164. Generating realistic patient data
Invited abstract in session MD-2: Patient to room, stream Sessions.
Monday, 13:30-15:00Room: NTNU, Realfagbygget R8
Authors (first author is the speaker)
| 1. | Tabea Brandt
|
| Lehr- und Forschungsgebiet Kombinatorische Optimierung, RWTH Aachen | |
| 2. | Christina Büsing
|
| Lehr- und Forschungsgebiet Kombinatorische Optimierung, RWTH Aachen University | |
| 3. | Johanna Leweke
|
| Research and Teaching Area Combinatorial Optimization, RWTH Aachen University |
Abstract
Developing algorithms for real-life problems that perform well in practice highly depends on the availability of realistic data for testing. Obtaining real-life data for optimization problems in health care, however, is often difficult. This is espacially true for any patient related optimization problems, e.g., for patient-to-room assignment, due to data privacy policies. Furthermore, obtained real-life data usually cannot be published which prohibits reproducibility of results by other researchers.
Therefore, often artificially generated instances are used.
In this talk, we present a configurable instance generator for the patient-to-room assignment problem. Configurability is in this case especially important as we observed in an extensive analysis of real-life data that, e.g., the probability distribution for patients' age and length of stay depend on the respective ward. We show in this talk how our instance generator can be used to create artificial instances that mimic the situation of a desired real-life ward and allow a feasible patient-to-room assignment. Further, we present combinatorial insights used to ensure that all generated instances are feasible for the specified room setting.
Keywords
- Modelling and simulation
- Analytics
- Performance evaluation
Status: accepted
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