Automatically Detecting the Position and Type of Psychiatric Evaluation Report Sections

Deya Banisakher, Naphtali Rishe, Mark A. Finlayson


Abstract
Psychiatric evaluation reports represent a rich and still mostly-untapped source of information for developing systems for automatic diagnosis and treatment of mental health problems. These reports contain free-text structured within sections using a convention of headings. We present a model for automatically detecting the position and type of different psychiatric evaluation report sections. We developed this model using a corpus of 150 sample reports that we gathered from the Web, and used sentences as a processing unit while section headings were used as labels of section type. From these labels we generated a unified hierarchy of labels of section types, and then learned n-gram models of the language found in each section. To model conventions for section order, we integrated these n-gram models with a Hierarchical Hidden Markov Model (HHMM) representing the probabilities of observed section orders found in the corpus, and then used this HHMM n-gram model in a decoding framework to infer the most likely section boundaries and section types for documents with their section labels removed. We evaluated our model over two tasks, namely, identifying section boundaries and identifying section types and orders. Our model significantly outperformed baselines for each task with an F1 of 0.88 for identifying section types, and a 0.26 WindowDiff (Wd) and 0.20 and (Pk) scores, respectively, for identifying section boundaries.
Anthology ID:
W18-5612
Volume:
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | Louhi | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
101–110
Language:
URL:
https://www.aclweb.org/anthology/W18-5612
DOI:
10.18653/v1/W18-5612
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-5612.pdf