Identify disorders in health records using Conditional Random Fields and Metamap: AEHRC at ShARe/CLEF 2013 eHealth Evaluation Lab Task 1

The Australian e-Health Research Centre (AEHRC) recently participated in the ShARe/CLEF eHealth Evaluation Lab Task 1. The goal of this task is to individuate mentions of disorders in free-text electronic health records and map disorders to SNOMED CT concepts in the UMLS metathesaurus. This paper details our participation to this ShARe/CLEF task. Our approaches are based on using the clinical natural language processing tool Metamap and Conditional Random Fields (CRF) to individuate mentions of disorders and then to map those to SNOMED CT concepts. Empirical results obtained on the 2013 ShARe/CLEF task highlight that our instance of Metamap (after ltering irrelevant semantic types), although achieving a high level of precision, is only able to identify a small amount of disorders (about 21% to 28%) from free-text health records. On the other hand, the addition of the CRF models allows for a much higher recall (57% to 79%) of disorders from free-text, without sensible detriment in precision. When evaluating the accuracy of the mapping of disorders to SNOMED CT concepts in the UMLS, we observe that the mapping obtained by our ltered instance of Metamap delivers state-of-the-art e ectiveness if only spans individuated by our system are considered (`relaxed' accuracy).

Local inter-session variability modelling for object classification

Object classification is plagued by the issue of session variation. Session variation describes any variation that makes one instance of an object look different to another, for instance due to pose or illumination variation. Recent work in the challenging task of face verification has shown that session variability modelling provides a mechanism to overcome some of these limitations. However, for computer vision purposes, it has only been applied in the limited setting of face verification. In this paper we propose a local region based intersession variability (ISV) modelling approach, and apply it to challenging real-world data. We propose a region based session variability modelling approach so that local session variations can be modelled, termed Local ISV. We then demonstrate the efficacy of this technique on a challenging real-world fish image database which includes images taken underwater, providing significant real-world session variations. This Local ISV approach provides a relative performance improvement of, on average, 23% on the challenging MOBIO, Multi-PIE and SCface face databases. It also provides a relative performance improvement of 35% on our challenging fish image dataset.

Overview of the ShARe/CLEF eHealth Evaluation Lab 2013

Discharge summaries and other free-text reports in healthcare transfer information between working shifts and geographic locations. Patients are likely to have difficulties in understanding their content, because of their medical jargon, non-standard abbreviations,and ward-specific idioms. This paper reports on an evaluation lab with an aim to support the continuum of care by developing methods and resources that make clinical reports in English easier to understand for patients, and which helps them in finding information related to their condition.

ShARe/CLEF eHealth Evaluation Lab 2013, Task 3: Information retrieval to address patients' questions when reading clinical reports

This paper presents the results of task 3 of the ShARe/CLEF eHealth Evaluation Lab 2013. This evaluation lab focuses on improving access to medical information on the web. The task objective was to investigate the effect of using additional information such as the discharge summaries and external resources such as medical ontologies on the IR effectiveness. The participants were allowed to submit up to seven runs, one mandatory run using no additional information or external resources, and three each using or not using discharge summaries.

Effect of a single session of exercise on lipoprotein(a)

Lipoprotein(a) (Lp(a)) is bound to apolipoprotein B-100 by disulfide linkage and is associated in the upper density range of low density lipoprotein cholesterol. Persons with elevated concentrations of Lp(a) are regarded as having an increased risk for premature coronary artery disease. Although many studies exist evaluating the effects of a single session of exercise on lipids and lipoproteins, little information is available concerning the effects of exercise on Lp(a). Therefore, the purpose of this study was to determine the effects of a single exercise session on plasma Lp(a). Twelve physically active men completed two 30-min submaximal treadmill exercise sessions: low intensity (LI, 50% VO2max) and high intensity (HI, 80% VO2max). Blood samples were obtained immediately before and after exercise. Total cholesterol (LI: before 4.22 +/- 0.26, after 4.24 +/- 0.28; HI: before 4.24 +/- 0.31, after 4.11 +/- 0.28 mmol . l(-1), mean +/- SE) and triglyceride (LI: before 1.14 +/- 0.16, after 1.06 +/- 0.16; HI: before 1.12 +/- 0.19, after 1.21 +/- 0.19 mmol . l(-1)) concentrations did not differ immediately after either exercise session, nor did Lp(a) concentrations differ immediately after either exercise session (LI: before 4.1 +/- 2.2, after 4.0 +/- 2.1; HI: before 3.9 +/- 2.2, after 3.7 +/- 2.0 mg . dl(-1)). These results suggest that neither a low nor a high intensity exercise session lasting 30 min in duration has an immediate effect on plasma Lp(a).

