Psychiatric symptom recognition without labeled data using distributional representations of phrases and on-line knowledge

Abstract

Objective Mental health is becoming an increasingly important topic in healthcare. Psychiatric symptoms, which consist of subjective descriptions of the patient’s experience, as well as the nature and severity of mental disorders, are critical to support the phenotypic classification for personalized prevention, diagnosis, and intervention of mental disorders. However, few automated approaches have been proposed to extract psychiatric symptoms from clinical text, mainly due to (a) the lack of annotated corpora, which are time-consuming and costly to build, and (b) the inherent linguistic difficulties that symptoms present as they are not well-defined clinical concepts like diseases. The goal of this study is to investigate techniques for recognizing psychiatric symptoms in clinical text without labeled data. Instead, external knowledge in the form of publicly available “seed” lists of symptoms is leveraged using unsupervised distributional representations. Materials and Methods First, psychiatric symptoms are collected from three online repositories of healthcare knowledge for consumers—MedlinePlus, Mayo Clinic, and the American Psychiatric Association—for use as seed terms. Candidate symptoms in psychiatric notes are automatically extracted using phrasal syntax patterns. In particular, the 2016 CEGS N-GRID challenge data serves as the psychiatric note corpus. Second, three corpora—psychiatric notes, psychiatric forum data, and MIMIC II—are adopted to generate distributional representations with . Finally, semantic similarity between the distributional representations of the seed symptoms and candidate symptoms is calculated to assess the relevance of a phrase. Experiments were performed on a set of psychiatric notes from the CEGS N-GRID 2016 Challenge. Results & Conclusion Our method demonstrates good performance at extracting symptoms from an unseen corpus, including symptoms with no word overlap with the provided seed terms. Semantic similarity based on the distributional representation outperformed baseline methods. Our experiment yielded two interesting results. First, distributional representations built from social media data outperformed those built from clinical data. And second, the distributional representation model built from sentences resulted in better representations of phrases than the model built from phrase alone.

A natural language processing challenge for clinical records: Research Domains Criteria (RDoC) for psychiatry

“…Given the scarcity of mental health records available to the research community, and as a way of encouraging researchers to pursue their own research questions on these data, we included in this issue peer-reviewed articles on the uses of RDoC data outside of de-identification and symptom severity classification. In the novel data use track, Zhang et al [14] took an unsupervised approach to finding psychiatric disorder symptoms in the RDoC data. They used external sources as “seed” terms for symptoms and built a system for recognizing symptoms based on their syntactic characteristics and semantic similarity to seed terms.…”

“…These data could be used for mental health-related research questions that go beyond what is posed by the challenge organizers. The novel data use track was for participants who wanted to utilize the RDoC data to address new research questions, e.g., [14–16]. …”

Psychiatric stressor recognition from clinical notes to reveal association with suicide

“…Initial psychiatric evaluation records are produced by psychiatrists to document psychiatric signs and symptoms, disorders, and other medical conditions in order to decide the course of treatment. 23 All of the records are de-identified.…”

Machine learning in mental health: A systematic scoping review of methods and applications

“…Deep Learning (word2vec) [299] Research Articles [299] Depression DT [303], kNN [134,298], NN [295], Regression [294,296], RF [134], SVM [134], Linear Discriminant Analysis [134] Survey [296,303,304], Social Media [298], Electronic Health Records [295], Imaging [134,294], Biological [134,296] Healthy Ageing RF [304] Survey [304] Psychosis SVM, Multiple Kernel Learning [297] Imaging [297] Schizophrenia RF [291], SVM [291,293], Linear Discriminant Analysis [291], kNN [291] Insurance [291], Imaging [293] Substance Use Topic modelling [306] Interview [306] Symptom Severity NN [301] Clinical Notes [301] Wellbeing BN [302], SVM [302], Deep Learning (paragraph2vec) [300], NN [307] Clinical Notes [300,302] As an emerging field, there are understandably significant gaps for future research to address. It is evident that the majority of papers focus on diagnosis and detection, particularly on depression, suicide risk and cognitive decline.…”