Cited by 139Machine Learning in HealthcareArtificial Intelligence in HealthcareEmergency and Acute Care Studies

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Text mining approach to predict hospital admissions using early medical records from the emergency department

Lucini, Filipe R., Fogliatto, Flavio S., da Silveira, Giovani J.C., Neyeloff, Jeruza L., Anzanello, Michel J., Kuchenbecker, Ricardo S., Schaan, Beatriz D.·2017·International Journal of Medical Informatics

DOI 10.1016/j.ijmedinf.2017.01.001

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Abstract

Objective Emergency department (ED) overcrowding is a serious issue for hospitals. Early information on short-term inward bed demand from patients receiving care at the ED may reduce the overcrowding problem, and optimize the use of hospital resources. In this study, we use text mining methods to process data from early ED patient records using the SOAP framework, and predict future hospitalizations and discharges.Design We try different approaches for pre-processing of text records and to predict hospitalization. Sets-of-words are obtained via binary representation, term frequency, and term frequency-inverse document frequency. Unigrams, bigrams and trigrams are tested for feature formation. Feature selection is based on χ 2 and F-score metrics. In the prediction module, eight text mining methods are tested: Decision Tree, Random Forest, Extremely Randomized Tree, AdaBoost, Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine (Kernel linear) and Nu-Support Vector Machine (Kernel linear).Measurements Prediction performance is evaluated by F1-scores. Precision and Recall values are also informed for all text mining methods tested.Results Nu-Support Vector Machine was the text mining method with the best overall performance. Its average F1-score in predicting hospitalization was 77.70%, with a standard deviation (SD) of 0.66%.Conclusions The method could be used to manage daily routines in EDs such as capacity planning and resource allocation. Text mining could provide valuable information and facilitate decision-making by inward bed management teams.

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Authors: Lucini, F. R., Fogliatto, F. S., da Silveira, G. J. C., Neyeloff, J. L., Anzanello, M. J., Kuchenbecker, R. S., & Schaan, B. D.
Affiliations: Universidade Federal do Rio Grande do Sul (BR)
Corresponding author: Lucini, Filipe R.
ORCID: Lucini, Filipe R.
Fogliatto, Flavio S.
Neyeloff, Jeruza L.
Anzanello, Michel J.
Kuchenbecker, Ricardo S.
Schaan, Beatriz D.
Venue: International Journal of Medical Informatics
Publisher: Elsevier BV
Published: 2017-04
Volume: 100
Pages: 1-8
ISSN: 1386-5056 (ISSN-L)
DOI: 10.1016/j.ijmedinf.2017.01.001
OpenAlex: W2568818165
Cited by: 139
References: 61
Citations by year: 2026: 2 · 2025: 12 · 2024: 13 · 2023: 11 · 2022: 20 · 2021: 23 · 2020: 21 · 2019: 20 · 2018: 14 · 2017: 3
FWCI: 9.46
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: International Journal of Medical Informatics (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
Keywords: emergency department overcrowding, hospital admission prediction, text mining, SOAP notes, electronic health records, hospital bed demand, capacity planning, resource allocation, feature selection, text preprocessing, support vector machine
MeSH terms: Bayes Theorem, Emergency Service, Hospital, Hospitalization, Humans, Medical Records, Logistic Models, Data Mining, Support Vector Machine

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Text mining approach to predict hospital admissions using early medical records from the emergency department

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