Publications by authors named "Susan Zelisko"

Article Synopsis
  • Acute Respiratory Distress Syndrome (ARDS) is a serious condition diagnosed via radiology reports, and this study explores methods for its identification.
  • Researchers compared a traditional keyword model to a natural language processing (NLP) approach using machine learning, analyzing 533 patients and 9,255 reports.
  • The NLP model outperformed the traditional method, achieving an accuracy of 83.0% compared to 67.3%, and a higher positive predictive value, suggesting NLP can better identify ARDS cases.
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This study examines health outcomes in burn patients with sepsis. We hypothesized that burn patients with sepsis would have an increased odds risk for in-hospital death and longer intensive care unit (ICU) stays. This was a retrospective cohort of consecutive patients admitted to the burn ICU with total BSA (TBSA) ≥10% and/or inhalation injury between January 2008 and March 2015.

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To develop an algorithm to identify sepsis and sepsis with organ dysfunction/septic shock in burn-injured patients incorporating criteria from the American Burn Association sepsis definition that possesses good test characteristics compared with International Classification of Diseases, Ninth Revision-Clinical Modification (ICD-9) codes and an algorithm previously validated in nonburn-injured septic patients (Martin et al method). This was a retrospective cohort study of consecutive patients admitted to the burn intensive care unit between January 2008 and March 2015. Of the 4761 admitted, 8.

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CAPriCORN, the Chicago Area Patient Centered Outcomes Research Network, is one of the eleven PCORI-funded Clinical Data Research Networks. A collaboration of six academic medical centers, a Chicago public hospital, two VA hospitals and a network of federally qualified health centers, CAPriCORN addresses the needs of a diverse community and overlapping populations. To capture complete medical records without compromising patient privacy and confidentiality, the network created policies and mechanisms for patient consultation, central IRB approval, de-identification, de-duplication, and integration of patient data by study cohort, randomization and sampling, re-identification for consent by providers and patients, and communication with patients to elicit patient-reported outcomes through validated instruments.

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The Gene Expression Barcode project, http://barcode.luhs.org, seeks to determine the genes expressed for every tissue and cell type in humans and mice.

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Data captured in electronic medical records (EMRs) and paper charts have enormous potential for clinical research and to improve the quality of health care; however, accessing, organizing, and analyzing these data pose significant challenges. To address these challenges, this article reports development of a web-based application that provides for local clinical data capture as well as integration of patient data directly from an institutional EMR. A web-based system was created using an existing institutional application development framework.

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