15 research outputs found
Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data
The collection of large, complex datasets has become common across a wide
variety of domains. Visual analytics tools increasingly play a key role in
exploring and answering complex questions about these large datasets. However,
many visualizations are not designed to concurrently visualize the large number
of dimensions present in complex datasets (e.g. tens of thousands of distinct
codes in an electronic health record system). This fact, combined with the
ability of many visual analytics systems to enable rapid, ad-hoc specification
of groups, or cohorts, of individuals based on a small subset of visualized
dimensions, leads to the possibility of introducing selection bias--when the
user creates a cohort based on a specified set of dimensions, differences
across many other unseen dimensions may also be introduced. These unintended
side effects may result in the cohort no longer being representative of the
larger population intended to be studied, which can negatively affect the
validity of subsequent analyses. We present techniques for selection bias
tracking and visualization that can be incorporated into high-dimensional
exploratory visual analytics systems, with a focus on medical data with
existing data hierarchies. These techniques include: (1) tree-based cohort
provenance and visualization, with a user-specified baseline cohort that all
other cohorts are compared against, and visual encoding of the drift for each
cohort, which indicates where selection bias may have occurred, and (2) a set
of visualizations, including a novel icicle-plot based visualization, to
compare in detail the per-dimension differences between the baseline and a
user-specified focus cohort. These techniques are integrated into a medical
temporal event sequence visual analytics tool. We present example use cases and
report findings from domain expert user interviews.Comment: IEEE Transactions on Visualization and Computer Graphics (TVCG),
Volume 26 Issue 1, 2020. Also part of proceedings for IEEE VAST 201
Visual Analysis of High-Dimensional Event Sequence Data via Dynamic Hierarchical Aggregation
Temporal event data are collected across a broad range of domains, and a
variety of visual analytics techniques have been developed to empower analysts
working with this form of data. These techniques generally display aggregate
statistics computed over sets of event sequences that share common patterns.
Such techniques are often hindered, however, by the high-dimensionality of many
real-world event sequence datasets because the large number of distinct event
types within such data prevents effective aggregation. A common coping strategy
for this challenge is to group event types together as a pre-process, prior to
visualization, so that each group can be represented within an analysis as a
single event type. However, computing these event groupings as a pre-process
also places significant constraints on the analysis. This paper presents a
dynamic hierarchical aggregation technique that leverages a predefined
hierarchy of dimensions to computationally quantify the informativeness of
alternative levels of grouping within the hierarchy at runtime. This allows
users to dynamically explore the hierarchy to select the most appropriate level
of grouping to use at any individual step within an analysis. Key contributions
include an algorithm for interactively determining the most informative set of
event groupings from within a large-scale hierarchy of event types, and a
scatter-plus-focus visualization that supports interactive hierarchical
exploration. While these contributions are generalizable to other types of
problems, we apply them to high-dimensional event sequence analysis using
large-scale event type hierarchies from the medical domain. We describe their
use within a medical cohort analysis tool called Cadence, demonstrate an
example in which the proposed technique supports better views of event sequence
data, and report findings from domain expert interviews.Comment: To Appear in IEEE Transactions on Visualization and Computer Graphics
(TVCG), Volume 26 Issue 1, 2020. Also part of proceedings for IEEE VAST 201
Visual Query System to Help Users Refine Queries from High-Dimensional Data: A Case Study
Temporal queries are normally issued for cohort selection from the high-dimensional dataset in many contexts, such as medical related research areas. The idea was inspired by the difficulties when interacting with the i2b2 system, an NIH-funded National Center for Biomedical Computing based at Partners HealthCare System, which seldom provides informative feedbacks and interactive exploration about the clinical events of each query or the expecting follow-up cohort. Considering the complexity and time-consuming nature of complicated temporal queries, it would be frustrating when iterative query refining is needed. The paper presents a newly designed web-based visual query system to facilitate refining the initial temporal query to select a satisfactory cohort for a given research. A detailed interface design associated with the query time frame and the implementation of the visual query algorithm that enables advanced arbitrary temporal query logic is included. In addition, a case study with 3 participants in medical related research areas was conducted that shows the system was overall useful to help the users to gain an idea about their follow-up queries.Master of Science in Information Scienc