ZusammenfassungDer kompetente, verantwortungsvolle und effiziente Umgang mit Daten gewinnt in der informationstechnisch geprägten Welt eine immer größere Relevanz in allen gesellschaftlichen und ...ökonomischen Bereichen. Data Engineering und Data Science werden dadurch zu immer wichtigeren Schlüsseldisziplinen, worauf Universitäten und Hochschulen mit einem zunehmenden Angebot an Bachelor- und Masterstudiengängen im Bereich Data Science reagieren. Die Task Force/Arbeitsgruppe „Data Science“ der Gesellschaft für Informatik e. V. hat sich daher das Ziel gesetzt, Empfehlungen für die Gestaltung von Data-Science-Masterstudiengängen auszuarbeiten, die als Diskussionsgrundlage für die Akkreditierung entsprechender Studiengänge dienen sollen. Hintergrund und Inhalte dieser Empfehlungen werden in diesem Beitrag genauer vorgestellt.
•First comprehensive review paper on Surgical Data Science.•Multi-round Delphi process with experts from 51 institutions to define open challenges and next steps.•List of publicly accessible surgical ...data sets.•List of currently released products and clinical success stories.•List of registered clinical trials relevant for the field.
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Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.
Why we need a small data paradigm Hekler, Eric B; Klasnja, Predrag; Chevance, Guillaume ...
BMC medicine,
07/2019, Letnik:
17, Številka:
1
Journal Article
Recenzirano
Odprti dostop
There is great interest in and excitement about the concept of personalized or precision medicine and, in particular, advancing this vision via various 'big data' efforts. While these methods are ...necessary, they are insufficient to achieve the full personalized medicine promise. A rigorous, complementary 'small data' paradigm that can function both autonomously from and in collaboration with big data is also needed. By 'small data' we build on Estrin's formulation and refer to the rigorous use of data by and for a specific N-of-1 unit (i.e., a single person, clinic, hospital, healthcare system, community, city, etc.) to facilitate improved individual-level description, prediction and, ultimately, control for that specific unit.
The purpose of this piece is to articulate why a small data paradigm is needed and is valuable in itself, and to provide initial directions for future work that can advance study designs and data analytic techniques for a small data approach to precision health. Scientifically, the central value of a small data approach is that it can uniquely manage complex, dynamic, multi-causal, idiosyncratically manifesting phenomena, such as chronic diseases, in comparison to big data. Beyond this, a small data approach better aligns the goals of science and practice, which can result in more rapid agile learning with less data. There is also, feasibly, a unique pathway towards transportable knowledge from a small data approach, which is complementary to a big data approach. Future work should (1) further refine appropriate methods for a small data approach; (2) advance strategies for better integrating a small data approach into real-world practices; and (3) advance ways of actively integrating the strengths and limitations from both small and big data approaches into a unified scientific knowledge base that is linked via a robust science of causality.
Small data is valuable in its own right. That said, small and big data paradigms can and should be combined via a foundational science of causality. With these approaches combined, the vision of precision health can be achieved.
The tremendous amount of data collected from connected devices and social media has created a high demand for new skills to help organizations gain the power of big data. With a shorter completion ...time, graduate certificates appeared to be a desirable alternative for working professionals to develop these skills. However, it was unclear if graduate certificates met the job market's needs. Quantitative text analysis was used to analyze data science skills in more than 5,000 data science job descriptions and skills taught in 588 required courses in 166 graduate certificates to investigate if skills needed in the industry were taught in these graduate certificates. The results showed that 21% of skills were common across seven job categories. 'Team' was a non-technical skill in high demand, while 'Python' and 'SQL' were the technical skills in high demand. Although Business offered the highest number of certificates, a lower number of data-science skills were taught in these certificates. The findings also suggested that job categories in the EDISON Data Science Professional Profiles could be grouped into two separate profile groups. The findings of this research provide a comprehensive list of skills that are common in a wide range of data science jobs for universities to consider while they develop their data science programs.
Time for data science to professionalise Steuer, Detlef
Significance (Oxford, England),
August 2020, 2020-08-01, 20200801, Letnik:
17, Številka:
4
Journal Article
Data science must become a profession if it is to get to grips with the issues of data ethics, argues Detlef Steuer
Data science must become a profession if it is to get to grips with the issues of ...data ethics, argues Detlef Steuer.
Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance ...imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses
. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset
. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.
ICES: Data, Discovery, Better Health Schull, Michael J; Azimaee, Mahmoud; Marra, Marcel ...
International journal of population data science,
03/2020, Letnik:
4, Številka:
2
Journal Article
Recenzirano
Odprti dostop
ICES was founded in 1992 to study the health care system and promote effective, efficient and equitable health care. Over 27 years later, the goal remains largely unchanged, though the institute has ...grown in size and impact. Created as an independent not-for-profit research institute and given what was, at the time, unprecedented access to administrative health data records for the population of Ontario, ICES’ initial focus was to better understand the delivery of hospital services and translate its findings into better health care and policy. From modest beginnings with a handful of researchers located in a few hospital offices, ICES has grown to encompass a community of almost 500 scientists and staff across a network of seven physical sites in Ontario. The original focus on hospital-based services has expanded significantly and now includes research and analysis of community-based health services, health policy, Indigenous health, social determinants of health, and data science.
Quomodo dicitur “data science” Latine? Jay, Matthew A.; Borja, Mario Cortina
Significance (Oxford, England),
August 2020, 2020-08-01, 20200801, Letnik:
17, Številka:
4
Journal Article
Odprti dostop
The headline asks: “How do you say ‘data science’ in Latin?” But perhaps the more pertinent question is, why should we care? Matthew A. Jay and Mario Cortina Borja look to the past to better ...understand a thoroughly modern phrase
The headline asks: “How do you say 'data science' in Latin?” But perhaps the more pertinent question is, why should we care? Matthew A. Jay and Mario Cortina Borja look to the past to better understand a thoroughly modern phrase.
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