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Jeffrey B. Weilburg

Researcher at Harvard University

Publications -  73
Citations -  4334

Jeffrey B. Weilburg is an academic researcher from Harvard University. The author has contributed to research in topics: Population & Panic. The author has an hindex of 28, co-authored 71 publications receiving 4001 citations. Previous affiliations of Jeffrey B. Weilburg include Syracuse University & Partners HealthCare.

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A mega-analysis of genome-wide association studies for major depressive disorder

Stephan Ripke, +115 more
- 01 Apr 2013 - 
TL;DR: This article conducted a genome-wide association studies (GWAS) mega-analysis for major depressive disorder (MDD) using more than 1.2 million autosomal and X chromosome single-nucleotide polymorphisms (SNPs) in 18,759 independent and unrelated subjects of recent European ancestry.
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The neuropsychiatry of the cerebellum - insights from the clinic.

TL;DR: The broader role of the cerebellum in the pathogenesis of these neuropsychiatric symptoms is discussed, and the possibility of using cerebellar stimulation to treat psychiatric disorders by enhancing Cerebellar modulation of cognition and emotion is revisited.
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Effect of computerized order entry with integrated decision support on the growth of outpatient procedure volumes: seven-year time series analysis.

TL;DR: Substantial decreases in the growth of outpatient CT and US procedure volume coincident with ROE implementation (supplemented by DS for CT) were observed and the utilization of outpatient MR imaging decreased less impressively.
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QT interval and antidepressant use: a cross sectional study of electronic health records

TL;DR: A modest prolongation of QT interval with citalopram was confirmed and additional antidepressants with similar observed risk were identified, and Pharmacovigilance studies using electronic health record data may be a useful method of identifying potential risk associated with treatments.
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Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model

TL;DR: The application of bioinformatics tools such as NLP should enable accurate and efficient determination of longitudinal outcomes, enabling existing EMR data to be applied to clinical research, including biomarker investigations.