COVID-19 and the Futures of Machine Learning (original) (raw)
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Data Science in Healthcare: COVID-19 and Beyond
International Journal of Environmental Research and Public Health, 2022
Data science is an interdisciplinary field that applies numerous techniques, such as machine learning (ML), neural networks (NN) and artificial intelligence (AI), to create value, based on extracting knowledge and insights from available ‘big’ data. The recent advances in data science and AI have had a major impact on healthcare already, as can be seen in the recent biomedical literature. Improved sharing and analysis of medical data results in earlier and better diagnoses, and more patient-tailored treatments. This increased data sharing, in combination with advances in health data management, works hand-in-hand with trends such as increased patient-centricity (with shared decision making), self-care (e.g., using wearables), and integrated healthcare delivery. Using data science and AI, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population level. AI can be applied in all three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. ML algorithms can make predictions on how a disease will develop or respond to treatment, deep learning algorithms can find malignant tumors in magnetic resonance (MR) images and digital pathology images, and natural language-processing (NLP) algorithms can analyze unstructured documents with high speed and accuracy. These are just a few examples of what data science can do. This Special Issue focuses on how data science and AI are used in healthcare, and on related topics such as data sharing and data management. Since this Special Issue contains papers from 2020 to 2022, naturally there are a few papers about the COVID-19 pandemic.
Unlocking the potential of big data and AI in medicine: insights from biobanking
Frontiers in Medicine, 2024
Big data and artificial intelligence are key elements in the medical field as they are expected to improve accuracy and efficiency in diagnosis and treatment, particularly in identifying biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. These applications belong to current research practice that is data-intensive. While the combination of imaging, pathological, genomic, and clinical data is needed to train algorithms to realize the full potential of these technologies, biobanks often serve as crucial infrastructures for data-sharing and data flows. In this paper, we argue that the ‘data turn’ in the life sciences has increasingly re-structured major infrastructures, which often were created for biological samples and associated data, as predominantly data infrastructures. These have evolved and diversified over time in terms of tackling relevant issues such as harmonization and standardization, but also consent practices and risk assessment. In line with the datafication, an increased use of AI-based technologies marks the current developments at the forefront of the big data research in life science and medicine that engender new issues and concerns along with opportunities. At a time when secure health data environments, such as European Health Data Space, are in the making, we argue that such meta-infrastructures can benefit both from the experience and evolution of biobanking, but also the current state of affairs in AI in medicine, regarding good governance, the social aspects and practices, as well as critical thinking about data practices, which can contribute to trustworthiness of such meta-infrastructures.
Symmetry, 2021
This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions ...
COVID-19, Big Data: how it will change the way we practice Medicine
QJM: An International Journal of Medicine, 2020
COVID-19 disease encompasses heterogeneous and sporadically severe effects. It is caused by SARS-CoV-2, a single-stranded positive-sense RNA virus (þssRNA), and produces mild infirmity for most affected; however, it can induce lingering problems, grave illness and death. Older, obese individuals and those with pre-existing medical conditions (such as diabetes, chronic respiratory, renal or cardiovascular disease) are at risk for critical syndromes. Personalized management of any disease exhibiting patient-heterogeneity, with variety of outcomes and potential remedies, can be enhanced by Big Data (BD) strategies; BD lies at the heart of efforts to comprehend and forecast the impact that coronavirus will have on all of us. Upholding the transdisciplinary approach of Aristotle and Leonardo, 1-3 data scientists are today helping healthcare workers, biomedexperts, epidemiologists and policymakers to aggregate, synthesize and exploit monitored data in planning diseaseprevention and treatment efforts (Figure 1). Complementing today's huge genomics advances, NIH's 'All-of-Us' initiative has opened ambitious COVID-19 investigations (http://www.allofus. nih.gov/), allowing studies of host-COVID-19 interactions via genomic constituents, including miRNAs. 4 Conventional monitoring assessing disease symptoms and signs entails checking over well-defined time-frameworks; consequently, there is granularity in available data, which may miss significant variability details. Conversely, the continuous collection of data by up-to-date personal biosensors-andsmartphones allows uninterrupted sign-assessments, 5 yielding reliable information regarding variability and modifications by interventions or treatments. Smartphone apps, analytics and artificial intelligence (AI), 6 all make finding and treating people with infectious ailments far more efficient than ever before. Today's connectivity provides ammunition to fight the pandemic in ways never heretofore achievable. Continuous
Frontiers in Big Data, 2021
