Tech Reports
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
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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We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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