Text mining approach to predict hospital admissions using early medical records from the Emergency Department Event as iCalendar

(Information Systems and Operations Management)

28 November 2018

1 - 2pm

Venue: The University of Auckland Business School, Level 3, Room 317, 12 Grafton Road, Auckland, 1010

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 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  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.

Bio:
Professor Flavio S Fogliatto holds a PhD in Industrial and Systems Engineering from Rutgers University, USA (1997). He was visiting scholar at the CNAM (Conservatoire National des Arts et Métiers – Paris, France) in 2005–2006. Currently he is Professor at the IE Department of the Federal University of Rio Grande do Sul (UFRGS), and Graduate Director of the IE Graduate Program in that same institution. He is also the President of FEEng, UFRGS’ School of Engineering Foundation.

His expertise is in the fields of Operations Research and Management. His main research interests are: healthcare operations management, quality control and optimization of products and processes, mass customization, and quantitative methods in production control. He received the IIE Transactions best paper award in 2002, the 2008 IEEE International Conference on Industrial Engineering and Engineering Management Best Conference Paper award, and best papers awards at the 2010 (Rotterdam, Holand) and 2012 (Rennes, France) editions of the Sensometrics Conference. His research has been published in the International Journal of Production Research, IIE Transactions, Food Quality & Preference, Chemometrics, Int J of Medical Informatics, and International Journal of Production Economics, among others. His h-index, measured in July 2018, is 13.0; his work has been cited 1,468 times (Scopus, 2018).

For more information contact:
Jaeseok Lee
Email: jaeseok.lee@auckland.ac.nz
Ext. 82170