In clinical documents, medical terms are often expressed in multi-word phrases. Traditional topic modelling approaches relying on the “bag-of-words” assumption are not effective in extracting topic themes from clinical documents. This paper proposes to first extract medical phrases using an off-the-shelf tool for medical concept mention extraction, and then train a topic model which takes a hierarchy of Pitman-Yor processes as prior for modelling the generation of phrases of arbitrary length. Experimental results on patients’ discharge summaries show that the proposed approach outperforms the state-of-the-art topical phrase extraction model on both perplexity and topic coherence measure and finds more interpretable topics.
|Title of host publication||Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)|
|Number of pages||7|
|Publication status||Published - 12 Feb 2016|
|Event||30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States|
Duration: 12 Feb 2016 → 17 Feb 2016
|Conference||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Period||12/02/16 → 17/02/16|