Appendix: Content Summaries of Best Papers for the 2018 IMIA Yearbook, Section Clinical
Natural Language Processing
Castro SM, Tseytlin E, Medvedeva O, Mitchell K, Visweswaran S, Bekhuis T, Jacobson
RS
Automated annotation and classification of BI-RADS assessment from radiology reports
J Biomed Inform 2017 May;69:177-87
This paper presents a system for automatically extracting and classifying mammography
information from radiology reports written in English. This is a well conducted study
comparing rule-based and machine learning methods for BI-RADS (Breast Imaging Reporting
and Data System) categories extraction from breast radiology reports (Conditional
Random Field, F=0.95), together with their laterality (Partial decision trees, F=0.91-0.93).
It can be noted that the study addresses types of clinical texts and entities that
are less challenging than others. However, the corpus offers high text variety with
reports from 18 hospitals. While the authors are neither experts on rule-based or
machine learning methods, they present an excellent report of their work from the
application point of view: describing the caveats of reproducing or adapting previous
work, and using toolkits at their disposal towards the targeted application goal.
Pérez A, Weegar R, Casillas A, Gojenola K, Oronoz M, Dalianis H
Semi-supervised medical entity recognition: A study on Spanish and Swedish clinical
corpora
J Biomed Inform 2017 Jul;71:16-30
This paper addresses named entity extraction from clinical corpora in Swedish and
Spanish. It studies the influence of two types of unsupervised word representations
on clinical information extraction performance: word embeddings as obtained by the
word2vec method, clustered using K-means, and Brown clusters. The authors go beyond
reporting experiments on two languages other than English and also offer methodological
insight on the contribution of different classifiers and unsupervised features when
little training data is available. The study is original in comparing the same set
of configurations based on ensembles of word representations in both corpora, although
different types of entities are annotated in the two languages (Drug/Diseases for
Spanish, and Body Part/ Disorder/Finding for Swedish).
Tapi Nzali MD, Bringay S, Lavergne C, Mollevi C, Opitz T
What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer
JMIR Med Inform 2017 Jul 31;5(3):e23
This study reports on social media analysis relying on two corpora on the topic of
breast cancer in French. This paper presents a strong use case and good explanations
of why the work is significant from multiple points of view: social media, quality
of life in cancer patients, and clinical questionnaire development. The authors use
Latent Dirichlet Analysis (LDA) to detect the topics in breast cancer messages from
Facebook groups and in forum posts. After a balanced analysis of the LDA results,
the automatically identified topics are aligned with internationally standardized
self-administered questionnaires used in cancer clinical trials in order to validate
the results and identify gaps in the questionnaires. The study draws conclusions that
may bring an impact on the maintenance of the international questionnaires.
