Herbert S. Chase

Learn More
Multi-item adverse drug event (ADE) associations are associations relating multiple drugs to possibly multiple adverse events. The current standard in pharmacovigilance is bivariate association analysis, where each single drug-adverse effect combination is studied separately. The importance and difficulty in the detection of multi-item ADE associations was(More)
In this article, we present a new pharmacovigilance data mining technique based on the biclustering paradigm, which is designed to identify drug groups that share a common set of adverse events (AEs) in the spontaneous reporting system (SRS) of the US Food and Drug Administration (FDA). A taxonomy of biclusters is developed, revealing that a significant(More)
Adverse drug events (ADEs) create a serious problem causing substantial harm to patients. An executable standardized knowledgebase of drug-ADE relations which is publicly available would be valuable so that it could be used for ADE detection. The literature is an important source that could be used to generate a knowledgebase of drug-ADE pairs. In this(More)
The identification of post-marketed adverse drug events (ADEs) is paramount to health care. Spontaneous reporting systems (SRS) are currently the mainstay in pharmacovigilance. Recently, electronic health records (EHRs) have emerged as a promising and effective complementary resource to SRS, as they contain a more complete record of the patient, and do not(More)
OBJECTIVE To identify some of the challenges that medical residents face in addressing their information needs in an inpatient setting, by examining how voice capture in natural language of clinical questions fits into workflow, and by characterizing the focus, format, and semantic content and complexity of their questions. DESIGN Internal medicine(More)
BACKGROUND AND OBJECTIVES Fibroblast growth factor 23 plays an important role in regulating phosphate and vitamin D homeostasis. Elevated levels of fibroblast growth factor 23 are independently associated with mortality in patients with CKD and ESRD. Whether fibroblast growth factor 23 levels are elevated and associated with adverse outcomes in patients(More)
Electronic health records (EHRs) are an important source of data for detection of adverse drug reactions (ADRs). However, adverse events are frequently due not to medications but to the patients' underlying conditions. Mining to detect ADRs from EHR data must account for confounders. We developed an automated method using natural-language processing (NLP)(More)
OBJECTIVE Data-mining algorithms that can produce accurate signals of potentially novel adverse drug reactions (ADRs) are a central component of pharmacovigilance. We propose a signal-detection strategy that combines the adverse event reporting system (AERS) of the Food and Drug Administration and electronic health records (EHRs) by requiring signaling in(More)
BACKGROUND Adverse drug events (ADE) cause considerable harm to patients, and consequently their detection is critical for patient safety. The US Food and Drug Administration maintains an adverse event reporting system (AERS) to facilitate the detection of ADE in drugs. Various data mining approaches have been developed that use AERS to detect signals(More)
Knowledge acquisition of relations between biomedical entities is critical for many automated biomedical applications, including pharmacovigilance and decision support. Automated acquisition of statistical associations from biomedical and clinical documents has shown some promise. However, acquisition of clinically meaningful relations (i.e. specific(More)