Unsupervised learning methods for efficient geographic clustering and identification of disease disparities with applications to county-level colorectal cancer incidence in California.

McMahon, Mallory E; Doroshenko, Lyubov; Roostaei, Javad; Cho, Hyunsoon; Haider, Mansoor A

McMahon, Mallory E; Doroshenko, Lyubov; Roostaei, Javad; Cho, Hyunsoon; Haider, Mansoor A.

Afiliación

McMahon ME; Department of Mathematics, Box 8205, North Carolina State University, Raleigh, NC, 27695-8205, USA.
Doroshenko L; Department of Economics and Finance, La Sapienza University of Rome, 00185, Roma, Italy.
Roostaei J; Department of Environmental Sciences and Engineering, UNC Gillings School of Global Public Health Chapel Hill, Raleigh, NC, 27599-7400, USA.
Cho H; Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea.
Haider MA; Department of Mathematics, Box 8205, North Carolina State University, Raleigh, NC, 27695-8205, USA. mahaider@ncsu.edu.

Health Care Manag Sci ; 25(4): 574-589, 2022 Dec.

Article en En | MEDLINE | ID: mdl-35732967

Asunto(s)

Neoplasias Colorrectales; Aprendizaje Automático no Supervisado; Humanos; Incidencia; Análisis por Conglomerados; Algoritmos; Neoplasias Colorrectales/epidemiología

Palabras clave

Colorectal cancer; K-Means clustering; Optimization; Self-organizing map; Spatial clustering

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Colorrectales / Aprendizaje Automático no Supervisado Tipo de estudio: Diagnostic_studies / Incidence_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Health Care Manag Sci Asunto de la revista: SERVICOS DE SAUDE Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Países Bajos

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