Using machine learning to support better and intelligent visualisation for genomic data

Massive amounts of genomic data are created for the advent of Next Generation Sequencing technologies. Great technological advances in methods of characterising the human diseases, including genetic and environmental factors, make it a great opportunity to understand the diseases and to find new diagnoses and treatments. Translating medical data becomes more and more rich and challenging. Visualisation can greatly aid the processing and integration of complex data. Genomic data visual analytics is rapidly evolving alongside 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 effectively and speed up expert decisions about the best treatment of an individual patient’s needs. However, meaningful visual analysis of such large genomic data remains a serious challenge. Visualising these complex genomic data requires not only simply plotting of data but should also lead to better decisions. Machine learning has the ability to make prediction and aid in decision-making. Machine learning and visualisation are both effective ways to deal with big data, but they focus on different purposes. Machine learning applies statistical learning techniques to automatically identify patterns in data to make highly accurate prediction, while visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. Clinicians, experts and researchers intend to use both visualisation and machine learning to analyse their complex genomic data, but it is a serious challenge for them to understand and trust machine learning models in the serious medical industry. The main goal of this thesis is to study the feasibility of intelligent and interactive visualisation which combined with machine learning algorithms for medical data analysis. A prototype has also been developed to illustrate the concept that visualising genomics data from childhood cancers in meaningful and dynamic ways could lead to better decisions. Machine learning algorithms are used and illustrated during visualising the cancer genomic data in order to provide highly accurate predictions. This research could open a new and exciting path to discovery for disease diagnostics and therapies

A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom

Multimodal medical data fusion has emerged as a transformative approach in smart healthcare, enabling a comprehensive understanding of patient health and personalized treatment plans. In this paper, a journey from data to information to knowledge to wisdom (DIKW) is explored through multimodal fusion for smart healthcare. We present a comprehensive review of multimodal medical data fusion focused on the integration of various data modalities. The review explores different approaches such as feature selection, rule-based systems, machine learning, deep learning, and natural language processing, for fusing and analyzing multimodal data. This paper also highlights the challenges associated with multimodal fusion in healthcare. By synthesizing the reviewed frameworks and theories, it proposes a generic framework for multimodal medical data fusion that aligns with the DIKW model. Moreover, it discusses future directions related to the four pillars of healthcare: Predictive, Preventive, Personalized, and Participatory approaches. The components of the comprehensive survey presented in this paper form the foundation for more successful implementation of multimodal fusion in smart healthcare. Our findings can guide researchers and practitioners in leveraging the power of multimodal fusion with the state-of-the-art approaches to revolutionize healthcare and improve patient outcomes.Comment: This work has been submitted to the ELSEVIER for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Immersive analytics for oncology patient cohorts

This thesis proposes a novel interactive immersive analytics tool and methods to interrogate the cancer patient cohort in an immersive virtual environment, namely Virtual Reality to Observe Oncology data Models (VROOM). The overall objective is to develop an immersive analytics platform, which includes a data analytics pipeline from raw gene expression data to immersive visualisation on virtual and augmented reality platforms utilising a game engine. Unity3D has been used to implement the visualisation. Work in this thesis could provide oncologists and clinicians with an interactive visualisation and visual analytics platform that helps them to drive their analysis in treatment efficacy and achieve the goal of evidence-based personalised medicine. The thesis integrates the latest discovery and development in cancer patients’ prognoses, immersive technologies, machine learning, decision support system and interactive visualisation to form an immersive analytics platform of complex genomic data. For this thesis, the experimental paradigm that will be followed is in understanding transcriptomics in cancer samples. This thesis specifically investigates gene expression data to determine the biological similarity revealed by the patient's tumour samples' transcriptomic profiles revealing the active genes in different patients. In summary, the thesis contributes to i) a novel immersive analytics platform for patient cohort data interrogation in similarity space where the similarity space is based on the patient's biological and genomic similarity; ii) an effective immersive environment optimisation design based on the usability study of exocentric and egocentric visualisation, audio and sound design optimisation; iii) an integration of trusted and familiar 2D biomedical visual analytics methods into the immersive environment; iv) novel use of the game theory as the decision-making system engine to help the analytics process, and application of the optimal transport theory in missing data imputation to ensure the preservation of data distribution; and v) case studies to showcase the real-world application of the visualisation and its effectiveness

Review of innovative immersive technologies for healthcare applications

Immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), can connect people using enhanced data visualizations to better involve stakeholders as integral members of the process. Immersive technologies have started to change the research on multidimensional genomic data analysis for disease diagnostics and treatments. Immersive technologies are highlighted in some research for health and clinical needs, especially for precision medicine innovation. The use of immersive technology for genomic data analysis has recently received attention from the research community. Genomic data analytics research seeks to integrate immersive technologies to build more natural human-computer interactions that allow better perception engagements. Immersive technologies, especially VR, help humans perceive the digital world as real and give learning output with lower performance errors and higher accuracy. However, there are limited reviews about immersive technologies used in healthcare and genomic data analysis with specific digital health applications. This paper contributes a comprehensive review of using immersive technologies for digital health applications, including patient-centric applications, medical domain education, and data analysis, especially genomic data visual analytics. We highlight the evolution of a visual analysis using VR as a case study for how immersive technologies step, can by step, move into the genomic data analysis domain. The discussion and conclusion summarize the current immersive technology applications’ usability, innovation, and future work in the healthcare domain, and digital health data visual analytics

iGPSe: A Visual Analytic System for Integrative Genomic Based Cancer Patient Stratification

