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Your search keyword '"N, Nicolotti"' showing total 2 results
Start Over Author "N, Nicolotti" Topic decision trees
2 results on '"N, Nicolotti"'

1. Gemelli decision tree Algorithm to Predict the need for home monitoring or hospitalization of confirmed and unconfirmed COVID-19 patients (GAP-Covid19): preliminary results from a retrospective cohort study.

Author

Vetrugno G, Laurenti P, Franceschi F, Foti F, D'Ambrosio F, Cicconi M, LA Milia DI, Di Pumpo M, Carini E, Pascucci D, Boccia S, Pastorino R, Damiani G, De-Giorgio F, Oliva A, Nicolotti N, Cambieri A, Ghisellini R, Murri R, Sabatelli G, Musolino M, and Gasbarrini A

Subjects
Aged, COVID-19 epidemiology, COVID-19 virology, COVID-19 Testing, Cohort Studies, Decision Making, Computer-Assisted, Female, Follow-Up Studies, Humans, Italy epidemiology, Machine Learning, Male, Monitoring, Physiologic, Prognosis, Retrospective Studies, SARS-CoV-2 isolation & purification, Algorithms, COVID-19 diagnosis, COVID-19 therapy, Decision Trees, Home Care Services statistics & numerical data, Hospitalization statistics & numerical data
Abstract

Objective: To develop a deep learning-based decision tree for the primary care setting, to stratify adult patients with confirmed and unconfirmed coronavirus disease 2019 (COVID-19), and to predict the need for hospitalization or home monitoring., Patients and Methods: We performed a retrospective cohort study on data from patients admitted to a COVID hospital in Rome, Italy, between 5 March 2020 and 5 June 2020. A confirmed case was defined as a patient with a positive nasopharyngeal RT-PCR test result, while an unconfirmed case had negative results on repeated swabs. Patients' medical history and clinical, laboratory and radiological findings were collected, and the dataset was used to train a predictive model for COVID-19 severity., Results: Data of 198 patients were included in the study. Twenty-eight (14.14%) had mild disease, 62 (31.31%) had moderate disease, 64 (32.32%) had severe disease, and 44 (22.22%) had critical disease. The G2 value assessed the contribution of each collected value to decision tree building. On this basis, SpO2 (%) with a cut point at 92 was chosen for the optimal first split. Therefore, the decision tree was built using values maximizing G2 and LogWorth. After the tree was built, the correspondence between inputs and outcomes was validated., Conclusions: We developed a machine learning-based tool that is easy to understand and apply. It provides good discrimination in stratifying confirmed and unconfirmed COVID-19 patients with different prognoses in every context. Our tool might allow general practitioners visiting patients at home to decide whether the patient needs to be hospitalized.

Published
2021
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2. Gemelli decision tree Algorithm to Predict the need for home monitoring or hospitalization of confirmed and unconfirmed COVID-19 patients (GAP-Covid19): preliminary results from a retrospective cohort study

Author

G, Vetrugno, P, Laurenti, F, Franceschi, F, Foti, F, D'Ambrosio, M, Cicconi, D I, LA Milia, M, Di Pumpo, E, Carini, D, Pascucci, S, Boccia, R, Pastorino, G, Damiani, F, De-Giorgio, A, Oliva, N, Nicolotti, A, Cambieri, R, Ghisellini, R, Murri, G, Sabatelli, M, Musolino, A, Gasbarrini, and Giuseppe, Zuccalà

Subjects
Male, Monitoring, Decision Making, Cohort Studies, Machine Learning, COVID-19 Testing, Computer-Assisted, General practitioners, Settore MED/41 - ANESTESIOLOGIA, Humans, Physiologic, Settore MED/42 - IGIENE GENERALE E APPLICATA, Decision Making, Computer-Assisted, Primary health care, Aged, Monitoring, Physiologic, Retrospective Studies, SARS-CoV-2, Settore MED/09 - MEDICINA INTERNA, Decision Trees, COVID-19, Prognosis, Home Care Services, Hospitalization, Community-based care, Italy, Female, Algorithms, Follow-Up Studies
Abstract

To develop a deep learning-based decision tree for the primary care setting, to stratify adult patients with confirmed and unconfirmed coronavirus disease 2019 (COVID-19), and to predict the need for hospitalization or home monitoring.We performed a retrospective cohort study on data from patients admitted to a COVID hospital in Rome, Italy, between 5 March 2020 and 5 June 2020. A confirmed case was defined as a patient with a positive nasopharyngeal RT-PCR test result, while an unconfirmed case had negative results on repeated swabs. Patients' medical history and clinical, laboratory and radiological findings were collected, and the dataset was used to train a predictive model for COVID-19 severity.Data of 198 patients were included in the study. Twenty-eight (14.14%) had mild disease, 62 (31.31%) had moderate disease, 64 (32.32%) had severe disease, and 44 (22.22%) had critical disease. The G2 value assessed the contribution of each collected value to decision tree building. On this basis, SpO2 (%) with a cut point at 92 was chosen for the optimal first split. Therefore, the decision tree was built using values maximizing G2 and LogWorth. After the tree was built, the correspondence between inputs and outcomes was validated.We developed a machine learning-based tool that is easy to understand and apply. It provides good discrimination in stratifying confirmed and unconfirmed COVID-19 patients with different prognoses in every context. Our tool might allow general practitioners visiting patients at home to decide whether the patient needs to be hospitalized.

Published
2021

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