Wrappers for Feature Subset Selection in CRF based Clinical Information Extraction
We present our methodology and the results for Task 1 of the CLEF eHealth Evaluation Lab 2016. This benchmark addresses clinical information extraction related to nursing shift changes, whereas the challenge is to maximize the correctness in structuring written free text records by automatically identifying relevant text snippets. Our approach is focused on the exploration of relevant features for conditional random fields. We use wrappers for feature subset selection in conjunction with parameter optimization to consider how the learning algorithm and the dataset interact. First, we composed a feature set based on Stanford CoreNLP, latent Dirichlet allocation, regular expressions, and the ontologies of WordNet and UMLS. Next, the heuristic methods best first and greedy (hill climbing) with forward and backward direction have been applied for feature evaluation and selection. Experimental results show that our system outperforms the baseline with a macro averaged F1 of 0.311 using all features and 0.382 by performing feature selection.
CLEF