Emergency Medical Text Classifier: New system improves processing and classification of triage notes.
OBJECTIVE
Automated syndrome classification aims to aid near real-time syndromic surveillance to serve as an early warning system for disease outbreaks, using Emergency Department (ED) data. We present a system that improves the automatic classification of an ED record with triage note into one or more syndrome categories using the vector space model coupled with a 'learning' module that employs a pseudo-relevance feedback mechanism.


METHODS
Terms from standard syndrome definitions are used to construct an initial reference dictionary for generating the syndrome and triage note vectors. Based on cosine similarity between the vectors, each record is classified into a syndrome category. We then take terms from the top-ranked records that belong to the syndrome of interest as feedback. These terms are added to the reference dictionary and the process is repeated to determine the final classification. The system was tested on two different datasets for each of three syndromes: Gastro-Intestinal (GI), Respiratory (Resp) and Fever-Rash (FR). Performance was measured in terms of sensitivity (Se) and specificity (Sp).


RESULTS
The use of relevance feedback produced high values of sensitivity and specificity for all three syndromes in both test sets: GI: 90% and 71%, Resp: 97% and 73%, FR: 100% and 87%, respectively, in test set 1, and GI: 88% and 69%, Resp: 87% and 61%, FR: 97% and 71%, respectively, in test set 2.


CONCLUSIONS
The new system for pre-processing and syndromic classification of ED records with triage notes achieved improvements in Se and Sp. Our results also demonstrate that the system can be tuned to achieve different levels of performance based on user requirements.
School of Information and Library Science, University of North Carolina at Chapel Hill, NC.
Stephanie W
SW
Haas
School of Nursing, University of North Carolina at Chapel Hill, NC ; School of Medicine, University of North Carolina at Chapel Hill, NC.
Debbie
D
Travers
School of Medicine, University of North Carolina at Chapel Hill, NC.
Anna
A
Waller
School of Nursing, University of North Carolina at Chapel Hill, NC.
Deepika
D
Mahalingam
School of Medicine, University of North Carolina at Chapel Hill, NC.
John
J
Crouch
School of Nursing, University of North Carolina at Chapel Hill, NC ; School of Public Health, University of North Carolina at Chapel Hill, NC.
Todd A
TA
Schwartz
School of Information and Library Science, University of North Carolina at Chapel Hill, NC ; School of Medicine, University of North Carolina at Chapel Hill, NC.
Javed
J
Mostafa
Disease outbreaks; electronic health records/classification; machine learning; natural language processing; public health informatics; public health surveillance/methods