Code for: "Evaluation of Random Forest and Ensemble Methods at Predicting Complications Following Cardiac Surgery"

Dataset

Description

This R code was written to develop prediction models for severe postoperative complications following cardiac surgery. It is part of Linda Lapp's PhD Thesis and also a conference proceedings paper "Evaluation of Random Forest and Ensemble Methods at Predicting Complications Following Cardiac Surgery", published at Artificial Intelligence in Medicine (AIME) 2019 conference (https://aime19.aimedicine.info/).

The code consists of (1) data preparation, (2) training and testing data based on experiments, (3) prediction model development, and (4) evaluation of prediction models.
Date made available14 Jun 2022
PublisherUniversity of Strathclyde

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  • Preop_Models.R

    File: application/octet-stream, 14.1 KB

    Type: Dataset

    Licence: CC BY 4.0

  • 1 Conference contribution book

  • Evaluation of random forest and ensemble methods at predicting complications following cardiac surgery

    Lapp, L., Bouamrane, M.-M., Kavanagh, K., Roper, M., Young, D. & Schraag, S., 30 May 2019, Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings. Wilk, S., ten Teije, A. & Riaño, D. (eds.). Cham: Springer, p. 376–385 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11526 LNAI).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

    Open Access
    File
    8 Citations (Scopus)
    28 Downloads (Pure)

  • Euan Minto Prize

    Alkhurayyif, Y. A. A. (Recipient), Almaghairbe, R. H. G. (Recipient), Kheirbakhsh Abadi, A. (Recipient), Smith, L. (Recipient), Davies, S. (Recipient), Foster, C. (Recipient), Nicol, E. (Recipient), Goodfellow, M. (Recipient), Canning, C. (Recipient) & Gibson, R. C. (Recipient), 2018

    Prize: Prize (including medals and awards)

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