Burden and Impact of Reactogenicity among Adults Receiving COVID-19 Vaccines in the United States and Canada: Results from a Prospective Observational Study.

As SARS-CoV-2 variants continue to emerge, vaccination remains a critical tool to reduce the COVID-19 burden. Vaccine reactogenicity and the impact on work productivity/daily activities are recognized as contributing factors to vaccine hesitancy. To encourage continued COVID-19 vaccination, a more complete understanding of the differences in reactogenicity and impairment due to vaccine-related side effects across currently available vaccines is necessary. The 2019nCoV-406 study (n = 1367) was a prospective observational study of reactogenicity and associated impairments in adults in the United States and Canada who received an approved/authorized COVID-19 vaccine. Compared with recipients of mRNA COVID-19 booster vaccines, a smaller percentage of NVX-CoV2373 booster recipients reported local and systemic reactogenicity. This study's primary endpoint (percentage of participants with ≥50% overall work impairment on ≥1 of the 6 days post-vaccination period) did not show significant differences. However, the data suggest that NVX-CoV2373 booster recipients trended toward being less impaired overall than recipients of an mRNA booster; further research is needed to confirm this observed trend. The results of this real-world study suggest that NVX-CoV2373 may be a beneficial vaccine option with limited impact on non-work activities, in part due to the few reactogenicity events after vaccination.

A Rule Based Intelligent Software to Predict Length of Stay and the Mortality Rate in Trauma Patients in the Intensive Care Unit.

Background: Intensive Care Unit (ICU) has the highest mortality rate in the world. ICU has special equipment that leads to the hospital's most costly parts. The length of stay in the ICU is a special issue, and reducing this time is a practical approach. We aimed to use artificial intelligence to help early and timely diagnosis of the disease to help with health. Methods: We designed a rule-based intelligent system to predict the length of stay and the mortality rate of trauma patients in ICU. A neuro-Fuzzy and eight machine learning models were used to predict the mortality rate in trauma patients in ICU. The performances of these techniques were evaluated with accuracy, sensitivity, specificity, and area under the ROC curve. Decision-Table was used to predict the length of stay in trauma patients in ICU. For comparison, eight machine learning models were used. The method is compared based on Mean absolute error and relative absolute error (%). Results: Neuro-Fuzzy expert system and Decision-Table showed better results than other techniques. Accuracy, sensitivity, specificity, and ROC Area of Nero-Fuzzy are 83.6735, 0.9744, 0.3000, 0.8379, and 1, respectively. The mean absolute error and Relative absolute error (%) of the Decision-Table model are 4.5426 and 65.4391, respectively. Conclusion: Neuro-Fuzzy expert system with the highest level of accuracy and a Decision-Table with the lowest Mean absolute error, which are rule-based models, are the best models. Therefore, these models are recommended as a valuable tool for prediction parameters of ICU as well as medical decision-making.

Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review.

Background and Aim: Schizophrenia and bipolar disorder (BD) are critical and high-risk inherited mental disorders with debilitating symptoms. Worldwide, 3% of the population suffers from these disorders. The mortality rate of these patients is higher compared to other people. Current procedures cannot effectively diagnose these disorders because it takes an average of 10 years from the onset of the first symptoms to the definitive diagnosis of the disease. Machine learning (ML) techniques are used to meet this need. This study aimed to summarize information on the use of ML techniques for predicting schizophrenia and BD to help early and timely diagnosis of the disease. Methods: A systematic literature search included articles published until January 19, 2020 in 3 databases. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. PRISMA guidelines were followed to conduct the study, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess included papers. Results: In this review, 1243 papers were retrieved through database searches, of which 15 papers were included based on full-text assessment. ML techniques were used to predict schizophrenia and BDs. The main algorithms applied were support vector machine (SVM) (10 studies), random forests (RF) (5 studies), and gradient boosting (GB) (3 studies). Input and output characteristics were very diverse and have been kept to enable future research. RFs algorithms demonstrated significantly higher accuracy and sensitivity than SVM and GB. GB demonstrated significantly higher specificity than SVM and RF. We found no significant difference between RF and SVM in terms of specificity. Conclusion: ML can precisely predict results and assist in making clinical decisions-concerning schizophrenia and BD. RF often performed better than other algorithms in supervised learning tasks. This study identified gaps in the literature and opportunities for future psychological ML research.

Evaluating hospital information system according to ISO 9241 part 12.

OBJECTIVES: Compliance with standards in designing information systems leads to better utilization and ease of use for users. In this study, the compliance of a widely used hospital information system (HIS) with ISO 9241 part 12 was assessed. METHODS: This applied research is a descriptive, cross-sectional study in which the HIS of 8 hospitals affiliated with Kerman University of Medical Sciences was evaluated based on ISO 9241 part 12. Data were collected by using ISO 9241/12 checklist. The data was analyzed in SPSS 16 using descriptive statistics. RESULTS: The analysis of data showed that the total compliance of the software with the ISO 9241/12 was 72%. The compliance of the software based on different groups of recommendations was 79% with Organization of information, 91% with Graphic objects, and 58% with Coding techniques. Compliance with different subgroups of ISO recommendations ranged from 28% related to "color coding" in coding techniques to 97% related to "General recommendation for graphical objects" in Graphic objects. CONCLUSION: According to this study, the design of a widely used HIS has fairly good compliance with the standard but still suffers from some problems. Considering the role of accurate, valid and timely information in management of the hospitals, and the difficulty of system optimization after implementation, it is necessary that software developers follow existing standards when designing health information systems.

Prognosis and Early Diagnosis of Ductal and Lobular Type in Breast Cancer Patient.

BACKGROUND: Breast cancer is one of the most common cancers with a high mortality rate among women. Prognosis and early diagnosis of breast cancer among women society reduce considerable rate of their mortality. Nowadays, due to this illness, try to be setting up intelligent systems, which can predict and early diagnose this cancer, and reduce mortality of women society. METHODS: Overall, 208 samples were collected from 2014 to 2015 from two oncologist offices and Javadalaemeh Clinic in Kerman, southeastern Iran. Data source was medical records of patients, then 64 data mining models in MATLAB and WEKA software were used, eventually these measured precision and accuracy of data mining models. RESULTS: Among 64 data mining models, Bayes-Net model had 95.67% of accuracy and 95.70% of precision; therefore, was introduced as the best model for prognosis and diagnosis of breast cancer. CONCLUSION: Intelligent and reliable data mining models are proposed. Hence, these models are recommended as a useful tool for breast cancer prediction as well as medical decision-making.

Machine learning models in breast cancer survival prediction.

BACKGROUND: Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. OBJECTIVE: The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. METHODS: We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. RESULTS: Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). CONCLUSIONS: This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of accuracy. Therefore, this model is recommended as a useful tool for breast cancer survival prediction as well as medical decision making.