Browsing by Subject "Medical records -- Data processing"

Now showing 1 - 6 of 6
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    Advanced natural language processing and temporal mining for clinical discovery
    (2015-08-17) Mehrabi, Saeed; Jones, Josette F.; Palakal, Mathew J.; Chien, Stanley Yung-Ping; Liu, Xiaowen; Schmidt, C. Max
    There has been vast and growing amount of healthcare data especially with the rapid adoption of electronic health records (EHRs) as a result of the HITECH act of 2009. It is estimated that around 80% of the clinical information resides in the unstructured narrative of an EHR. Recently, natural language processing (NLP) techniques have offered opportunities to extract information from unstructured clinical texts needed for various clinical applications. A popular method for enabling secondary uses of EHRs is information or concept extraction, a subtask of NLP that seeks to locate and classify elements within text based on the context. Extraction of clinical concepts without considering the context has many complications, including inaccurate diagnosis of patients and contamination of study cohorts. Identifying the negation status and whether a clinical concept belongs to patients or his family members are two of the challenges faced in context detection. A negation algorithm called Dependency Parser Negation (DEEPEN) has been developed in this research study by taking into account the dependency relationship between negation words and concepts within a sentence using the Stanford Dependency Parser. The study results demonstrate that DEEPEN, can reduce the number of incorrect negation assignment for patients with positive findings, and therefore improve the identification of patients with the target clinical findings in EHRs. Additionally, an NLP system consisting of section segmentation and relation discovery was developed to identify patients' family history. To assess the generalizability of the negation and family history algorithm, data from a different clinical institution was used in both algorithm evaluations.
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    Flexible models of time-varying exposures
    (2015-05) Wang, Chenkun; Gao, Sujuan; Liu, Hai; Yu, Zhangsheng; Callahan, Christopher M.
    With the availability of electronic medical records, medication dispensing data offers an unprecedented opportunity for researchers to explore complex relationships among longterm medication use, disease progression and potential side-effects in large patient populations. However, these data also pose challenges to existing statistical models because both medication exposure status and its intensity vary over time. This dissertation focused on flexible models to investigate the association between time-varying exposures and different types of outcomes. First, a penalized functional regression model was developed to estimate the effect of time-varying exposures on multivariate longitudinal outcomes. Second, for survival outcomes, a regression spline based model was proposed in the Cox proportional hazards (PH) framework to compare disease risk among different types of time-varying exposures. Finally, a penalized spline based Cox PH model with functional interaction terms was developed to estimate interaction effect between multiple medication classes. Data from a primary care patient cohort are used to illustrate the proposed approaches in determining the association between antidepressant use and various outcomes.
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    Giving patients granular control of personal health information: Using an ethics “Points to Consider” to inform informatics system designers
    (http://dx.doi.org/10.1016/j.ijmedinf.2013.08.010, 2013-12) Meslin, Eric M.; Alpert, Sheri A.; Carroll, Aaron E.; Odell, Jere D.; Tierney, William M.; Schwartz, Peter H.
    OBJECTIVE: There are benefits and risks of giving patients more granular control of their personal health information in electronic health record (EHR) systems. When designing EHR systems and policies, informaticists and system developers must balance these benefits and risks. Ethical considerations should be an explicit part of this balancing. Our objective was to develop a structured ethics framework to accomplish this. METHODS: We reviewed existing literature on the ethical and policy issues, developed an ethics framework called a "Points to Consider" (P2C) document, and convened a national expert panel to review and critique the P2C. RESULTS: We developed the P2C to aid informaticists designing an advanced query tool for an electronic health record (EHR) system in Indianapolis. The P2C consists of six questions ("Points") that frame important ethical issues, apply accepted principles of bioethics and Fair Information Practices, comment on how questions might be answered, and address implications for patient care. DISCUSSION: The P2C is intended to clarify what is at stake when designers try to accommodate potentially competing ethical commitments and logistical realities. The P2C was developed to guide informaticists who were designing a query tool in an existing EHR that would permit patient granular control. While consideration of ethical issues is coming to the forefront of medical informatics design and development practices, more reflection is needed to facilitate optimal collaboration between designers and ethicists. This report contributes to that discussion.
