Emilio Soria-Olivas
Biographic Data
| ID | 4270486 |
|---|---|
| NAME | Emilio Soria-Olivas |
| GIVEN NAMES | Emilio |
| FAMILY NAME | Soria-Olivas |
| SIGNATURE | SORIA-OLIVAS E |
| AFFILIATIONS | Universitat de València |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 0 |
Machine Learning for Mortality Analysis in Patients with Covid-19
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…
Physical Activity Monitoring and Acceptance of a Commercial Activity Tracker in Adult Patients with Haemophilia
Physical activity (PA) is highly beneficial for people with haemophilia (PWH), however, studies that objectively monitor the PA in this population are scarce. This study aimed to monitor the daily PA and analyse its evolution over time in a cohort of PWH using a commercial activity tracker. In addition, this work analyses the relationship between PA levels, demographics, and joint health status, as well as the acceptance and adherence to the acti…
Visual Data Mining With Self-organizing Maps for 'Self-monitoring' Data Analysis
Data collected in psychological studies are mainly characterized by containing a large number of variables (multidimensional data sets). Analyzing multidimensional data can be a difficult task, especially if only classical approaches are used (hypothesis tests, analyses of variance, linear models, etc.). Regarding multidimensional models, visual techniques play an important role because they can show the relationships among variables in a data se…
No prominent works on this page.
Visual Data Mining With Self-organizing Maps for 'Self-monitoring' Data Analysis
Data collected in psychological studies are mainly characterized by containing a large number of variables (multidimensional data sets). Analyzing multidimensional data can be a difficult task, especially if only classical approaches are used (hypothesis tests, analyses of variance, linear models, etc.). Regarding multidimensional models, visual techniques play an important role because they can show the relationships among variables in a data se…
Physical Activity Monitoring and Acceptance of a Commercial Activity Tracker in Adult Patients with Haemophilia
Physical activity (PA) is highly beneficial for people with haemophilia (PWH), however, studies that objectively monitor the PA in this population are scarce. This study aimed to monitor the daily PA and analyse its evolution over time in a cohort of PWH using a commercial activity tracker. In addition, this work analyses the relationship between PA levels, demographics, and joint health status, as well as the acceptance and adherence to the acti…
Machine Learning for Mortality Analysis in Patients with Covid-19
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…
Computer Science (2 works) · Internal Medicine (2 works) · Medicine (2 works) · 2019-20 coronavirus outbreak (1 work) · Activity monitor (1 work) · Activity tracker (1 work) · Artificial Intelligence (1 work) · Artificial Intelligence (1 work) · Artificial Intelligence in Healthcare (1 work) · Betacoronavirus (1 work)