Factors influencing robustness and effectiveness of conditional random fields in active learning frameworks

Active learning approaches reduce the annotation cost required by traditional supervised approaches to reach the same effectiveness by actively selecting informative instances during the learning phase. However, effectiveness and robustness of the learnt models are influenced by a number of factors. In this paper we investigate the factors that affect the effectiveness, more specifically in terms of stability and robustness, of active learning models built using conditional random fields (CRFs) for information extraction applications. Stability, defined as a small variation of performance when small variation of the training data or a small variation of the parameters occur, is a major issue for machine learning models, but even more so in the active learning framework which aims to minimise the amount of training data required. The factors we investigate are a) the choice of incremental vs. standard active learning, b) the feature set used as a representation of the text (i.e., morphological features, syntactic features, or semantic features) and c) Gaussian prior variance as one of the important CRFs parameters. Our empirical findings show that incremental learning and the Gaussian prior variance lead to more stable and robust models across iterations. Our study also demonstrates that orthographical, morphological and contextual features as a group of basic features play an important role in learning effective models across all iterations.

Comparative genomics of koala, cattle and sheep strains of Chlamydia pecorum

Background Chlamydia pecorum is an important pathogen of domesticated livestock including sheep, cattle and pigs. This pathogen is also a key factor in the decline of the koala in Australia. We sequenced the genomes of three koala C. pecorum strains, isolated from the urogenital tracts and conjunctiva of diseased koalas. The genome of the C. pecorum VR629 (IPA) strain, isolated from a sheep with polyarthritis, was also sequenced. Results Comparisons of the draft C. pecorum genomes against the complete genomes of livestock C. pecorum isolates revealed that these strains have a conserved gene content and order, sharing a nucleotide sequence similarity > 98%. Single nucleotide polymorphisms (SNPs) appear to be key factors in understanding the adaptive process. Two regions of the chromosome were found to be accumulating a large number of SNPs within the koala strains. These regions include the Chlamydia plasticity zone, which contains two cytotoxin genes (toxA and toxB), and a 77 kbp region that codes for putative type III effector proteins. In one koala strain (MC/MarsBar), the toxB gene was truncated by a premature stop codon but is full-length in IPTaLE and DBDeUG. Another five pseudogenes were also identified, two unique to the urogenital strains C. pecorum MC/MarsBar and C. pecorum DBDeUG, respectively, while three were unique to the koala C. pecorum conjunctival isolate IPTaLE. An examination of the distribution of these pseudogenes in C. pecorum strains from a variety of koala populations, alongside a number of sheep and cattle C. pecorum positive samples from Australian livestock, confirmed the presence of four predicted pseudogenes in koala C. pecorum clinical samples. Consistent with our genomics analyses, none of these pseudogenes were observed in the livestock C. pecorum samples examined. Interestingly, three SNPs resulting in pseudogenes identified in the IPTaLE isolate were not found in any other C. pecorum strain analysed, raising questions over the origin of these point mutations. Conclusions The genomic data revealed that variation between C. pecorum strains were mainly due to the accumulation of SNPs, some of which cause gene inactivation. The identification of these genetic differences will provide the basis for further studies to understand the biology and evolution of this important animal pathogen. Keywords: Chlamydia pecorum; Single nucleotide polymorphism; Pseudogene; Cytotoxin

The security and privacy of usage policies and provenance logs in an Information Accountability Framework

The potential benefits of shared eHealth records systems are promising for the future of improved healthcare. However, the uptake of such systems is hindered by concerns over the security and privacy of patient information. The use of Information Accountability and so called Accountable-eHealth (AeH) systems has been proposed to balance the privacy concerns of patients with the information needs of healthcare professionals. However, a number of challenges remain before AeH systems can become a reality. Among these is the need to protect the information stored in the usage policies and provenance logs used by AeH systems to define appropriate use of information and hold users accountable for their actions. In this paper, we discuss the privacy and security issues surrounding these accountability mechanisms, define valid access to the information they contain, discuss solutions to protect them, and verify and model an implementation of the access requirements as part of an Information Accountability Framework.

2nd International Workshop on Secure and Privacy-Aware Information Management in eHealth

Information security and privacy in the healthcare domain is a complex and challenging problem for computer scientists, social scientists, law experts and policy makers. Appropriate healthcare provision requires specialized knowledge, is information intensive and much patient information is of a particularly sensitive nature. Electronic health record systems provide opportunities for information sharing which may enhance healthcare services, for both individuals and populations. However, appropriate information management measures are essential for privacy preservation...

Information accountability and health big data analytics: A consent-based model

With the ever increasing amount of eHealth data available from various eHealth systems and sources, Health Big Data Analytics promises enticing benefits such as enabling the discovery of new treatment options and improved decision making. However, concerns over the privacy of information have hindered the aggregation of this information. To address these concerns, we propose the use of Information Accountability protocols to provide patients with the ability to decide how and when their data can be shared and aggregated for use in big data research. In this paper, we discuss the issues surrounding Health Big Data Analytics and propose a consent-based model to address privacy concerns to aid in achieving the promised benefits of Big Data in eHealth.

