International journal of medical informatics
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Diabetes is a chronic noncommunicable disease with high incidence rate. Diabetics without early diagnosis or standard treatment may contribute to serious multisystem complications, which can be life threatening. Timely detection and intervention of prediabetes is very important to prevent diabetes, because it is inevitable in the development and progress of the disease. ⋯ Tongue features can improve the prediction accuracy of the diabetes risk prediction model formed by classical machine learning model significantly. In addition to the excellent performance, Stacking model and ResNet50 model which were recommended had non-invasive operation and were easy to use. Stacking model and ResNet50 model had high precision, low false positive rate and low misdiagnosis rate on detecting hyperglycemia. While on detecting blood glucose value in critical state, Stacking model and ResNet50 model had a high sensitivity, a low false negative rate and a low missed diagnosis rate. The study had proved that the differential changes of tongue features reflected the abnormal glucose metabolism, thus the diabetes risk prediction model formed by a combined TCM tongue diagnosis and machine learning technique was feasible.
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Accurate differentiation and prioritization in emergency department (ED) triage is important to identify high-risk patients and to efficiently allocate of finite resources. Using data available from patients with suspected cardiovascular disease presenting at ED triage, this study aimed to train and compare the performance of four common machine learning models to assist in decision making of triage levels. ⋯ Four machine learning models had good discriminative ability of triage. XGBoost demonstrated a slight advantage over other models. These models could be used for differential triage of low-risk patients and high-risk patients as a strategy to improve efficiency and allocation of finite resources.
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Randomized Controlled Trial
Use of the FITT framework to understand patients' experiences using a real-time medication monitoring pill bottle linked to a mobile-based HIV self-management app: A qualitative study.
The purpose of this work was to conduct an in-depth analysis to understand patients' experiences using a real-time medication monitoring pill bottle linked to an HIV self-management app. ⋯ This study demonstrated that tracking medication adherence and receiving push-notification medication reminders through the electronic pill bottle connected to the app encourages and supports PLWH in adhering to their medication regimens. Findings from this work highlight the importance of adequate consideration of the needs of intended users in designing customizable mobile health technology, including HIV-related stigma, disclosure of HIV status and antiretroviral therapy regimens.
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Internet-based self-monitoring intervention offers accessibleand convenient weight management. This review aimed to systematically review the evidence on the effectiveness of internet-based self-monitoring intervention for overweight and obese adolescents. ⋯ Internet-based self-monitoring intervention is a possible approach for overweight and obese adolescents to reduce their BMI. Further well-designed RCTs with follow-up data and large sample sizes are needed to ensure the robustness of the evidence.
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The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care. ⋯ This review study unveils that there is a paucity of information on evidence of real-world use of Big Data analytics in healthcare. This is because, the usability studies have considered only qualitative approach which describes potential benefits but does not take into account the quantitative study. Also, majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data analytics in developing countries.