Overview of the ShARe/CLEF eHealth Evaluation Lab 2014

This paper reports on the 2nd ShARe/CLEFeHealth evaluation lab which continues our evaluation resource building activities for the medical domain. In this lab we focus on patients' information needs as opposed to the more common campaign focus of the specialised information needs of physicians and other healthcare workers. The usage scenario of the lab is to ease patients and next-of-kins' ease in understanding eHealth information, in particular clinical reports. The 1st ShARe/CLEFeHealth evaluation lab was held in 2013. This lab consisted of three tasks. Task 1 focused on named entity recognition and normalization of disorders; Task 2 on normalization of acronyms/abbreviations; and Task 3 on information retrieval to address questions patients may have when reading clinical reports. This year's lab introduces a new challenge in Task 1 on visual-interactive search and exploration of eHealth data. Its aim is to help patients (or their next-of-kin) in readability issues related to their hospital discharge documents and related information search on the Internet. Task 2 then continues the information extraction work of the 2013 lab, specifically focusing on disorder attribute identification and normalization from clinical text. Finally, this year's Task 3 further extends the 2013 information retrieval task, by cleaning the 2013 document collection and introducing a new query generation method and multilingual queries. De-identified clinical reports used by the three tasks were from US intensive care and originated from the MIMIC II database. Other text documents for Tasks 1 and 3 were from the Internet and originated from the Khresmoi project. Task 2 annotations originated from the ShARe annotations. For Tasks 1 and 3, new annotations, queries, and relevance assessments were created. 50, 79, and 91 people registered their interest in Tasks 1, 2, and 3, respectively. 24 unique teams participated with 1, 10, and 14 teams in Tasks 1, 2 and 3, respectively. The teams were from Africa, Asia, Canada, Europe, and North America. The Task 1 submission, reviewed by 5 expert peers, related to the task evaluation category of Effective use of interaction and targeted the needs of both expert and novice users. The best system had an Accuracy of 0.868 in Task 2a, an F1-score of 0.576 in Task 2b, and Precision at 10 (P@10) of 0.756 in Task 3. The results demonstrate the substantial community interest and capabilities of these systems in making clinical reports easier to understand for patients. The organisers have made data and tools available for future research and development.

Day in the Lab : Urban Informatics Research Lab, Queensland University of Technology

The Urban Informatics Research Lab brings together a group of people who focus their research on interdisciplinary topics at the intersection of social, spatial, and technical research domains—that is, people, place, and technology. Those topics are spread across the breadth of urban life—its contemporary issues and its needs, as well as the design opportunities that we have as individuals, groups, communities, and as a whole society. The lab’s current research areas include urban planning and design, civic innovation, mobility and transportation, education and connected learning, environmental sustainability, and food and urban agriculture. The common denominator of the lab’s approach is user-centered design research directed toward understanding, conceptualizing, developing, and evaluating sociotechnical practices as well as the opportunities afforded by innovative digital technology in urban environments.

Teleoperation of a 7 DOF humanoid robot arm using human arm accelerations and EMG signals

We present our work on tele-operating a complex humanoid robot with the help of bio-signals collected from the operator. The frameworks (for robot vision, collision avoidance and machine learning), developed in our lab, allow for a safe interaction with the environment, when combined. This even works with noisy control signals, such as, the operator’s hand acceleration and their electromyography (EMG) signals. These bio-signals are used to execute equivalent actions (such as, reaching and grasping of objects) on the 7 DOF arm.

Lab coats and leggings: When science and dance connect it’s quite a show

Dancing is an activity most people associate with after-hours exploits: parties, weddings, the lounge rooms of friends with great vinyl collections, a night out at the ballet – or television shows such as So You Think You Can Dance, Dancing With The Stars or Got To Dance...