Henning Nilius: An Explainable Machine Learning Tool for Pre-Surgery Screening
Henning Nilius, Resident at Insel Gruppe, shared a post on LinkedIn about a recent article he and his co-authord, published in The Lancet Digital Health, adding:
“The core project of my PhD has now been published in The Lancet Digital Health.
MBD Check is an explainable, machine-learning-based screening tool to help decide whether a patient with a suspected mild bleeding disorder needs further haematology work-up before surgery.
We were able to externally validate at a second hospital, correctly identifying nearly 90% of patients with a mild bleeding disorder, and clinicians across surgery, anaesthesiology, and haematology rated its usability as excellent.
It is implemented as a simple Shiny web app and takes about a minute to complete.
You can read the paper and try the tool.
Thank you to my supervisors/co-referee (Michael Nagler and Christos Nakas) and our co-authors for making this possible.”
Title: Development, validation, and user-centric evaluation of an interpretable machine learning decision support tool for the preoperative prediction of mild bleeding disorders (MBD-Check): a prospective diagnostic prediction study
Authors: Henning Nilius, Jonas Kaufmann, Marcel Adler, Fabrizio Minervini, Anna Wieland Greguare-Sander, Lorenzo Alberio, Bernhard Gerber, Dino Kröll, Sajitha Veerakatty, Alexander Kashev, Sigve Haug, Thomas C. Sauter, Andreas Koster, Gabor Erdoes, Janna Hastings, Jerrold H. Levy, Christos Nakas, Michael Nagler
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