17,004 research outputs found
Artificial intelligence for dementia drug discovery and trials optimization
Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation
Artificial intelligence for dementia drug discovery and trials optimization
Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi‐disciplinary approach can promote data‐driven decision‐making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation
Artificial intelligence for dementia drug discovery and trials optimization
Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation
Big data analytics in the healthcare industry: A systematic review and roadmap for practical implementation in Nigeria
Introduction: The introduction of digitization of healthcare data has posed both challenges and opportunities within the industry. Big Data Analytics (BDA) has emerged as a powerful tool, facilitating data-driven decision-making and revolutionizing patient care.
Purpose: The research aimed to analyze diverse perspectives on big data in healthcare, assess BDA's application in the sector, examine contexts, synthesize findings, and propose an implementation roadmap and future research directions.
Methodology: Using an SLR protocol by Nazir et al. (2019), sources like Google Scholar, IEEE, ScienceDirect, Springer, and Elsevier were searched with 18 queries. Inclusion criteria yielded 37 articles, with five more added through citation searches, totaling 42.
Results: The study uncovers diverse healthcare viewpoints on big data's transformative potential, precision medicine, resource optimization, and challenges like security and interoperability. BDA empowers clinical choices, early disease detection, and personalized medicine. Future areas include ethics, interpretable AI, real-time BDA, multi-omics integration, AI-driven drug discovery, mental health, resource constraints, health disparities, secure data sharing, and human-AI collaboration.
Conclusion: This study illuminates Big Data Analytics' transformative potential in healthcare, revealing diverse applications and emphasizing ethical complexities. Integrated data analysis is advocated for patient-centric services.
Recommendation: Balancing BDA's power with privacy, guidelines, and regulations is vital. Implementing the Nigerian healthcare roadmap can optimize outcomes, address challenges, and enhance efficiency. Future research should focus on ethics, interpretable AI, real-time BDA, and mental health integration
Mathematical modeling and big data analytics in biomedicine
The World's total data is doubling every two years. Data expansion includes growth in quantity,
complexity, and types of data. The enormous rate of generation and on-line access to data is profoundly
changing the way how business is conducted. Biomedical data include research and development data,
clinical data, activity and cost data, patient behavior data, basic science data, and standards and ontologies,
among others. Furthermore, Big Data approaches are increasingly needed for utilization of results
from various Omics studies. Specific applications include predictive and content analytics that support
drug discovery and optimization, the development of new diagnostic methods, and personalization of
medicine. Biomedical data vary in granularity, quality, and complexity. There is a variety of sources and
data formats - web pages, publications, technical reports, and databases. The challenge is to make the
transition from data to actionable knowledge
Big Data Transforms Discovery-Utilization Therapeutics Continuum.
Enabling omic technologies adopt a holistic view to produce unprecedented insights into the molecular underpinnings of health and disease, in part, by generating massive high-dimensional biological data. Leveraging these systems-level insights as an engine driving the healthcare evolution is maximized through integration with medical, demographic, and environmental datasets from individuals to populations. Big data analytics has accordingly emerged to add value to the technical aspects of storage, transfer, and analysis required for merging vast arrays of omic-, clinical-, and eco-datasets. In turn, this new field at the interface of biology, medicine, and information science is systematically transforming modern therapeutics across discovery, development, regulation, and utilization
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Big data from electronic health records for early and late translational cardiovascular research: challenges and potential.
AIMS: Cohorts of millions of people's health records, whole genome sequencing, imaging, sensor, societal and publicly available data present a rapidly expanding digital trace of health. We aimed to critically review, for the first time, the challenges and potential of big data across early and late stages of translational cardiovascular disease research. METHODS AND RESULTS: We sought exemplars based on literature reviews and expertise across the BigData@Heart Consortium. We identified formidable challenges including: data quality, knowing what data exist, the legal and ethical framework for their use, data sharing, building and maintaining public trust, developing standards for defining disease, developing tools for scalable, replicable science and equipping the clinical and scientific work force with new inter-disciplinary skills. Opportunities claimed for big health record data include: richer profiles of health and disease from birth to death and from the molecular to the societal scale; accelerated understanding of disease causation and progression, discovery of new mechanisms and treatment-relevant disease sub-phenotypes, understanding health and diseases in whole populations and whole health systems and returning actionable feedback loops to improve (and potentially disrupt) existing models of research and care, with greater efficiency. In early translational research we identified exemplars including: discovery of fundamental biological processes e.g. linking exome sequences to lifelong electronic health records (EHR) (e.g. human knockout experiments); drug development: genomic approaches to drug target validation; precision medicine: e.g. DNA integrated into hospital EHR for pre-emptive pharmacogenomics. In late translational research we identified exemplars including: learning health systems with outcome trials integrated into clinical care; citizen driven health with 24/7 multi-parameter patient monitoring to improve outcomes and population-based linkages of multiple EHR sources for higher resolution clinical epidemiology and public health. CONCLUSION: High volumes of inherently diverse ('big') EHR data are beginning to disrupt the nature of cardiovascular research and care. Such big data have the potential to improve our understanding of disease causation and classification relevant for early translation and to contribute actionable analytics to improve health and healthcare
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