Even before COVID-19, the bioinformatics labs and life science industry were investing extensively in ecosystems of technological and analytical applications/appliances to store, curate, share, integrate, and analyze large amounts of data. With the pandemic coming at an accelerating pace, a series of global research actions are being implemented to strive against the virus and its effects and to create data-driven investigations to support more agile responses to future events 1. Innovative solutions in COVID-19 research require more efficient and effective data management strategies and practices. Cancer research is an excellent example of the adoption of the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles (Wilkinson et al., 2016) on precision oncology (Deist et al., 2020; Delgado and Llorente, 2020; Vesteghem et al., 2020) and major cancer data repositories, such as the NIH Cancer Research Data Commons, are gradually adhering to these principles. In a broad sense, understanding the whole scenario of a worldwide virus outbreak such as the COVID-19 pandemic has been a great challenge. It involves a huge effort to put together facts and a large volume of data related to the dynamics of the real world, as well as past and current results of scientific research. At the same time, cancer researchers are pivoting portions of their investigations and contributing with their expertise and resources to scientific studies of the SARS-Cov-2. Their findings have been wide in scope, ranging from
2021
SARS-CoV-2, the new coronavirus causing COVID-19, is one of the most contagious disease of past decades. COVID-19 is different only in that everyone is encountering it for the first time during this pandemic. The world has gone from complete ignorance to a blitz of details in a matter of months. The foremost challenge that the scientific community faces is to understand the growth and transmission capability of the virus. As the world grapples with the global pandemic, people are spending more time than ever before living and working in the digital milieu, and the adoption of Artificial Intelligence (AI) is propelled to an unprecedented level especially as AI has already proven to play an important role in counteracting COVID-19. AI and Data Science are rapidly becoming important tools in clinical research, precision medicine, biomedical discovery and medical diagnostics. Machine learning (ML) and their subsets, such as deep learning, are also referred to as cognitive computing due ...
2021
Machine learning can be defined as a comprehensive range of tools utilized for recognizing patterns in data. Owing to its reliance on artificial intelligence in lieu of age-old, traditional methods, machine learning has established itself as an exceedingly quicker way of discerning patterns and trends from bulk data. The advanced system can even update itself on the availability of new data. This paper intends to elucidate different techniques involved in machine learning that have facilitated the prediction, detection and restriction of infectious diseases in the past few decades. Moreover, in light of the unprecedented COVID-19 pandemic, such tools and techniques have been utilized extensively by smart cities to curb the proliferation of the SARS-CoV-2 virus. However, the strengths and weaknesses of this approach remain abstruse and therefore, this review also aims to evaluate the role of machine learning in the recent coronavirus outbreak.<br>
Artificial Intelligence and Big Data in Public Health
International Journal of Environmental Research and Public Health
Artificial intelligence and automation are topics dominating global discussions on the future of professional employment, societal change, and economic performance. In this paper, we describe fundamental concepts underlying AI and Big Data and their significance to public health. We highlight issues involved and describe the potential impacts and challenges to medical professionals and diagnosticians. The possible benefits of advanced data analytics and machine learning are described in the context of recently reported research. Problems are identified and discussed with respect to ethical issues and the future roles of professionals and specialists in the age of artificial intelligence.
From Big Data to Precision Medicine
Frontiers in Medicine, 2019
For over a decade the term “Big data” has been used to describe the rapid increase in volume, variety and velocity of information available, not just in medical research but in almost every aspect of our lives. As scientists, we now have the capacity to rapidly generate, store and analyse data that, only a few years ago, would have taken many years to compile. However, “Big data” no longer means what it once did. The term has expanded and now refers not to just large data volume, but to our increasing ability to analyse and interpret those data. Tautologies such as “data analytics” and “data science” have emerged to describe approaches to the volume of available information as it grows ever larger. New methods dedicated to improving data collection, storage, cleaning, processing and interpretation continue to be developed, although not always by, or for, medical researchers. Exploiting new tools to extract meaning from large volume information has the potential to drive real change in clinical practice, from personalized therapy and intelligent drug design to population screening and electronic health record mining. As ever, where new technology promises “Big Advances,” significant challenges remain. Here we discuss both the opportunities and challenges posed to biomedical research by our increasing ability to tackle large datasets. Important challenges include the need for standardization of data content, format, and clinical definitions, a heightened need for collaborative networks with sharing of both data and expertise and, perhaps most importantly, a need to reconsider how and when analytic methodology is taught to medical researchers. We also set “Big data” analytics in context: recent advances may appear to promise a revolution, sweeping away conventional approaches to medical science. However, their real promise lies in their synergy with, not replacement of, classical hypothesis-driven methods. The generation of novel, data-driven hypotheses based on interpretable models will always require stringent validation and experimental testing. Thus, hypothesis-generating research founded on large datasets adds to, rather than replaces, traditional hypothesis driven science. Each can benefit from the other and it is through using both that we can improve clinical practice.