The Effect of Color on Short-term Memory in Information Visualization

Kang, Xin

doi:10.1145/2968220.2968237

Cited by 11 publications

(13 citation statements)

References 6 publications

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“…In this study, we found that combining the metrics covering all four aspects of discrimination, calibration, clinical usefulness and overall accuracy into a single CEM improved the efficiency of cognitive decision-making (according to Miller's Law 79 for selecting the optimal ensemble models. While AUC does evaluate diagnostic or predictive performance of the model, it does not directly reflect patient benefit.…”

Section: Discussionmentioning

confidence: 82%

Cardiac surgery risk prediction using ensemble machine learning to incorporate legacy risk scores: A benchmarking study

et al. 2023

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Objective The introduction of new clinical risk scores (e.g. European System for Cardiac Operative Risk Evaluation (EuroSCORE) II) superseding original scores (e.g. EuroSCORE I) with different variable sets typically result in disparate datasets due to high levels of missingness for new score variables prior to time of adoption. Little is known about the use of ensemble learning to incorporate disparate data from legacy scores. We tested the hypothesised that Homogenenous and Heterogeneous Machine Learning (ML) ensembles will have better performance than ensembles of Dynamic Model Averaging (DMA) for combining knowledge from EuroSCORE I legacy data with EuroSCORE II data to predict cardiac surgery risk. Methods Using the National Adult Cardiac Surgery Audit dataset, we trained 12 different base learner models, based on two different variable sets from either EuroSCORE I (LogES) or EuroScore II (ES II), partitioned by the time of score adoption (1996–2016 or 2012–2016) and evaluated on holdout set (2017–2019). These base learner models were ensembled using nine different combinations of six ML algorithms to produce homogeneous or heterogeneous ensembles. Performance was assessed using a consensus metric. Results Xgboost homogenous ensemble (HE) was the highest performing model (clinical effectiveness metric (CEM) 0.725) with area under the curve (AUC) (0.8327; 95% confidence interval (CI) 0.8323–0.8329) followed by Random Forest HE (CEM 0.723; AUC 0.8325; 95%CI 0.8320–0.8326). Across different heterogenous ensembles, significantly better performance was obtained by combining siloed datasets across time (CEM 0.720) than building ensembles of either 1996–2011 ( t-test adjusted, p = 1.67×10−6) or 2012–2019 ( t-test adjusted, p = 1.35×10−193) datasets alone. Conclusions Both homogenous and heterogenous ML ensembles performed significantly better than DMA ensemble of Bayesian Update models. Time-dependent ensemble combination of variables, having differing qualities according to time of score adoption, enabled previously siloed data to be combined, leading to increased power, clinical interpretability of variables and usage of data.

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Section: Discussionmentioning

confidence: 82%

Cardiac surgery risk prediction using ensemble machine learning to incorporate legacy risk scores: A benchmarking study

et al. 2023

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“…The results indicated in Kang (2016) study intended to recognize some colors features on short-term memory for informational visualization. The basic colors are violet, red, yellow, and Cyan, which can attract attention and recall information.…”

Section: Research In Infographic and Visual Instructionmentioning

confidence: 99%

“…Color helps in differentiating the world around us where people acquire the largest amount of surrounding information through these visuals. Many studies have proven that there is a relationship between colors and the process of recalling (Kang, 2016;Shaltout, 2016). This indicates that colors possess different abilities to capture the recipient's attention, to enhance his memory, and to contribute to clarifying comparisons and to conduct an information analysis.…”

Section: Introductionmentioning

confidence: 99%

Message Design: Color Impact and its Effectiveness on Designing Instructional Infographic

