A Systematic Approach to Configuring MetaMap for Optimal Performance

Abstract

Background: MetaMap is a valuable tool for processing biomedical texts to identify concepts. Although MetaMap is highly configurative, configuration decisions are not straightforward. Objective: To develop a systematic, data-driven methodology for configuring MetaMap for optimal performance. Methods: MetaMap, the word2vec model, and the phrase model were used to build a pipeline. For unsupervised training, the phrase and word2vec models used abstracts related to clinical decision support as input. During testing, MetaMap was configured with the default option, one behavior option, and two behavior options. For each configuration, cosine and soft cosine similarity scores between identified entities and gold-standard terms were computed for 40 annotated abstracts (422 sentences). The similarity scores were used to calculate and compare the overall percentages of exact matches, similar matches, and missing gold-standard terms among the abstracts for each configuration. The results were manually spot-checked. The precision, recall, and F-measure (β =1) were calculated. Results: The percentages of exact matches and missing gold-standard terms were 0.6– 0.79 and 0.09–0.3 for one behavior option, and 0.56– 0.8 and 0.09–0.3 for two behavior options, respectively. The percentages of exact matches and missing terms for soft cosine similarity scores exceeded those for cosine similarity scores. The average precision, recall, and F-measure were 0.59, 0.82, and 0.68 for exact matches, and 1.00, 0.53, and 0.69 for missing terms, respectively. Conclusion: We demonstrated a systematic approach that provides objective and accurate evidence guiding MetaMap configurations for optimizing performance. Combining objective evidence and the current practice of using principles, experience, and intuitions outperforms a single strategy in MetaMap configurations. Our methodology, reference codes, measurements, results, and workflow are valuable references for optimizing and configuring MetaMap.

Keyphrase Identification Using Minimal Labeled Data with Hierarchical Contexts and Transfer Learning

“…Now, only 3148 unlabeled articles remained, and we created synthetic KP and marked the labels to create a synthetic labeled dataset for the CDSS domain with a 1:2 train-validation split. Cohen’s kappa rates for the first 42 (GS42) abstracts were 0.93 (between annotators 1 and 2) and 0.73 (between annotators 1 and 3) [37]. For the second set of abstracts (GS91), Cohen’s kappa rates were 0.87 (between annotators 1 and 2) and 0.97 (between annotators 1 and 3).…”