Browsing by Author "Binkheder, Samar"
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Item Analyzing Patterns of Literature-Based Phenotyping Definitions for Text Mining Applications(IEEE, 2018-06) Binkheder, Samar; Wu, Heng-Yi; Quinney, Sara; Li, Lang; BioHealth Informatics, School of Informatics and ComputingPhenotyping definitions are widely used in observational studies that utilize population data from Electronic Health Records (EHRs). Biomedical text mining supports biomedical knowledge discovery. Therefore, we believe that mining phenotyping definitions from the literature can support EHR-based clinical research. However, information about these definitions presented in the literature is inconsistent, diverse, and unknown, especially for text mining usage. Therefore, we aim to analyze patterns of phenotyping definitions as a first step toward developing a text mining application to improve phenotype definition. A set random of observational studies was used for this analysis. Term frequency-inverse document frequency (TF-IDF) and Term Frequency (TF) were used to rank the terms in the 3958 sentences. Finally, we present preliminary results analyzing phenotyping definitions patterns.Item Best Practices for Health Informatician Involvement in Interprofessional Health Care Teams(Thieme, 2018-01) Holden, Richard J.; Binkheder, Samar; Patel, Jay; Viernes, Sara Helene P.; BioHealth Informatics, School of Informatics and ComputingAcademic and nonacademic health informatics (HI) professionals (informaticians) serve on interprofessional health care teams with other professionals, such as physicians, nurses, pharmacists, dentists, and nutritionists. Presently, we argue for investing greater attention to the role health informaticians play on interprofessional teams and the best practices to support this role.Item Correction: PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature(BMC, 2022-07-20) Binkheder, Samar; Wu, Heng‑Yi; Quinney, Sara K.; Zhang, Shijun; Zitu, Md. Muntasir; Chiang, Chien‑Wei; Wang, Lei; Jones, Josette; Li, Lang; BioHealth Informatics, School of Informatics and ComputingPhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature. Binkheder S, Wu HY, Quinney SK, Zhang S, Zitu MM, Chiang CW, Wang L, Jones J, Li L. J Biomed Semantics. 2022 Jun 11;13(1):17. doi: 10.1186/s13326-022-00272-6. PMID: 35690873Item PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature(Springer, 2022) Binkheder, Samar; Wu, Heng-Yi; Quinney, Sara K.; Zhang, Shijun; Zitu, Md. Muntasir; Chiang, Chien-Wei; Wang, Lei; Jones, Josette; Li, Lang; BioHealth Informatics, School of Informatics and ComputingBackground Adverse events induced by drug-drug interactions are a major concern in the United States. Current research is moving toward using electronic health record (EHR) data, including for adverse drug events discovery. One of the first steps in EHR-based studies is to define a phenotype for establishing a cohort of patients. However, phenotype definitions are not readily available for all phenotypes. One of the first steps of developing automated text mining tools is building a corpus. Therefore, this study aimed to develop annotation guidelines and a gold standard corpus to facilitate building future automated approaches for mining phenotype definitions contained in the literature. Furthermore, our aim is to improve the understanding of how these published phenotype definitions are presented in the literature and how we annotate them for future text mining tasks. Results Two annotators manually annotated the corpus on a sentence-level for the presence of evidence for phenotype definitions. Three major categories (inclusion, intermediate, and exclusion) with a total of ten dimensions were proposed characterizing major contextual patterns and cues for presenting phenotype definitions in published literature. The developed annotation guidelines were used to annotate the corpus that contained 3971 sentences: 1923 out of 3971 (48.4%) for the inclusion category, 1851 out of 3971 (46.6%) for the intermediate category, and 2273 out of 3971 (57.2%) for exclusion category. The highest number of annotated sentences was 1449 out of 3971 (36.5%) for the “Biomedical & Procedure” dimension. The lowest number of annotated sentences was 49 out of 3971 (1.2%) for “The use of NLP”. The overall percent inter-annotator agreement was 97.8%. Percent and Kappa statistics also showed high inter-annotator agreement across all dimensions. Conclusions The corpus and annotation guidelines can serve as a foundational informatics approach for annotating and mining phenotype definitions in literature, and can be used later for text mining applications.Item Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research(Wiley, 2018) Zhang, Pengyue; Wu, Heng-Yi; Chiang, Chien-Wei; Binkheder, Samar; Wang, Xueying; Zeng, Donglin; Quinney, Sara K.; Li, Lang; BioHealth Informatics, School of Informatics and ComputingDrug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research.Item U.S. Hospitals' Web-Based Patient Engagement ActivitiesJones, Josette; Zolnoori, Maryam; Binkheder, Samar; Schilling, Katherine; Lenox, Michelle; Pondugala, Lakshmi RavaliThe purpose of this poster is to describe how U.S. Hospitals use their websites to meet the National e-Health Collaborative (NeHC) patient engagement criteria and to explore trends, challenges, opportunities for hospitals when it comes to leveraging websites for patient engagement.