scholarly journals Critical Care, Critical Data

2019 ◽  
Vol 10 ◽  
pp. 117959721985656 ◽  
Author(s):  
Christopher V Cosgriff ◽  
Leo Anthony Celi ◽  
David J Stone
Keyword(s):  
Artificial Intelligence ◽  
Critical Care ◽  
Clinical Data ◽  
Continuous Monitoring ◽  
Data Science ◽  
Clinical Decision ◽  
Critical Care Medicine ◽  
Data Driven ◽  
Critical Data

As big data, machine learning, and artificial intelligence continue to penetrate into and transform many facets of our lives, we are witnessing the emergence of these powerful technologies within health care. The use and growth of these technologies has been contingent on the availability of reliable and usable data, a particularly robust resource in critical care medicine where continuous monitoring forms a key component of the infrastructure of care. The response to this opportunity has included the development of open databases for research and other purposes; the development of a collaborative form of clinical data science intended to fully leverage these data resources, and the creation of data-driven applications for purposes such as clinical decision support. Most recently, data levels have reached the thresholds required for the development of robust artificial intelligence features for clinical purposes. The systematic capture and analysis of clinical data in both individuals and populations allows us to begin to move toward precision medicine in the intensive care unit (ICU). In this perspective review, we examine the fundamental role of data as we present the current progress that has been made toward an artificial intelligence (AI)-supported, data-driven precision critical care medicine.

scholarly journals Out of the loop? On the radical and the routine in urban big data

Urban Studies ◽  
2021 ◽  
pp. 004209802110140
Author(s):  
Sarah Barns
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Computational Models ◽  
Data Driven ◽  
Urban Systems ◽  
Out Of The Loop ◽  
Urban Futures ◽  
Urban Behaviour ◽  
The City

This commentary interrogates what it means for routine urban behaviours to now be replicating themselves computationally. The emergence of autonomous or artificial intelligence points to the powerful role of big data in the city, as increasingly powerful computational models are now capable of replicating and reproducing existing spatial patterns and activities. I discuss these emergent urban systems of learned or trained intelligence as being at once radical and routine. Just as the material and behavioural conditions that give rise to urban big data demand attention, so do the generative design principles of data-driven models of urban behaviour, as they are increasingly put to use in the production of replicable, autonomous urban futures.

Against generalisation: Data-driven decisions need context to be human-compatible

2021 ◽  
pp. 026638212110619
Author(s):  
Sharon Richardson
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Theoretical Frameworks ◽  
The Past ◽  
Real World Applications ◽  
Contextual Framework ◽  
Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

An Official Multi-Society Statement: The Role of Clinical Research Results in the Practice of Critical Care Medicine

2012 ◽  
Vol 185 (10) ◽  
pp. 1117-1124 ◽  
Author(s):  
Mark R. Tonelli ◽  
J. Randall Curtis ◽  
Kalpalatha K. Guntupalli ◽  
Gordon D. Rubenfeld ◽  
Alejandro C. Arroliga ◽  
...  
Keyword(s):  
Critical Care ◽  
Clinical Research ◽  
Critical Care Medicine ◽  
Research Results

scholarly journals Remote work and human capital: new challenges and threats (international experience)

2021 ◽  
pp. 107-119
Author(s):  
Oleksandr Burov
Keyword(s):  
Artificial Intelligence ◽  
Human Capital ◽  
Data Science ◽  
Well Being ◽  
Business Environment ◽  
Remote Work ◽  
Science Data ◽  
Creative Abilities ◽  
The Impact

Keywords: human capital, remote work, cybersecurity, workforce, digital economics The article considers the role of human capital in the transitionto the remote work. The analysis of world changes in the field of safe and effectiveuse of digital business environment and qualification of workforce in the conditions ofgrowth of remote work is carried out. The analysis was conducted in the following areas:general features of the digitalizing in crisis and innovation, a new paradigm of business«Data is the new gold», the organization of the workforce in the transition to teleworking,the priorities of today's professions, the problems of cybersecurity in teleworking. It has been articulated that the main requirements for the today’s workforce are intellectualand creative abilities, competence in the field of creation and use of ICT, bigdata (data science, data mining, data analytics) and artificial intelligence, the role ofwhich has grown even more due to the COVID-19 pandemic. The human component ofintellectual capital (in the form of knowledge, skills and competencies, as well as intellectualand creative abilities) is gaining new importance in the digital economy.The analysis of relationship of the crisis and innovation made on the basis of the ClarivateDerwent report has demonstrated the impact of the pandemic on the global lifecycle of research and innovation projects in the first half of 2020, namely that COVID-19violated innovation strategy of the innovative leaders worldwide. The analysis hasdemonstrated: in the new conditions of accelerated digitalization, ingenuity and speed ofdecision-making and innovation are needed more than ever. These priorities will affectthe world economy in the coming year.Special attention in analysis has been paid to the new business paradigm related touse and role of data. It was highlighted that digitization generates vast amounts of datathat offer many opportunities for business, human well-being, and the environment. As aresult, new capabilities and opportunities arise for business with the ecosystem of cooperationand partnership, as well as collaboration of stakeholders.The core of changes in digitalization is reskilling and upskilling of the workforce accountingnew workplaces and new requirements for them. It is recognized that talentmanagement and creative people selection can be the main engine in future transformationof economics, and workforce becomes an effective pole for investments. At the sametime, it is argued that remote worker is outside the scope of corporate protection, and virtuallyany production information, like human capital, becomes much more vulnerablein such conditions and requires appropriate cybersecurity methods.As a conclusion, it is articulated that the ability of companies to use big data is beginningto play a significant role in the economy, which in turn requires the involvementand training of data processing and analysis specialists. The direction of professions thatis being actively formed recently — data science — is one of the most priority in the labormarket. At the same time, the labor market needs skills and abilities in the field of interpersonalcommunication (soft skills), which are able to ensure the effective operation ofpeople and systems of hybrid intelligence «human-artificial intelligence».For the further research it has been recommended a comprehensive study of protectionof objects and subjects of intellectual property in open networks.

scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine

10.2196/16607 ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. e16607 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and a mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

scholarly journals Data-driven Discovery of Mathematical and Physical Relations in Oncology Data using Human-understandable Machine Learning

2021 ◽  
Author(s):  
Daria Kurz ◽  
Carlos Salort S&aacutenchez ◽  
Cristian Axenie
Keyword(s):  
Machine Learning ◽  
Cancer Progression ◽  
Clinical Data ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Biological Data ◽  
Rate Of Change ◽  
Learning System ◽  
Data Driven ◽  
Computational Oncology

For decades, researchers have used the concepts of rate of change and differential equations to model and forecast neoplastic processes. This expressive mathematical apparatus brought significant insights in oncology by describing the unregulated proliferation and host interactions of cancer cells, as well as their response to treatments. Now, these theories have been given a new life and found new applications. With the advent of routine cancer genome sequencing and the resulting abundance of data, oncology now builds an "arsenal" of new modeling and analysis tools. Models describing the governing physical laws of tumor-host-drug interactions can be now challenged with biological data to make predictions about cancer progression. Our study joins the efforts of the mathematical and computational oncology community by introducing a novel machine learning system for data-driven discovery of mathematical and physical relations in oncology. The system utilizes computational mechanisms such as competition, cooperation, and adaptation in neural networks to simultaneously learn the statistics and the governing relations between multiple clinical data covariates. Targeting an easy adoption in clinical oncology, the solutions of our system reveal human-understandable properties and features hidden in the data. As our experiments demonstrate, our system can describe nonlinear conservation laws in cancer kinetics and growth curves, symmetries in tumor's phenotypic staging transitions, the pre-operative spatial tumor distribution, and up to the nonlinear intracellular and extracellular pharmacokinetics of neoadjuvant therapies. The primary goal of our work is to enhance or improve the mechanistic understanding of cancer dynamics by exploiting heterogeneous clinical data. We demonstrate through multiple instantiations that our system is extracting an accurate human-understandable representation of the underlying dynamics of physical interactions central to typical oncology problems. Our results and evaluation demonstrate that using simple - yet powerful - computational mechanisms, such a machine learning system can support clinical decision making. To this end, our system is a representative tool of the field of mathematical and computational oncology and offers a bridge between the data, the modeler, the data scientist, and the practising clinician.

scholarly journals Commentary on Neil Selwyn’s LAK18 Keynote Address

2019 ◽  
Vol 6 (3) ◽  
Author(s):  
Paul Prinsloo
Keyword(s):  
Learning Analytics ◽  
Data Science ◽  
Keynote Address ◽  
Research Focus ◽  
Critical Data ◽  
Royal Household

In his keynote, Neil Selwyn not only acknowledged his role as ‘outsider’ to the field of learning analytics, but also intentionally assumed the role of “idiot”. In my commentary I assume that Selwyn’s embrace of being an idiot was more than just self-deprecating humour or a disclaimer aimed to prepare the audience for his provocations. In a Medieval carnival, the clown, fool or community idiot was crowned king, and for the duration of the carnival, could make fun of the royal household, blaspheme and provoke, all licenced by his or her role at that moment in time. Selwyn acknowledged that his own position was and continue to be informed by Critical Data Studies (CDS), an emerging research focus and discourse aimed at troubling much of current accepted and unquestioned assumptions and practices in the broader context of data science. I reflect and comment on Selwyn’s keynote by firstly mapping some of the key tenets of CDS, before addressing some aspects of the keynote and two aspect of his “learning analytics wish-list” namely “giving students control” and “seeing ethics in terms of power, not in terms of protection."

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