Althoff, T, Clark K, Leskovec, J. Large-scale Analysis of Counseling Conversations:
An Application of Natural Language Processing to Mental Health. Trans Assoc Comput
Linguist 2016(4):463-76
This paper presents an analysis of counselor/patient interactions in text message
conversations collected from a free hotline. The authors characterize counseling conversation
relying on a wide range of methods including discourse analysis, statistical language
modeling, and sentiment analysis. They evidence actionable conversation strategies
that are associated with better conversation outcomes: successful counselors exhibit
an ability to adapt creatively to each new counseling situation, their reaction to
ambiguity with check questions and appreciation language is well received, they conduct
conversations efficiently by spending less time to understand the patient’s issue
and more time in problem-solving, and they facilitate a change in perspective. These
results may be used towards improving practice recommendations and counselor training.
Kilicoglu, H, Demner-Fushman, D. Bio-SCoRes: A Smorgasbord Architecture for Coreference
Resolution in Biomedical Text. PLoS One. 2016 Mar 2;11(3):e0148538
This paper addresses coreference resolution, which is a fundamental and challenging
task in natural language processing. The authors offer a very instructive and informed
definition of coreference as well as a comprehensive literature review of the state
of the art in coreference resolution. They describe and evaluate a general method
for biomedical coreference resolution called BioSCoRes, implemented in a publicly
available toolkit. The evaluation broadly covers the biomedical domain by relying
on two existing corpora (one clinical, one biological), as well as a newly developed
and shared corpus of drug label inserts. It also offers a detailed performance report
on each type of coreference. BioSCoRes obtains overall results that come close (on
the clinical corpus) or exceed (on the other two corpora) the state-of-the art.
Morid, MA, Fiszman, M, Raja, K, Jonnalagadda, SR, Del Fiol, G. Classification of clinically
useful sentences in clinical evidence resources. J Biomed Inform. 2016 Apr;60:14-22
This paper presents a method for classifying sentences from a variety of evidence-based
clinical decision support knowledge sources according to clinical usefulness. This
work offers a specific definition of actionable, clinically useful sentences. Then,
it proceeds to explore advanced NLP methods to extract rich features for sentence
classification. A feature ablation study supports the proposed feature-rich approach
and shows that the system performs with an F-measure of at least 73% on different
text genres. This work is exemplary in exploring fundamental approaches towards the
practical goal of providing real-time clinical information at the point of care, while
setting up a technical framework that will facilitate the integration of the research
results in a clinical setting.
Shivade C, de Marneffe MC, Fosler-Lussier E, Lai AM. Identification, characterization,
and grounding of gradable terms in clinical text. Proceedings of the 15th Workshop
on Biomedical Natural Language Processing. 2016:17-26
This paper presents an analysis of gradable adjectives found in clinical text to qualify
medical findings. The authors use existing methods to automatically identify gradable
adjectives in clinical corpora and estimate prevalence at about 30% of adjectives.
Focusing on four clinical phenomena, the authors show that the gradable adjectives
used to qualify these phenomena in a clinical corpus can be reliably associated to
numerical value intervals using a probabilistic model. This very original work relies
on a regular expression-based analysis of a clinical description of selected phenomena
comprising a gradable adjective along with a numerical value. It offers a first step
towards the interpretation of statements using gradable adjectives by grounding their
meaning to value intervals, which would facilitate clinical decision-making.
Wu, Y, Denny, JC, Rosenbloom, ST, Miller, RA, Giuse, DA, Wang, L, Blanquicett, C,
Soysal, E, Xu, J, Xu, H. A long journey to short abbreviations: developing an open-source
framework for clinical abbreviation recognition and disambiguation (CARD). J Am Med
Inform Assoc 2017 Apr 1;24(e1):e79-e86
This paper presents an open source framework for clinical abbreviation recognition
and disambiguation via entity linking to the Unified Medical Language System (UMLS).
This work builds on a large body of research on different aspects of abbreviation
resolution including recognition of abbreviated entities in clinical text, the extraction
of possible long forms or senses from knowledge bases, and the disambiguation of a
given entity that leverages context to identify the unique sense of a given abbreviated
entity. The overall framework offers performance that exceeds the state-of-the-art
on two shared datasets. In addition, the tool may be tailored to specific needs by
allowing the use of customized resources.
