본문 바로가기
HOME> 논문 > 논문 검색상세

논문 상세정보

Journal of biomedical informatics v.75 suppl., 2017년, pp.S138 - S148   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

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

Tran, Tung (Department of Computer Science, University of Kentucky, 329 Rose Street, Lexington, KY 40506, USA ) ; Kavuluru, Ramakanth (Department of Computer Science, University of Kentucky, 329 Rose Street, Lexington, KY 40506, USA ) ;
  • 초록  

    Abstract 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 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. Highlights Psychiatric conditions are predicted based on the “history of present illness” text field. Deep neural networks resulted in a 3% improvement in micro F-score over linear models. Recurrent neural networks (RNNs) with attention helped in model interpretation. CNNs and RNNs complemented each other in per-condition evaluations. Graphical abstract [DISPLAY OMISSION]


  • 주제어

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

 활용도 분석

  • 상세보기

    amChart 영역
  • 원문보기

    amChart 영역

원문보기

무료다운로드
  • 원문이 없습니다.
유료다운로드

유료 다운로드의 경우 해당 사이트의 정책에 따라 신규 회원가입, 로그인, 유료 구매 등이 필요할 수 있습니다. 해당 사이트에서 발생하는 귀하의 모든 정보활동은 NDSL의 서비스 정책과 무관합니다.

원문복사신청을 하시면, 일부 해외 인쇄학술지의 경우 외국학술지지원센터(FRIC)에서
무료 원문복사 서비스를 제공합니다.

NDSL에서는 해당 원문을 복사서비스하고 있습니다. 위의 원문복사신청 또는 장바구니 담기를 통하여 원문복사서비스 이용이 가능합니다.

이 논문과 함께 출판된 논문 + 더보기