Publications by authors named "Francisco Orchard"
Article Synopsis
- PANDEM-Source (PS) is a tool designed to collect and integrate public health data from various sources to enhance surveillance and management of infectious diseases, particularly for pandemic situations.
- Developed during the COVID-19 pandemic through the EU-funded Horizon 2020 PANDEM-2 project, PS emerged from collaboration among 19 partners, including public health agencies and first responders.
- The tool standardizes both traditional and non-traditional data sources, aids in calculating health indicators, and can generate synthetic data for training in pandemic preparedness, making it adaptable for monitoring various diseases and threats across different regions.
BackgroundThe European Centre for Disease Prevention and Control (ECDC) systematically collates information from sources to rapidly detect early public health threats. The lack of a freely available, customisable and automated early warning tool using data from Twitter prompted the ECDC to develop epitweetr, which collects, geolocates and aggregates tweets generating signals and email alerts.AimThis study aims to compare the performance of epitweetr to manually monitoring tweets for the purpose of early detecting public health threats.
View Article and Find Full Text PDFBackground: Intervening in and preventing diabetes distress requires an understanding of its causes and, in particular, from a patient's perspective. Social media data provide direct access to how patients see and understand their disease and consequently show the causes of diabetes distress.
Objective: Leveraging machine learning methods, we aim to extract both explicit and implicit cause-effect relationships in patient-reported diabetes-related tweets and provide a methodology to better understand the opinions, feelings, and observations shared within the diabetes online community from a causality perspective.
Background: The amount of available textual health data such as scientific and biomedical literature is constantly growing and becoming more and more challenging for health professionals to properly summarize those data and practice evidence-based clinical decision making. Moreover, the exploration of unstructured health text data is challenging for professionals without computer science knowledge due to limited time, resources, and skills. Current tools to explore text data lack ease of use, require high computational efforts, and incorporate domain knowledge and focus on topics of interest with difficulty.
View Article and Find Full Text PDFIntroduction: Little research has been done to systematically evaluate concerns of people living with diabetes through social media, which has been a powerful tool for social change and to better understand perceptions around health-related issues. This study aims to identify key diabetes-related concerns in the USA and primary emotions associated with those concerns using information shared on Twitter.
Research Design And Methods: A total of 11.