Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19

Massimo Giotta, Paolo Trerotoli (), Vincenzo Ostilio Palmieri, Francesca Passerini, Piero Portincasa, Ilaria Dargenio, Jihad Mokhtari, Maria Teresa Montagna and Danila De Vito
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Massimo Giotta: School of Specialization in Medical Statistics and Biometry, School of Medicine, University of Bari Aldo Moro, 70121 Bari, Italy
Paolo Trerotoli: Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70121 Bari, Italy
Vincenzo Ostilio Palmieri: Department of Biomedical Science and Human Oncology, University of Bari Aldo Moro, 70121 Bari, Italy
Francesca Passerini: Department of Biomedical Science and Human Oncology, University of Bari Aldo Moro, 70121 Bari, Italy
Piero Portincasa: Department of Biomedical Science and Human Oncology, University of Bari Aldo Moro, 70121 Bari, Italy
Ilaria Dargenio: School of Specialization in Medical Statistics and Biometry, School of Medicine, University of Bari Aldo Moro, 70121 Bari, Italy
Jihad Mokhtari: Department of Basic Medical Sciences, Neurosciences, and Sense Organs, Medical School, University of Bari Aldo Moro, 70121 Bari, Italy
Maria Teresa Montagna: Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70121 Bari, Italy
Danila De Vito: Department of Basic Medical Sciences, Neurosciences, and Sense Organs, Medical School, University of Bari Aldo Moro, 70121 Bari, Italy

IJERPH, 2022, vol. 19, issue 20, 1-12

Abstract: 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.

Keywords: COVID-19; machine learning; clinical aspect; prognostic markers; haematochemical parameters; prediction (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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