Alyahya¹,

Nasser²

2019

IJLTER

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This study aims to contribute to the development of message design. This designing is aimed to enhance student memory and support their knowledge recall. Therefore, the study assesses the impact of colors (Red-Green-Purple-Cyan) and their effect when used in designing educational infographic message for students with relation to their retrieve functionality. To achieve the goal of the study, the researchers follow the quasi-experiment methodology, using five tools which vary in terms of words, sentences, and images. A total of 87 female students at King Saud University from different fields (Islamic Studies, Quranic Studies, Art Education, Special Needs Education, Psychology and Computer Science). The study found that there is a significant relationship (0.05 ≥ α) between the colors usages in designing educational infographic message and level of students' recall for students. It also found the significant level at (0. 05 ≥ α) impact of colors, particularly for red, on recall's level of the students when used in designing educational infographic message. Also, the researchers recommend studying different colors in divergent fields to present an educational message such as educational media, educational software, educational websites, images, books, social networking etc. which can enhance the learning competence of the students.

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“…The Miller’s law observed that the human working memory is limited to holding on to an average of seven items in the short-term memory. [13] This is particularly relevant in a scenario where a clinician would need to select from a number of ML models based on a panel of performance metrics. The split-attention effect cognition theory indicates that a single integrated source of information enhances knowledge acquisition better than separated sources of information.…”

Section: Introductionmentioning

confidence: 99%

Performance drift is a major barrier to the safe use of machine learning in cardiac surgery

Dong

Sinha

Zhai

et al. 2023

Preprint

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Objectives The Society of Thoracic Surgeons (STS), and EuroSCORE II (ES II) risk scores, are the most commonly used risk prediction models for adult cardiac surgery post-operative in-hospital mortality. However, they are prone to miscalibration over time, and poor generalisation across datasets and their use remain controversial. It has been suggested that using Machine Learning (ML) techniques, a branch of Artificial intelligence (AI), may improve the accuracy of risk prediction. Despite increased interest, a gap in understanding the effect of dataset drift on the performance of ML over time remains a barrier to its wider use in clinical practice. Dataset drift occurs when a machine learning system underperforms because of a mismatch between the dataset it was developed and the data on which it is deployed. Here we analyse this potential concern in a large United Kingdom (UK) database. Methods: A retrospective analyses of prospectively routinely gathered data on adult patients undergoing cardiac surgery in the UK between 2012-2019. We temporally split the data 70:30 into a training and validation subset. ES II and five ML mortality prediction models were assessed for relationships between and within variable importance drift, performance drift and actual dataset drift using temporal and non-temporal invariant consensus scoring, combining geometric average results of all metrics as the Clinical Effective Metric (CEM). Results: A total of 227,087 adults underwent cardiac surgery during the study period with a mortality rate of 2.76%. There was a strong evidence of decrease in overall performance across all models (p < 0.0001). Xgboost (CEM 0.728 95CI: 0.728-0.729) and Random Forest (CEM 0.727 95CI 0.727-0.728) were the best overall performing models both temporally and non-temporally. ES II perfomed worst across all comparisons. Sharp changes in variable importance and dataset drift between 2017-10 to 2017-12, 2018-06 to 2018-07 and 2018-12 to 2019-02 mirrored effects of performance decrease across models. Conclusions: Combining the metrics covering all four aspects of discrimination, calibration, clinical usefulness and overall accuracy into a single consensus metric improved the efficiency of cognitive decision-making. All models show a decrease in at least 3 of the 5 individual metrics. CEM and variable importance drift detection demonstrate the limitation of logistic regression methods used for cardiac surgery risk prediction and the effects of dataset drift. Future work will be required to determine the interplay between ML and whether ensemble models could take advantage of their respective performance advantages.

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The Effect of Color on Short-term Memory in Information Visualization

Cited by 11 publications

References 6 publications

Cardiac surgery risk prediction using ensemble machine learning to incorporate legacy risk scores: A benchmarking study

Cardiac surgery risk prediction using ensemble machine learning to incorporate legacy risk scores: A benchmarking study

Message Design: Color Impact and its Effectiveness on Designing Instructional Infographic

Performance drift is a major barrier to the safe use of machine learning in cardiac surgery

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