Bmc Med Inform Decis
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Bmc Med Inform Decis · Jan 2014
Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection.
The key aim of triage in chest pain patients is to identify those with high risk of adverse cardiac events as they require intensive monitoring and early intervention. In this study, we aim to discover the most relevant variables for risk prediction of major adverse cardiac events (MACE) using clinical signs and heart rate variability. ⋯ It is observed that a few predictors outperformed the whole set of variables in predicting MACE within 72 h. We conclude that more predictors do not necessarily guarantee better prediction results. Furthermore, machine learning-based variable selection seems promising in discovering a few relevant and significant measures as predictors.
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Bmc Med Inform Decis · Jan 2014
ReviewCongruence between patients' preferred and perceived participation in medical decision-making: a review of the literature.
Patients are increasingly expected and asked to be involved in health care decisions. In this decision-making process, preferences for participation are important. In this systematic review we aim to provide an overview the literature related to the congruence between patients' preferences and their perceived participation in medical decision-making. We also explore the direction of mismatched and outline factors associated with congruence. ⋯ This review suggests that a similar approach to all patients is not likely to meet patients' wishes, since preferences for participation vary among patients. Health care professionals should be sensitive to patients individual preferences and communicate about patients' participation wishes on a regular basis during their illness trajectory.
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Bmc Med Inform Decis · Jan 2014
Randomized Controlled TrialPracticing evidence based medicine at the bedside: a randomized controlled pilot study in undergraduate medical students assessing the practicality of tablets, smartphones, and computers in clinical life.
Practicing evidence-based medicine is an important aspect of providing good medical care. Accessing external information through literature searches on computer-based systems can effectively achieve integration in clinical care. We conducted a pilot study using smartphones, tablets, and stationary computers as search devices at the bedside. The objective was to determine possible differences between the various devices and assess students' internet use habits. ⋯ Using a mobile device at the bedside to perform an extensive search is not suitable for students who prefer using computers. However, mobility is regarded as a substantial advantage, and therefore future applications might facilitate quick and simple searches at the bedside.
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Bmc Med Inform Decis · Jan 2014
Comparative StudyThe effect of a decision aid intervention on decision making about coronary heart disease risk reduction: secondary analyses of a randomized trial.
Decision aids offer promise as a practical solution to improve patient decision making about coronary heart disease (CHD) prevention medications and help patients choose medications to which they are likely to adhere. However, little data is available on decision aids designed to promote adherence. ⋯ Decision aids can play an important role in improving decisions about CHD prevention and increasing patient-provider discussions and intent to reduce CHD risk.
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Bmc Med Inform Decis · Jan 2014
Derivation and validation of a search algorithm to retrospectively identify mechanical ventilation initiation in the intensive care unit.
The development and validation of automated electronic medical record (EMR) search strategies are important for establishing the timing of mechanical ventilation initiation in the intensive care unit (ICU).Thus, we sought to develop and validate an automated EMR search algorithm (strategy) for time zero, the moment of mechanical ventilation initiation in the critically ill patient. ⋯ The use of an electronic search strategy resulted in highly accurate extraction of mechanical ventilation initiation in the ICU. The search algorithm of mechanical ventilation initiation is highly efficient and reliable and can facilitate both clinical research and patient care management in a timely manner.