Abstract
Timely discharge of hospitalized patients can prevent complications and reduce costs. In this paper, we have investigated whether outpatient services, i.e., diagnostic exams or specialist consultations provided by external wards, have a clear impact on Length of Stay (LOS). In particular, we have worked on an event log of more than 7000 real patient traces, logging the sequence of outpatient services provided during hospitalization, and we have classified the traces into long (more or equal than 20 days) versus short (less than 20 days) LOS, resorting to a deep learning approach that adopts a Long Short-Term Memory (LSTM) network. The very high quality of the classification results suggests that outpatient services play a significant role in determining LOS, and hospitals should work on optimizing their organization.
| Lingua originale | Inglese |
|---|---|
| Pagine | 1-5 |
| Numero di pagine | 5 |
| Stato di pubblicazione | Pubblicato - 2025 |
| Evento | Process Management in the AI Era 2025 - 4th International Workshop on Process Management in the AI era (PMAI25) @ECAI 2025 - Bologna, Italy Durata: 1 gen 2025 → … |
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| ???event.eventtypes.event.conference??? | Process Management in the AI Era 2025 - 4th International Workshop on Process Management in the AI era (PMAI25) @ECAI 2025 |
|---|---|
| Città | Bologna, Italy |
| Periodo | 1/01/25 → … |
Keywords
- LSTM
- Length of Stay
- Process trace classification