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Major depressive disorder (MDD) is a prevalent psychiatric disorder that isassociated with significant healthcare burden worldwide. Phenotyping of MDD canhelp early diagnosis and consequently may have significant advantages inpatient management. In prior research MDD phenotypes have been extracted fromstructured Electronic Health Records (EHR) or using Electroencephalographic(EEG) data with traditional machine learning models to predict MDD phenotypes.However, MDD phenotypic information is also documented in free-text EHR data,such as clinical notes. While clinical notes may provide more accuratephenotyping information, natural language processing (NLP) algorithms must bedeveloped to abstract such information. Recent advancements in NLP resulted instate-of-the-art neural language models, such as Bidirectional EncoderRepresentations for Transformers (BERT) model, which is a transformer-basedmodel that can be pre-trained from a corpus of unsupervised text data and thenfine-tuned on specific tasks. However, such neural language models have beenunderutilized in clinical NLP tasks due to the lack of large training datasets.In the literature, researchers have utilized the distant supervision paradigmto train machine learning models on clinical text classification tasks tomitigate the issue of lacking annotated training data. It is still unknownwhether the paradigm is effective for neural language models. In this paper, wepropose to leverage the neural language models in a distant supervisionparadigm to identify MDD phenotypes from clinical notes. The experimentalresults indicate that our proposed approach is effective in identifying MDDphenotypes and that the Bio- Clinical BERT, a specific BERT model for clinicaldata, achieved the best performance in comparison with conventional machinelearning models.

Neural Language Models with Distant Supervision to Identify Major Depressive Disorder from Clinical Notes