Context-Aware Experience Extraction from Online Health Forums

Abstract—Online health forums provide a large repository for patients, caregivers, and researchers to seek valuable information. The extraction of patient-reported personal health experience from the forums has many important applications. For example, medical researchers can discover trustable knowledge from the extracted experience. Patients can search for peers with similar experience and connect with them. In this paper, we model the extraction of patient experience as a classification problem: classifying each sentence in a forum post as containing patient experience or not containing patient experience. We propose to exploit the sentence context information for such experience extraction task, and classify the context information into global context and local context. A unified Context-Aware expeRience Extraction (CARE) framework is proposed to incorporate these two types of context information. Our experimental results show that the global context can significantly improve the experience extraction accuracy, while the local context can also improve the performance when less labeled data is available

People on Drugs: Credibility of User Statements in Health Communities

Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information

Qualitative website analysis of information on birth after caesarean section

Date of Acceptance: 10/08/2015 © 2015 Peddie et al.Peer reviewedPublisher PD

The conceptual and practical ethical dilemmas of using health discussion board posts as research data.

Increasing numbers of people living with a long-term health condition are putting personal health information online, including on discussion boards. Many discussion boards contain material of potential use to researchers; however, it is unclear how this information can and should be used by researchers. To date there has been no evaluation of the views of those individuals sharing health information online regarding the use of their shared information for research purposes

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

Business Ontology for Evaluating Corporate Social Responsibility

This paper presents a software solution that is developed to automatically classify companies by taking into account their level of social responsibility. The application is based on ontologies and on intelligent agents. In order to obtain the data needed to evaluate companies, we developed a web crawling module that analyzes the company’s website and the documents that are available online such as social responsibility report, mission statement, employment structure, etc. Based on a predefined CSR ontology, the web crawling module extracts the terms that are linked to corporate social responsibility. By taking into account the extracted qualitative data, an intelligent agent, previously trained on a set of companies, computes the qualitative values, which are then included in the classification model based on neural networks. The proposed ontology takes into consideration the guidelines proposed by the “ISO 26000 Standard for Social Responsibility”. Having this model, and being aware of the positive relationship between Corporate Social Responsibility and financial performance, an overall perspective on each company’s activity can be configured, this being useful not only to the company’s creditors, auditors, stockholders, but also to its consumers.corporate social responsibility, ISO 26000 Standard for Social Responsibility, ontology, web crawling, intelligent agent, corporate performance, POS tagging, opinion mining, sentiment analysis

Are you an effective teacher of reading?

Reading occurs in our lives on a constant basis. Nevertheless, defining reading is not easy. Different people use the term reading for different purposes, which can cause much confusion. For the context of the language classroom this article will concern itself with the notion of reading as the extraction of meaning from a written text . In other words, the text is viewed as a vehicle of communication from the writer to the reader; Aebersold and Field (1997) acknowledge this by stating that it is the interaction between the text and reader that constitutes actual reading. However, simply stating that this is what constitutes reading is to risk forgetting that, in the reading class, the most important thing is that both the teacher and the student should understand the reading process

An ‘objective-centred’ approach to course redesign: using learning objectives to integrate e-learning

This article describes the process of integrating e-learning into the M-level research methods course Research Synthesis for Policy and Practice. It explores an ‘objective-centred’ approach to course redesign. This entails using learning objectives as the basis for developing online activities and integrating technological tools. This article describes what this ‘objectives approach’ meant in practice and illustrates the importance of learning objectives for the redesign process. Embedding elearning into the course provides new opportunities to meet existing objectives in an innovative, and hopefully more effective, way. Technological tools provide the scope to extend and develop new learning objectives to better meet the needs of students. Whilst objectives are central to the redesign, the article highlights the significant role played by other types of knowledge, namely tutor experience, student views and research