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Using Visual Analytics to Inform Rheumatoid Arthritis Patient Choices

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Abstract

Individuals diagnosed with chronic diseases often face difficult, potentially life-altering, treatment decisions. Without sufficient knowledge, it can be difficult for a patient to make an informed decision. An essential element of medical care is educating the patient regarding disease outcomes and treatment options, thereby reducing feelings of uncertainty and increasing confidence in the resulting decision. It has been shown that incorporating decision aids (DAs) based on serious computer games into medical care can increase health knowledge and assist decision-making of patients with a variety of diseases. We discuss the benefits and challenges of using serious games as patient decision aids. We focus on rheumatoid arthritis (RA), a chronic disease that primarily affects the joints of the hand. We propose the use of serious games to enable RA patients to safely explore the uncertain effects of treatments through an avatar that performs common daily tasks, which are known to cause problems for RA patients, and experiences the side effects. We discuss the engineering challenges in building such a system and propose a data-driven approach using medical imagery to communicate the effects of the disease.
... These trainings are particularly useful for acquiring skills and cognitive processes not easily taught in a classroom setting, such as strategic and analytical thinking, planning and execution, problem solving, decision-making, and adaptation to rapid change (Foundation of American Scientists, 2006). More recent accounts also added rehabilitation (Cornforth et al., 2015), patient choice education (Mihail, Jacobs, Goldsmith, & Lohr, 2015), and expertise training . ...
... 123e144;Loh, Anantachai, Byun, & Lenox, 2007). A database of captured action data can be (data)mined to yield analytics and insights for training and improvement: e.g., visualize players' navigational paths (Chittaro, Ranon, & Ieronutti, 2006;Loh, Sheng, & Li, 2015;Zacharias, 2006), measure changes in their proficiency levels , differentiate the novices from the experts (Loh & Sheng, 2014, and diagnose potential problems (Liu, Shen, Mei, Ji, & Miao, 2013;Mihail et al., 2015), to name a few. Hopefully, in the near future, serious games can mature into the "tools for improving decision-making skills and performance" (Krulak, 1997;Michael & Chen, 2005;Sawyer & Rejeski, 2002) e as originally intended. ...
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Three Gameplay Action-Decision (GAD) profiles: Explorer, Fulfiller, and Quitter, have been identified based on individual’s decision-making actions and navigational behaviors in situ serious games. The ability to profile trainees using serious games can yield new analytics and insights towards training and learning performance improvement, including the identification of weaknesses or potential training needs in the players towards adaptive training, and the creation of new diagnostics for prescriptive training, retraining, and remediation. Similarity measures of players’ in-game course of actions (COAs) have been shown to be a viable approach in differentiating novices from experts in serious games. In this study, we examined and compared several popular similarity measures to see if any measure, or combination of measures, would be viable in differentiating players based on their GAD profiles in serious games. Our findings revealed that similarity measures, while significant in their predicting abilities individually, could gain more strength from one another in combination. More research is needed to create or develop new metrics and methods for players’ action and behavioral profiling in Serious Games Analytics.
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