Danielle L. Mowery

Also published as: Danielle L Mowery, Danielle Mowery


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Investigating the Documentation of Electronic Cigarette Use in the Veteran Affairs Electronic Health Record: A Pilot Study
Danielle Mowery | Brett South | Olga Patterson | Shu-Hong Zhu | Mike Conway
BioNLP 2017

In this paper, we present pilot work on characterising the documentation of electronic cigarettes (e-cigarettes) in the United States Veterans Administration Electronic Health Record. The Veterans Health Administration is the largest health care system in the United States with 1,233 health care facilities nationwide, serving 8.9 million veterans per year. We identified a random sample of 2000 Veterans Administration patients, coded as current tobacco users, from 2008 to 2014. Using simple keyword matching techniques combined with qualitative analysis, we investigated the prevalence and distribution of e-cigarette terms in these clinical notes, discovering that for current smokers, 11.9% of patient records contain an e-cigarette related term.

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A Corpus Analysis of Social Connections and Social Isolation in Adolescents Suffering from Depressive Disorders
Jia-Wen Guo | Danielle L Mowery | Djin Lai | Katherine Sward | Mike Conway
Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality

Social connection and social isolation are associated with depressive symptoms, particularly in adolescents and young adults, but how these concepts are documented in clinical notes is unknown. This pilot study aimed to identify the topics relevant to social connection and isolation by analyzing 145 clinical notes from patients with depression diagnosis. We found that providers, including physicians, nurses, social workers, and psychologists, document descriptions of both social connection and social isolation.


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Vocabulary Development To Support Information Extraction of Substance Abuse from Psychiatry Notes
Sumithra Velupillai | Danielle L. Mowery | Mike Conway | John Hurdle | Brent Kious
Proceedings of the 15th Workshop on Biomedical Natural Language Processing

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Assessing the Feasibility of an Automated Suggestion System for Communicating Critical Findings from Chest Radiology Reports to Referring Physicians
Brian E. Chapman | Danielle L. Mowery | Evan Narasimhan | Neel Patel | Wendy Chapman | Marta Heilbrun
Proceedings of the 15th Workshop on Biomedical Natural Language Processing

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Towards Automatically Classifying Depressive Symptoms from Twitter Data for Population Health
Danielle L. Mowery | Albert Park | Craig Bryan | Mike Conway
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)

Major depressive disorder, a debilitating and burdensome disease experienced by individuals worldwide, can be defined by several depressive symptoms (e.g., anhedonia (inability to feel pleasure), depressed mood, difficulty concentrating, etc.). Individuals often discuss their experiences with depression symptoms on public social media platforms like Twitter, providing a potentially useful data source for monitoring population-level mental health risk factors. In a step towards developing an automated method to estimate the prevalence of symptoms associated with major depressive disorder over time in the United States using Twitter, we developed classifiers for discerning whether a Twitter tweet represents no evidence of depression or evidence of depression. If there was evidence of depression, we then classified whether the tweet contained a depressive symptom and if so, which of three subtypes: depressed mood, disturbed sleep, or fatigue or loss of energy. We observed that the most accurate classifiers could predict classes with high-to-moderate F1-score performances for no evidence of depression (85), evidence of depression (52), and depressive symptoms (49). We report moderate F1-scores for depressive symptoms ranging from 75 (fatigue or loss of energy) to 43 (disturbed sleep) to 35 (depressed mood). Our work demonstrates baseline approaches for automatically encoding Twitter data with granular depressive symptoms associated with major depressive disorder.


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Towards Developing an Annotation Scheme for Depressive Disorder Symptoms: A Preliminary Study using Twitter Data
Danielle Mowery | Craig Bryan | Mike Conway
Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality

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BluLab: Temporal Information Extraction for the 2015 Clinical TempEval Challenge
Sumithra Velupillai | Danielle L Mowery | Samir Abdelrahman | Lee Christensen | Wendy Chapman
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)


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Generating Patient Problem Lists from the ShARe Corpus using SNOMED CT/SNOMED CT CORE Problem List
Danielle Mowery | Mindy Ross | Sumithra Velupillai | Stephane Meystre | Janyce Wiebe | Wendy Chapman
Proceedings of BioNLP 2014


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Medical diagnosis lost in translation – Analysis of uncertainty and negation expressions in English and Swedish clinical texts
Danielle L Mowery | Sumithra Velupillai | Wendy W Chapman
BioNLP: Proceedings of the 2012 Workshop on Biomedical Natural Language Processing


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Distinguishing Historical from Current Problems in Clinical Reports – Which Textual Features Help?
Danielle Mowery | Henk Harkema | John Dowling | Jonathan Lustgarten | Wendy Chapman
Proceedings of the BioNLP 2009 Workshop


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Temporal Annotation of Clinical Text
Danielle Mowery | Henk Harkema | Wendy Chapman
Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing