A Crowd Monitoring Framework using Emotion Analysis of Social Media for Emergency Management in Mass Gatherings

In emergency management for mass gatherings, the knowledge about crowd types can highly assist with providing timely response and effective resource allocation. Crowd monitoring can be achieved using computer vision based approaches and sensory data analysis. The emergence of social media platforms presents an opportunity to capture valuable information about how people feel and think. However, the literature shows that there are a limited number of studies that use social media in crowd monitoring and/or incorporate a unified crowd model for consistency and interoperability. This paper presents a novel framework for crowd monitoring using social media. It includes a standard crowd model to represent different types of crowds. The proposed framework considers the effect of emotion on crowd behaviour and uses the emotion analysis of social media to identify the crowd types in an event. An experiment using historical data to validate our framework is described

Integrating Social Media with Ontologies for Real-Time Crowd Monitoring and Decision Support in Mass Gatherings

Situation awareness plays an essential role in making real-time decisions in mass gatherings. In the last few years, social media data analysis has been proved to be an effective approach to enable and enhance situation awareness. Mass gathering events are dynamic and critical environments where thousands of people attend. During the event, there is a potential for injuries and other health hazards, and thus it is critical for emergency medical services to access real-time and situational awareness information, especially concerning the nature of the crowd. It has been well recognized in the literature that crowd mood and behaviour can have a direct impact on the crowd safety and patient presentation rates. We describe a mobile social media-enabled crowd monitoring architecture that aims to improve emergency management decision-making by analysing the data from social networks in real-time. The proposed architecture incorporates a crowd behaviour classification model, which facilitates real-time situation awareness and provides a better understanding of analysis results. Awareness and perception of crowd mood and behaviour during the event can significantly improve prediction of patient presentation rates; leading to timely and effective medical care provision. The implementation and evaluation of the proposed framework on an Android mobile phone is described

Enhancing rule-based text classification of neurosurgical notes using filtered feature weight vectors

Clinicians need to record clinical encounters in written or spoken language, not only for its work-flow naturalness but also for its expressivity, precision, and capacity to convey all required information, which codified structure data is incapable of. Therefore, the structured data which is required for aggregation and analysis must be obtained from clinical text as a later step. Specialised areas of medicine use their own clinical language and clinical coding systems, resulting in unique challenges for the extraction process. Rule-based information extraction have been used effectively in commercial systems and are favoured because they are easily understood and controlled. However, there is promising research into the use of machine language techniques for extracting information, and this research explores the effectiveness of a hybrid rule-based and machine learning-based audit coding system developed for the neurosurgical department of a major trauma hospital

Developing a Contextual Model towards Understanding Low Back Pain

Recent advances in mobile computing and sensor technology have provided new opportunities in data collection and analysis, especially in the medical fields of research. Low back pain is a key area within chronic pain management. It is a widespread problem and a major contributor towards disability worldwide. Researchers have concluded that pain can be an individualistic experience. Evidence from other fields of research show that studying the context of the phenomena can allow for a better understanding of its nature. Existing studies may not consider the full context of the patients’ pain, and collect data infrequently (e.g. monthly or yearly). An explanation for this could be due to the cost and difficulty of collecting such data in the past. In this research, we propose a descriptive contextual model that extends a current low back pain model, with contextual attributes and factors. The goal of this research is to provide researchers with a descriptive contextual classification of variables into their respective factors, and to guide future studies in collecting such data, by utilizing advances in mobile and sensor technology

Intelligent audit code generation from free text in the context of neurosurgery

Clinical auditing requires codified data for aggregation and analysis of patterns. However in the medical domain obtaining structured data can be difficult as the most natural, expressive and comprehensive way to record a clinical encounter is through natural language. The task of creating structured data from naturally expressed information is known as information extraction. Specialised areas of medicine use their own language and data structures; the translation process has unique challenges, and often requires a fresh approach. This research is devoted to creating a novel semi-automated method for generating codified auditing data from clinical notes recorded in a neurosurgical department in an Australian teaching hospital. The method encapsulates specialist knowledge in rules that instantaneously make precise decisions for the majority of the matches, followed up by dictionary-based matching of the remaining text

Empirical study of user experience on mobile data collection for chronic low back pain

The majority of all IS implementation projects fails. McFarlan (1981) identified risk factors associated with organizational IT projects and created a model to predict project risk. The McFarlan Risk Model (MRM) provides a useful approach for the diagnosis and mitigation of IT project risks but can be improved in its predictive ability. In this paper, we suggest to augment the model, beyond its original three dimensions. Based on recent literature, which points to the importance of culture, specifically corporate culture, we develop an extension to McFarlan’s model and assess the added value of this extended model through the evaluation of two business cases. Expert evaluations using the Extended McFarlan Risk Model (EMRM) indicate higher predictive power in the differentiation of project success and failure, based on differences in the model’s culture dimension

Integrating contextual and online self-reported data for personalized healthcare: a tennis elbow case study

Advances in sensors and mobile technology have helped evolve the use of eHealth, especially in the field of Chronic Pain. Chronic pain is a widespread problem where self-management is important. Current studies tend to collect data at sparse intervals due to the cost involved in collecting data using traditional instruments. We demonstrate how technology enables richer data collection frequencies to analyse the influence of patients’ context on their pain levels. In this paper, we present a case study as an add-on analysis to a clinical trial for Tennis Elbow. We explore the usefulness of on-line key data collected at higher frequencies in explaining or discovering changes in pain. This dataset allowed us to learn that there are no associations with temperature and humidity to this type of pain, that patients tend to have different pain experiences, and that pain at night tends to be higher than overall or activity-related pain

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Advances in e-health and mobile health monitoring

E-health as a new industrial phenomenon and a field of research integrates medical informatics, public health and healthcare business, aiming to facilitate the provision of more accessible healthcare services, such as remote health monitoring, reducing healthcare costs and enhancing patient experience [...