Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Benedikt, Langenberger"'
Autor:
Viktoria Steinbeck, Anja Yvonne Bischof, Lukas Schöner, Benedikt Langenberger, David Kuklinski, Alexander Geissler, Christoph Pross, Reinhard Busse
Publikováno v:
International Journal for Equity in Health, Vol 23, Iss 1, Pp 1-11 (2024)
Abstract Background As patient-reported outcomes (PROs) gain prominence in hip and knee arthroplasty (HA and KA), studies indicate PRO variations between genders. Research on the specific health domains particularly impacted is lacking. Hence, we aim
Externí odkaz:
https://doaj.org/article/8fb2377ee2ba48ef9ef1117baeefca89
Autor:
Benedikt Langenberger, Daniel Schrednitzki, Andreas M. Halder, Reinhard Busse, Christoph M. Pross
Publikováno v:
Bone & Joint Research, Vol 12, Iss 9, Pp 512-521 (2023)
Aims: A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three
Externí odkaz:
https://doaj.org/article/26a38672668f4b3a833aaddf51d5d43b
Autor:
Benedikt Langenberger, Natalie Baier, Frank‐Christian Hanke, Jacqueline Fahrentholz, Christina Gorny, Stephanie Sehlen, Katrin Christiane Reber, Sebastian Liersch, Ralf Radomski, Jens Haftenberger, Hans Jürgen Heppner, Reinhard Busse, Verena Vogt
Publikováno v:
Nursing Open, Vol 9, Iss 2, Pp 1477-1485 (2022)
Abstract Aim To estimate the cost‐effectiveness of an intervention facilitating the early detection of adverse drug events through the means of health professional training and the application of a digital screening tool. Design Multi‐centred non
Externí odkaz:
https://doaj.org/article/1102c5803178457595a1834391c82d6f
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 22, Iss 1, Pp 1-14 (2022)
Abstract Objectives To systematically review studies using machine learning (ML) algorithms to predict whether patients undergoing total knee or total hip arthroplasty achieve an improvement as high or higher than the minimal clinically important dif
Externí odkaz:
https://doaj.org/article/d797e742ef03465fb108afb95fe9537b
Publikováno v:
PLoS ONE, Vol 18, Iss 11, p e0293723 (2023)
BackgroundRetrospective hospital quality indicators can only be useful if they are trustworthy signals of current or future quality. Despite extensive longitudinal quality indicator data and many hospital quality public reporting initiatives, researc
Externí odkaz:
https://doaj.org/article/d85f46c935004b01bc6628366dfacb83
Publikováno v:
PLoS ONE, Vol 18, Iss 1, p e0279540 (2023)
Our aim was to predict future high-cost patients with machine learning using healthcare claims data. We applied a random forest (RF), a gradient boosting machine (GBM), an artificial neural network (ANN) and a logistic regression (LR) to predict high
Externí odkaz:
https://doaj.org/article/7d0dc76ed8454f09ad9b5d1c7220b2d2
Autor:
Benedikt, Langenberger, Natalie, Baier, Frank-Christian, Hanke, Jacqueline, Fahrentholz, Christina, Gorny, Stephanie, Sehlen, Katrin Christiane, Reber, Sebastian, Liersch, Ralf, Radomski, Jens, Haftenberger, Hans Jürgen, Heppner, Reinhard, Busse, Verena, Vogt
Publikováno v:
Nursing open. 9(2)
To estimate the cost-effectiveness of an intervention facilitating the early detection of adverse drug events through the means of health professional training and the application of a digital screening tool.Multi-centred non-randomized controlled tr