• Publications
  • Influence
2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text
TLDR
The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks, which showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations.
Evaluating temporal relations in clinical text: 2012 i2b2 Challenge
TLDR
A corpus of discharge summaries annotated with temporal information was provided to be used for the development and evaluation of temporal reasoning systems, and the best systems overwhelmingly adopted a rule based approach for value normalization.
De-identification of patient notes with recurrent neural networks
TLDR
The first de-identification system based on artificial neural networks (ANNs), which requires no handcrafted features or rules, unlike existing systems, is introduced, which outperforms the state-of-the-art systems.
Viewpoint Paper: Evaluating the State-of-the-Art in Automatic De-identification
TLDR
An overview of this de-identification challenge is provided, the data and the annotation process are described, the evaluation metrics are explained, the nature of the systems that addressed the challenge are discussed, the results of received system runs are analyzed, and directions for future research are identified.
Extracting medication information from clinical text
TLDR
Although rule-based systems dominated the top 10, the best performing system was a hybrid and durations and reasons were the most difficult for all systems to detect.
Evaluating the state of the art in coreference resolution for electronic medical records
TLDR
It is shown that machine-learning and rule-based approaches worked best when augmented with external knowledge sources and coreference clues extracted from document structure and the systems performed better in coreference resolution when provided with ground truth mentions.
CLPsych 2019 Shared Task: Predicting the Degree of Suicide Risk in Reddit Posts
The shared task for the 2019 Workshop on Computational Linguistics and Clinical Psychology (CLPsych’19) introduced an assessment of suicide risk based on social media postings, using data from Reddit
Viewpoint Paper: Recognizing Obesity and Comorbidities in Sparse Data
  • Özlem Uzuner
  • Computer Science
    J. Am. Medical Informatics Assoc.
  • 1 July 2009
TLDR
i2b2 provided data for, and invited the development of, automated systems that can classify obesity and its comorbidities into these four classes based on individual discharge summaries, and refers to the categories Present, Absent, Questionable, and Unmentioned as classes.
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