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Tech Report kmi-00-07 Abstract


Robust Outcome Prediction for Intensive-Care Patients
Techreport ID: kmi-00-07
Date: 2000
Author(s): Marco Ramoni, Paola Sebastiani and Richard Dybowski
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Missing data are a major plague of medical databases in general, and of Intensive Care Units databases in particular. The time pressure of work in an Intensive Care Unit pushes the physicians to omit randomly or selectively record data. These different omission strategies give rise to different patterns of missing data and the recommended approach of completing the database using median imputation and fitting a logistic regression model can lead to significant biases. This paper applies a new classification method, called robust Bayes classifier, that does not rely on any particular assumption about the pattern of missing data and compares it to the median imputation approach using a database of 324 Intensive Care Unit patients. 1. Knowledge Media Institute, The Open University 2. Department ofÊMathematics, Imperial College of Science, Technology and Medicine 3. Department of Medicine, King's College London

Publication(s):

Also in Methods of Information in Medicine, 2000.
 
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Future Internet is...


Future Internet
With over a billion users, today's Internet is arguably the most successful human artifact ever created. The Internet's physical infrastructure, software, and content now play an integral part of the lives of everyone on the planet, whether they interact with it directly or not. Now nearing its fifth decade, the Internet has shown remarkable resilience and flexibility in the face of ever increasing numbers of users, data volume, and changing usage patterns, but faces growing challenges in meetings the needs of our knowledge society. Globally, many major initiatives are underway to address the need for more scientific research, physical infrastructure investment, better education, and better utilisation of the Internet. Within Japan, USA and Europe major new initiatives have begun in the area.

To succeed the Future Internet will need to address a number of cross-cutting challenges including:

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