Human factors in financial trading: an analysis of trading incidents

Objective: This study tests the reliability of a system (FINANS) to collect and analyse incident reports in the financial trading domain, and is guided by a human factors taxonomy used to describe error in the trading domain. Background: Research indicates the utility of applying human factors theory to understand error in finance, yet empirical research is lacking. We report on the development of the first system for capturing and analysing human factors-related issues in operational trading incidents. Method: In study 1, 20 incidents are analysed by an expert user group against a referent standard to establish the reliability of FINANS. Study 2 analyses 750 incidents using distribution, mean, pathway and associative analysis to describe the data. Results: Kappa scores indicate that categories within FINANS can be reliably used to identify and extract data on human factors-related problems underlying trading incidents. Approximately 1% of trades (n=750) lead to an incident. Slip/lapse (61%), situation awareness (51%), and teamwork (40%) were found to be the most common problems underlying incidents. For the most serious incidents, problems in situation awareness and teamwork were most common. Conclusion: We show that (i) experts in the trading domain can reliably and accurately code human factors in incidents, (ii) 1% of trades incur error and (iii) poor teamwork skills and situation awareness underpin the most critical incidents. Application: This research provides data crucial for ameliorating risk within financial trading organizations, with implications for regulation and policy

Prospects and pitfalls in combining eye tracking data and verbal reports

It is intuitively appealing to try to combine eye-tracking data and verbal reports when investigating medical image interpretation. However, before collecting such data, important decisions have to be made including exactly when and how to collect the verbal reports. The purpose of this methodological article is to reflect upon the pros and cons of different solutions and to offer some guidelines to investigators. We start by exploring the ontology of vision and speech production and the epistemology of eye movements to grasp what fixations and verbal reports actually reflect. We are also interested in the major constraints of the two systems. Second, we elaborate on two dominant investigational approaches to verbal accounts, namely concurrent think-aloud and Chi’s explanations, and move on to other approaches. Third, we present and critically evaluate studies from the literature on medical image interpretation that have sought to contrast or integrate eye movement data and verbal reports. Fourth, we conclude with some practical guidelines and suggestions for further research.              

Aerospace Medicine and Biology: a Continuing Bibliography with Indexes (Supplement 328)

This bibliography lists 104 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during September, 1989. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 324)

This bibliography lists 200 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during May, 1989. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Automation of Patient Trajectory Management: A deep-learning system for critical care outreach

The application of machine learning models to big data has become ubiquitous, however their successful translation into clinical practice is currently mostly limited to the field of imaging. Despite much interest and promise, there are many complex and interrelated barriers that exist in clinical settings, which must be addressed systematically in advance of wide-spread adoption of these technologies. There is limited evidence of comprehensive efforts to consider not only their raw performance metrics, but also their effective deployment, particularly in terms of the ways in which they are perceived, used and accepted by clinicians. The critical care outreach team at St Vincent’s Public Hospital want to automatically prioritise their workload by predicting in-patient deterioration risk, presented as a watch-list application. This work proposes that the proactive management of in-patients at risk of serious deterioration provides a comprehensive case-study in which to understand clinician readiness to adopt deep-learning technology due to the significant known limitations of existing manual processes. Herein is described the development of a proof of concept application uses as its input the subset of real-time clinical data available in the EMR. This data set has the noteworthy challenge of not including any electronically recorded vital signs data. Despite this, the system meets or exceeds similar benchmark models for predicting in-patient death and unplanned ICU admission, using a recurrent neural network architecture, extended with a novel data-augmentation strategy. This augmentation method has been re-implemented in the public MIMIC-III data set to confirm its generalisability. The method is notable for its applicability to discrete time-series data. Furthermore, it is rooted in knowledge of how data entry is performed within the clinical record and is therefore not restricted in applicability to a single clinical domain, instead having the potential for wide-ranging impact. The system was presented to likely end-users to understand their readiness to adopt it into their workflow, using the Technology Adoption Model. In addition to confirming feasibility of predicting risk from this limited data set, this study investigates clinician readiness to adopt artificial intelligence in the critical care setting. This is done with a two-pronged strategy, addressing technical and clinically-focused research questions in parallel

Systems delays in the management of malignant breast disease

Centralised multidisciplinary management of breast cancer occurs in KwaZulu-Natal, South Africa and requires a diagnostic and staging pathway at the referring hospital. Delays in this pathway are unknown. This study, conducted at a referring hospital, R K Khan (RKK), quantifies and analyses these delays. A retrospective folder review included all patients with breast cancer diagnosed at RKK from January 2008 to January 2009. Data extraction included demographic data, time to diagnosis and initial staging using a standardised datasheet. Specific care steps were identified, namely delays to initial imaging with mammography, pathology confirmation, staging workup and eventual referral to a centralised breast clinic

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 359)

This bibliography lists 164 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Jan. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Aerospace Medicine and Biology: A continuing bibliography with indexes

This bibliography lists 417 reports, articles and other documents introduced into the NASA scientific and technical information system in March 1985

Anomaly detection prototype for log-based predictive maintenance at INFN-CNAF tier-1

Splitting the evolution of HEP from the one of computational resources needed to perform analyses is, nowadays, not possible. Each year, in fact, LHC produces dozens of PetaBytes of data (e.g. collision data, particle simulation, metadata etc.) that need orchestrated computing resources for storage, computational power and high throughput networks to connect centers. As a consequence of the LHC upgrade, the Luminosity of the experiment will increase by a factor of 10 over its originally designed value, entailing a non negligible technical challenge at computing centers: it is expected, in fact, an uprising in the amount of data produced and processed by the experiment. With this in mind, the HEP Software Foundation took action and released a road-map document describing the actions needed to prepare the computational infrastructure to support the upgrade. As a part of this collective effort, involving all computing centres of the Grid, INFN-CNAF has set a preliminary study towards the development of AI driven maintenance paradigm. As a contribution to this preparatory study, this master thesis presents an original software prototype that has been developed to handle the task of identifying critical activity time windows of a specific service (StoRM). Moreover, the prototype explores the viability of a content extraction via Text Processing techniques, applying such strategies to messages belonging to anomalous time windows