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Housing is an environmental social determinant of health that is linked to mortality and clinical outcomes. We developed a lexicon of housing-related concepts and rule-based natural language processing methods for identifying these housing-related concepts within clinical text. We piloted our methods on several test cohorts: a synthetic cohort generated by ChatGPT for initial infrastructure testing, a cohort with substance use disorders (SUD), and a cohort diagnosed with problems related to housing and economic circumstances (HEC). Our methods successfully identified housing concepts in our ChatGPT notes (recall = 1.0, precision = 1.0), our SUD population (recall = 0.9798, precision = 0.9898), and our HEC population (recall = N/A, precision = 0.9160)., Competing Interests: None., (© The Author(s) 2024.)

We present our open-source pipeline for quickly enhancing open data sets with research-focused expansions and show its effectiveness on a cornerstone open data set released by the Cook County government in Illinois. The City of Chicago and Cook County were both early adopters of open data portals and have made a wide variety of data available to the public; we focus on the medical examiner case archive which provides information about deaths recorded by Cook County's Office of the Medical Examiner, including overdoses invaluable to substance use disorder research. Our pipeline derives key variables from open data and links to other publicly available data sets in support of accelerating translational research on substance use disorders. Our methods apply to location-based analyses of overdoses in general and, as an example, we highlight their impact on opioid research. We provide our pipeline as open-source software to act as open infrastructure for open data to help fill the gap between data release and data use., (©2022 AMIA - All rights reserved.)

We present findings on using natural language processing to classify tobacco-related entries from problem lists found within patient's electronic health records. Problem lists describe health-related issues recorded during a patient's medical visit; these problems are typically followed up upon during subsequent visits and are updated for relevance or accuracy. The mechanics of problem lists vary across different electronic health record systems. In general, they either manifest as pre-generated generic problems that may be selected from a master list or as text boxes where a healthcare professional may enter free text describing the problem. Using commonly-available natural language processing tools, we classified tobacco-related problems into three classes: active-user, former-user, and non-user; we further demonstrate that rule-based post-processing may significantly increase precision in identifying these classes (+32%, +22%, +35% respectively). We used these classes to generate tobacco time-spans that reconstruct a patient's tobacco-use history and better support secondary data analysis. We bundle this as an open-source toolkit with flow visualizations indicating how patient tobacco-related behavior changes longitudinally, which can also capture and visualize contradicting information such as smokers being flagged as having never smoked., (©2020 AMIA - All rights reserved.)

We present sig2db as an open-source solution for clinical data warehouses desiring to process natural language from prescription instructions, often referred to as "sigs". In electronic prescribing, the sig is typically an unstructured text field intended to capture all requirements for medication administration. The sig captures certain fields that the structured data may lack such as days supply, time of day, or meal-time considerations. Our open-source software package facilitates the workflow needed to process sigs into a structured format usable by clinical data warehouses. Our solution focuses on extracting concepts from prescriptions in order to understand the intended semantics by leveraging known natural language processing tools. We demonstrate the utility of concept extraction from sigs and present our findings in processing 1023 unique sigs from 5.7 million unique prescriptions., (©2020 AMIA - All rights reserved.)

Drug repurposing is the identification of novel indication(s) for existing medications. Health claims data provide a burgeoning resource to evaluate pharmacotherapies with repurposing potential. To demonstrate a workflow for drug repurposing using claims data, we assessed the association between prescription of bupropion and stimulant use disorder (StUD) remission. Using the Truven Marketscan database, 96,156 individuals with a StUD were identified. Logistic regression was used to model the association between new bupropion prescriptions and remission while controlling for age, sex, region, StUD severity, antidepressant co-prescriptions, and comorbid mood and attention disorders. Prescription of bupropion within 30 days offirst documented StUD diagnosis increased odds of a subsequent remission diagnosis by 2.1 times (99% confidence interval: 1.09-3.89) in individuals with an amphetamine use disorder, but not those with a cocaine use disorder. This work provides a framework for reverse-translational drug repurposing, which may be applied to many other medical conditions.

We present DELVE (Document ExpLoration and Visualization Engine), a framework for developing interactive visualizations as modular Web-applications to assist researchers with exploratory literature search. The goal for web-applications driven by DELVE is to better satisfy the information needs of researchers and to help explore and understand the state of research in scientific liter ature by providing immersive visualizations that both contain facets and are driven by facets derived from the literature. We base our framework on principles from user-centered design and human-computer interaction (HCI). Preliminary evaluations demon strate the usefulness of DELVE's techniques: (1) a clinical researcher immediately saw that her original query was inappropriate simply due to the frequencies displayed via generalized clouds and (2) a muscle biologist quickly learned of vocabulary differences found between two disciplines that were referencing the same idea, which we feel is critical for interdisciplinary work. We dis cuss the underlying category-theoretic model of our framework and show that it naturally encourages the development of reusable visualizations by emphasizing interoperability.

We introduce a tool that extracts clinical data sets and provides visualizations from clinical data warehouses that use the Informatics for Integrating Biology and the Bedside (i2b2) query tool. Our tool, i2b2t2 (i2b2 to Tableau), can extract and visualize any i2b2 query into a portable format that researchers can easily explore without needing a highly technical or statistical background. This user-friendly format provides a quick visual summary of the queried population and is easily extendable to develop more intricate and robust visualizations. Extraction and visualization can be provided as a service by clinical data warehouses to expedite the release of data sets for research. i2b2t2 also encourages visualization as a self-service; a motivated researcher can develop custom visualizations for exploration or publication.