EHealth Trends in Europe 2005-2007: A Population-Based Survey

Introduction: This study presents COVID-Twitter-BERT (CT-BERT), a transformer-based model that is pre-trained on a large corpus of COVID-19 related Twitter messages. CT-BERT is specifically designed to be used on COVID-19 content, particularly from social media, and can be utilized for various natural language processing tasks such as classification, question-answering, and chatbots. This paper aims to evaluate the performance of CT-BERT on different classification datasets and compare it with BERT-LARGE, its base model.Methods: The study utilizes CT-BERT, which is pre-trained on a large corpus of COVID-19 related Twitter messages. The authors evaluated the performance of CT-BERT on five different classification datasets, including one in the target domain. The model's performance is compared to its base model, BERT-LARGE, to measure the marginal improvement. The authors also provide detailed information on the training process and the technical specifications of the model.Results: The results indicate that CT-BERT outperforms BERT-LARGE with a marginal improvement of 10-30% on all five classification datasets. The largest improvements are observed in the target domain. The authors provide detailed performance metrics and discuss the significance of these results.Discussion: The study demonstrates the potential of pre-trained transformer models, such as CT-BERT, for COVID-19 related natural language processing tasks. The results indicate that CT-BERT can improve the classification performance on COVID-19 related content, especially on social media. These findings have important implications for various applications, such as monitoring public sentiment and developing chatbots to provide COVID-19 related information. The study also highlights the importance of using domain-specific pre-trained models for specific natural language processing tasks. Overall, this work provides a valuable contribution to the development of COVID-19 related NLP models.

The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.

This study explored the use of the four major Norwegian mental-health-related online discussion forums; who participate, why, and what implications use may have. The objective was to provide a basis for proposing relevant research questions and issues for public policy attention. A total of 492 responses to a web-based questionnaire were received. The respondents, predominantly women (78%) in the age range 18--35 years, found forum participation useful for information, and social contact and support. A majority (75%) found it easier to discuss personal problems online than face-to-face, and almost half say they discuss problems online that they do not discuss face-to-face. A majority would not have participated had they not had the option of using a pseudonym. Respondents perceive discussion groups as a supplement rather than a replacement of traditional mental health services, reporting no change in the amount or type of service used. A clear majority want professionals to take an active role in these types of forum. Comments from respondents indicate that forums may have an empowering effect. We believe that online interaction can have unique benefits for people suffering from mental disorders. Professionals will need new knowledge and perceptions of their roles, and public authorities will have to decide their role in influencing the quality of services offered, and the social values conveyed, to those who seek help through the Internet.

Background: The electronic health record (EHR) has been fully established in all Norwegian hospitals. Patient-accessible electronic health records (PAEHRs) are available to citizens aged 16 years and older through the national health portal Helsenorge. Objective: This study aimed at understanding how patients use PAEHRs. Three research questions were addressed in order to explore (1) characteristics of users, (2) patients’ use of the service, and (3) patient experience with the service. Methods: We conducted an online survey of users who had accessed their EHR online at least once through the national health portal. Patients from two of the four health regions in Norway were invited to participate. Quantitative data were supplemented by qualitative information. Results: A total of 1037 respondents participated in the survey, most of whom used the PAEHR regularly (305/1037, 29.4%) or when necessary (303/1037, 29.2%). Service utilization was associated with self-reported health, age, gender, education, and health care professional background. Patients found the service useful to look up health information (687/778, 88.3%), keep track of their treatment (684/778, 87.9%), prepare for a hospital appointment (498/778, 64.0%), and share documents with their general practitioner (292/778, 37.5%) or family (194/778, 24.9%). Most users found it easy to access their EHR online (965/1037, 93.1%) and did not encounter technical challenges. The vast majority of respondents (643/755, 85.2%) understood the content, despite over half of them acknowledging some difficulties with medical terms or phrases. The overall satisfaction with the service was very high (700/755, 92.7%). Clinical advantages to the patients included enhanced knowledge of their health condition (565/691, 81.8%), easier control over their health status (685/740, 92.6%), better self-care (571/653, 87.4%), greater empowerment (493/674, 73.1%), easier communication with health care providers (493/618, 79.8%), and increased security (655/730, 89.7%). Patients with complex, long-term or chronic conditions seemed to benefit the most. PAEHRs were described as useful, informative, effective, helpful, easy, practical, and safe. Conclusions: PAEHRs in Norway are becoming a mature service and are perceived as useful by patients. Future studies should include experimental designs focused on specific populations or chronic conditions that are more likely to achieve clinically meaningful benefits. Continuous evaluation programs should be conducted to assess implementation and changes of wide-scale routine services over time.