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    Living kidney donor follow-up in a statewide health information exchange: health services utilization, health outcomes and policy implications
    (2016-05-24) Henderson, Macey Leigh; Stone, Cynthia L.; Dixon, Brian; Harle, Chris; Menachemi, Nir; Holmes, Ann; Fry-Revere, Sigrid
    Living donors have contributed about 6,000 kidneys per year in the past 10 years, but more than 100,000 individuals are still waiting for a kidney transplant. Living kidney donors undergo a major surgical procedure without direct medical benefit to themselves, but comprehensive follow-up information on living donors’ health is unfortunately limited. Expert recommendations suggest capturing clinical information beyond traditional sources to improve surveillance of co-morbid conditions from living kidney donors. Currently the United Network for Organ Sharing is responsible for collecting and reporting follow-up data for all living donors from U.S. transplant centers. Under policy implemented in February of 2013, transplant centers must submit follow-up date for two years after donation, but current processes often yield to incomplete and untimely reporting. This dissertation uses a statewide Health Information Exchange as a new clinical data source to 1) retrospectively identify a cohort of living kidney donors, 2) understand their follow-up care patterns, and 3) observe selected clinical outcomes including hypertension, diabetes and post-donation renal function.
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    Mapping the rules: conceptual and logical relationships in a system for pediatric clinical decision support
    (2013-10-07) Ralston, Rick K.; Odell, Jere D.; Whipple, Elizabeth C.; Liu, Gilbert C.
    The Child Health Improvement through Computer Automation (CHICA) system uses evidence-based guidelines and information collected in the clinic and stored in an electronic medical record (EMR) to inform physician and patient decision making. CHICA helps physicians to identify and select relevant screenings and also provides personalized, just-in-time information for patients. This system relies on a database of Medical Logic Modules (MLMS) written in the Arden Rules syntax. These MLMs store observations (StorObs) during the clinical encounter which trigger potential screenings and preventive health interventions for discussion with the patient or for follow up at the next visit. This poster shows how informationists worked with the CHICA team to describe the MLMs using standard vocabularies, including Medical Subject Headings (MeSH) and Logical Observation Identifiers Names and Codes (LOINC). After assigning keywords to the database of MLMs, the informationists used visualization tools to generate maps. These maps show how rules are related by logic (shared StorObs) and by concept (shared vocabulary). The CHICA team will use these maps to identify gaps in the clinical decision support database and (if needed) to develop rules which bridge related but currently isolated concepts.
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    Web-based geotemporal visualization of healthcare data
    (2014-10-09) Bloomquist, Samuel W.; Fang, Shiaofen; Tuceryan, Mihran; Xia, Yuni
    Healthcare data visualization presents challenges due to its non-standard organizational structure and disparate record formats. Epidemiologists and clinicians currently lack the tools to discern patterns in large-scale data that would reveal valuable healthcare information at the granular level of individual patients and populations. Integrating geospatial and temporal healthcare data within a common visual context provides a twofold benefit: it allows clinicians to synthesize large-scale healthcare data to provide a context for local patient care decisions, and it better informs epidemiologists in making public health recommendations. Advanced implementations of the Scalable Vector Graphic (SVG), HyperText Markup Language version 5 (HTML5), and Cascading Style Sheets version 3 (CSS3) specifications in the latest versions of most major Web browsers brought hardware-accelerated graphics to the Web and opened the door for more intricate and interactive visualization techniques than have previously been possible. We developed a series of new geotemporal visualization techniques under a general healthcare data visualization framework in order to provide a real-time dashboard for analysis and exploration of complex healthcare data. This visualization framework, HealthTerrain, is a concept space constructed using text and data mining techniques, extracted concepts, and attributes associated with geographical locations. HealthTerrain's association graph serves two purposes. First, it is a powerful interactive visualization of the relationships among concept terms, allowing users to explore the concept space, discover correlations, and generate novel hypotheses. Second, it functions as a user interface, allowing selection of concept terms for further visual analysis. In addition to the association graph, concept terms can be compared across time and location using several new visualization techniques. A spatial-temporal choropleth map projection embeds rich textures to generate an integrated, two-dimensional visualization. Its key feature is a new offset contour method to visualize multidimensional and time-series data associated with different geographical regions. Additionally, a ring graph reveals patterns at the fine granularity of patient occurrences using a new radial coordinate-based time-series visualization technique.