Unsupervised meta-clustering identifies risk clusters in acute myeloid leukemia based on clinical and genetic profiles.

Jan-Niklas EckardtChristoph RölligKlaus H MetzelerPeter HeisigSebastian StasikJulia-Annabell GeorgiFrank KroschinskyFriedrich StölzelUwe PlatzbeckerKarsten SpiekermannUtz KrugJan BraessDennis GörlichCristina SauerlandBernhard WoermannTobias HeroldWolfgang HiddemannCarsten Muller-TidowHubert ServeClaudia Dorothea BaldusKerstin Schäfer-EckartMartin KaufmannStefan W KrauseMathias HänelWolfgang E BerdelChristoph SchliemannJiri MayerMaher Hanoun Johannes ScheteligKarsten WendtMartin Bornhäuser Christian ThiedeJan Moritz Middeke

Published in: Communications medicine (2023)

Dynamic data-driven models are likely more suitable for risk stratification in the context of increasingly complex medical data than rigid hypothesis-driven models to allow for a more personalized treatment allocation and gain novel insights into disease biology.

Keyphrases

genome wide
healthcare
machine learning
electronic health record
copy number
single cell
gene expression
combination therapy
data analysis