Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Goldstein, E. B., Buscombe, D., Lazarus, E. D., Mohanty, S. D., Rafique, S. N., Anarde, K. A., Ashton, A. D., Beuzen, T., Castagno, K. A., Cohn, N., Conlin, M. P., Ellenson, A., Gillen, M., Hovenga, P. A., Over, J.-S. R., Palermo, R., Ratliff, K. M., Reeves, I. R. B., Sanborn, L. H., Straub, J. A., Taylor, L. A., Wallace E. J., Warrick, J., Wernette, P., Williams, H. E. Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement. Earth and Space Science, 8(9), (2021): e2021EA001896, https://doi.org/10.1029/2021EA001896.Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.The authors gratefully acknowledge support from the U.S. Geological Survey (G20AC00403 to EBG and SDM), NSF (1953412 to EBG and SDM; 1939954 to EBG), Microsoft AI for Earth (to EBG and SDM), The Leverhulme Trust (RPG-2018-282 to EDL and EBG), and an Early Career Research Fellowship from the Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine (to EBG). U.S. Geological Survey researchers (DB, J-SRO, JW, and PW) were supported by the U.S. Geological Survey Coastal and Marine Hazards and Resources Program as part of the response and recovery efforts under congressional appropriations through the Additional Supplemental Appropriations for Disaster Relief Act, 2019 (Public Law 116-20; 133 Stat. 871)

No outcome disparities in patients with diffuse large B-cell lymphoma and a low socioeconomic status

Introduction: In patients with diffuse large B-cell lymphoma (DLBCL) socioeconomic status (SES) is associated with outcome in several population-based studies. The aim of this study was to further investigate the existence of disparities in treatment and survival. Methods: A population-based cohort study was performed including 343 consecutive patients with DLBCL, diagnosed between 2005 and 2012, in the North-west of the Netherlands. SES was based on the socioeconomic position within the Netherlands by use of postal code and categorized as low, intermediate or high. With multivariable logistic regression and Cox proportional hazard models the association between SES and respectively treatment and overall survival (OS) was evaluated. Results: Two-third of patients was positioned in low SES. Irrespective of SES an equal proportion of patients received standard immunochemotherapy. SES was not a significant risk indicator for OS (intermediate versus low SES: hazard ratio (HR) 1.31 (95% CI 0.78-2.18); high versus low SES: HR 0.83 (95% CI 0.48-1.46)). The mortality risk remained significantly increased with higher age, advanced performance status, elevated LDH and presence of comorbidity. Conclusion: Within the setting of free access to health care, in this cohort of patients with DLBCL no disparities in treatment and survival were seen in those with lower SES. (C) 2017 Elsevier Ltd. All rights reserved

Biomedical informatics and translational medicine

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams

Personnel performance management

Individual performance evaluation is also referred to as performance appraisal, performance evaluation, performance management, performance review, and personal planning and development. These terms reflect the various uses to which this activity may be put and the desire for individual performance improvement. Whichever definition and method is adopted, it is imperative that the employee knows and understands from the outset what his/her work consists in, how long it takes to perform, and the expectations of the manager. An individual worker's view of what constitutes good or acceptable performance may not match the expectations or values of his or her peers, immediate superiors or employer. Therefore, there needs to be a clear understanding of work requirements by all parties. Position descriptions can be used to assist that process, and they can be seen as forming part of the contract between employer and employee. The accuracy, comprehensiveness and clarity of position descriptions will influence common understandings of performance requirements and expectations. This chapter covers the above concepts and includes a description of methods used to analyse work, which are indispensable when preparing position descriptions, selection criteria and ways of documenting these to enable performance management relative to position requirements to take place. The use of performance indicators is also covered. Appropriate systems need to be in place to enable managers, both administrative and clinical, to evaluate the process of the provision of services. Better information about the relationships between inputs, processes, costs and outcomes is expected to lead to better decision making, improved methods of service delivery and hence improved outcomes. Personnel constitute a vital factor in the health care industry, as they perform the processes that constitute health service delivery. Sound personnel management, continuously aiming to improve work performance, will lead to improved outcomes for health care consumers

Evaluation of organisational performance

Performance has two dimensions - effectiveness, and efficiency. Measurement requires definitions of what is to be measured. Effectiveness is a measure of the extent to which objectives are achieved, whereas efficiency is a measure of the cost of trying to achieve those objectives. An organisation's objectives are set out in its strategic plan along with its mission statement, and organisational objectives are operationalised at successively lower levels of the organisation. Therefore, measurement of outcomes involves definition of relevant criteria and measures. Chapter 16 explains a variety of techniques that may be employed for work performance measurement. This chapter is concerned with the measurement of collective or organisational performance

Advancing national health professional cooperation to improve health outcomes

Abstrac