Details

Autor(en) / Beteiligte

• Strobl, Marina
• Vetter, Julia
• Halmerbauer, Gerhard
• Königswieser, Tilman
• Winkler, Stephan M.

Titel

Using Explainable Artificial Intelligence for Data Based Detection of Complications in Records of Patient Treatments

Ist Teil von

• Computer Aided Systems Theory – EUROCAST 2022, p.173-180

Ort / Verlag

Cham: Springer Nature Switzerland

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• We analyze data of 18,000 patients for identifying models that are able to detect complications in the data of surgeries and other medical treatments. High quality detection models are found using data available for those patients, for whom general data as well as risk factors are available. For identifying these detection models we use explainable artificial intelligence, namely symbolic regression by genetic programming with three different levels of model complexity with respect to model size and complexity of functions used as building blocks for the identified models.