Abstract
The introduction of pre-trained language models in natural language processing (NLP) based on deep learning and the availability of electronic health records (EHRs) presents a great opportunity to transfer the “knowledge” learned from data in the general domain to enable the analysis of unstructured textual data in clinical domains. This study explored the feasibility of applying NLP to a small EHR dataset to investigate the power of transfer learning to facilitate the process of patient screening in psychiatry. A total of 500 patients were randomly selected from a medical center database. Three annotators with clinical experience reviewed the notes to make diagnoses for major/minor depression, bipolar disorder, schizophrenia, and dementia to form a small and highly imbalanced corpus. Several state-of-the-art NLP methods based on deep learning along with pre-trained models based on shallow or deep transfer learning were adapted to develop models to classify the aforementioned diseases. We hypothesized that the models that rely on transferred knowledge would be expected to outperform the models learned from scratch. The experimental results demonstrated that the models with the pre-trained techniques outperformed the models without transferred knowledge by micro-avg. and macro-avg. F-scores of 0.11 and 0.28, respectively. Our results also suggested that the use of the feature dependency strategy to build multi-labeling models instead of problem transformation is superior considering its higher performance and simplicity in the training process.
Highlights
The diagnosis and classification of mental disorders are commonly based on the International Statistical Classification of Diseases and Related Health Problems (ICD) and the Diagnostic and Statistical Manual of Mental Disorders (DSM) system
The summaries were manually chart reviewed by board-certified clinical psychiatrists (CJK and CSW) to determine whether the patients had schizophrenia (SCZ), bipolar disorder (BPD), major depressive disorders (MDD), minor or other depressive disorder, and/or dementia (DD)
For the pre-trained models, we used GloVe 6B-300d, which was trained on Wikipedia and Gigaword and empirically outperformed other word embedding methods on several medical natural language processing (NLP) tasks [20, 30]
Summary
The diagnosis and classification of mental disorders are commonly based on the International Statistical Classification of Diseases and Related Health Problems (ICD) and the Diagnostic and Statistical Manual of Mental Disorders (DSM) system. These diagnostic criteria are derived from the clinical observation of the symptoms, signs, and course of mental diseases. The use of antidepressants for bipolar depression might induce mood swings or a manic episode. Another example is that depressive symptoms are frequently observed in patients with dementia. Antidepressant treatment might increase the risk of cerebrovascular accidents among these patients [1]
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