Summary of Forecasting Mortality Associated Emergency Department Crowding, by Jalmari Nevanlinna et al.
Forecasting mortality associated emergency department crowding
by Jalmari Nevanlinna, Anna Eidstø, Jari Ylä-Mattila, Teemu Koivistoinen, Niku Oksala, Juho Kanniainen, Ari Palomäki, Antti Roine
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A LightGBM model is used in this study to predict periods of high occupancy in emergency departments, which are associated with increased mortality rates. The model is trained on retrospective data from a large Nordic emergency department and is able to accurately forecast afternoon crowding up to 11am with an AUC of 0.82 (95% CI 0.78-0.86) and morning crowding up to 8am with an AUC of 0.79 (95% CI 0.75-0.83). This study demonstrates the feasibility of forecasting mortality-associated crowding using anonymous administrative data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Emergency departments are often overcrowded, which can lead to increased mortality rates. To help prevent this, researchers used a special type of model called LightGBM to look at past data from a big emergency department in Norway. They found that they could accurately predict when the department would be very busy later on in the day. This means that hospitals might be able to prepare for these times and reduce the number of deaths. |
Keywords
» Artificial intelligence » Auc
