Authors:
Lior Rokach
1
;
Roni Romano
2
and
Oded Maimon
2
Affiliations:
1
Ben-Gurion University of the Negev, Israel
;
2
Tel Aviv University, Israel
Keyword(s):
Medical Informatics, Text Classification, Machine Learning, Information Retrieval.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Substantial medical data such as discharge summaries and operative reports are stored in textual form. Databases containing free-text clinical narratives reports often need to be retrieved to find relevant information for clinical and research purposes. Terms that appear in these documents tend to appear in different contexts. The context of negation, a negative finding, is of special importance, since many of the most frequently described findings are those denied by the patient or subsequently “ruled out.” Hence, when searching free-text narratives for patients with a certain medical condition, if negation is not taken into account, many of the documents retrieved will be irrelevant. In this paper we examine the applicability of machine learning methods for automatic identification of negative context patterns in clinical narratives reports. We suggest two new simple algorithms and compare their performance with standard machine learning techniques such as neural networks and decis
ion trees. The proposed algorithms significantly improve the performance of information retrieval done on medical narratives.
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