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Identifying and Predicting Postoperative Infections Based on Readily Available Electronic Health Record Data

Autor: Meijden, S.L. van der, Boekel, A. van, Schinkelshoek, L., Goor, H. van, Boer, M den, Steyerberg, E., Geerts, B., Arbous, S.

Publikováno v: Studies in Health Technology and Informatics, 302, pp. 348-349
Studies in Health Technology and Informatics, 302, 348-349

Contains fulltext : 292886.pdf (Publisher’s version ) (Open Access) Identification of postoperative infections based on retrospective patient data is currently done using manual chart review. We used a validated, automated labelling method based on

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::a097798ae76bb505ab8685d53622dc87
https://hdl.handle.net/2066/292886

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Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records

Autor: Vagliano, I., Schut, M.C., Abu-Hanna, A., Dongelmans, D.A., Lange, D.W. de, Gommers, D., Cremer, O.L., Bosman, R.J., Rigter, S., Wils, E.J., Frenzel, T., Jong, R. de, Peters, M.A.A., Kamps, M.J.A., Ramnarain, D., Nowitzky, R., Nooteboom, F.G.C.A., Ruijter, W. de, Urlings-Strop, L.C., Smit, E.G.M., Mehagnoul-Schipper, D.J., Dormans, T., Jager, C.P.C. de, Hendriks, S.H.A., Achterberg, S., Oostdijk, E., Reidinga, A.C., Festen-Spanjer, B., Brunnekreef, G.B., Cornet, A.D., Tempel, W. van den, Boelens, A.D., Koetsier, P., Lens, J., Faber, H.J., Karakus, A., Entjes, R., Jong, P. de, Rettig, T.C.D., Reuland, M.C., Arbous, S., Fleuren, L.M., Dam, T.A., Thoral, P.J., Lalisang, R.C.A., Tonutti, M., Bruin, D.P. de, Elbers, P.W.G., Keizer, N.F. de, Dutch COVID-19 Res Consortium, Dutch ICU Data Sharing Against COV

Publikováno v: International Journal of Medical Informatics, 167
International Journal of Medical Informatics, 167:104863. Elsevier Ireland Ltd
International Journal of Medical Informatics, 167. ELSEVIER IRELAND LTD
Vagliano, I, Schut, M C, Abu-Hanna, A, Dongelmans, D A, de Lange, D W, Gommers, D, Cremer, O L, Bosman, R J, Rigter, S, Wils, E-J, Frenzel, T, de Jong, R, Peters, M A A, Kamps, M J A, Ramnarain, D, Nowitzky, R, Nooteboom, F G C A, de Ruijter, W, Urlings-Strop, L C, Smit, E G M, Mehagnoul-Schipper, D J, Dormans, T, de Jager, C P C, Hendriks, S H A, Achterberg, S, Oostdijk, E, Reidinga, A C, Festen-Spanjer, B, Brunnekreef, G B, Cornet, A D, van den Tempel, W, Boelens, A D, Koetsier, P, Lens, J, Faber, H J, Karakus, A, Entjes, R, de Jong, P, Rettig, T C D, Reuland, M C, Arbous, S, Fleuren, L M, Dam, T A, Thoral, P J, Lalisang, R C A, Tonutti, M, de Bruin, D P, Elbers, P W G & de Keizer, N F 2022, ' Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients : A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records ', International Journal of Medical Informatics, vol. 167, 104863 . https://doi.org/10.1016/j.ijmedinf.2022.104863
International journal of medical informatics, 167:104863. Elsevier Ireland Ltd

Purpose: To assess, validate and compare the predictive performance of models for in-hospital mortality of COVID-19 patients admitted to the intensive care unit (ICU) over two different waves of infections. Our models were built with high-granular El

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b6a0687e3d7bae60f5dd4796a7ff407
https://doi.org/10.1016/j.ijmedinf.2022.104863

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