Finding Similar People to Guide Life Choices

Du, Fan; Plaisant, Catherine; Spring, Neil; Shneiderman, Ben

doi:10.1145/3025453.3025777

Cited by 42 publications

(27 citation statements)

References 31 publications

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“…Although self-reported confidence is a common metric in information visualization evaluation [68], it can be subject to biases [56], [57]. Findings from cognitive bias research suggest that confidence metrics need to be calibrated [69] and put in context with task accuracy.…”

Section: Visualization Researchmentioning

confidence: 99%

A Task-Based Taxonomy of Cognitive Biases for Information Visualization

Dimara

Franconeri

Plaisant

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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Information visualization designers strive to design data displays that allow for efficient exploration, analysis, and communication of patterns in data, leading to informed decisions. Unfortunately, human judgment and decision making are imperfect and often plagued by cognitive biases. There is limited empirical research documenting how these biases affect visual data analysis activities. Existing taxonomies are organized by cognitive theories that are hard to associate with visualization tasks. Based on a survey of the literature we propose a task-based taxonomy of 154 cognitive biases organized in 7 main categories. We hope the taxonomy will help visualization researchers relate their design to the corresponding possible biases, and lead to new research that detects and addresses biased judgment and decision making in data visualization.

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Section: Visualization Researchmentioning

confidence: 99%

A Task-Based Taxonomy of Cognitive Biases for Information Visualization

Dimara

Franconeri

Plaisant

et al. 2020

IEEE Trans. Visual. Comput. Graphics

Self Cite

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“…Moreover, we can use our methods for other applications, such as career decision support, financial analysis, and cybersecurity systems. For instance, as similar to the existing works [7][8][9], in a career decision support system, we can suggest similar students or workers to help the user select the next career.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

Comparative visual analytics for assessing medical records with sequence embedding

Guo

Fujiwara

et al. 2020

Visual Informatics

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Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare. Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence. However, such analysis is not straightforward due to the characteristics of medical records: high dimensionality, irregularity in time, and sparsity. To address this challenge, we introduce a method for similarity calculation of medical records. Our method employs event and sequence embeddings. While we use an autoencoder for the event embedding, we apply its variant with the self-attention mechanism for the sequence embedding. Moreover, in order to better handle the irregularity of data, we enhance the self-attention mechanism with consideration of different time intervals. We have developed a visual analytics system to support comparative studies of patient records. To make a comparison of sequences with different lengths easier, our system incorporates a sequence alignment method. Through its interactive interface, the user can quickly identify patients of interest and conveniently review both the temporal and multivariate aspects of the patient records. We demonstrate the effectiveness of our design and system with case studies using a real-world dataset from the neonatal intensive care unit of UC Davis.

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“…Du el al. [20], [21] introduced two career path recommendation techniques that provide suggestions and potential outcomes by summarizing the outcome of similar users. In EventAction [20], all of the records that similar to select one are displayed in a list of calendar views ( Fig.…”

Section: Visual Event Prediction and Recommendationmentioning

confidence: 99%

Visual Progression Analysis of Event Sequence Data

Guo

Jin

Gotz

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identification and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a significant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help define those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2, which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2: (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design.

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Finding Similar People to Guide Life Choices

Cited by 42 publications

References 31 publications

A Task-Based Taxonomy of Cognitive Biases for Information Visualization

A Task-Based Taxonomy of Cognitive Biases for Information Visualization

Comparative visual analytics for assessing medical records with sequence embedding

Visual Progression Analysis of Event Sequence Data

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