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Lau CW, Qu Z, Draper D, Quan R, Braytee A, Bluff A, Zhang D, Johnston A, Kennedy PJ, Simoff S, Nguyen QV, Catchpoole D. Virtual reality for the observation of oncology models (VROOM): immersive analytics for oncology patient cohorts. Sci Rep 2022; 12:11337. [PMID: 35790803 PMCID: PMC9256599 DOI: 10.1038/s41598-022-15548-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 06/24/2022] [Indexed: 11/08/2022] Open
Abstract
The significant advancement of inexpensive and portable virtual reality (VR) and augmented reality devices has re-energised the research in the immersive analytics field. The immersive environment is different from a traditional 2D display used to analyse 3D data as it provides a unified environment that supports immersion in a 3D scene, gestural interaction, haptic feedback and spatial audio. Genomic data analysis has been used in oncology to understand better the relationship between genetic profile, cancer type, and treatment option. This paper proposes a novel immersive analytics tool for cancer patient cohorts in a virtual reality environment, virtual reality to observe oncology data models. We utilise immersive technologies to analyse the gene expression and clinical data of a cohort of cancer patients. Various machine learning algorithms and visualisation methods have also been deployed in VR to enhance the data interrogation process. This is supported with established 2D visual analytics and graphical methods in bioinformatics, such as scatter plots, descriptive statistical information, linear regression, box plot and heatmap into our visualisation. Our approach allows the clinician to interrogate the information that is familiar and meaningful to them while providing them immersive analytics capabilities to make new discoveries toward personalised medicine.
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Affiliation(s)
- Chng Wei Lau
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta, Australia.
| | - Zhonglin Qu
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta, Australia
| | | | - Rosa Quan
- School of Psychology, Western Sydney University, Penrith, Australia
| | - Ali Braytee
- School of Computer Science, University of Technology Sydney, Ultimo, Australia
| | | | - Dongmo Zhang
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta, Australia
| | - Andrew Johnston
- School of Computer Science, University of Technology Sydney, Ultimo, Australia
| | - Paul J Kennedy
- School of Computer Science, University of Technology Sydney, Ultimo, Australia
| | - Simeon Simoff
- MARCS Institute and School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta, Australia
| | - Quang Vinh Nguyen
- MARCS Institute and School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta, Australia
| | - Daniel Catchpoole
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta, Australia.
- School of Computer Science, University of Technology Sydney, Ultimo, Australia.
- Biospecimen Research Services, Children's Cancer Research Unit, The Kids Research Institute, The Children's Hospital at Westmead, Westmead, Australia.
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Alcaide D, Aerts J. A visual analytic approach for the identification of ICU patient subpopulations using ICD diagnostic codes. PeerJ Comput Sci 2021; 7:e430. [PMID: 33954230 PMCID: PMC8049127 DOI: 10.7717/peerj-cs.430] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 02/15/2021] [Indexed: 05/03/2023]
Abstract
A large number of clinical concepts are categorized under standardized formats that ease the manipulation, understanding, analysis, and exchange of information. One of the most extended codifications is the International Classification of Diseases (ICD) used for characterizing diagnoses and clinical procedures. With formatted ICD concepts, a patient profile can be described through a set of standardized and sorted attributes according to the relevance or chronology of events. This structured data is fundamental to quantify the similarity between patients and detect relevant clinical characteristics. Data visualization tools allow the representation and comprehension of data patterns, usually of a high dimensional nature, where only a partial picture can be projected. In this paper, we provide a visual analytics approach for the identification of homogeneous patient cohorts by combining custom distance metrics with a flexible dimensionality reduction technique. First we define a new metric to measure the similarity between diagnosis profiles through the concordance and relevance of events. Second we describe a variation of the Simplified Topological Abstraction of Data (STAD) dimensionality reduction technique to enhance the projection of signals preserving the global structure of data. The MIMIC-III clinical database is used for implementing the analysis into an interactive dashboard, providing a highly expressive environment for the exploration and comparison of patients groups with at least one identical diagnostic ICD code. The combination of the distance metric and STAD not only allows the identification of patterns but also provides a new layer of information to establish additional relationships between patient cohorts. The method and tool presented here add a valuable new approach for exploring heterogeneous patient populations. In addition, the distance metric described can be applied in other domains that employ ordered lists of categorical data.
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Affiliation(s)
- Daniel Alcaide
- Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Jan Aerts
- Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
- UHasselt, I-BioStat, Data Science Institute, Hasselt, Belgium
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Qu Z, Lau CW, Nguyen QV, Zhou Y, Catchpoole DR. Visual Analytics of Genomic and Cancer Data: A Systematic Review. Cancer Inform 2019; 18:1176935119835546. [PMID: 30890859 PMCID: PMC6416684 DOI: 10.1177/1176935119835546] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 01/29/2019] [Indexed: 12/12/2022] Open
Abstract
Visual analytics and visualisation can leverage the human perceptual system to
interpret and uncover hidden patterns in big data. The advent of next-generation
sequencing technologies has allowed the rapid production of massive amounts of
genomic data and created a corresponding need for new tools and methods for
visualising and interpreting these data. Visualising genomic data requires not
only simply plotting of data but should also offer a decision or a choice about
what the message should be conveyed in the particular plot; which methodologies
should be used to represent the results must provide an easy, clear, and
accurate way to the clinicians, experts, or researchers to interact with the
data. Genomic data visual analytics is rapidly evolving in parallel with
advances in high-throughput technologies such as artificial intelligence (AI)
and virtual reality (VR). Personalised medicine requires new genomic
visualisation tools, which can efficiently extract knowledge from the genomic
data and speed up expert decisions about the best treatment of individual
patient’s needs. However, meaningful visual analytics of such large genomic data
remains a serious challenge. This article provides a comprehensive systematic
review and discussion on the tools, methods, and trends for visual analytics of
cancer-related genomic data. We reviewed methods for genomic data visualisation
including traditional approaches such as scatter plots, heatmaps, coordinates,
and networks, as well as emerging technologies using AI and VR. We also
demonstrate the development of genomic data visualisation tools over time and
analyse the evolution of visualising genomic data.
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Affiliation(s)
- Zhonglin Qu
- School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW, Australia
| | - Chng Wei Lau
- School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW, Australia
| | - Quang Vinh Nguyen
- School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW, Australia.,The MARCS Institute, Western Sydney University, Penrith, NSW, Australia
| | - Yi Zhou
- School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW, Australia
| | - Daniel R Catchpoole
- The Tumour Bank, Children's Cancer Research Unit, Kids Research, The Children's Hospital at Westmead, Westmead, NSW, Australia.,Discipline of Paediatrics and Child Health, Faculty of Medicine, The University of Sydney, Sydney, NSW, Australia.,Faculty of Information Technology, The University of Technology Sydney, Ultimo, NSW, Australia
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Khalid NEA, Yusoff M, Kamaru-Zaman EA, Kamsani II. Multidimensional Data Medical Dataset Using Interactive Visualization Star Coordinate Technique. PROCEDIA COMPUTER SCIENCE 2014; 42:247-254. [DOI: 10.1016/j.procs.2014.11.059] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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