A key purpose of building a model from clinical data is to predict the outcomes of future individual patients. This work introduces a Bayesian patient-specific predictive framework for constructing predictive models from data that are optimized to predict well for a particular patient case. The construction of such <i>patient-specific models</i> is influenced by the particular history, symptoms, laboratory results, and other features of the patient case at hand. This approach is in contrast to the commonly used <i>population-wide models</i> that are constructed to perform well on average on all future cases.
The new patient-specific method described in this research uses Bayesian network models, carries out Bayesian model averaging over a set of models to predict the outcome of interest for the patient case at hand, and employs a patient-specific heuristic to locate a set of suitable models to average over. Two versions of the method are developed that differ in the representation used for the conditional probability distributions in the Bayesian networks. One version uses a representation that captures only the so called <i>global structure</i> among the variables of a Bayesian network and the second representation captures additional <i>local structure</i> among the variables.
The patient-specific methods were experimentally evaluated on one synthetic dataset, 21 UCI datasets and three medical datasets. Their performance was measured using five different performance measures and compared to that of several commonly used methods for constructing predictive models including naïve Bayes, C4.5 decision tree, logistic regression, neural networks, k-Nearest Neighbor and Lazy Bayesian Rules. Over all the datasets, both patient-specific methods performed better on average on all performance measures and against all the comparison algorithms. The <i>global structure</i> method that performs Bayesian model averaging in conjunction with the patient-specific search heuristic had better performance than either model selection with the patient-specific heuristic or non-patient-specific Bayesian model averaging. However, the additional learning of local structure by the <i>local structure</i> method did not lead to significant improvements over the use of global structure alone. The specific implementation limitations of the local structure method may have limited its performance.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-11292007-232406 |
| Date | 29 January 2008 |
| Creators | Visweswaran, Shyam |
| Contributors | Marek J. Druzdzel, Tom Mitchell, Gregory F. Cooper, Milos Hauskrecht |
| Publisher | University of Pittsburgh |
| Source Sets | University of Pittsburgh |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.pitt.edu/ETD/available/etd-11292007-232406/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Pittsburgh or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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