Augmenting DSM-5 diagnostic criteria with self-attention-based BiLSTM models for psychiatric diagnosis.

BACKGROUND: Most previous studies make psychiatric diagnoses based on diagnostic terms. In this study we sought to augment Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) diagnostic criteria with deep neural network models to make psychiatric diagnoses based on psychiatric notes. METHODS: We augmented DSM-5 diagnostic criteria with self-attention-based bidirectional long short-term memory (BiLSTM) models to identify schizophrenia, bipolar, and unipolar depressive disorders. Given that the diagnostic criteria for psychiatric diagnosis include a certain symptom profile and functional impairment, we first extracted psychiatric symptoms and functional features with two approaches, including a lexicon-based approach and a dependency parsing approach. Then, we incorporated free-text discharge notes and extracted features for psychiatric diagnoses with the proposed models. RESULTS: The micro-averaged F1 scores of the two automatic annotation approaches were greater than 0.8. BiLSTM models with self-attention outperformed the rule-based models with DSM-5 criteria in the prediction of schizophrenia and bipolar disorder, while the latter outperformed the former in predicting unipolar depressive disorder. Approaches for augmenting DSM-5 criteria with a self-attention-based BiLSTM outperformed both pure rule-based and pure deep neural network models. In terms of classification of psychiatric diagnoses, we observed that the performance for schizophrenia and bipolar disorder was acceptable. CONCLUSION: This DSM-5-augmented deep neural network models showed good performance in identifying psychiatric diagnoses from psychiatric notes. We conclude that it is possible to establish a model that consults clinical notes to make psychiatric diagnoses comparably to physicians. Further research will be extended to outpatient notes and other psychiatric disorders.

TCMGeneDIT: a database for associated traditional Chinese medicine, gene and disease information using text mining.

BACKGROUND: Traditional Chinese Medicine (TCM), a complementary and alternative medical system in Western countries, has been used to treat various diseases over thousands of years in East Asian countries. In recent years, many herbal medicines were found to exhibit a variety of effects through regulating a wide range of gene expressions or protein activities. As available TCM data continue to accumulate rapidly, an urgent need for exploring these resources systematically is imperative, so as to effectively utilize the large volume of literature. METHODS: TCM, gene, disease, biological pathway and protein-protein interaction information were collected from public databases. For association discovery, the TCM names, gene names, disease names, TCM ingredients and effects were used to annotate the literature corpus obtained from PubMed. The concept to mine entity associations was based on hypothesis testing and collocation analysis. The annotated corpus was processed with natural language processing tools and rule-based approaches were applied to the sentences for extracting the relations between TCM effectors and effects. RESULTS: We developed a database, TCMGeneDIT, to provide association information about TCMs, genes, diseases, TCM effects and TCM ingredients mined from vast amount of biomedical literature. Integrated protein-protein interaction and biological pathways information are also available for exploring the regulations of genes associated with TCM curative effects. In addition, the transitive relationships among genes, TCMs and diseases could be inferred through the shared intermediates. Furthermore, TCMGeneDIT is useful in understanding the possible therapeutic mechanisms of TCMs via gene regulations and deducing synergistic or antagonistic contributions of the prescription components to the overall therapeutic effects. The database is now available at http://tcm.lifescience.ntu.edu.tw/. CONCLUSION: TCMGeneDIT is a unique database that offers diverse association information on TCMs. This database integrates TCMs with biomedical studies that would facilitate clinical research and elucidate the possible therapeutic mechanisms of TCMs and gene regulations.

Gene ontology annotation by density and gravitation models.

Gene Ontology (GO) is developed to provide standard vocabularies of gene products in different databases. The process of annotating GO terms to genes requires curators to read through lengthy articles. Methods for speeding up or automating the annotation process are thus of great importance. We propose a GO annotation approach using full-text biomedical documents for directing more relevant papers to curators. This system explores word density and gravitation relationships between genes and GO terms. Different density and gravitation models are built and several evaluation criteria are employed to assess the effects of the proposed methods.

Enhancing performance of protein and gene name recognizers with filtering and integration strategies.

Named entity (NE) recognition is a fundamental task in biological relationship mining. This paper considers protein/gene collocates extracted from biological corpora as restrictions to enhance the precision rate of protein/gene name recognition. In addition, we integrate the results of multiple NE recognizers to improve the recall rates. Yapex and KeX, and ABGene and Idgene are taken as examples of protein and gene name recognizers, respectively. The precision of Yapex increases from 70.90 to 85.84% at the low expense of the recall rate (i.e., it only decreases 2.44%) when collocates are incorporated. When both filtering and integration strategies are employed together, the Yapex-based integration with KeX shows good performance, i.e., the F-score increases by 7.83% compared to the pure Yapex method. The results of gene recognition show the same tendency. The ABGene-based integration with Idgene shows a 10.18% F-score increase compared to the pure ABGene method. These successful methodologies can be easily extended to other name finders in biological documents.