Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa

Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use these models to explore the use of geographical features in predicting colorectal cancer survival curves for patients in the state of Iowa, spanning the years 1989 to 2012. Specifically, we compare model performance using a newly defined metric -- area between the curves (ABC) -- to assess (a) whether survival curves can be reasonably predicted for colorectal cancer patients in the state of Iowa, (b) whether geographical features improve predictive performance, and (c) whether a simple binary representation or richer, spectral clustering-based representation perform better. Our findings suggest that survival curves can be reasonably estimated on average, with predictive performance deviating at the five-year survival mark. We also find that geographical features improve predictive performance, and that the best performance is obtained using richer, spectral analysis-elicited features.Comment: 8 page

Towards the Evolution of Multi-Layered Neural Networks: A Dynamic Structured Grammatical Evolution Approach

Current grammar-based NeuroEvolution approaches have several shortcomings. On the one hand, they do not allow the generation of Artificial Neural Networks (ANNs composed of more than one hidden-layer. On the other, there is no way to evolve networks with more than one output neuron. To properly evolve ANNs with more than one hidden-layer and multiple output nodes there is the need to know the number of neurons available in previous layers. In this paper we introduce Dynamic Structured Grammatical Evolution (DSGE): a new genotypic representation that overcomes the aforementioned limitations. By enabling the creation of dynamic rules that specify the connection possibilities of each neuron, the methodology enables the evolution of multi-layered ANNs with more than one output neuron. Results in different classification problems show that DSGE evolves effective single and multi-layered ANNs, with a varying number of output neurons

A predictive model for kidney transplant graft survival using machine learning

Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney donor risk index. A machine learning method may provide improved prediction of transplant outcomes and help decision-making. A popular tree-based machine learning method, random forest, was trained and evaluated with the same data originally used to develop the risk index (70,242 observations from 1995-2005). The random forest successfully predicted an additional 2,148 transplants than the risk index with equal type II error rates of 10%. Predicted results were analyzed with follow-up survival outcomes up to 240 months after transplant using Kaplan-Meier analysis and confirmed that the random forest performed significantly better than the risk index (p<0.05). The random forest predicted significantly more successful and longer-surviving transplants than the risk index. Random forests and other machine learning models may improve transplant decisions.Comment: This work has been published: Pahl ES, Street WN, Johnson HJ, Reed AI. "A Predictive Model for Kidney Transplant Graft Survival Using Machine Learning." 4th International Conference on Computer Science and Information Technology (COMIT 2020), November 28-29, 2020, Dubai, UAE. ISBN: 978-1-925953-30-5. Volume 10, Number 16.10.5121/csit.2020.10160

Detection in the United Kingdom of the Neisseria gonorrhoeae FC428 clone, with ceftriaxone resistance and intermediate resistance to azithromycin, October to December 2018

We describe detection in the United Kingdom (UK) of the drug-resistant Neisseria gonorrhoeae FC428 clone, with ceftriaxone resistance and intermediate azithromycin resistance. Two female patients developed infection following contact with UK-resident men from the same sexual network linked to travel to Ibiza, Spain. One case failed treatment with ceftriaxone, and azithromycin and gentamicin, before successful treatment with ertapenem. Both isolates had indistinguishable whole-genome sequences. Urgent action is essential to contain this drug-resistant strain