Eva-Maria Schwienhorst-Stich, Dana Kropff, Katharina Kersken, Sarah König, Tobias Leutritz, Sandra Parisi, Clara Schlittenhardt, Jörg Schmid, Anne Simmenroth, Jana Jünger, Janina Zirkel
[The elective planetary health: Climate, environment and health at the faculty of medicine in Würzburg: Concept, didactic methods and evaluation results]
Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen
hematoma expansion patients intracerebral hemorrhage ich key predictor poor prognosis potentially amenable treatment study aimed build classification model predict patients ich using deep learning algorithms without using advanced radiological features data atach2 trial antihypertensive treatment acute cerebral hemorrhage utilized variables included models chosen per literature consensus salient variables associated defined increase either 33 6 ml hematoma volume first 24 h multiple machine learning algorithms employed using iterative feature selection outcome balancing methods 70 patients used training 30 internal validation compared ml models logistic regression model calculated auc accuracy sensitivity specificity internal validation models respective models among 1000 patients included atach2 trial 924 complete parameters included analytical cohort median interquartile range iqr initial hematoma volume 993mm3 5031817 252 best performing model across feature selection groups sampling cohorts using artificial neural network ann testing cohort auc 0702 95 ci 06310774 8 hidden layer nodes traditional logistic regression yielded auc 0658 95 ci 06410675 models performed less accuracy lower auc initial hematoma volume time initial ct head initial sbp emerged relevant variables across best performing models developed multiple ml algorithms predict ann classifying best without advanced radiographic features although auc modestly better models larger heterogenous dataset needed build better generalize models