International journal of medical informatics
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The positive impact of computerized physician order entry (CPOE) systems on prescription safety must be considered in light of the persistence of certain types of medication-prescription errors. We performed a systematic review, based on the PRISMA statement, to analyze the prevalence of prescription errors related to the use of CPOE systems. ⋯ The reporting of prescription errors should be continued because the weaknesses of CPOE systems are potential sources of error. Analysis of the mechanisms behind CPOE errors can reveal areas for improvement.
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Critical limb ischemia (CLI) is a complication of advanced peripheral artery disease (PAD) with diagnosis based on the presence of clinical signs and symptoms. However, automated identification of cases from electronic health records (EHRs) is challenging due to absence of a single definitive International Classification of Diseases (ICD-9 or ICD-10) code for CLI. ⋯ The CLI-NLP algorithm for identification of CLI from narrative clinical notes in an EHR had excellent PPV and has potential for translation to patient care as it will enable automated identification of CLI cases for quality projects, clinical decision support tools and support a learning healthcare system.
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Electronic Health Record systems (EHRs) offer numerous benefits in health care but also pose certain risks. As we progress toward the implementation of EHRs, a more in-depth understanding of attitudes that influence overall levels of EHR support is required. ⋯ The factors identified in the present study present actionable insights that may increase awareness about EHRs. The survey illustrates that both the public and physicians acknowledge the benefits and support EHRs on the condition that sufficient guarantees are provided about privacy and security.
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Mortality prediction of hospitalized patients is an important problem. Over the past few decades, several severity scoring systems and machine learning mortality prediction models have been developed for predicting hospital mortality. By contrast, early mortality prediction for intensive care unit patients remains an open challenge. Most research has focused on severity of illness scoring systems or data mining (DM) models designed for risk estimation at least 24 or 48h after ICU admission. ⋯ The results show that although there are many values missing in the first few hour of ICU admission, there is enough signal to effectively predict mortality during the first 6h of admission. The proposed framework, in particular the one that uses the ensemble learning approach - EMPICU Random Forest (EMPICU-RF) offers a base to construct an effective and novel mortality prediction model in the early hours of an ICU patient admission, with an improved performance profile.
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Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. ⋯ Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time with the ultimate goal to inform patient care decisions, and that the performance of these techniques with this particular dataset may be on par with that of classical methods.