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Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions.
Xie, Feng; Ong, Marcus Eng Hock; Liew, Johannes Nathaniel Min Hui; Tan, Kenneth Boon Kiat; Ho, Andrew Fu Wah; Nadarajan, Gayathri Devi; Low, Lian Leng; Kwan, Yu Heng; Goldstein, Benjamin Alan; Matchar, David Bruce; Chakraborty, Bibhas; Liu, Nan.
Afiliación
  • Xie F; Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
  • Ong MEH; Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
  • Liew JNMH; Department of Emergency Medicine, Singapore General Hospital, Singapore.
  • Tan KBK; Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
  • Ho AFW; Department of Emergency Medicine, Singapore General Hospital, Singapore.
  • Nadarajan GD; Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
  • Low LL; Department of Emergency Medicine, Singapore General Hospital, Singapore.
  • Kwan YH; Department of Emergency Medicine, Singapore General Hospital, Singapore.
  • Goldstein BA; Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
  • Matchar DB; Department of Family Medicine and Continuing Care, Singapore General Hospital, Singapore.
  • Chakraborty B; Programme in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore.
  • Liu N; Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore.
JAMA Netw Open ; 4(8): e2118467, 2021 08 02.
Article en En | MEDLINE | ID: mdl-34448870
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Admisión del Paciente / Medición de Riesgo / Servicio de Urgencia en Hospital / Gravedad del Paciente / Aprendizaje Automático Tipo de estudio: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Límite: Aged / Female / Humans / Male / Middle aged País/Región como asunto: Asia Idioma: En Revista: JAMA Netw Open Año: 2021 Tipo del documento: Article País de afiliación: Singapur Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Admisión del Paciente / Medición de Riesgo / Servicio de Urgencia en Hospital / Gravedad del Paciente / Aprendizaje Automático Tipo de estudio: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Límite: Aged / Female / Humans / Male / Middle aged País/Región como asunto: Asia Idioma: En Revista: JAMA Netw Open Año: 2021 Tipo del documento: Article País de afiliación: Singapur Pais de publicación: Estados Unidos