Autistic Spectrum Disorder (ASD) is a neurological condition associated with communication, repetitive, and social challenges. ASD screening is the process of detecting potential autistic traits in individuals using tests conducted by a medical professional, a caregiver, or a parent. These tests often contain large numbers of items to be covered by the user and they generate a score based on scoring functions designed by psychologists and behavioural scientists. Potential technologies that may improve the reliability and accuracy of ASD tests are Artificial Intelligence and Machine Learning. This paper presents a new framework for ASD screening based on Ensembles Learning called Ensemble Classification for Autism Screening (ECAS). ECAS employs a powerful learning method that considers constructing multiple classifiers from historical cases and controls and then utilizes these classifiers to predict autistic traits in test instances. ECAS performance has been measured on a real dataset related to cases and controls of children and using different Machine Learning techniques. The results revealed that ECAS was able to generate better classifiers from the children dataset than the other Machine Learning methods considered in regard to levels of sensitivity, specificity, and accuracy.

Aims: The current study investigates the student nurses' attitudes and opinions towards their health promotion role during the COVID-19 pandemic using social media. Background: Social media and networking have become the most secure modes of communication among health care providers and their clients during the COVID-19 pandemic all over the world. However, it is the primary means of disseminating health information about disease prevention and control. Methods: A cross-sectional study was conducted on 296 student nurses aged 19-49 enrolled at twelve Jordanian universities (6 public and six private) in Jordan. The research team developed the self-administered questionnaire to explore the student nurses' attitudes towards their health promotion role during the COVID-19 pandemic using social media and the Internet. Results: Findings revealed that the student nurses had positive attitudes towards their health promotion role during the COVID-19 pandemic. The majority of student nurses are using social media to raise the awareness of their community about COVID-19 prevention. Conclusion: The current research findings provide baseline data on the student nurses' attitudes about the proper utilization of social media to enhance their community health about COVID-19. Given the student nurses' positive attitudes about their role in COVID-19 health promotion, we strongly recommend that they be provided with the necessary knowledge and skills to demonstrate effective health education.

In this paper, we review the literature to find suitable information retrieval techniques for EHealth. Also discussed NLP techniques that have been proved their capability to extract valuable information in unstructured data from EHR. One of the best NLP techniques used for searching free text is LSI, due to its capability of finding semantic terms and in rich the search results by finding the hidden relations between terms. LSI uses a mathematical model called SVD, which is not scalable for large amounts of data due to its complexity and exhausts the memory, and a review for recent applications of LSI was discussed.

No abstract