Abstract：
BACKGROUND:Efficient planning of hospital bed usage is a necessary condition to minimize the hospital costs. In the presented work we deal with the problem of occupancy forecasting in the scale of several months, with a focus on personnel's holiday planning. METHODS:We construct a model based on a set of recursive neural networks, which performs an occupancy prediction using historical admission and release data combined with external factors such as public and school holidays. The model requires no personal information on patients or staff. It is optimized for a 60 days forecast during the summer season (May-September). RESULTS:An average mean absolute percentage error (MAPE) of 6.24% was computed on 8 validation sets. CONCLUSIONS:The proposed machine learning model has shown to be competitive to standard time-series forecasting models and can be recommended for incorporation in medium-size hospitals automatized scheduling and decision making.
journal_name
BMC Med Inform Decis Makjournal_title
BMC medical informatics and decision makingauthors
Kutafina E,Bechtold I,Kabino K,Jonas SMdoi
10.1186/s12911-019-0776-1subject
Has Abstractpub_date
2019-03-07 00:00:00pages
39issue
1issn
1472-6947pii
10.1186/s12911-019-0776-1journal_volume
19pub_type
杂志文章abstract：BACKGROUND:Emergency room reports pose specific challenges to natural language processing techniques. In this setting, violence episodes on women, elderly and children are often under-reported. Categorizing textual descriptions as containing violence-related injuries (V) vs. non-violence-related injuries (NV) is thus a...
journal_title：BMC medical informatics and decision making
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journal_title：BMC medical informatics and decision making
pub_type： 杂志文章
doi：10.1186/1472-6947-12-132
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journal_title：BMC medical informatics and decision making
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doi：10.1186/1472-6947-7-6
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doi：10.1186/1472-6947-7-5
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pub_type： 杂志文章,评审
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