024
Advancement of Artificial Intelligence (AI) capabilities in medicine can help address many pressing problems in healthcare. However, AI research endeavors in healthcare may not be clinically relevant, may have unrealistic expectations, or may not be explicit enough about their limitations. A diverse and well-functioning multidisciplinary team (MDT) can help identify appropriate and achievable AI research agendas in healthcare, and advance medical AI technologies by developing AI algorithms as well as addressing the shortage of appropriately labeled datasets for machine learning. In this paper, our team of engineers, clinicians and machine learning experts share their experience and lessons learned from their two-year-long collaboration on a natural language processing (NLP) research project. We highlight specific challenges encountered in cross-disciplinary teamwork, dataset creation for NLP research, and expectation setting for current medical AI technologies.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Procesamiento de Lenguaje Natural / Inteligencia Artificial / Toma de Decisiones Clínicas / Aprendizaje Automático Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Int J Med Inform Asunto de la revista: INFORMATICA MEDICA Año: 2018 Tipo del documento: Article Pais de publicación: Irlanda

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Procesamiento de Lenguaje Natural / Inteligencia Artificial / Toma de Decisiones Clínicas / Aprendizaje Automático Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Int J Med Inform Asunto de la revista: INFORMATICA MEDICA Año: 2018 Tipo del documento: Article Pais de publicación: Irlanda