Using Visualization to Illustrate Machine Learning Models for Genomic Data

Qu, Zhonglin; Zhou, Yi; Nguyễn, Quang Vinh; Catchpoole, Daniel

doi:10.1145/3290688.3290719

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Viewing the spatial positioning of the individual patient in a 3D virtual genomic world enables clinicians to find patients’ genomic similarities and differences. The integrated machine learning algorithms would allow clinicians to uncover genomic relationships and inform decision-making for treatment regimens with more breadth and better accuracy [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Understanding cancer patient cohorts in virtual reality environment for better clinical decisions: a usability study

Qu,

Nguyen,

Lau

et al. 2023

BMC Med Inform Decis Mak

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Background Visualising patient genomic data in a cohort with embedding data analytics models can provide relevant and sensible patient comparisons to assist a clinician with treatment decisions. As immersive technology is actively used around the medical world, there is a rising demand for an efficient environment that can effectively display genomic data visualisations on immersive devices such as a Virtual Reality (VR) environment. The VR technology will allow clinicians, biologists, and computer scientists to explore a cohort of individual patients within the 3D environment. However, demonstrating the feasibility of the VR prototype needs domain users’ feedback for future user-centred design and a better cognitive model of human–computer interactions. There is limited research work for collecting and integrating domain knowledge into the prototype design. Objective A usability study for the VR prototype–-Virtual Reality to Observe Oncology data Models (VROOM) was implemented. VROOM was designed based on a preliminary study among medical users. The goals of this usability study included establishing a baseline of user experience, validating user performance measures, and identifying potential design improvements that are to be addressed to improve efficiency, functionality, and end-user satisfaction. Methods The study was conducted with a group of domain users (10 males, 10 females) with portable VR devices and camera equipment. These domain users included medical users such as clinicians and genetic scientists and computing domain users such as bioinformatics and data analysts. Users were asked to complete routine tasks based on a clinical scenario. Sessions were recorded and analysed to identify potential areas for improvement to the data visual analytics projects in the VR environment. The one-hour usability study included learning VR interaction gestures, running visual analytics tool, and collecting before and after feedback. The feedback was analysed with different methods to measure effectiveness. The statistical method Mann–Whitney U test was used to analyse various task performances among the different participant groups, and multiple data visualisations were created to find insights from questionnaire answers. Results The usability study investigated the feasibility of using VR for genomic data analysis in domain users’ daily work. From the feedback, 65% of the participants, especially clinicians (75% of them), indicated that the VR prototype is potentially helpful for domain users’ daily work but needed more flexibility, such as allowing them to define their features for machine learning part, adding new patient data, and importing their datasets in a better way. We calculated the engaged time for each task and compared them among different user groups. Computing domain users spent 50% more time exploring the algorithms and datasets than medical domain users. Additionally, the medical domain users engaged in the data visual analytics parts (approximately 20%) longer than the computing domain users.

show abstract

Section: Introductionmentioning

confidence: 99%

Understanding cancer patient cohorts in virtual reality environment for better clinical decisions: a usability study

Qu,

Nguyen,

Lau

et al. 2023

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…Viewing the spatial positioning of the individual patient in a 3D virtual genomic world enables clinicians to nd patients' genomic similarities and differences. The integrated machine learning algorithms would allow clinicians to uncover genomic relationships and inform decision-making for treatment regimens with more breadth and better accuracy [13,14].…”

mentioning

confidence: 99%

Understanding Cancer Patient Cohorts in Virtual Reality Environment for Better Clinical Decisions: A Usability Study

Nguyễn

Lau

et al. 2023

Preprint

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Background Visualising patient genomic data in a cohort with embedding data analytics models can provide relevant and sensible patient comparisons to assist a clinician with treatment decisions. As immersive technology is actively used around the medical world, there is a rising demand for an efficient environment that can effectively display genomic data visualisations on immersive devices such as a Virtual Reality (VR) environment. The VR technology will allow clinicians, biologists, and computer scientists to explore a cohort of individual patients within the 3D environment. However, demonstrating the feasibility of the VR prototype needs domain users’ feedback for future user-centric design and a better cognitive model of human-computer interactions. There is limited research work for collecting and integrating domain knowledge into the prototype design. Objective A usability study for the VR prototype—Virtual Reality to Observe Oncology data Models (VROOM) was implemented. VROOM was designed based on a preliminary study among medical users. The goals of this usability study included establishing a baseline of user experience, validating user performance measures, and identifying potential design improvements that are to be addressed to improve efficiency, functionality, and end-user satisfaction. Methods The study was conducted with a group of domain users (10 males, 10 females) with portable VR devices and camera equipment. These domain users included medical users such as clinicians and genetic scientists and computing domain users such as bioinformatics and data analysts. Users were asked to complete routine tasks based on a clinical scenario. Sessions were recorded and analysed to identify potential areas for improvement to the data visual analytics projects in the VR environment. The one-hour usability study included learning VR interaction gestures, running visual analytics tool, and collecting before and after feedback. The feedback was analysed with different methods to measure effectiveness. The statistical method Mann-Whitney U test was used to analyse various task performances among the different participant groups, and multiple data visualisations were created to find insights from questionnaire answers. Results The usability study investigated the feasibility of using VR for genomic data analysis in domain users’ daily work. From the feedback, 65% of the participants, especially clinicians (75% of them), indicated that the VR prototype is potentially helpful for domain users’ daily work but needed more flexibility, such as allowing them to define their features for machine learning part, adding new patient data, and importing their datasets in a better way. We calculated the engaged time for each task and compared them among different user groups. Computing domain users spent 50% more time exploring the algorithms and datasets than the medical domain users. Additionally, the medical domain users engaged in the data visual analytics parts (approximately 20%) longer than the computing domain users.

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Intelligent and Immersive Visual Analytics of Health Data

Lau

Catchpoole

et al. 2020

Advanced Computational Intelligence in Healthcare-7

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Using Visualization to Illustrate Machine Learning Models for Genomic Data

Cited by 5 publications

References 10 publications

Understanding cancer patient cohorts in virtual reality environment for better clinical decisions: a usability study

Understanding cancer patient cohorts in virtual reality environment for better clinical decisions: a usability study

Understanding Cancer Patient Cohorts in Virtual Reality Environment for Better Clinical Decisions: A Usability Study

Intelligent and Immersive Visual Analytics of Health Data

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