Predicting mental conditions based on “history of present illness” in psychiatric notes with deep neural networks

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


Background: Applications of natural language processing to mental health notes are not common given the sensitive nature of the associated narratives. The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) changed this scenario by providing the first set of neuropsychiatric notes to participants. This study summarizes our efforts and results in proposing a novel data use case for this dataset as part of the third track in this shared task. Objective: We explore the feasibility and effectiveness of predicting a set of common mental conditions a patient has based on the short textual description of patient's history of present illness typically occurring in the beginning of a psychiatric initial evaluation note. Materials and methods: We clean and process the 1000 records made available through the N-GRID clinical NLP task into a key-value dictionary and build a dataset of 986 examples for which there is a narrative for history of present illness as well as Yes/No responses with regards to presence of specific mental conditions. We propose two independent deep neural network models: one based on convolutional neural networks (CNN) and another based on recurrent neural networks with hierarchical attention (ReHAN), the latter of which allows for interpretation of model decisions. We conduct experiments to compare these methods to each other and to baselines based on linear models and named entity recognition (NER). Results: Our CNN model with optimized thresholding of output probability estimates achieves best overall mean micro-F score of 63.144% for 11 common mental conditions with statistically significant gains (p<0.05) over all other models. The ReHAN model with interpretable attention mechanism scored 61.904% mean micro-F1 score. Both models’ improvements over baseline models (support vector machines and NER) are statistically significant. The ReHAN model additionally aids in interpretation of the results by surfacing important words and sentences that lead to a particular prediction for each instance. Conclusions: Although the history of present illness is a short text segment averaging 300 words, it is a good predictor for a few conditions such as anxiety, depression, panic disorder, and attention deficit hyperactivity disorder. Proposed CNN and RNN models outperform baseline approaches and complement each other when evaluating on a per-label basis.

Original languageEnglish
Pages (from-to)S138-S148
JournalJournal of Biomedical Informatics
StatePublished - Nov 2017

Bibliographical note

Funding Information:
We thank anonymous reviewers for their constructive criticism, suggestions for improving readability, and recommendations for better evaluations. Thanks to Richard Charnigo for advising us on the paired t -test needed to assess statistical significance of our results. We are grateful to the U.S. National Library of Medicine for providing the primary support for this work through grant R21LM012274 . We are thankful for additional support by the National Center for Advancing Translational Sciences through grant UL1TR001998 and the Kentucky Lung Cancer Research Program through grant PO2 41514000040001 . Finally, we are grateful to the organizers of the N-GRID clinical NLP shared task and the support through NIH grants MH106933 and R13LM011411 that made the task and the associated workshop possible. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Appendix A

Publisher Copyright:
© 2017


  • Convolutional and recurrent neural networks
  • Hierarchical attention networks
  • Multi-label text classification
  • Psychiatric condition prediction

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications


Dive into the research topics of 'Predicting mental conditions based on “history of present illness” in psychiatric notes with deep neural networks'. Together they form a unique fingerprint.

Cite this