Allogeneic hematopoietic cell transplantation (HCT) effectively treats high-risk hematologic diseases but can entail HCT-specific complications, which may be minimized by appropriate patient management, supported by accurate, individual risk estimation. However, almost all HCT risk scores are limited to a single risk assessment before HCT without incorporation of additional data. We developed machine learning models that integrate both baseline patient data and time-dependent laboratory measurements to individually predict mortality and cytomegalovirus (CMV) reactivation after HCT at multiple time points per patient. These gradient boosting machine models provide well-calibrated, time-dependent risk predictions and achieved areas under the receiver-operating characteristic of 0.92 and 0.83 and areas under the precision-recall curve of 0.58 and 0.62 for prediction of mortality and CMV reactivation, respectively, in a 21-day time window. Both models were successfully validated in a prospective, non-interventional study and performed on par with expert hematologists in a pilot comparison.

Allogeneic hematopoietic cell transplantation (HCT) treats high-risk hematologic diseases effectively but can entail HCT-specific complications, which may be minimized by appropriate patient management and accurate, individual risk estimation. Existing clinical scores typically provide a single risk assessment before HCT and do not incorporate additional data as it becomes available. We developed machine learning models which integrate both baseline patient data and time-dependent laboratory measurements to individually predict mortality and cytomegalovirus (CMV) reactivation after HCT at multiple time points per patient. These models provide well-calibrated time-dependent risk predictions and achieved areas under the receiver-operating characteristic of 0.92 and 0.83 and areas under the precision-recall curve of 0.58 and 0.62 for prediction of mortality and CMV reactivation, respectively, in a 21-day time window. Both were successfully validated in a non-interventional, prospective study and performed on par with expert hematologists in a pilot comparison.

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The ACGT project which aims to foster the sharing of research result from both, clinical and molecular research for the benefit of cancer patients uses ontology-driven semantic services. One novelty ACGT provides is a tool named ObTiMA which allows to build questionnaires directly from the ACGT Master Ontology. This will facilitate the process of creating Case Report Forms. Furthermore, the clinical data collected is already annotated in the terms of the ontology.