Methods of information in medicine
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The current study sought to evaluate whether nursing narratives can be used to predict postoperative length of hospital stay (LOS) following curative surgery for ovarian cancer. ⋯ The current study sought to determine whether elements of nursing narratives could be used to predict postoperative LOS among elderly ovarian cancer patients. Results indicated that nursing narratives that used the words "urination," "food supply," "bowel mobility," and "pain" significantly predicted postoperative LOS in the study population. Additionally, it was found that machine learning could effectively predict LOS based on quantitative characteristics of nursing narratives.
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The design of computerized systems able to support automated detection of threatening conditions in critically ill patients such as systemic inflammatory response syndrome (SIRS) and sepsis has been fostered recently. The increase of research work in this area is due to both the growing digitalization in health care and the increased appreciation of the importance of early sepsis detection and intervention. To be able to understand the variety of systems and their characteristics as well as performances, a systematic literature review is required. Existing reviews on this topic follow a rather restrictive searching methodology or they are outdated. As much progress has been made during the last 5 years, an updated review is needed to be able to keep track of current developments in this area of research. ⋯ The review demonstrated the high variety of research in this context successfully. A clear trend is observable toward the use of data-driven algorithms, and a lack of research could be identified in covering the pediatric population as well as acknowledging SIRS as an independent and threatening condition. The quality as well as the significance of the presented evaluations for assessing the performances of the algorithms in clinical routine settings are often not meeting the current standard of scientific work. Our future interest will be concentrated on these realistic settings by implementing and evaluating SIRS detection approaches as well as considering factors to make the CDSS useable in clinical routine from both technical and medical perspectives.
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Health information systems have developed rapidly and considerably during the last decades, taking advantage of many new technologies. Robots used in operating theaters represent an exceptional example of this trend. Yet, the more these systems are designed to act autonomously and intelligently, the more complex and ethical questions arise about serious implications of how future hybrid clinical team-machine interactions ought to be envisioned, in situations where actions and their decision-making are continuously shared between humans and machines. ⋯ The expected significant changes in the relationship of humans and machines can only be appropriately analyzed and considered by inter- and multidisciplinary collaboration. Fundamentally new approaches are needed to construct the reasonable concepts surrounding hybrid action that will take into account the ascription of responsibility to the radically different types of human versus nonhuman intelligent agents involved.
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Trauma is a global burden. Emergency medical services (EMS) provide care for individuals who have serious injuries or suffered a major trauma. ⋯ The review indicates that TMA and TPN are accompanying telemedical concepts in out-of-hospital trauma care. Well-designed populated studies are needed to fully assess the effect of telemedicine in acute trauma care. Therefore, evidence regarding the effectiveness of telemedicine in prehospital setting for trauma patients is still limited.
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To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements. ⋯ The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient's reason for visit regardless of modeling approach. Natural language processing and neural networks that incorporate patient-reported outcome free text may increase predictive accuracy for hospital admission.