SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Figure 16: Differences in distributions of MEDSAT environmental features between 2019 and 2020. The Kolmogorov-Smirnov (KS) test [27] showed that, except for low and high vegetation LAI, all the other environmental features have significantly different distributions between the two years. Specifically, variables related to air pollution (NO2, ozone, and PM2.5) have significantly decreased in 2020. Interestingly, the amount of total aerosols has on the other hand increased. The amount of trees as observed from satellite imagery has decreased, while the amount of built environment has increased. Finally, all the climate-related variables, such as temperature, solar radiation, the two wind components have also exhibited increased values in 2020.
[18] Lisa EM Hopcroft, Jon Massey, Helen J Curtis, Brian Mackenna, Richard Croker, Andrew D Brown, Thomas O'Dwyer, Orla Macdonald, David Evans, Peter Inglesby, et al. Data-driven identification of unusual prescribing behavior: Analysis and use of an interactive data tool using 6 months of primary care data from 6500 practices in england. JMIR Medical Informatics, 11:e44237, 2023.
[19] Matthias Hunger, Larissa Schwarzkopf, Margit Heier, Annette Peters, Rolf Holle, and KORA Study Group. Official statistics and claims data records indicate non-response and recall bias within survey-based estimates of health care utilization in the older population. BMC health services research, 13:1-11, 2013.
[20] Stephen Ilyas and Joanna Moncrieff. Trends in prescriptions and costs of drugs for mental disorders in england, 1998-2010. The British Journal of Psychiatry, 200(5):393-398, 2012.
[21] Kishore Kumar Jagadeesan, James Grant, Sue Griffin, Ruth Barden, and Barbara Kasprzyk-Hordern. Prana: an r package to calculate and visualize england nhs primary care prescribing data. BMC Medical Informatics and Decision Making, 22(1):5, 2022.
[22] Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30, 2017.
[23] Kévin Le Rest, David Pinaud, Pascal Monestiez, Joël Chadoeuf, and Vincent Bretagnolle. Spatial leave-one-out cross-validation for variable selection in the presence of spatial autocorrelation. Global ecology and biogeography, 23(7):811-820, 2014.
[40] Shane Timmons, Frances McGinnity, Cameron Belton, Martina Barjaková, and Peter Lunn. It depends on how you ask: measuring bias in population surveys of compliance with Covid-19 public health guidance. J Epidemiol Community Health, 75(4):387–389, 2021.
[41] Nuffield Trust. Poor areas left behind on standards of GP care, research reveals. https://www.nuffieldtrust.org.uk/news-item/poor-areas-left-behind-on-standards-of-gp-care-research-reveals, 2018. [Online; accessed 3-June-2023].
[42] Parliament UK. Population estimates HP registers: why the difference? https://commonlibrary.parliament.uk/population-estimates-gp-registers-why-the-difference, 2016. [Online; accessed 3-June-2023].
[43] Roozbeh Valavi, Jane Elith, José J Lahoz-Monfort, and Gurutzeta Guillera-Arroita. block cv: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods in Ecology and Evolution, 10(2):225-232, 2019.
[44] WASDI platform. Earth Observation tech for everyone. https://www.wasdi.cloud, 2023. [Online; accessed 3-June-2023].
[45] Yixue Xia and Patrice Forget. Opioid and gabapentinoid prescriptions in england from 2015 to 2020. Plos one, 17(11):e0276867, 2022.
[46] Christopher Yeh, Chenlin Meng, Sherrie Wang, Anne Driscoll, Erik Rozi, Patrick Liu, Ji-hyeon Lee, Marshall Burke, David B Lobell, and Stefano Ermon. Sustainbench: Benchmarks for monitoring the sustainable development goals with machine learning. arXiv preprint arXiv:2111.04724, 2021.