Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis

Autor: Martin Stocker, Salhab el Helou, Luregn J Schlapbach, Juliette van Gijsel, Frans B Plötz, Jan Janota, René F Kornelisse, Dirk Lehnick, Niek B. Achten, Rob Moonen, Albertine E Donker, Urs Zimmerman, Wendy van Herk, Imant Daunhawer, Rita K van den Tooren-de Groot, Annemarie M. C. van Rossum, Angelique Hoffmann-Haringsma, Sven Wellmann, Madan Roy, Jantien W Wieringa, Laura H van der Meer-Kappelle, Frank A B A Schuerman, Sourabh Dutta, Maren Tomaske, Julia E. Vogt, Esther de Vries, Sintha D Sie, Amerik C. de Mol

Publikováno v: Stocker, M, Daunhawer, I, van Herk, W, el Helou, S, Dutta, S, Schuerman, F A B A, van den Tooren-de Groot, R K, Wieringa, J W, Janota, J, van der Meer-Kappelle, L H, Moonen, R, Sie, S D, de Vries, E, Donker, A E, Zimmerman, U, Schlapbach, L J, de Mol, A C, Hoffmann-Haringsma, A, Roy, M, Tomaske, M, Kornelisse, R F, van Gijsel, J, Plötz, F B, Wellmann, S, Achten, N B, Lehnick, D, van Rossum, A M C & Vogt, J E 2022, ' Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis ', Pediatric Infectious Disease Journal, vol. 41, no. 3, pp. 248-254 . https://doi.org/10.1097/INF.0000000000003344
Pediatric Infectious Disease Journal, 41(3), 248-254. Lippincott Williams and Wilkins
The Pediatric infectious disease journal, 41(3), 248-254. Lippincott Williams & Wilkins
Pediatric Infectious Disease Journal, 41(3), 248-254. Lippincott Williams and Wilkins Ltd.
Pediatric infectious disease journal, 41(3), 248-254. Lippincott Williams and Wilkins

Background: Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic performance. The aim of this study was to use machine learning to analyze the diagnostic accuracy of risk f

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb09507fffc3911b56d3f583d5e66116
https://research.vumc.nl/en/publications/cabbad12-9f51-4d21-8b28-01a829d44d56

PET-Guided Attention Network for Segmentation of Lung Tumors from PET/CT Images

Autor: Imant Daunhawer, Thomas Weikert, Julia E. Vogt, Bram Stieltjes, Varaha Karthik Pattisapu, Alexander W. Sauter

Publikováno v: Lecture Notes in Computer Science ISBN: 9783030712778
GCPR
Lecture Notes in Computer Science, 12544
Pattern Recognition

PET/CT imaging is the gold standard for the diagnosis and staging of lung cancer. However, especially in healthcare systems with limited resources, costly PET/CT images are often not readily available. Conventional machine learning models either proc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cfda3ac1ec50b02e3123fdf5c1ecfc2f
https://doi.org/10.1007/978-3-030-71278-5_32

Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning

Autor: Marc Pfister, Hatice Cakal, Julia E. Vogt, Imant Daunhawer, Severin Kasser, Gilbert Koch, Janina Tütsch, Sven Wellmann, Lea Sieber

Publikováno v: Pediatric research. 86(1)

Machine learning models may enhance the early detection of clinically relevant hyperbilirubinemia based on patient information available in every hospital. We conducted a longitudinal study on preterm and term born neonates with serial measurements o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ca847226fee52d109713001c4d36972b
https://pubmed.ncbi.nlm.nih.gov/30928997

Biases in the Football Betting Market

Autor: Sven Kosub, Imant Daunhawer, David Schoch

Publikováno v: SSRN Electronic Journal.

This research uses football online betting odds of a broad variety of matches and bookmakers to identify known biases in odds pricing, namely the favorite-longshot bias and the away-favorite bias. Furthermore, it tries to answer the question whether

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::612569c8d5bd4142b7e2c8e0e89bb0bf
https://doi.org/10.2139/ssrn.2977118