ML models for severity classification and length-of-stay forecasting in emergency units
Date
2023-03-02
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Journal Title
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Publisher
Elsevier
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0957-4174
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Abstract
Length-of-stay (LoS) prediction and severity classification for patients in emergency units in a clinic or hospital
are crucial problems for public and private health networks. An accurate estimation of these parameters is
essential for better planning resources, which are usually scarce. Although it is possible to find several works
that propose traditional Machine Learning (ML) models to face these challenges, few works have exploited
advances in Natural Language Processing (NLP) on Spanish raw-text vector representations. Consequently, we
take advantage of those advances, incorporating sentence embeddings in traditional ML models to improve
predictions. Moreover, we apply a strategy based on SHapley Additive exPlanations (SHAP) values to provide
explanations for these predictions. The results of our case study demonstrate an increase in the accuracy of
the predictions using raw text with a minimum preprocessing. The precision increased by up to 2% in the
classification of the patient’s post-care destination and by up to 8% in the prediction of LoS in the hospital.
This evidence encourages practitioners to use available text to anticipate the patient’s need for hospitalization
more accurately at the earliest stage of the care process.
Description
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Citation
Expert Systems With Applications, Vol. 223, N° 1 (2023)
Keywords
Length-of-stay prediction, Applied machine learning, Text embeddings, Emergency units, Explanaible artificial intelligence
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Atribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL)