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Educated agreement: it's value regarding retrolective investigation and also health-related technology development.
Conclusions Mobility and flexibility is important for both m-health and e-health. Five keywords that characterize the definitions of e-health and m-health are "health", "mobile", "use", "information", and "technology". E-health or m-health cannot replace human actors because e-health and m-health consist of social and material interactions. Using e-health and m-health is, thus, about developing healthcare without compromising native relics.Objectives Longitudinal data are prevalent in clinical research; due to their correlated nature, special analysis must be used for this type of data. Creatinine is an important marker in predicting end-stage renal disease, and it is recorded longitudinally. This study compared the prediction performance of linear regression (LR), linear mixed-effects model (LMM), least-squares support vector regression (LS-SVR), and mixed-effects least-squares support vector regression (MLS-SVR) methods to predict serum creatinine as a longitudinal outcome. Methods We used a longitudinal dataset of hemodialysis patients in Hamadan city between 2013 and 2016. To evaluate the performance of the methods in serum creatinine prediction, the data was divided into two sets of training and testing samples. Then LR, LMM, LS-SVR, and MLS-SVR were fitted. The prediction performance was assessed and compared in terms of mean squared error (MSE), mean absolute error (MAE), mean absolute prediction error (MAPE), and determination coefficient (R 2). Variable importance was calculated using the best model to select the most important predictors. Results The MLS-SVR outperformed the other methods in terms of the least prediction error; MSE = 1.280, MAE = 0.833, and MAPE = 0.129 for the training set and MSE = 3.275, MAE = 1.319, and MAPE = 0.159 for the testing set. Also, the MLS-SVR had the highest R 2, 0.805 and 0.654 for both the training and testing samples, respectively. Blood urea nitrogen was the most important factor in the prediction of creatinine. Conclusions The MLS-SVR achieved the best serum creatinine prediction performance in comparison to LR, LMM, and LS-SVR.Objectives Electronic Health Records (EHRs)-based surveillance systems are being actively developed for detecting adverse drug reactions (ADRs), but this is being hindered by the difficulty of extracting data from unstructured records. This study performed the analysis of ADRs from nursing notes for drug safety surveillance using the temporal difference method in reinforcement learning (TD learning). selleck chemicals llc Methods Nursing notes of 8,316 patients (4,158 ADR and 4,158 non-ADR cases) admitted to Ajou University Hospital were used for the ADR classification task. A TD(λ) model was used to estimate state values for indicating the ADR risk. For the TD learning, each nursing phrase was encoded into one of seven states, and the state values estimated during training were employed for the subsequent testing phase. We applied logistic regression to the state values from the TD(λ) model for the classification task. Results The overall accuracy of TD-based logistic regression of 0.63 was comparable to that of two machine-learning methods (0.64 for a naïve Bayes classifier and 0.63 for a support vector machine), while it outperformed two deep learning-based methods (0.58 for a text convolutional neural network and 0.61 for a long short-term memory neural network). Most importantly, it was found that the TD-based method can estimate state values according to the context of nursing phrases. Conclusions TD learning is a promising approach because it can exploit contextual, time-dependent aspects of the available data and provide an analysis of the severity of ADRs in a fully incremental manner.Objectives To identify the effects of a mobile-app-based self-management program for elderly hemodialysis patients on their sick-role behavior, basic psychological needs, and self-efficacy. Methods A nonequivalent control group with a non-synchronized design was utilized, and 60 participants (30 in each of the experimental and control groups) were recruited from Chungnam National University Hospital from March to August 2018. The program consisted of continuous training on how to use the mobile-app, self-checking via the app, message transfer through Electronic Medical Records, and feedback. The control group received the usual care. Data were analyzed using the χ2-test, the t-test, the repeated-measures ANOVA, and the McNemar test. A formalized messaging program was developed, and the app was developed with consideration of the specific physical and cognitive limitations of the elderly. Results Comparisons were conducted between the experimental (n = 28) and control (n = 28) groups. Statistically significant increases in sick-role behavior, basic psychological needs, and self-efficacy were found in the experimental group (p less then 0.001). Physiological parameters were maintained within the normal ranges in the experimental group, and the number of non-adherent patients decreased, although the change was not statistically significant. Conclusions The mobile-app-based self-management program developed in this study increased the sick-role behavior, basic psychological needs, and self-efficacy of elderly hemodialysis patients, while physiological parameters were maintained within the normal range. Future studies are needed to develop management systems for high-risk hemodialysis patients and family-sharing apps to manage non-adherent patients.Objectives Recently, wearable device technology has gained more popularity in supporting a healthy lifestyle. Hence, researchers have begun to put significant efforts into studying the direct and indirect benefits of wearable devices for health and wellbeing. This paper summarizes recent studies on the use of consumer wearable devices to improve physical activity, mental health, and health consciousness. Methods A thorough literature search was performed from several reputable databases, such as PubMed, Scopus, ScienceDirect, arXiv, and bioRxiv mainly using "wearable device research" as a keyword, no earlier than 2018. As a result, 25 of the most recent and relevant papers included in this review cover several topics, such as previous literature reviews (9 papers), wearable device accuracy (3 papers), self-reported data collection tools (3 papers), and wearable device intervention (10 papers). Results All the chosen studies are discussed based on the wearable device used, complementary data, study design, and data processing method.
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