529 research outputs found

    Biomedical Informatics Applications for Precision Management of Neurodegenerative Diseases

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    Modern medicine is in the midst of a revolution driven by “big data,” rapidly advancing computing power, and broader integration of technology into healthcare. Highly detailed and individualized profiles of both health and disease states are now possible, including biomarkers, genomic profiles, cognitive and behavioral phenotypes, high-frequency assessments, and medical imaging. Although these data are incredibly complex, they can potentially be used to understand multi-determinant causal relationships, elucidate modifiable factors, and ultimately customize treatments based on individual parameters. Especially for neurodegenerative diseases, where an effective therapeutic agent has yet to be discovered, there remains a critical need for an interdisciplinary perspective on data and information management due to the number of unanswered questions. Biomedical informatics is a multidisciplinary field that falls at the intersection of information technology, computer and data science, engineering, and healthcare that will be instrumental for uncovering novel insights into neurodegenerative disease research, including both causal relationships and therapeutic targets and maximizing the utility of both clinical and research data. The present study aims to provide a brief overview of biomedical informatics and how clinical data applications such as clinical decision support tools can be developed to derive new knowledge from the wealth of available data to advance clinical care and scientific research of neurodegenerative diseases in the era of precision medicine

    Processing of Electronic Health Records using Deep Learning: A review

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    Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

    Integration of Wearable Technologies into Patients’ Electronic Medical Records

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    Health Wearable Technologies are becoming more popular all around the world. Smart watches and fitness trackers are currently being used by many and the utilization is expected to continue to grow. The innovative technology will certainly play a key role to the optimal operating of future society, especially with applications in healthcare. This article will introduce the concept of wearable technology with advantages and challenges of its application in the healthcare industry

    Data integration and analysis for circadian medicine

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    Data integration, data sharing, and standardized analyses are important enablers for data-driven medical research. Circadian medicine is an emerging field with a particularly high need for coordinated and systematic collaboration between researchers from different disciplines. Datasets in circadian medicine are multimodal, ranging from molecular circadian profiles and clinical parameters to physiological measurements and data obtained from (wearable) sensors or reported by patients. Uniquely, data spanning both the time dimension and the spatial dimension (across tissues) are needed to obtain a holistic view of the circadian system. The study of human rhythms in the context of circadian medicine has to confront the heterogeneity of clock properties within and across subjects and our inability to repeatedly obtain relevant biosamples from one subject. This requires informatics solutions for integrating and visualizing relevant data types at various temporal resolutions ranging from milliseconds and seconds to minutes and several hours. Associated challenges range from a lack of standards that can be used to represent all required data in a common interoperable form, to challenges related to data storage, to the need to perform transformations for integrated visualizations, and to privacy issues. The downstream analysis of circadian rhythms requires specialized approaches for the identification, characterization, and discrimination of rhythms. We conclude that circadian medicine research provides an ideal environment for developing innovative methods to address challenges related to the collection, integration, visualization, and analysis of multimodal multidimensional biomedical data.Peer Reviewe

    Literature Study On Some Computational Healthcare Systems For Sustainability

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    Medical data released and shared through the cloud are very popular in practice, and information and knowledge bases can be enriched and shared through the cloud. The revolution presented by the cloud and big data can have a huge impact on the healthcare industry, and a new healthcare system is evolving. This is why we need to design a more appropriate health care system to meet the challenges presented by this revolution. The diversity of data sources requires a uniform standard of heterogeneous data management. On the one hand, due to the diversification of medical equipment, the data formats and the amount of data generated by various devices may be quite different, which requires that the system support data access by various medical devices to ensure high scalability and satisfy actual medical needs. On the other hand, the system needs to convert the received data into a unified standard to improve the efficiency of data storage, query, retrieval, processing, and analysis. This paper presents Literature Study on Computational Healthcare Systems for sustainability

    Translational Research in the Era of Precision Medicine: Where We Are and Where We Will Go

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    The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of "multi-omics" analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient

    Bioinformatics: Challenges in Integrating Biological Information

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    Bioinformatics has become a buzzword in today's world of science. About twenty or thirty years ago, people saw biology and computer science as two completely different fields. However, at present, it seems that a new bridge has been built that connects these two fields, and this new field is called bioinformatics. Bioinformatics is an challenging and exciting new multidisciplinary field. This field combines methods of statistics, mathematics, computer science and information technology to solve problems based on biological data. Advances in experimental technology have generated large amounts of data. Therefore, managing and extracting useful information from these sources is essential if we are to discover new knowledge to advance our understanding of life. In this article, we try to discuss what is bioinformatics?, The basic knowledge needed to understand bionformatics and the reasons that are very important for biologists to learn and apply bioinformatics. In addition, we also discuss an introduction to biological terminology that will be widely used in the field of bioinformatics. We are confident that this review will provide valuable insights and serve as a starting point for researchers to become acquainted with and study bioinformatics.

    Review Focus On Computational Healthcare Tools For Sustainability

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    The medical industry is experiencing an increase in the amount of data generated in terms of complexity, diversity, and timeliness; the industry increasingly relies on the collection and analysis of data. Therefore, to make better decisions, we need to collect data and conduct effective analysis. The cloud is a good choice for on-demand services for storing, processing, and analyzing data. Medical data released and shared through the cloud are very popular in practice, and information and knowledge bases can be enriched and shared through the cloud. The revolution presented by the cloud and big data can have a huge impact on the healthcare industry, and a new healthcare system is evolving. This is why we need to design a more appropriate health care system to meet the challenges presented by this revolution. The diversity of data sources requires a uniform standard of heterogeneous data management. On the one hand, due to the diversification of medical equipment, the data formats and the amount of data generated by various devices may be quite different, which requires that the system support data access by various medical devices to ensure high scalability and satisfy actual medical needs. On the other hand, the system needs to convert the received data into a unified standard to improve the efficiency of data storage, query, retrieval, processing, and analysis. This paper presents Review Study On Existing Computational Healthcare Tools For Sustainability
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