e-Science and artificial neural networks in cancer management

SUMMARY We describe the origins of this project, its aims and its relevance to e-Science research. Particle physicists at the University of Manchester with experience of artificial neural networks (ANNs) have collaborated with clinicians at the University of Dundee to produce an ANN that is intended to predict survival rates and to indicate management profiles for cancer patients. Comparisons are made between typical data handling problems in particle physics and health care. The problems associated with data procurement, namely reliability and censoring are described, together with a discussion of how these problems were addressed. The inputs to the ANN and its decision output are discussed. The reliability of the ANN is assessed quantitatively. The prototype secure Web-based interface, which allows clinicians to input new patient data to the central node at the University of Manchester and to obtain prognoses from anywhere in the world is presented. For each topic, the e-Science relevance is described and underlined

Assessing the sprinkler activation predictive capability of the BRANZFIRE fire model

This paper describes an investigation into the sprinkler response time predictive capability of the BRANZFIRE fire model. A set of 22 fire/sprinkler experiments are simulated where the sprinkler activation time and the heat release rate (HRR) for each individual experiment had been determined. The experiments provided data for use in validating the sprinkler activation prediction algorithms in the BRANZFIRE zone model. A set of base case values were chosen and input files constructed for the simulations. The experiments were then simulated by the fire model using both the NIST/ JET ceiling jet and Alpert’s ceiling jet options (which are the two ceiling jet correlations available in the BRANZFIRE zone model). The fire model included a heat transfer calculation for the temperature of the heat sensitive sprinkler element. Different sprinkler operational parameters such as the conduction factor, response time index (RTI) and the sprinkler depth below ceiling were also varied to assess the sensitivity of their effect on the activation time. Results showed that using the NIST/JET ceiling jet algorithm gave a closer prediction of the sprinkler response time in a small room than Alpert’s correlation. This was expected, since the former includes the effect of a hot upper layer while the latter applies to unconfined ceilings. The experiments available for comparison had been conducted inside an enclosure with a developing hot upper layer. The findings also signified that changing the sprinkler operational parameters can change the predicted sprinkler activation time significantly

Artificial Neural Networks in Cancer Management

An Instrument for Prediction of Cancer-Related Survival (CIPCS) has been implemented. The intent of this system is to provide an Internet/web access for practical clinicians to assist in making prognosis/decision in cancer patient treatment or management.