J
John Pestian
Researcher at Cincinnati Children's Hospital Medical Center
Publications - 101
Citations - 3607
John Pestian is an academic researcher from Cincinnati Children's Hospital Medical Center. The author has contributed to research in topics: Suicidal ideation & Poison control. The author has an hindex of 29, co-authored 94 publications receiving 3184 citations. Previous affiliations of John Pestian include Hospital Research Foundation & Eastern Virginia Medical School.
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Proceedings ArticleDOI
A shared task involving multi-label classification of clinical free text
John Pestian,Chris Brew,Pawel Matykiewicz,DJ Hovermale,Neil D. Johnson,K. Bretonnel Cohen,Włodzisław Duch +6 more
TL;DR: A shared task involving the assignment of ICD-9-CM codes to radiology reports resulted in the first freely distributable corpus of fully anonymized clinical text, suggesting that human-like performance on this task is within the reach of currently available technologies.
Journal ArticleDOI
Effect of Nebulized Ipratropium on the Hospitalization Rates of Children with Asthma
TL;DR: Among children with a severe exacerbation of asthma, the addition of ipratropium bromide to albuterol and corticosteroid therapy significantly decreases the hospitalization rate.
Journal ArticleDOI
Sentiment Analysis of Suicide Notes: A Shared Task.
John Pestian,Pawel Matykiewicz,Michelle Linn-Gust,Brett R. South,Özlem Uzuner,Jan Wiebe,K. Bretonnel Cohen,John F. Hurdle,Chris Brew +8 more
TL;DR: A shared task involving the assignment of emotions to suicide notes resulted in the corpus of fully anonymized clinical text and annotated suicide notes, suggesting that human-like performance on this task is within the reach of currently available technologies.
Journal ArticleDOI
Suicide Note Classification Using Natural Language Processing: A Content Analysis
TL;DR: An attempt to determine the role of computational algorithms in understanding a suicidal patient's thoughts, as represented by suicide notes is presented, and it is hypothesized that machine learning algorithms can categorize suicide notes as as mental health professionals and psychiatric physician trainees do.
Journal ArticleDOI
Evaluating the state of the art in coreference resolution for electronic medical records.
TL;DR: 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.