Ordinal convolutional neural networks for predicting RDoC positive valence psychiatric symptom severity scores

Anthony Rios, Ramakanth Kavuluru

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Background The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) provided a set of 1000 neuropsychiatric notes to participants as part of a competition to predict psychiatric symptom severity scores. This paper summarizes our methods, results, and experiences based on our participation in the second track of the shared task. Objective Classical methods of text classification usually fall into one of three problem types: binary, multi-class, and multi-label classification. In this effort, we study ordinal regression problems with text data where misclassifications are penalized differently based on how far apart the ground truth and model predictions are on the ordinal scale. Specifically, we present our entries (methods and results) in the N-GRID shared task in predicting research domain criteria (RDoC) positive valence ordinal symptom severity scores (absent, mild, moderate, and severe) from psychiatric notes. Methods We propose a novel convolutional neural network (CNN) model designed to handle ordinal regression tasks on psychiatric notes. Broadly speaking, our model combines an ordinal loss function, a CNN, and conventional feature engineering (wide features) into a single model which is learned end-to-end. Given interpretability is an important concern with nonlinear models, we apply a recent approach called locally interpretable model-agnostic explanation (LIME) to identify important words that lead to instance specific predictions. Results Our best model entered into the shared task placed third among 24 teams and scored a macro mean absolute error (MMAE) based normalized score (100·(1-MMAE)) of 83.86. Since the competition, we improved our score (using basic ensembling) to 85.55, comparable with the winning shared task entry. Applying LIME to model predictions, we demonstrate the feasibility of instance specific prediction interpretation by identifying words that led to a particular decision. Conclusion In this paper, we present a method that successfully uses wide features and an ordinal loss function applied to convolutional neural networks for ordinal text classification specifically in predicting psychiatric symptom severity scores. Our approach leads to excellent performance on the N-GRID shared task and is also amenable to interpretability using existing model-agnostic approaches.

Original languageEnglish
Pages (from-to)S85-S93
JournalJournal of Biomedical Informatics
Volume75
DOIs
StatePublished - Nov 2017

Bibliographical note

Funding Information:
We thank anonymous reviewers whose valuable comments helped improve the readability and methodological aspects of this manuscript. We are grateful to the U.S. National Library of Medicine for offering the primary support for this work through grant R21LM012274 . We are also 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 . We are very grateful to the organizers of the N-GRID clinical NLP shared task and the support through NIH grants MH106933 (PI: Kohane) and R13LM011411 (PI: Uzuner) 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.

Publisher Copyright:
© 2017 Elsevier Inc.

Keywords

  • Convolutional neural networks
  • Model interpretability
  • Ordinal regression
  • Research domain criteria
  • Text classification

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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