Managing and sharing health data through information accountability protocols

Concerns over the security and privacy of patient information are one of the biggest hindrances to sharing health information and the wide adoption of eHealth systems. At present, there are competing requirements between healthcare consumers' (i.e. patients) requirements and healthcare professionals' (HCP) requirements. While consumers want control over their information, healthcare professionals want access to as much information as required in order to make well-informed decisions and provide quality care. In order to balance these requirements, the use of an Information Accountability Framework devised for eHealth systems has been proposed. In this paper, we take a step closer to the adoption of the Information Accountability protocols and demonstrate their functionality through an implementation in FluxMED, a customisable EHR system.

The design and implementation of an Information Accountability Framework for eHealth systems

This tutorial primarily focuses on the implementation of Information Accountability (IA) protocols defined in an Information Accountability Framework (IAF) in eHealth systems. Concerns over the security and privacy of patient information are one of the biggest hindrances to sharing health information and the wide adoption of eHealth systems. At present, there are competing requirements between healthcare consumers' (i.e. patients) requirements and healthcare professionals' (HCP) requirements. While consumers want control over their information, healthcare professionals want access to as much information as required in order to make well-informed decisions and provide quality care. This conflict is evident in the review of Australia's PCEHR system and in recent studies of patient control of access to their eHealth information. In order to balance these requirements, the use of an Information Accountability Framework devised for eHealth systems has been proposed. Through the use of IA protocols, so-called Accountable-eHealth systems (AeH) create an eHealth environment where health information is available to the right person at the right time without rigid barriers whilst empowering the consumers with information control and transparency. In this half-day tutorial, we will discuss and describe the technical challenges surrounding the implementation of the IAF protocols into existing eHealth systems and demonstrate their use. The functionality of the protocols and AeH systems will be demonstrated, and an example of the implementation of the IAF protocols into an existing eHealth system will be presented and discussed.

Review and evaluation of mindfulness-based iPhone apps

Background There is growing evidence for the positive impact of mindfulness on wellbeing. Mindfulness-based mobile apps may have potential as an alternative delivery medium for training. While there are hundreds of such apps, there is little information on their quality. Objective This study aimed to conduct a systematic review of mindfulness-based iPhone mobile apps and to evaluate their quality using a recently-developed expert rating scale, the Mobile Application Rating Scale (MARS). It also aimed to describe features of selected high-quality mindfulness apps. Methods A search for “mindfulness” was conducted in iTunes and Google Apps Marketplace. Apps that provided mindfulness training and education were included. Those containing only reminders, timers or guided meditation tracks were excluded. An expert rater reviewed and rated app quality using the MARS engagement, functionality, visual aesthetics, information quality and subjective quality subscales. A second rater provided MARS ratings on 30% of the apps for inter-rater reliability purposes. Results The “mindfulness” search identified 700 apps. However, 94 were duplicates, 6 were not accessible and 40 were not in English. Of the remaining 560, 23 apps met inclusion criteria and were reviewed. The median MARS score was 3.2 (out of 5.0), which exceeded the minimum acceptable score (3.0). The Headspace app had the highest average score (4.0), followed by Smiling Mind (3.7), iMindfulness (3.5) and Mindfulness Daily (3.5). There was a high level of inter-rater reliability between the two MARS raters. Conclusions Though many apps claim to be mindfulness-related, most were guided meditation apps, timers, or reminders. Very few had high ratings on the MARS subscales of visual aesthetics, engagement, functionality or information quality. Little evidence is available on the efficacy of the apps in developing mindfulness.

An information accountability perspective to eHealth security and privacy

Rigid security boundaries hinder the proliferation of eHealth. Through active audit logs, accountable-eHealth systems alleviate privacy concerns and enhance information availability.

Analysis of word embeddings and sequence features for clinical information extraction

This study investigates the use of unsupervised features derived from word embedding approaches and novel sequence representation approaches for improving clinical information extraction systems. Our results corroborate previous findings that indicate that the use of word embeddings significantly improve the effectiveness of concept extraction models; however, we further determine the influence that the corpora used to generate such features have. We also demonstrate the promise of sequence-based unsupervised features for further improving concept extraction.

A participatory design agenda for ubiquitous computing and multimodal interaction: A case study of dental practice

This paper reflects upon our attempts to bring a participatory design approach to design research into interfaces that better support dental practice. The project brought together design researchers, general and specialist dental practitioners, the CEO of a dental software company and, to a limited extent, dental patients. We explored the potential for deployment of speech and gesture technologies in the challenging and authentic context of dental practices. The paper describes the various motivations behind the project, the negotiation of access and the development of the participant relationships as seen from the researchers' perspectives. Conducting participatory design sessions with busy professionals demands preparation, improvisation, and clarity of purpose. The paper describes how we identified what went well and when to shift tactics. The contribution of the paper is in its description of what we learned in bringing participatory design principles to a project that spanned technical research interests, commercial objectives and placing demands upon the time of skilled professionals. Copyright © 2010 ACM, Inc