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Internal and Emergency Medicine

Erschienen in:

15.02.2020 | IM - COMMENTARY

Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study

verfasst von: Greta Falavigna

Erschienen in: Internal and Emergency Medicine | Ausgabe 6/2020

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Excerpt

The article by [1] focuses on a very interesting topic for EDs, i.e., predicting the length of stay of patients. The relevance of this topic is widely recognized in the literature, since it is linked to a twofold problem: on one hand, physicians clearly care about people’s health; on the other hand, they have to take budget constraints into account [2, 3]. Starting from these considerations, models for assessing the pressure of the medical care system are very welcome. …

Bacchi S, Gluck S, Tan Y, Chim I, Cheng J, Gilbert T, Menon DK, Jannes J, Kleinig T, Koblar S (2020) Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study. Intern Emerg Med 1:1–7

Casagranda I, Costantino G, Falavigna G, Furlan R, Ippoliti R (2016) Artificial neural networks and risk stratification models in Emergency Departments: the policy maker's perspective. Health Policy 120(1):111–119CrossRef

Falavigna G, Costantino G, Furlan R, Quinn JV, Ungar A, Ippoliti R (2019) Artificial neural networks and risk stratification in emergency departments. Intern Emerg Med 14(2):291–299CrossRef

Costantino G, Falavigna G, Solbiati M, Casagranda I, Sun BC, Grossman SA, Quinn JV, Reed MJ, Ungar A, Montano N, Furlan R (2016) Neural networks as a tool to predict syncope risk in the Emergency Department. Ep Europace 19(11):1891–1895CrossRef

Siegenfeld AF, Bar-Yam Y (2019) An introduction to complex systems science and its applications. arXiv preprint https://arxiv.org/1912.05088

Žižka J, Dařena F, Svoboda A (2020) Text mining with machine learning. CRC Press, Boca Raton https://doi.org/10.1201/9780429469275CrossRef

Titel: Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study
verfasst von: Greta Falavigna
Publikationsdatum: 15.02.2020
Verlag: Springer International Publishing
Erschienen in: Internal and Emergency Medicine / Ausgabe 6/2020
Print ISSN: 1828-0447
Elektronische ISSN: 1970-9366
DOI: https://doi.org/10.1007/s11739-020-02291-6

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Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study

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