Will Bridewell
Will Bridewell
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Reducing Overfitting in Process Model Induction
We note that previous methods for inductive process modeling tend to overfit the training data, which suggests ensemble learning as a likely response. We introduce a new approach that induces a set of process models from different samples of the training data and uses them to guide a final search through the space of model structures.
Will Bridewell
,
Narges Bani Asadi
,
Pat Langley
,
Ljupčo Todorovski
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Inducing Hierarchical Process Models in Dynamic Domains
Research on inductive process modeling combines background knowledge with time-series data to construct explanatory models, but previous work has placed few constraints on search through the model space. We present an extended formalism that organizes process knowledge in a hierarchical manner and a system that carries out constrained search using this knowledge.
Ljupčo Todorovski
,
Will Bridewell
,
Oren Shiran
,
Pat Langley
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Evaluation of Negation Phrases in Narrative Clinical Reports
Automatically identifying findings or diseases described in clinical textual reports requires determining whether clinical observations are present or absent. We evaluate the use of negation phrases and the frequency of negation in free-text clinical reports.
Wendy W. Chapman
,
Will Bridewell
,
Paul Hanbury
,
Gregory F. Cooper
,
Bruce G. Buchanan
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