4. Modelling Survival Data in Medical Research

Please provide a short abstract of 100 to 250 words. The abstract should not contain any undefined abbreviations or unspecified references. Work planned but not completed should not appear in the abstract.

User Modeling and User-Adapted Interaction

The need for interpretable and accountable intelligent systems grows along with the prevalence of artificial intelligence applications used in everyday life. Explainable intelligent systems are designed to self-explain the reasoning behind system decisions and predictions, and researchers from different disciplines work together to define, design, and evaluate interpretable systems. However, scholars from different disciplines focus on different objectives and fairly independent topics of interpretable machine learning research, which poses challenges for identifying appropriate design and evaluation methodology and consolidating knowledge across efforts. To this end, this paper presents a survey and framework intended to share knowledge and experiences of XAI design and evaluation methods across multiple disciplines. Aiming to support diverse design goals and evaluation methods in XAI research, after a thorough review of XAI related papers in the fields of machine learning, visualization, and human-computer interaction, we present a categorization of interpretable machine learning design goals and evaluation methods to show a mapping between design goals for different XAI user groups and their evaluation methods. From our findings, we develop a framework with step-by-step design guidelines paired with evaluation methods to close the iterative design and evaluation cycles in multidisciplinary XAI teams. Further, we provide summarized ready-to-use tables of evaluation methods and recommendations for different goals in XAI research.

A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems

In 2008, Cathy O’Neil, a data scientist at the hedge fund D.E. Shaw, watched as the entire financial sector around her imploded.1 The financial crisis gave O’Neil, a self-proclaimed math nerd with a Ph.D., a front row seat to the weaponization of mathematics. During the financial crisis, complex mathematical models designed and marketed to be superior to human ability and beyond the confines of human subjectivity turned out to be farces—layers of false assumptions coated in difficult-to-understand layers of mathematics. In order to dupe both the public and themselves, financial analysts at banks used models and algorithms as a justification for their subjective and flawed inputs. Ten years after the financial crisis, O’Neil sees these nefarious models infiltrating every aspect of modern life. These “Weapons of Math Destruction”—as O’Neil calls them—are (1) “opaque,” (2) “beyond dispute or appeal,” and (3) disproportionally impact the underprivileged.2 These Weapons of Math Destruction, or WMDs, replace the role of traditional subjective decision makers and cause those affected by their decisionmaking to adjust their ways of life to the models. In Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, O’Neil highlights different WMDs that have infiltrated various areas of American life.

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Background Fungal diseases range from relatively-minor superficial and mucosal infections to severe, life-threatening systemic infections. Delayed diagnosis and treatment can lead to poor patient outcomes and high medical costs. The overall burden of fungal diseases in the United States is challenging to quantify, because they are likely substantially underdiagnosed. Methods To estimate the total, national, direct medical costs associated with fungal diseases from a healthcare payer perspective, we used insurance claims data from the Truven Health MarketScan 2014 Research Databases, combined with hospital discharge data from the 2014 Healthcare Cost and Utilization Project National Inpatient Sample and outpatient visit data from the 2005-2014 National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey. All costs were adjusted to 2017 dollars. Results We estimate that fungal diseases cost more than $7.2 billion in 2017, including $4.5 billion from 75055 hospitalizations and $2.6 billion from 8993230 outpatient visits. Hospitalizations for Candida infections (n = 26735, total cost $1.4 billion) and Aspergillus infections (n = 14820, total cost $1.2 billion) accounted for the highest total hospitalization costs of any disease. Over half of outpatient visits were for dermatophyte infections (4981444 visits, total cost $802 million), and 3639037 visits occurred for non-invasive candidiasis (total cost $1.6 billion). Conclusions Fungal diseases impose a considerable economic burden on the healthcare system. Our results likely underestimate their true costs, because they are underdiagnosed. More comprehensive estimates of the public health impact of these diseases are needed to improve their recognition, prevention, diagnosis, and treatment.

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Estimation of Direct Healthcare Costs of Fungal Diseases in the United States.

Finding the differences and similarities between two datasets is a common analytics task. With temporal event sequence data, this task is complex because of the many ways single events and event sequences can differ between the two datasets (or cohorts) of records: the structure of the event sequences (e.g., event order, co-occurring events, or event frequencies), the attributes of events and records (e.g., patient gender), or metrics about the timestamps themselves (e.g., event duration). In exploratory analyses, running statistical tests to cover all cases is time-consuming and determining which results are significant becomes cumbersome. Current analytics tools for comparing groups of event sequences emphasize a purely statistical or purely visual approach for comparison. This paper presents a taxonomy of metrics for comparing cohorts of temporal event sequences, showing that the problem-space is bounded. We also present a visual analytics tool, CoCo (for "Cohort Comparison"), which implements balanced integration of automated statistics with an intelligent user interface to guide users to significant, distinguishing features between the cohorts. Lastly, we describe two early case studies: the first with a research team studying medical team performance in the emergency department and the second with pharmacy researchers.

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Cohort Comparison of Event Sequences with Balanced Integration of Visual Analytics and Statistics

The growing volume and variety of data presents both opportunities and challenges for visual analytics. Addressing these challenges is needed for big data to provide valuable insights and novel solutions for business, security, social media, and healthcare. In the case of temporal event sequence analytics it is the number of events in the data and variety of temporal sequence patterns that challenges users of visual analytic tools. This paper describes 15 strategies for sharpening analytic focus that analysts can use to reduce the data volume and pattern variety. Four groups of strategies are proposed: (1) extraction strategies, (2) temporal folding, (3) pattern simplification strategies, and (4) iterative strategies. For each strategy, we provide examples of the use and impact of this strategy on volume and/or variety. Examples are selected from 20 case studies gathered from either our own work, the literature, or based on email interviews with individuals who conducted the analyses and developers who observed analysts using the tools. Finally, we discuss how these strategies might be combined and report on the feedback from 10 senior event sequence analysts.

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Coping with Volume and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus

Recommender systems are being widely used to assist people in making decisions, for example, recommending films to watch or books to buy. Despite its ubiquity, the problem of presenting the recommendations of temporal event sequences has not been studied. We propose EventAction, which to our knowledge, is the first attempt at a prescriptive analytics interface designed to present and explain recommendations of temporal event sequences. EventAction provides a visual analytics approach to (1) identify similar records, (2) explore potential outcomes, (3) review recommended temporal event sequences that might help achieve the users' goals, and (4) interactively assist users as they define a personalized action plan associated with a probability of success. Following the design study framework, we designed and deployed EventAction in the context of student advising and reported on the evaluation with a student review manager and three graduate students.

EventAction: Visual analytics for temporal event sequence recommendation

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.

Visual Progression Analysis of Event Sequence Data

People often seek examples of similar individuals to guide their own life choices. For example, students making academic plans refer to friends; patients refer to acquaintances with similar conditions, physicians mention past cases seen in their practice. How would they want to search for similar people in databases? We discuss the challenge of finding similar people to guide life choices and report on a need analysis based on 13 interviews. Our PeerFinder prototype enables users to find records that are similar to a seed record, using both record attributes and temporal events found in the records. A user study with 18 participants and four experts shows that users are more engaged and more confident about the value of the results to provide useful evidence to guide life choices when provided with more control over the search process and more context for the results, even at the cost of added complexity.

Fan Du

Papers

Cohort Comparison of Event Sequences with Balanced Integration of Visual Analytics and Statistics

Coping with Volume and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus

EventAction: Visual analytics for temporal event sequence recommendation

Visual Progression Analysis of Event Sequence Data

Finding Similar People to Guide Life Choices: Challenge, Design, and Evaluation