  Automatic phenotype concept recognition from unstructured text remains a
challenging task in biomedical text mining research. Previous works that
address the task typically use dictionary-based matching methods, which can
achieve high precision but suffer from lower recall. Recently, machine
learning-based methods have been proposed to identify biomedical concepts,
which can recognize more unseen concept synonyms by automatic feature learning.
However, most methods require large corpora of manually annotated data for
model training, which is difficult to obtain due to the high cost of human
annotation. In this paper, we propose PhenoTagger, a hybrid method that
combines both dictionary and machine learning-based methods to recognize Human
Phenotype Ontology (HPO) concepts in unstructured biomedical text. We first use
all concepts and synonyms in HPO to construct a dictionary, which is then used
to automatically build a distantly supervised training dataset for machine
learning. Next, a cutting-edge deep learning model is trained to classify each
candidate phrase (n-gram from input sentence) into a corresponding concept
label. Finally, the dictionary and machine learning-based prediction results
are combined for improved performance. Our method is validated with two HPO
corpora, and the results show that PhenoTagger compares favorably to previous
methods. In addition, to demonstrate the generalizability of our method, we
retrained PhenoTagger using the disease ontology MEDIC for disease concept
recognition to investigate the effect of training on different ontologies.
Experimental results on the NCBI disease corpus show that PhenoTagger without
requiring manually annotated training data achieves competitive performance as
compared with state-of-the-art supervised methods.
