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Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19.
Giotta, Massimo; Trerotoli, Paolo; Palmieri, Vincenzo Ostilio; Passerini, Francesca; Portincasa, Piero; Dargenio, Ilaria; Mokhtari, Jihad; Montagna, Maria Teresa; De Vito, Danila.
Afiliación
  • Giotta M; School of Specialization in Medical Statistics and Biometry, School of Medicine, University of Bari Aldo Moro, 70121 Bari, Italy.
  • Trerotoli P; Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70121 Bari, Italy.
  • Palmieri VO; Department of Biomedical Science and Human Oncology, University of Bari Aldo Moro, 70121 Bari, Italy.
  • Passerini F; Department of Biomedical Science and Human Oncology, University of Bari Aldo Moro, 70121 Bari, Italy.
  • Portincasa P; Department of Biomedical Science and Human Oncology, University of Bari Aldo Moro, 70121 Bari, Italy.
  • Dargenio I; School of Specialization in Medical Statistics and Biometry, School of Medicine, University of Bari Aldo Moro, 70121 Bari, Italy.
  • Mokhtari J; Department of Basic Medical Sciences, Neurosciences, and Sense Organs, Medical School, University of Bari Aldo Moro, 70121 Bari, Italy.
  • Montagna MT; Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70121 Bari, Italy.
  • De Vito D; Department of Basic Medical Sciences, Neurosciences, and Sense Organs, Medical School, University of Bari Aldo Moro, 70121 Bari, Italy.
Int J Environ Res Public Health ; 19(20)2022 10 11.
Article en En | MEDLINE | ID: mdl-36293594
Many studies have identified predictors of outcomes for inpatients with coronavirus disease 2019 (COVID-19), especially in intensive care units. However, most retrospective studies applied regression methods to evaluate the risk of death or worsening health. Recently, new studies have based their conclusions on retrospective studies by applying machine learning methods. This study applied a machine learning method based on decision tree methods to define predictors of outcomes in an internal medicine unit with a prospective study design. The main result was that the first variable to evaluate prediction was the international normalized ratio, a measure related to prothrombin time, followed by immunoglobulin M response. The model allowed the threshold determination for each continuous blood or haematological parameter and drew a path toward the outcome. The model's performance (accuracy, 75.93%; sensitivity, 99.61%; and specificity, 23.43%) was validated with a k-fold repeated cross-validation. The results suggest that a machine learning approach could help clinicians to obtain information that could be useful as an alert for disease progression in patients with COVID-19. Further research should explore the acceptability of these results to physicians in current practice and analyze the impact of machine learning-guided decisions on patient outcomes.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Health_economic_evaluation / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Int J Environ Res Public Health Año: 2022 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Suiza
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Health_economic_evaluation / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Int J Environ Res Public Health Año: 2022 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Suiza