Biomedical Informatics

Apr 30, 2013

clinical informatics social network analysis ontologies

Will Bridewell

Research Scientist in Artificial Intelligence

My research interests include the relationship between attention, cognition, and intentional action.

Publications

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

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

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

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

Science as an Anomaly-Driven Enterprise: A Computational Approach to Generating Acceptable Theory Revisions in the Face of Anomalous Data

To determine whether anomaly-driven approaches to discovery produce more accurate models than the standard approaches, we built a program called Kalpana. We also used Kalpana to explore means for identifying those anomaly resolutions that are acceptable to domain experts.

Will Bridewell

Extracting Plausible Explanations of Anomalous Data

An explanation generator implementing (part of) John Stuart Mill’s Method of Induction was constructed that divides the available data into meaningful subsets to better resolve the anomalies. We found that using relevant subsets of data can provide plausible explanations not generated when using all the data and that identifying plausible explanations can help select among equally possible revisions.

Will Bridewell, Bruce G. Buchanan

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

A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries

We developed a simple regular expression algorithm called NegEx that implements several phrases indicating negation, filters out sentences containing phrases that falsely appear to be negation phrases, and limits the scope of the negation phrases.

Wendy W. Chapman, Will Bridewell, Paul Hanbury, Gregory F. Cooper, Bruce G. Buchanan