Multiprobabilistic prediction in early medical diagnoses

This paper describes the methodology of providing multiprobability predictions for proteomic mass spectrometry data. The methodology is based on a newly developed machine learning framework called Venn machines. Is allows to output a valid probability interval. The methodology is designed for mass spectrometry data. For demonstrative purposes, we applied this methodology to MALDI-TOF data sets in order to predict the diagnosis of heart disease and early diagnoses of ovarian cancer and breast cancer. The experiments showed that probability intervals are narrow, that is, the output of the multiprobability predictor is similar to a single probability distribution. In addition, probability intervals produced for heart disease and ovarian cancer data were more accurate than the output of corresponding probability predictor. When Venn machines were forced to make point predictions, the accuracy of such predictions is for the most data better than the accuracy of the underlying algorithm that outputs single probability distribution of a label. Application of this methodology to MALDI-TOF data sets empirically demonstrates the validity. The accuracy of the proposed method on ovarian cancer data rises from 66.7 % 11 months in advance of the moment of diagnosis to up to 90.2 % at the moment of diagnosis. The same approach has been applied to heart disease data without time dependency, although the achieved accuracy was not as high (up to 69.9 %). The methodology allowed us to confirm mass spectrometry peaks previously identified as carrying statistically significant information for discrimination between controls and cases.

The political economy of bovine tuberculosis in Great Britain

A brief history of bovine tuberculosis (bTB) and its control in Great Britain is presented. Numerous diverse policies to control the disease in man, cattle and wildlife have been pursued over the last 100 years and many millions of pounds have been spent. After notable success in reducing the incidence and prevalence of bTB in cattle in GB from the 1950s to the mid-1980s, the number of cattle slaughtered has increased with increased geographical spread continually since that time with a high point of bTB incidence in 2008. This increase appeared to coincide with changing policy regarding the control of the disease in badgers with a more humane approach adopted and with strengthened protection for badgers through legislation. Indeed, much controversy has been involved in the debate on the role of badgers in disease transmission to cattle and the need for their control as vectors of the disease with various commissioned research projects, trials, public consultations and media attention. The findings of two social science investigations presented as examples showed that citizens generally believed that bTB in cattle is an important issue that needs to be tackled but objected to badgers being killed, whilst cattle farmers were willing to pay around £17/animal/year for a bTB cattle vaccine. It is noted that successes regarding the control of bTB in other countries have combined both cattle and wildlife controls and had strong involvement from industry working with government.