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Time-to-birth prediction models and the influence of expert opinions

Gilles Vandewiele (UGent) , Isabelle Dehaene (UGent) , Olivier Janssens (UGent) , Femke Ongenae (UGent) , Femke De Backere (UGent) , Filip De Turck (UGent) , Kristien Roelens (UGent) , Sofie Van Hoecke (UGent) and Thomas Demeester (UGent)
(2019) ARTIFICIAL INTELLIGENCE IN MEDICINE, AIME 2019. In Lecture Notes in Computer Science 11526. p.286-291
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Abstract
Preterm birth is the leading cause of death among children under five years old. The pathophysiology and etiology of preterm labor are not yet fully understood. This causes a large number of unnecessary hospitalizations due to high--sensitivity clinical policies, which has a significant psychological and economic impact. In this study, we present a predictive model, based on a new dataset containing information of 1,243 admissions, that predicts whether a patient will give birth within a given time after admission. Such a model could provide support in the clinical decision-making process. Predictions for birth within 48 h or 7 days after admission yield an Area Under the Curve of the Receiver Operating Characteristic (AUC) of 0.72 for both tasks. Furthermore, we show that by incorporating predictions made by experts at admission, which introduces a potential bias, the prediction effectiveness increases to an AUC score of 0.83 and 0.81 for these respective tasks.

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Citation

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MLA
Vandewiele, Gilles, et al. “Time-to-Birth Prediction Models and the Influence of Expert Opinions.” ARTIFICIAL INTELLIGENCE IN MEDICINE, AIME 2019, edited by David Riaño et al., vol. 11526, Springer, 2019, pp. 286–91, doi:10.1007/978-3-030-21642-9_36.
APA
Vandewiele, G., Dehaene, I., Janssens, O., Ongenae, F., De Backere, F., De Turck, F., … Demeester, T. (2019). Time-to-birth prediction models and the influence of expert opinions. In D. Riaño, S. Wilk, & A. ten Teije (Eds.), ARTIFICIAL INTELLIGENCE IN MEDICINE, AIME 2019 (Vol. 11526, pp. 286–291). https://doi.org/10.1007/978-3-030-21642-9_36
Chicago author-date
Vandewiele, Gilles, Isabelle Dehaene, Olivier Janssens, Femke Ongenae, Femke De Backere, Filip De Turck, Kristien Roelens, Sofie Van Hoecke, and Thomas Demeester. 2019. “Time-to-Birth Prediction Models and the Influence of Expert Opinions.” In ARTIFICIAL INTELLIGENCE IN MEDICINE, AIME 2019, edited by David Riaño, Szymon Wilk, and Annette ten Teije, 11526:286–91. Springer. https://doi.org/10.1007/978-3-030-21642-9_36.
Chicago author-date (all authors)
Vandewiele, Gilles, Isabelle Dehaene, Olivier Janssens, Femke Ongenae, Femke De Backere, Filip De Turck, Kristien Roelens, Sofie Van Hoecke, and Thomas Demeester. 2019. “Time-to-Birth Prediction Models and the Influence of Expert Opinions.” In ARTIFICIAL INTELLIGENCE IN MEDICINE, AIME 2019, ed by. David Riaño, Szymon Wilk, and Annette ten Teije, 11526:286–291. Springer. doi:10.1007/978-3-030-21642-9_36.
Vancouver
1.
Vandewiele G, Dehaene I, Janssens O, Ongenae F, De Backere F, De Turck F, et al. Time-to-birth prediction models and the influence of expert opinions. In: Riaño D, Wilk S, ten Teije A, editors. ARTIFICIAL INTELLIGENCE IN MEDICINE, AIME 2019. Springer; 2019. p. 286–91.
IEEE
[1]
G. Vandewiele et al., “Time-to-birth prediction models and the influence of expert opinions,” in ARTIFICIAL INTELLIGENCE IN MEDICINE, AIME 2019, Poznan, Poland, 2019, vol. 11526, pp. 286–291.
@inproceedings{8628809,
  abstract     = {{Preterm birth is the leading cause of death among children under five years old. The pathophysiology and etiology of preterm labor are not yet fully understood. This causes a large number of unnecessary hospitalizations due to high--sensitivity clinical policies, which has a significant psychological and economic impact. In this study, we present a predictive model, based on a new dataset containing information of 1,243 admissions, that predicts whether a patient will give birth within a given time after admission. Such a model could provide support in the clinical decision-making process. Predictions for birth within 48 h or 7 days after admission yield an Area Under the Curve of the Receiver Operating Characteristic (AUC) of 0.72 for both tasks. Furthermore, we show that by incorporating predictions made by experts at admission, which introduces a potential bias, the prediction effectiveness increases to an AUC score of 0.83 and 0.81 for these respective tasks.}},
  author       = {{Vandewiele, Gilles and Dehaene, Isabelle and Janssens, Olivier and Ongenae, Femke and De Backere, Femke and De Turck, Filip and Roelens, Kristien and Van Hoecke, Sofie and Demeester, Thomas}},
  booktitle    = {{ARTIFICIAL INTELLIGENCE IN MEDICINE, AIME 2019}},
  editor       = {{Riaño, David and Wilk, Szymon and ten Teije, Annette}},
  isbn         = {{9783030216412}},
  issn         = {{0302-9743}},
  language     = {{eng}},
  location     = {{Poznan, Poland}},
  pages        = {{286--291}},
  publisher    = {{Springer}},
  title        = {{Time-to-birth prediction models and the influence of expert opinions}},
  url          = {{http://doi.org/10.1007/978-3-030-21642-9_36}},
  volume       = {{11526}},
  year         = {{2019}},
}
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