Zobrazeno 1 - 10
of 3 117
pro vyhledávání: '"Van Calster, A."'
Autor:
Van Calster, Ben, Collins, Gary S., Vickers, Andrew J., Wynants, Laure, Kerr, Kathleen F., Barreñada, Lasai, Varoquaux, Gael, Singh, Karandeep, Moons, Karel G. M., Hernandez-boussard, Tina, Timmerman, Dirk, Mclernon, David J., Van Smeden, Maarten, Steyerberg, Ewout W.
A myriad of measures to illustrate performance of predictive artificial intelligence (AI) models have been proposed in the literature. Selecting appropriate performance measures is essential for predictive AI models that are developed to be used in m
Externí odkaz:
http://arxiv.org/abs/2412.10288
Prediction models are used to predict an outcome based on input variables. Missing data in input variables often occurs at model development and at prediction time. The missForestPredict R package proposes an adaptation of the missForest imputation a
Externí odkaz:
http://arxiv.org/abs/2407.03379
Autor:
Gao, Shan, Albu, Elena, Putter, Hein, Stijnen, Pieter, Rademakers, Frank, Cossey, Veerle, Debaveye, Yves, Janssens, Christel, Van Calster, Ben, Wynants, Laure
Objective Hospitals register information in the electronic health records (EHR) continuously until discharge or death. As such, there is no censoring for in-hospital outcomes. We aimed to compare different dynamic regression modeling approaches to pr
Externí odkaz:
http://arxiv.org/abs/2405.01986
Autor:
Carriero, Alex, Luijken, Kim, de Hond, Anne, Moons, Karel GM, van Calster, Ben, van Smeden, Maarten
Risk prediction models are increasingly used in healthcare to aid in clinical decision making. In most clinical contexts, model calibration (i.e., assessing the reliability of risk estimates) is critical. Data available for model development are ofte
Externí odkaz:
http://arxiv.org/abs/2404.19494
Autor:
Albu, Elena, Gao, Shan, Stijnen, Pieter, Rademakers, Frank, Janssens, Christel, Cossey, Veerle, Debaveye, Yves, Wynants, Laure, Van Calster, Ben
Prognostic outcomes related to hospital admissions typically do not suffer from censoring, and can be modeled either categorically or as time-to-event. Competing events are common but often ignored. We compared the performance of random forest (RF) m
Externí odkaz:
http://arxiv.org/abs/2404.16127
Publikováno v:
Diagn Progn Res 8, 14 (2024)
Random forests have become popular for clinical risk prediction modelling. In a case study on predicting ovarian malignancy, we observed training c-statistics close to 1. Although this suggests overfitting, performance was competitive on test data. W
Externí odkaz:
http://arxiv.org/abs/2402.18612
Autor:
Gehringer, Celina K, Martin, Glen P, Van Calster, Ben, Hyrich, Kimme L, Verstappen, Suzanne M M, Sergeant, Jamie C
Multinomial prediction models (MPMs) have a range of potential applications across healthcare where the primary outcome of interest has multiple nominal or ordinal categories. However, the application of MPMs is scarce, which may be due to the added
Externí odkaz:
http://arxiv.org/abs/2312.12008
Publikováno v:
Diagnostic and Prognostic Research, Vol 8, Iss 1, Pp 1-14 (2024)
Abstract Background Random forests have become popular for clinical risk prediction modeling. In a case study on predicting ovarian malignancy, we observed training AUCs close to 1. Although this suggests overfitting, performance was competitive on t
Externí odkaz:
https://doaj.org/article/e0a3450ab9244d92851909742ecf816b
Autor:
Charlotte Van Calster, Wouter Everaerts, Inge Geraerts, An De Groef, An-Kathleen Heroes, Tessa De Vrieze, Ceyhun Alar, Nele Devoogdt
Publikováno v:
BMC Urology, Vol 24, Iss 1, Pp 1-11 (2024)
Abstract Background Patients undergoing treatment for prostate cancer may develop lymphoedema of the midline region. This has a substantial impact on a patient’s quality of life and its diagnosis is often delayed or missed. Therefore, the purpose o
Externí odkaz:
https://doaj.org/article/84ab9b8714744ed08a9b165db3905b2e
Autor:
Reinke, Annika, Tizabi, Minu D., Baumgartner, Michael, Eisenmann, Matthias, Heckmann-Nötzel, Doreen, Kavur, A. Emre, Rädsch, Tim, Sudre, Carole H., Acion, Laura, Antonelli, Michela, Arbel, Tal, Bakas, Spyridon, Benis, Arriel, Blaschko, Matthew, Buettner, Florian, Cardoso, M. Jorge, Cheplygina, Veronika, Chen, Jianxu, Christodoulou, Evangelia, Cimini, Beth A., Collins, Gary S., Farahani, Keyvan, Ferrer, Luciana, Galdran, Adrian, van Ginneken, Bram, Glocker, Ben, Godau, Patrick, Haase, Robert, Hashimoto, Daniel A., Hoffman, Michael M., Huisman, Merel, Isensee, Fabian, Jannin, Pierre, Kahn, Charles E., Kainmueller, Dagmar, Kainz, Bernhard, Karargyris, Alexandros, Karthikesalingam, Alan, Kenngott, Hannes, Kleesiek, Jens, Kofler, Florian, Kooi, Thijs, Kopp-Schneider, Annette, Kozubek, Michal, Kreshuk, Anna, Kurc, Tahsin, Landman, Bennett A., Litjens, Geert, Madani, Amin, Maier-Hein, Klaus, Martel, Anne L., Mattson, Peter, Meijering, Erik, Menze, Bjoern, Moons, Karel G. M., Müller, Henning, Nichyporuk, Brennan, Nickel, Felix, Petersen, Jens, Rafelski, Susanne M., Rajpoot, Nasir, Reyes, Mauricio, Riegler, Michael A., Rieke, Nicola, Saez-Rodriguez, Julio, Sánchez, Clara I., Shetty, Shravya, van Smeden, Maarten, Summers, Ronald M., Taha, Abdel A., Tiulpin, Aleksei, Tsaftaris, Sotirios A., Van Calster, Ben, Varoquaux, Gaël, Wiesenfarth, Manuel, Yaniv, Ziv R., Jäger, Paul F., Maier-Hein, Lena
Publikováno v:
Nature methods, 1-13 (2024)
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in im
Externí odkaz:
http://arxiv.org/abs/2302.01790