Computers in biology and medicine
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The ISET (Instrument for SElf-Triage) is a validated pen-and-paper instrument for patient self-triage in ophthalmic emergency departments. The aim of the present study is to develop a validated computer-assisted ISET (ca-ISET) with a touch screen. ⋯ A ca-ISET prototype was developed, with minor textual modification of the pen-and-paper version. The new ca-ISET was validated by comparing against triage decided by the regular triage assistant.
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This study sought to predict postsurgical seizure freedom from pre-operative diagnostic test results and clinical information using a rapid automated approach, based on supervised learning methods in patients with drug-resistant focal seizures suspected to begin in temporal lobe. ⋯ Supervised machine learning using multimodal compared to unimodal data accurately predicted postsurgical outcome in patients with atypical MTLE.
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Cohort identification is important in both population health management and research. In this project we sought to assess the use of text queries for cohort identification. Specifically we sought to determine the incremental value of unstructured data queries when added to structured queries for the purpose of patient cohort identification. ⋯ This project demonstrates the value and limitation of free text queries in patient cohort identification from large data sets. The clinical domain and prevalence of the inclusion and exclusion criteria in the patient population influence the utility and yield of this approach.
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An estimated 6.5 million patients in the United States are affected by chronic wounds, with more than US$25 billion and countless hours spent annually for all aspects of chronic wound care. There is a need for an intelligent software tool to analyze wound images, characterize wound tissue composition, measure wound size, and monitor changes in wound in between visits. Performed manually, this process is very time-consuming and subject to intra- and inter-reader variability. ⋯ The innovative aspects of this work include defining a four-dimensional probability map specific to wound characteristics, a computationally efficient method to segment wound images utilizing the probability map, and auto-calibration of wound measurements using the content of the image. These methods were applied to 80 wound images, captured in a clinical setting at the Ohio State University Comprehensive Wound Center, with the ground truth independently generated by the consensus of at least two clinicians. While the mean inter-reader agreement between the readers varied between 67.4% and 84.3%, the computer achieved an average accuracy of 75.1%.
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In the present paper, an attempt was made to find waveform-derived variables that would be useful for a more precise diagnosis of hypovolemia. In attempting this, arterial blood pressure graphs of 18 hypovolemic postoperative patients were analysed using a discrete Fourier transform. ⋯ Based on the values of A1, a preliminary study was performed in which an additional group of 14 hypovolemic and 14 normovolemic patients were categorized into hypovolemic and normovolemic groups using logistic regression. The method proved to be successful in identifying hypovolemic patients: the prediction was correct in 80% and wrong only in 20%, indicating that A1 is potentially a useful parameter in detecting hypovolemia.