Temporal Feature Selection with Symbolic Regression

Building and discovering useful features when constructing machine learning models is the central task for the machine learning practitioner. Good features are useful not only in increasing the predictive power of a model but also in illuminating the underlying drivers of a target variable. In this research we propose a novel feature learning technique in which Symbolic regression is endowed with a ``Range Terminal\u27\u27 that allows it to explore functions of the aggregate of variables over time. We test the Range Terminal on a synthetic data set and a real world data in which we predict seasonal greenness using satellite derived temperature and snow data over a portion of the Arctic. On the synthetic data set we find Symbolic regression with the Range Terminal outperforms standard Symbolic regression and Lasso regression. On the Arctic data set we find it outperforms standard Symbolic regression, fails to beat the Lasso regression, but finds useful features describing the interaction between Land Surface Temperature, Snow, and seasonal vegetative growth in the Arctic

Efficacy and safety of using mesh or grafts in surgery for anterior and/or posterior vaginal wall prolapse: systematic review and meta-analysis.

Background The efficacy and safety of mesh/graft in surgery for anterior or posterior pelvic organ prolapse is uncertain. Objectives To systematically review the efficacy and safety of mesh/graft for anterior or posterior vaginal wall prolapse surgery. Search strategy Electronic databases and conference proceedings were searched, experts and manufacturers contacted and reference lists of retrieved papers scanned. Selection criteria Randomised controlled trials (RCTs), non-randomised comparative studies, registries, case series involving at least 50 women, and RCTs published as conference abstracts from 2005 onwards. Data collection and analysis One reviewer screened titles/abstracts, undertook data extraction, and assessed study quality. Data analysis was conducted for three subgroups: anterior, posterior, and anterior and/or posterior repair (not reported separately). Results Forty-nine studies involving 4569 women treated with mesh/graft were included. Study quality was generally high. Median follow up was 13 months (range 1 to 51). In anterior repair, there was short-term evidence that mesh/graft (any type) significantly reduced objective prolapse recurrence rates compared with no mesh/graft (relative risk 0.48, 95% CI 0.32-0.72). Non-absorbable synthetic mesh had a significantly lower objective prolapse recurrence rate (8.8%, 48/548) than absorbable synthetic mesh (23.1%, 63/273) and biological graft (17.9%, 186/1041), but a higher erosion rate (10.2%, 68/666) than synthetic mesh (0.7%, 1/147) and biological graft (6.0%, 35/581). There was insufficient information to compare any of the other outcomes regardless of prolapse type. Conclusion Evidence for most outcomes was too sparse to provide meaningful conclusions. Rigorous long-term RCTs are required to determine the comparative efficacy of using mesh/graft.The National Institute for Health and Clinical Excellence Interventional Procedures Programme