Generating insights in uncharted territories:Real-time learning from data in critically ill patients-an implementer report

Introduction In the current situation, clinical patient data are often siloed in multiple hospital information systems. Especially in the intensive care unit (ICU), large volumes of clinical data are routinely collected through continuous patient monitoring. Although these data often contain useful information for clinical decision making, they are not frequently used to improve quality of care. During, but also after, pressing times, data-driven methods can be used to mine treatment patterns from clinical data to determine the best treatment options from a hospitals own clinical data. Methods In this implementer report, we describe how we implemented a data infrastructure that enabled us to learn in real time from consecutive COVID-19 ICU admissions. In addition, we explain our step-by-step multidisciplinary approach to establish such a data infrastructure. Conclusion By sharing our steps and approach, we aim to inspire others, in and outside ICU walls, to make more efficient use of data at hand, now and in the future

Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter

Objective Although the role of artificial intelligence (AI) in medicine is increasingly studied, most patients do not benefit because the majority of AI models remain in the testing and prototyping environment. The development and implementation trajectory of clinical AI models are complex and a structured overview is missing. We therefore propose a step-by-step overview to enhance clinicians' understanding and to promote quality of medical AI research. Methods We summarised key elements (such as current guidelines, challenges, regulatory documents and good practices) that are needed to develop and safely implement AI in medicine. Conclusion This overview complements other frameworks in a way that it is accessible to stakeholders without prior AI knowledge and as such provides a step-by-step approach incorporating all the key elements and current guidelines that are essential for implementation, and can thereby help to move AI from bytes to bedside

Observation of WWWWWW Production in pppp Collisions at s\sqrt s =13 TeV with the ATLAS Detector

International audienceThis Letter reports the observation of $WWW$ production and a measurement of its cross section using 139 fb$^{-1}$ of proton-proton collision data recorded at a center-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. Events with two same-sign leptons (electrons or muons) and at least two jets, as well as events with three charged leptons, are selected. A multivariate technique is then used to discriminate between signal and background events. Events from $WWW$ production are observed with a significance of 8.0 standard deviations, where the expectation is 5.4 standard deviations. The inclusive $WWW$ production cross section is measured to be $820 \pm 100\,\text{(stat)} \pm 80\,\text{(syst)}$ fb, approximately 2.6 standard deviations from the predicted cross section of $511 \pm 18$ fb calculated at next-to-leading-order QCD and leading-order electroweak accuracy

Observation of WWWWWW Production in pppp Collisions at s\sqrt s =13 TeV with the ATLAS Detector

International audienceThis Letter reports the observation of $WWW$ production and a measurement of its cross section using 139 fb$^{-1}$ of proton-proton collision data recorded at a center-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. Events with two same-sign leptons (electrons or muons) and at least two jets, as well as events with three charged leptons, are selected. A multivariate technique is then used to discriminate between signal and background events. Events from $WWW$ production are observed with a significance of 8.0 standard deviations, where the expectation is 5.4 standard deviations. The inclusive $WWW$ production cross section is measured to be $820 \pm 100\,\text{(stat)} \pm 80\,\text{(syst)}$ fb, approximately 2.6 standard deviations from the predicted cross section of $511 \pm 18$ fb calculated at next-to-leading-order QCD and leading-order electroweak accuracy

Observation of WWWWWW Production in pppp Collisions at s\sqrt s =13 TeV with the ATLAS Detector

International audienceThis Letter reports the observation of $WWW$ production and a measurement of its cross section using 139 fb$^{-1}$ of proton-proton collision data recorded at a center-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. Events with two same-sign leptons (electrons or muons) and at least two jets, as well as events with three charged leptons, are selected. A multivariate technique is then used to discriminate between signal and background events. Events from $WWW$ production are observed with a significance of 8.0 standard deviations, where the expectation is 5.4 standard deviations. The inclusive $WWW$ production cross section is measured to be $820 \pm 100\,\text{(stat)} \pm 80\,\text{(syst)}$ fb, approximately 2.6 standard deviations from the predicted cross section of $511 \pm 18$ fb calculated at next-to-leading-order QCD and leading-order electroweak accuracy

Observation of WWWWWW Production in pppp Collisions at s\sqrt s =13 TeV with the ATLAS Detector

International audienceThis Letter reports the observation of $WWW$ production and a measurement of its cross section using 139 fb$^{-1}$ of proton-proton collision data recorded at a center-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. Events with two same-sign leptons (electrons or muons) and at least two jets, as well as events with three charged leptons, are selected. A multivariate technique is then used to discriminate between signal and background events. Events from $WWW$ production are observed with a significance of 8.0 standard deviations, where the expectation is 5.4 standard deviations. The inclusive $WWW$ production cross section is measured to be $820 \pm 100\,\text{(stat)} \pm 80\,\text{(syst)}$ fb, approximately 2.6 standard deviations from the predicted cross section of $511 \pm 18$ fb calculated at next-to-leading-order QCD and leading-order electroweak accuracy

Observation of WWWWWW Production in pppp Collisions at s\sqrt s =13 TeV with the ATLAS Detector

International audienceThis Letter reports the observation of $WWW$ production and a measurement of its cross section using 139 fb$^{-1}$ of proton-proton collision data recorded at a center-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. Events with two same-sign leptons (electrons or muons) and at least two jets, as well as events with three charged leptons, are selected. A multivariate technique is then used to discriminate between signal and background events. Events from $WWW$ production are observed with a significance of 8.0 standard deviations, where the expectation is 5.4 standard deviations. The inclusive $WWW$ production cross section is measured to be $820 \pm 100\,\text{(stat)} \pm 80\,\text{(syst)}$ fb, approximately 2.6 standard deviations from the predicted cross section of $511 \pm 18$ fb calculated at next-to-leading-order QCD and leading-order electroweak accuracy