Background: Cancers are highly heterogeneous with different subtypes. These subtypes often possess different genetic variants, present different pathological phenotypes, and most importantly, show various clinical outcomes such as varied prognosis and response to treatment and likelihood for recurrence and metastasis. Recently, integrative genomics (or panomics) approaches are often adopted with the goal of combining multiple types of omics data to identify integrative biomarkers for stratification of patients into groups with different clinical outcomes. Results: In this paper we present a visual analytic system called Interactive Genomics Patient Stratification explorer (iGPSe) which significantly reduces the computing burden for biomedical researchers in the process of exploring complicated integrative genomics data. Our system integrates unsupervised clustering with graph and parallel sets visualization and allows direct comparison of clinical outcomes via survival analysis. Using a breast cancer dataset obtained from the The Cancer Genome Atlas (TCGA) project, we are able to quickly explore different combinations of gene expression (mRNA) and microRNA features and identify potential combined markers for survival prediction. Conclusions: Visualization plays an important role in the process of stratifying given population patients. Visual tools allowed for the selection of possibly features across various datasets for the given patient population. We essentially made a case for visualization for a very important problem in translational informatics.Comment: BioVis 2014 conferenc

The future of the healthcare industry : how will digital transformation create better healthcare?

This dissertation focuses on the analysis of the opportunities raised by emerging tech dynamics to improve care delivery. Specifically, it focuses on assessing its impact in the main healthcare activities covered in the prevention and diagnosis stages through the lens of three main stakeholder groups - the patients, the health professionals and the health service’s experts. In order to reach these objectives, an exploratory and qualitative research was conducted through the analysis of primary and secondary data, collected from existing literature and semi-structured interviews with the considered stakeholder groups. The conclusions suggest that tech dynamics can bring significant impact to the healthcare industry through three main key applications: (1) information generation, which have a major impact in the activities covered in the prevention stage; (2) information treatment, impacting both activities covered in prevention and diagnosis; and (3) experience improvement, mainly useful in activities that require in person interactions. By applying the tech dynamics into medical practice, the stakeholders may benefit from enhanced user experience, productivity and cost reductions which ultimately has a positive impact into the improvement of the quality of care delivered. Moreover, this impact is extended through other stakeholders in the life-science such as insurance companies and pharmaceuticals.Esta dissertação centra-se na análise das oportunidades que surgiram do desenvolvimento de novas dinâmicas tecnológicas no sentido de melhorar a prestação de cuidados de saúde. Em concreto, tem como foco a avaliação do impacto destas dinâmicas nas principais atividades médicas, desde a prevenção até ao diagnóstico e incluindo os três principais stakeholders - pacientes, médicos e administrativos. Neste sentido, foi realizada uma pesquisa exploratória e qualitativa com base na análise de dados primários e secundários, dados estes que foram recolhidos através de entrevistas semi-estruturadas com os stakeholders acima identificados e segundo a literatura já existente relativa ao tema. As conclusões sugerem que estas novas dinâmicas podem resultar num impacto significativo para a indústria da saúde através de três principais aplicações: (1) geração de informação, que tem um grande impacto nas atividades abrangidas pela fase de prevenção; (2) tratamento de informação, que impacta as atividades abrangidas pelas fases de prevenção e diagnóstico; e (3) melhoria da experiência do paciente, que é principalmente relevante nas atividades com maior interação pessoal. Com a aplicação destas dinâmicas tecnológicas na saúde, os diferentes stakeholders podem ser beneficiados através de: uma melhor experiência para o paciente, uma maior produtividade e reduções de custos que, em última análise, tem um impacto positivo na melhoria da qualidade dos cuidados prestados. Adicionalmente, estes impactos abrangem ainda outros agentes ligados à indústria da saúde, tais como companhias de seguros e farmacêuticas

Artificial Intelligence in Medicine: A New Way to Diagnose and Treat Disease

Artificial intelligence (AI) has immense potential to transform medicine by improving diagnostic accuracy and enabling personalized treatments. This paper explores how AI systems analyze medical images, lab tests, genetic data, and patient histories to detect disease earlier and guide therapy selection. Though still an emerging field, impressive results demonstrate AI can surpass human clinicians on diagnostic tasks. For example, an AI system detected breast cancer from mammograms more accurately than expert radiologists. In ophthalmology, AI outperformed ophthalmologists in diagnosing diabetic retinopathy. By finding subtle patterns in complex datasets, AI promises to catch diseases like cancer in early, more treatable stages. Beyond diagnosis, AI can identify optimal treatments for individual patients based on their genetic makeup and lifestyle factors. Researchers are also using AI to design new medications. While AI offers many benefits, challenges remain regarding clinician displacement, legal liability, data privacy, and the "black box" nature of AI reasoning. More research is needed, but it is clear that AI will fundamentally alter medical practice. AI empowers clinicians to provide earlier, more precise diagnoses and tailored therapies for patients. Though it will not replace doctors, by automating routine tasks and uncovering hidden insights, AI can free physicians to focus on holistic care. The future of medicine lies in humans and smart machines working together

AI in Medical Imaging Informatics: Current Challenges and Future Directions

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine