Background: previous studies have indicated on association between shift work and lipid profile disturbances. Lipid profile disturbances could be due to internal desynchronization. The aim of this study was to analyze whether there is relationship between shift work and serum lipids, fasting blood glucose and hypertension.

Introduction: Heart failure(HF) related hospitalization constitutes a significant proportion of healthcare cost. Unchanging rates of readmission during recent years, shows the importance of addressing this problem. Methods: Patients admitted with heart failure diagnosis in our institution during April 2018to August 2018 were selected. Clinical, para-clinical and imaging data were recorded. All included patients were followed up for 6 months. The primary endpoints of the study were prevalence of early readmission and the predictors of that. Secondary end points were in-hospital and 6-month post-discharge mortality rate and late readmission rate. Results: After excluding 94 patients due to missing data, 428 patients were selected. Mean age of patients was 58.5 years (±17.4) and 61% of patients were male. During follow-up, 99patients (24%) were readmitted. Early re-admission (30-day) occurred in 27 of the patients(6.6%). The predictors of readmission were older age ( P=0.006), lower LVEF (P <0.0001), higher body weight (P=0.01), ICD/CRT implantation ( P=0.001), Lower sodium ( P=0.01), higher Pro-BNP(P=0.01), Higher WBC count (P=0.01) and higher BUN level (P=0.02). Independent predictors of early readmission were history of device implantation (P=0.007), lower LVEF (P=0.016), QRS duration more than 120 ms (P=0.037), higher levels of BUN (P=0.008), higher levels of Pro-BNP(P=0.037) and higher levels of uric acid (P=0.035). Secondary end points including in-hospital and 6-month post-discharge mortality occurred in 11% and 14.4% of patients respectively. Conclusion: Lower age of our heart failure patients and high prevalence of ischemic cardiomyopathy, necessitate focusing on more preventable factors related to heart failure.

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Background Patients’ rights are integral to medical ethics. This study aimed to perform sentiment analysis and opinion mining on patients’ messages by a combination of lexicon-based and machine learning methods to identify positive or negative comments and to determine the different ward and staff names mentioned in patients’ messages. Methods The level of satisfaction and observance of the rights of 250 service recipients of the hospital was evaluated through the related checklists by the evaluator. In total, 822 Persian messages, composed of 540 negative and 282 positive comments, were collected and labeled by the evaluator. Pre-processing was performed on the messages and followed by 2 feature vectors which were extracted from the messages, including the term frequency–inverse document frequency (TFIDF) vector and a combination of the multifeature (MF) (a lexicon-based method) and TFIDF (MF + TFIDF) vectors. Six feature selectors and 5 classifiers were used in this study. For the evaluations, 5-fold cross-validation with different metrics including area under the receiver operating characteristic curve (AUC), accuracy (ACC), F1 score, sensitivity (SEN), specificity (SPE) and Precision-Recall Curves (PRC) were reported. Message tag detection, which featured different hospital wards and identified staff names mentioned in the study patients’ messages, was implemented by the lexicon-based method. Results The best classifier was Multinomial Naïve Bayes in combination with MF + TFIDF feature vector and SelectFromModel (SFM) feature selection (ACC = 0.89 ± 0.03, AUC = 0.87 ± 0.03, F1 = 0.92 ± 0.03, SEN = 0.93 ± 0.04, and SPE = 0.82 ± 0.02, PRC-AUC = 0.97). Two methods of assessment by the evaluator and artificial intelligence as well as survey systems were compared. Conclusion Our results demonstrated that the lexicon-based method, in combination with machine learning classifiers, could extract sentiments in patients’ comments and classify them into positive and negative categories. We also developed an online survey system to analyze patients’ satisfaction in different wards and to remove conventional assessments by the evaluator.