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Krumholz, Mark R.,Adamo, Angela,Fumagalli, Michele,Wofford, Aida,Calzetti, Daniela,Lee, Janice C.,Whitmore, Bradley C.,Bright, Stacey N.,Grasha, Kathryn,Gouliermis, Dimitrios A.,Kim, Hwihyun,Nair, Pre IOP Publishing 2015 The Astrophysical journal Vol.812 No.2
<P>We investigate a novel Bayesian analysis method, based on the Stochastically Lighting Up Galaxies (slug) code, to derive the masses, ages, and extinctions of star clusters from integrated light photometry. Unlike many analysis methods, slug correctly accounts for incomplete initial mass function (IMF) sampling, and returns full posterior probability distributions rather than simply probability maxima. We apply our technique to 621 visually confirmed clusters in two nearby galaxies, NGC 628 and NGC 7793, that are part of the Legacy Extragalactic UV Survey (LEGUS). LEGUS provides Hubble Space Telescope photometry in the NUV, U, B, V, and I bands. We analyze the sensitivity of the derived cluster properties to choices of prior probability distribution, evolutionary tracks, IMF, metallicity, treatment of nebular emission, and extinction curve. We find that slug's results for individual clusters are insensitive to most of these choices, but that the posterior probability distributions we derive are often quite broad, and sometimes multi-peaked and quite sensitive to the choice of priors. In contrast, the properties of the cluster population as a whole are relatively robust against all of these choices. We also compare our results from slug to those derived with a conventional non-stochastic fitting code, Yggdrasil. We show that slug's stochastic models are generally a better fit to the observations than the deterministic ones used by Yggdrasil. However, the overall properties of the cluster populations recovered by both codes are qualitatively similar.</P>
12th Korea Healthcare Congress 2021 김치국부터 마시지 말라 The Time for Digital Health is Almost Here
Harlan M. Krumholz 연세대학교의과대학 2022 Yonsei medical journal Vol.63 No.5
We are now on the cusp of massive adoption of digital health technologies. Medicine is becoming an information science intertwinedwith technology and data science. This talk aims to describe the current state of digital transformation in healthcare, toidentify reasons for enthusiasm and caution, and to provide a framework for thinking about what is necessary for hospitals andhealth systems to be confident about incorporating these innovations into practice. I have three key recommendations. First, weshould buy results, not claims. Those in positions that influence decisions about endorsing or purchasing digital products designedto improve care or outcomes ought to buy results, not claims or intermediate results. Moreover, although analytic validity and clinicalvalidity are important, they sometimes do not reflect the impact of a product in its entirety. Ultimately, we need to know whetherpatients benefit. Second, we should insist on transparency. The performance of a product cannot be a secret. The basis on whichdevelopers make claims about their products should be open to all, including patients. Better yet, data on which experts reach aconclusion should be shared, just as many companies share research data on drugs and devices. Third, we should be aware of unintendedadverse consequences. We should evaluate every intervention for unintended adverse consequences. Changes to systems,with all good intentions, can always go awry. In conclusion, insistence on good and evolving evidence is the best way to arriveat our destination: the use of innovations to improve outcomes.