TY - GEN
T1 - Constraint Programming for the Pandemic in Peru
AU - Ugarte, Willy
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Currently, the world requires techniques that match infected people and hospital beds together given various criteria such as the severity of infection, patient location, hospital capacity, etc. Deep Learning might seems to be a perfect fit for this: various configurations from a broad range of parameters that need to be reduced to a few solutions. But, this models require to be trained, hence the need for historical data on previous cases leading to a waste of time would in cleaning and consolidating a dataset and lengthy training sessions need to be performed with a variety of architectures. Nevertheless, formulating this problem as a Constraint Satisfaction Problem (CSP), the aforementioned downsides will not be present while still optimal results, and without the need for any historical data. In this paper, a CSP model is used to search for the best distribution of COVID-19 patients with a severity of patients requiring hospitalization and patients requiring ICU beds, in hospitals in a part of Lima.
AB - Currently, the world requires techniques that match infected people and hospital beds together given various criteria such as the severity of infection, patient location, hospital capacity, etc. Deep Learning might seems to be a perfect fit for this: various configurations from a broad range of parameters that need to be reduced to a few solutions. But, this models require to be trained, hence the need for historical data on previous cases leading to a waste of time would in cleaning and consolidating a dataset and lengthy training sessions need to be performed with a variety of architectures. Nevertheless, formulating this problem as a Constraint Satisfaction Problem (CSP), the aforementioned downsides will not be present while still optimal results, and without the need for any historical data. In this paper, a CSP model is used to search for the best distribution of COVID-19 patients with a severity of patients requiring hospitalization and patients requiring ICU beds, in hospitals in a part of Lima.
KW - Constraint programming
KW - CoViD
KW - CSP
KW - Pandemic
UR - https://www.scopus.com/pages/publications/85107367734
U2 - 10.1007/978-3-030-71503-8_23
DO - 10.1007/978-3-030-71503-8_23
M3 - Contribución a la conferencia
AN - SCOPUS:85107367734
SN - 9783030715021
T3 - Communications in Computer and Information Science
SP - 299
EP - 311
BT - Applied Technologies - Second International Conference, ICAT 2020, Proceedings
A2 - Botto-Tobar, Miguel
A2 - Montes León, Sergio
A2 - Camacho, Oscar
A2 - Chávez, Danilo
A2 - Torres-Carrión, Pablo
A2 - Zambrano Vizuete, Marcelo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Applied Technologies, ICAT 2020
Y2 - 2 December 2020 through 4 December 2020
ER -