J Med Syst
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Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. ⋯ Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.
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Lung cancer is still the most concerned disease around the world. Lung nodule generates in the pulmonary parenchyma which indicates the latent risk of lung cancer. Computer-aided pulmonary nodules detection system is necessary, which can reduce diagnosis time and decrease mortality of patients. ⋯ By means of an ensemble classifier, Random Forest (RF), this study acquires 93.2, 92.4, 94.8, 97.6% of accuracy, sensitivity, specificity, area under the curve (AUC), respectively. Compared with support vector machine (SVM) classifier, RF can reduce more false positive nodules and acquire larger AUC. With the help of this CAD system, radiologist can be provided with a great reference for pulmonary nodule diagnosis timely.
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It is recognized that the study of the disaster medical response (DMR) is a relatively new field. To date, there is no evidence-based literature that clearly defines the best medical response principles, concepts, structures and processes in a disaster setting. Much of what is known about the DMR results from descriptive studies and expert opinion. ⋯ The aim of the case study is to implement the SIMEDIS model to the DMRS of an international airport and to test the medical response plan to an airplane crash simulation at the airport. In order to identify good response options, the model then was used to study the effect of a number of interventional factors on the performance of the DMRS. Our study reflects the potential of SIMEDIS to model complex systems, to test different aspects of DMR, and to be used as a tool in experimental research that might make a substantial contribution to provide the evidence base for the effectiveness and efficiency of disaster medical management.
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In April 2015, Oregon Health & Science University (OHSU) deployed a web-based, electronic medical record-embedded application created by third party vendor Vynca Inc. to allow real-time education, and completion of Physician Orders for Life Sustaining Treatment (POLST). Forms are automatically linked to the Epic Systems™ electronic health record (EHR) patient header and submitted to a state Registry, improving efficiency, accuracy, and rapid access to and retrieval of these important medical orders. POLST Forms, implemented in Oregon in 1992, are standardized portable medical orders used to document patient treatment goals for end-of-life care. ⋯ Delays in registering a POLST Form may result in unwanted treatment if the paper form is not immediately available. An electronic POLST Form completion system (ePOLST) was implemented to support direct Registry submission. Other benefits of the system include single-sign-on, transmission of HL7 data for patient demographics and other relevant information, elimination of potential errors in form completion using internalized logic, built-in real-time video and text-based education materials for both patients and health care professionals, and mobile linkage for signature capture.