Demystifying Digital Records Processing... Step by Step, Byte by Byte

After briefly providing some background information on the acquisition of a digital processing workstation and the development of digital records processing workflows, this hands-on work session will provide an overview of at least one possible workflow for accessioning and processing born digital archival records. Attendees will walk through the process themselves and learn how to use specific (free) tools to better understand and process the materials at hand. Some possible tasks could include documenting basic file structure for ingesting and processing digital files, generating basic metadata about files (e.g. identify the number, size, and types/formats of files), identifying duplicate files and/or empty directories, and creating checksums (as a baseline for preservation). Come and take the next step with us

Content Matters--Teaching The Case For Reparations, 9-12

We offer specific materials and plans for teaching the structure and content of Ta-Nehisi Coates\u27 persuasive essay, The Case for Reparations, and building trustworthy relationships with and among students. By participating in this interactive session, you will practice teaching five specific high school appropriate lessons addressing requisite knowledge and skills for studying this essay, from real estate redlining to building academic vocabulary for rhetorical analysis

Characterizing environmental and phenotypic associations using information theory and electronic health records

The availability of up-to-date, executable, evidence-based medical knowledge is essential for many clinical applications, such as pharmacovigilance, but executable knowledge is costly to obtain and update. Automated acquisition of environmental and phenotypic associations in biomedical and clinical documents using text mining has showed some success. The usefulness of the association knowledge is limited, however, due to the fact that the specific relationships between clinical entities remain unknown. In particular, some associations are indirect relations due to interdependencies among the data. In this work, we develop methods using mutual information (MI) and its property, the data processing inequality (DPI), to help characterize associations that were generated based on use of natural language processing to encode clinical information in narrative patient records followed by statistical methods. Evaluation based on a random sample consisting of two drugs and two diseases indicates an overall precision of 81%. This preliminary study demonstrates that the proposed method is effective for helping to characterize phenotypic and environmental associations obtained from clinical reports

Information-theoretic classification of SNOMED improves the organization of context-sensitive excerpts from Cochrane Reviews

The emphasis on evidence based medicine (EBM) has placed increased focus on finding timely answers to clinical questions in presence of patients. Using a combination of natural language processing for the generation of clinical excerpts and information theoretic distance based clustering, we evaluated multiple approaches for the efficient presentation of context-sensitive EBM excerpts

The influence of year-end bonuses on colorectal cancer screening

The objective of the paper is to estimate the effect of physician bonus eligibility on CRC screening while controlling for patient and primary care physician characteristics. The study is retrospective, using a managed care plan’s claims data on fifty-year-old commercially insured patients in the years 2000 and 2001. The data also include links to enrollment and provider files. Multivariate logistic regression models are used to assess the association between CRC screening receipt and physician bonus eligibility. The results indicate that the probability that a patient received a CRC screening was approximately 3 percentage points higher in the year physicians were eligible for a bonus. There were also significant differences according to the gender of both the patient and physician, income, and race.

Selecting information in electronic health records for knowledge acquisition

AbstractKnowledge 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 associations) remains challenging because textual information is noisy and co-occurrence does not typically determine specific relations. In this work, we focus on acquisition of two types of relations from clinical reports: disease-manifestation related symptom (MRS) and drug-adverse drug event (ADE), and explore the use of filtering by sections of the reports to improve performance. Evaluation indicated that applying the filters improved recall (disease-MRS: from 0.85 to 0.90; drug-ADE: from 0.43 to 0.75) and precision (disease-MRS: from 0.82 to 0.92; drug-ADE: from 0.16 to 0.31). This preliminary study demonstrates that selecting information in narrative electronic reports based on the sections improves the detection of disease-MRS and drug-ADE types of relations. Further investigation of complementary methods, such as more sophisticated statistical methods, more complex temporal models and use of information from other knowledge sources, is needed