Will Bridewell
Will Bridewell
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Discovering Constraints for Inductive Process Modeling
Previous research on inductive process modeling, which constructs models from knowledge and time-series data, has relied on handcrafted constraints. In this paper, we report an approach to discovering such constraints from a set of models that have been ranked according to their error on observations.
Ljupčo Todorovski
,
Will Bridewell
,
Pat Langley
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Recognizing Deception: A Model of Dynamic Belief Attribution
We introduce a practical, computational framework that enables socially aware inference. We then demonstrate the framework’s ability to model a common, complex, and under-investigated aspect of human social behavior: deception.
Will Bridewell
,
Alistair M.C. Isaac
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Alignment and Clustering of Breast Cancer Patients by Longitudinal Treatment History
We present a novel approach to temporal clustering of patient treatment information based on the semantic similarity of longitudinal histories.
Wei-Nchih Lee
,
Will Bridewell
,
Amar K. Das
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Social Network Analysis of Physician Interactions: The Effect of Institutional Boundaries on Breast Cancer Care
We looked at registry-based data on breast cancer care at two neighboring healthcare institutions with a specific focus on whether organizational boundaries determine the physicians that a patient will see. From an initial patient-oriented data set, we developed a social network of physicians, modeling their interactions over the course of the provided treatments.
Will Bridewell
,
Amar K. Das
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A Computational Account of Everyday Abductive Inference
We review the main qualitative characteristics of everyday inference in human cognition and present a computational account that is consistent with them.
Will Bridewell
,
Pat Langley
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Code
Comparison of Semantic Similarity Measures for Application Specific Ontology Pruning
We propose that ontology pruning be used to remove unneeded concepts so that the resulting ontology better reflects the semantic distinctions of a particular domain. In this paper, we present a novel pruning strategy for drug ontologies.
Wei-Nchih Lee
,
Will Bridewell
,
Amar K. Das
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DOI
Combining Data-driven and Knowledge-guided Methods to Induce Interpretable Physiological Models
We review the paradigm of inductive process modeling and examine its application to human physiology.
Pat Langley
,
Will Bridewell
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Visualizing HIV Treatment Patterns with Network Models
Network visualization of temporal data offers insights into the practical application of treatment guidelines. Using publicly …
Will Bridewell
,
Wei-Nchih Lee
,
Amar K. Das
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Integrated Systems for Inducing Spatio-temporal Process Models
Chunki Park
,
Will Bridewell
,
Pat Langley
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Integrated Systems for Inducing Spatio-temporal Process Models
In the past, inductive process modeling systems have been limited to data sets that recorded change over time, but many interesting problems involve both spatial and temporal dynamics. To meet this challenge, we introduce SCISM, an integrated intelligent system which solves the task of inducing process models that account for spatial and temporal variation.
Chunki Park
,
Will Bridewell
,
Pat Langley
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