Unsupervised Information Extraction from Behaviour Change Literature.

This paper describes our approach to construct a scalable system for unsupervised information extraction from the behaviour change intervention literature. Due to the many different types of attribute to be extracted, we adopt a passage retrieval based framework that provides the most likely value for an attribute. Our proposed method is capable of addressing variable length passage sizes and different validation criteria for the extracted values corresponding to each attribute to be found.

The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation.

Background: Behaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evidence from behaviour change intervention (BCI) evaluations and promote the uptake of that evidence into a wide range of contexts. The scale and complexity of the task of synthesising and interpreting this evidence, and increasing evidence timeliness and accessibility, will require increased computer support.

Probability Statements Extraction with Constrained Conditional Random Fields.

This paper investigates how to extract probability statements from academic medical papers. In previous work we have explored traditional classification methods which led to numerous false negatives. This current work focuses on constraining classification output obtained from a Conditional Random Field (CRF) model to allow for domain knowledge constraints.

Thesaurus-Based Hierarchical Semantic Grouping of Medical Terms in Information Extraction.

In this paper we describe a semantic approach for grouping medical terms into a hierarchy of concepts based on the UMLS meta-thesaurus. The context of this work is Medical Recap, a Web system that automatically extracts risk information from PubMed abstracts, and then aggregates this knowledge into dependence graphs or Bayesian networks.

Extracting Dependence Relations from Unstructured Medical Text.

Dependence relations among disease and risk factors are a key ingredient in risk modeling and decision support models. Currently such information is either provided by experts (costly and time consuming) or extracted from data (if available). The published medical literature represents a promising source of such knowledge; however its manual processing is practically infeasible.

Person-Specific Standardized Vulnerability Assessment in Health and Social Care.

We describe an integrated person-specific standardized vulnerability assessment model designed to facilitate patient management in health and social care. Such a system is not meant to replace existing health and social assessment models but rather to complement them by providing a holistic picture of the vulnerabilities faced by a given patient. In fact, it should be seen as a screening tool for health and social care workers.

Extracting risk modeling information from medical articles.

Risk modeling in healthcare is both ubiquitous and in its infancy. On the one hand, a significant proportion of medical research focuses on determining the factors that influence the incidence, severity and treatment of diseases, which is a form of risk identification. Those studies typically investigate the micro-level of risk modeling, i.

Engineering risk analysis of a hospital oxygen supply system.

Reports from the Food and Drug Administration (FDA) and the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) have emphasized the potential for injury to patients caused by failures in oxygen supply systems. This article presents a model of patient risk related to the process of supplying oxygen at a single university hospital. One of the goals of the article is to illustrate how probabilistic risk analysis (PRA) can be used by hospitals to assess and mitigate risk and, therefore, to meet JCAHO requirements.