Identification of predictors and model for predicting prolonged length of stay in dengue patients.

Shahid Ansari, Md; Jain, Dinesh; Harikumar, Haripriya; Rana, Santu; Gupta, Sunil; Budhiraja, Sandeep; Venkatesh, Svetha

Shahid Ansari, Md; Jain, Dinesh; Harikumar, Haripriya; Rana, Santu; Gupta, Sunil; Budhiraja, Sandeep; Venkatesh, Svetha.

Afiliação

Shahid Ansari M; Department of Clinical Data Analytics, Max Super Specialty Hospital, 1, Press Enclave Road, Saket, New Delhi, 110017, India.
Jain D; Department of Clinical Data Analytics, Max Super Specialty Hospital, 1, Press Enclave Road, Saket, New Delhi, 110017, India. Dinesh.Jain@maxhealthcare.com.
Harikumar H; Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, Australia.
Rana S; Institute for Health Transformation, Deakin University, Geelong, VIC, Australia.
Gupta S; Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, Australia.
Budhiraja S; Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, Australia.
Venkatesh S; Department of Internal Medicine, Max Super Specialty Hospital, New Delhi, India.

Health Care Manag Sci ; 24(4): 786-798, 2021 Dec.

Article em En | MEDLINE | ID: mdl-34389924

Assuntos

Dengue; Hospitalização; Adolescente; Adulto; Criança; Dengue/epidemiologia; Dengue/terapia; Feminino; Hospitais; Humanos; Tempo de Internação; Modelos Logísticos; Masculino; Estudos Retrospectivos; Adulto Jovem

Palavras-chave

Dengue; Elastic-net; Healthcare; Patient's length of stay (LOS); Predictive models; Random forest

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Dengue / Hospitalização Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Child / Female / Humans / Male Idioma: En Ano de publicação: 2021 Tipo de documento: Article

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