Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records
TABLE IV: The performance of annotation results compared with the silver standard. All the numbers are averaged across EHRs in the testing set.
['Method', 'Precision', 'Recall', 'F1']
The proposed method achieves the precision of 0.7113, recall of 0.6805 and F1 of 0.6383. The F1 is significantly higher than those of all other baselines. Considering the association between phenotype and diseases in the silver standard, we believe that our method is more effective and the annotation results of our method can provide a better indication for disease diagnosis than the baselines.