Usually, in a hospital, the data generated by each department or section is treated in isolation, believing that there is no relationship between them. It is thought that while one department is in high demand, it can not influence that another may have the same demand or not have any demand. In this paper, we question this approach by considering information from departments as components of a large system in the hospital. Thus, we present an algorithm to predict the appointments of departments when data is not available using data from other departments. This algorithm uses a model based on multiple linear regression using a correlation matrix to measure the relationship between the departments with different time windows. After running our algorithm for different time windows and departments, we experimentally find that while we increase the extension of a time window and learn dependencies in the data, its corresponding precision decreases. Indeed, a month of data is the minimum sweet spot to leverage information from other departments and still provide accurate predictions. These results are important to develop per-department health policies under limited data, an interesting problem that we plan to investigate in future works.
|Number of pages||7|
|Journal||International Journal of Advanced Computer Science and Applications|
|State||Published - 2020|
Bibliographical noteFunding Information:
This research project was subsidized by Universidad Nacional de San Agusti[dotless]n de Arequipa, IAI-023-2018-UNSA. We would like to thank the "Research Center, Transfer of Technologies and Software Development R + D + i"-CiTeSoft-UNSA for their collaboration in the use of their equipment and facilities, for the development of this research work.
© Science and Information Organization.
- Correlation matrix
- Hospital appointments
- Machine learning
- Multi linear regression