Machine Learning for Mortality Analysis in Patients with Covid-19
Bibliographic Data
| ID | 15467212 |
|---|---|
| Authors | Manuel Sánchez-Montañés (0000-0002-5944-0532, Universidad Autónoma de Madrid), Pablo Rodríguez-Belenguer (0000-0001-5270-7452, Universitat de València), Antonio J Serrano-López (0000-0002-3863-8611, Universitat de València, corresponding author), Emilio Soria-Olivas (Universitat de València), Emilio Soria‐Olivas (0000-0002-9148-8405, Universitat de València), Yasser Alakhdar-Mohmara (Universitat de València) |
| Year | 2020 |
| Volume | 17 |
| Issue | 22 |
| Pages | 8386-8386 |
| Publication date | 2020-11-12 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph17228386 |
| PMID | 33198392 |
| OpenAlex | W3104955410 |
| Language | EN |
| Citations received | 3 |
| References cited | 34 |
This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis where the endpoint variable is the reason for hospital discharge (home or deceased). The different methods applied show the importance of variables such as age, O 2 saturation at Emergency Rooms (ER), and whether the patient comes from a nursing home. In addition, biclustering is used to globally analyze the patient-drug dataset, extracting segments of patients. We highlight the validity of the classifiers developed to predict the mortality, reaching an appreciable accuracy. Finally, interpretable decision rules for estimating the risk of mortality of patients can be obtained from the decision tree, which can be crucial in the prioritization of medical care and resources
2019-20 coronavirus outbreak · Betacoronavirus · Biology · Coronavirus disease 2019 (COVID-19 · Coronavirus Infections · Disease · Geography · Infectious disease (medical specialty · Medical emergency · MEDLINE · Outbreak · Pandemic · Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 · Artificial Intelligence in Healthcare · Computer Science · COVID-19 diagnosis using AI · Machine Learning in Healthcare · Medicine · Internal Medicine · Virology
| Citations per year | 0.5 |
|---|---|
| Citation span | 2020 - 2024 (5) |
| Highly cited | No |