Abstract
| Lingua originale | Inglese |
|---|---|
| pagine (da-a) | 2785-2794 |
| Numero di pagine | 10 |
| Rivista | European Review for Medical and Pharmacological Sciences |
| Volume | 25 |
| Numero di pubblicazione | 6 |
| DOI | |
| Stato di pubblicazione | Pubblicato - 2021 |
All Science Journal Classification (ASJC) codes
- Farmacologia (medica)
Keywords
- Aged
- Algorithms
- COVID-19
- COVID-19 Testing
- Cohort Studies
- Community-based care
- Computer-Assisted
- Decision Making
- Decision Trees
- Female
- Follow-Up Studies
- General practitioners
- Home Care Services
- Hospitalization
- Humans
- Italy
- Machine Learning
- Machine learning
- Male
- Monitoring
- Physiologic
- Primary health care
- Prognosis
- Retrospective Studies
- SARS-CoV-2
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In: European Review for Medical and Pharmacological Sciences, Vol. 25, N. 6, 2021, pag. 2785-2794.
Risultato della ricerca: Contributo in rivista › Articolo › peer review
TY - JOUR
T1 - 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
AU - Vetrugno, Giuseppe
AU - Laurenti, Patrizia
AU - Franceschi, Francesco
AU - Foti, F
AU - D'Ambrosio, F
AU - Cicconi, M
AU - LA Milia, D I
AU - Di Pumpo, M
AU - Carini, E
AU - Pascucci, D
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AU - Damiani, Gianfranco
AU - De-Giorgio, F
AU - Oliva, Antonio
AU - Nicolotti, N
AU - Cambieri, A
AU - Ghisellini, R
AU - Murri, Rita
AU - Sabatelli, G
AU - Musolino, M
AU - Gasbarrini, Antonio
AU - Montini, L
AU - Miele, Luca
AU - Group, Gemelli-Against-Covid
AU - Rome, null
AU - Abbate, Italy V.
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PY - 2021
Y1 - 2021
N2 - 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 G(2) 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 G(2) 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.
AB - 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 G(2) 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 G(2) 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.
KW - Aged
KW - Algorithms
KW - COVID-19
KW - COVID-19 Testing
KW - Cohort Studies
KW - Community-based care
KW - Computer-Assisted
KW - Decision Making
KW - Decision Trees
KW - Female
KW - Follow-Up Studies
KW - General practitioners
KW - Home Care Services
KW - Hospitalization
KW - Humans
KW - Italy
KW - Machine Learning
KW - Machine learning
KW - Male
KW - Monitoring
KW - Physiologic
KW - Primary health care
KW - Prognosis
KW - Retrospective Studies
KW - SARS-CoV-2
KW - Aged
KW - Algorithms
KW - COVID-19
KW - COVID-19 Testing
KW - Cohort Studies
KW - Community-based care
KW - Computer-Assisted
KW - Decision Making
KW - Decision Trees
KW - Female
KW - Follow-Up Studies
KW - General practitioners
KW - Home Care Services
KW - Hospitalization
KW - Humans
KW - Italy
KW - Machine Learning
KW - Machine learning
KW - Male
KW - Monitoring
KW - Physiologic
KW - Primary health care
KW - Prognosis
KW - Retrospective Studies
KW - SARS-CoV-2
UR - https://publicatt.unicatt.it/handle/10807/215073
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85103631077&origin=inward
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85103631077&origin=inward
U2 - 10.26355/eurrev_202103_25440
DO - 10.26355/eurrev_202103_25440
M3 - Article
SN - 2284-0729
VL - 25
SP - 2785
EP - 2794
JO - European Review for Medical and Pharmacological Sciences
JF - European Review for Medical and Pharmacological Sciences
IS - 6
ER -