Improving Event Time Prediction by Learning to Partition the Event Time Space

Recently developed survival analysis methods improve upon existing approaches by predicting the probability of event occurrence in each of a number pre-specified (discrete) time intervals. By avoiding placing strong parametric assumptions on the event density, this approach tends to improve prediction performance, particularly when data are plentiful. However, in clinical settings with limited available data, it is often preferable to judiciously partition the event time space into a limited number of intervals well suited to the prediction task at hand. In this work, we develop a method to learn from data a set of cut points defining such a partition. We show that in two simulated datasets, we are able to recover intervals that match the underlying generative model. We then demonstrate improved prediction performance on three real-world observational datasets, including a large, newly harmonized stroke risk prediction dataset. Finally, we argue that our approach facilitates clinical decision-making by suggesting time intervals that are most appropriate for each task, in the sense that they facilitate more accurate risk prediction.Comment: 16 pages, 5 figures, 2 table

Application of Machine Learning Alogorithms to Correct Images to Help the Color Blind

https://openriver.winona.edu/urc2018/1011/thumbnail.jp

Field Observations of a Multilevel Beach Cusp System and Their Swash Zone Dynamics

This paper presents the observed morphological evolution of a multilevel beach cusp system in Long Strand, Co. Cork, Ireland. The surveys were carried out with an Unmanned Aerial Vehicle (UAV) system between March and September 2019. From this site, three levels of beach cusps on the beachface (i.e., lower beach level, mid beach level and upper beach level), and critical cusp parameters are reported, including cusp spacing, cusp elevation, cusp depth, and cusp amplitude. Thus far, such an extensive dataset has not previously been reported in the literature from a single site. The evolution of the different cusp parameters is then linked with the hydrodynamics in the study area, and new prediction theories are proposed for the different cusp parameters. The Lower beach level cusps (1 < z < 2.5 m Irish Transverse Mercator (ITM)) changed with every tide and appeared when surf-similarity parameter-ξ0 < 1.55. These cusps had a mean cusp spacing of λmean = 11.09 m, which are closely linked with the predictions of the self-organisation theory (p < 0.05). In contrast, the Mid beach level cusps (2.5 < z < 3.5 m ITM) are less dynamic compared to the Lower beach level cusps and can persist between spring tidal cycles. They had a mean cusp spacing of λmean = 18.17 m. The Upper beach level cusps (approximately z = 6 m ITM) are above astronomical tide levels and have a mean cusp spacing of λmean = 40.26 m. They did not change significantly over the survey period due to a lack of major storm events. These findings give a better understanding of the evolution of different cusp parameters for a multilevel beach cusp system and can be used to formulate a global theory regarding their change over time

Management of patients with Graves' orbitopathy:initial assessment, management outside specialised centres and referral pathways

Graves’ orbitopathy (GO) is uncommon, but responsible for considerable morbidity. A coordinated approach between healthcare professionals is required in order to meet the needs of patients. Early diagnosis can be achieved by a simple clinical assessment. Low-cost effective interventions can be initiated by generalists, which may improve outcomes. Moderate-to-severe GO should be referred to specialised centres. Recommendations for clinical diagnosis, initial management and referral pathways are highlighted