International journal of medical informatics
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Predicting insulin-induced postprandial hypoglycemic events is critical for the safety of type 1 diabetes patients because an early warning of hypoglycemia facilitates correction of the insulin bolus before its administration. The postprandial hypoglycemic event counts can be lowered by reducing the size of the bolus based on a reliable prediction but at the cost of increasing the average blood glucose. ⋯ The results demonstrated that it is feasible to anticipate hypoglycemic events with a reasonable false-positive rate. The accuracy of the results and the trade-off between performance metrics allow its use in decision support systems for patients who wear insulin pumps.
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The landscape of mobile devices is changing and their present use by patients for healthcare purposes is unknown. An understanding of current attitudes and usage may help increase patient engagement through mobile applications. This study sought to determine characteristics of mobile device ownership among Emergency Department patients, patients' feelings regarding their use in healthcare, and desired functionality in mobile applications. ⋯ Ownership of smartphones is high across the Emergency Department population and patients are enthusiastic about using mobile devices as part of their care. Further study can elucidate opportunities to further integrate mobile device applications into patient care.
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We aimed to construct a mortality prediction model using the random forest (RF) algorithm for acute kidney injury (AKI) patients in the intensive care unit (ICU), and compared its performance with that of two other machine learning models and the customized simplified acute physiology score (SAPS) II model. ⋯ There is great potential for the RF model in mortality prediction for AKI patients in ICU. The RF model may be helpful to aid ICU clinicians to make timely clinical intervention decisions for AKI patients, which is critical to help reduce the in-hospital mortality of AKI patients. A prospective study is necessary to evaluate the clinical utility of the RF model.
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Background Children with medical complexity (CMC) are a growing population of medically fragile children with unique healthcare needs, who have recurrent emergency department (ED) and hospital admissions due to frequent acute escalations of their chronic conditions. Mobile health (mHealth) tools have been suggested to support CMC home monitoring and prevent admissions. No mHealth tool has ever been developed for CMC and challenges exist. ⋯ They suggested an mHealth tool for CMC to include the following functionalities: 1) symptom tracking, targeting commonly reported drivers (symptoms) of ED/hospital admissions; 2) user friendly (ease of data entry), using voice, radio buttons, and drop down menus; 3) a free-text field for reporting child's other symptoms and interventions attempted at home; 4) ability to directly access a health care provider (HCP) via text/email messaging, and to allow real-time sharing of child data to facilitate care, and 5) option to upload and post a photo or video of the child to allow a visual recall by the HCP. Conclusions Caregivers deemed a mHealth tool beneficial and offered a set of key functionalities to meet information needs for monitoring CMC's symptoms. Our future efforts will consist of creating a prototype of the mHealth tool and testing it for usability among CMC caregivers.
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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.