Time-Specific Metalearners for the Early Prediction of Sepsis

Marcus Vollmer, Christian F. Luz, Philipp Sodmann, Bhanu Sinha, Sven-olaf Kuhn

Research output: Contribution to conferencePaperAcademic

2 Citations (Scopus)


Motivation: Accounting for complex clinical dynamics in sepsis patients while aiming at an automated analysis of hourly (non-)validated data is challenging. The algorithm has to deal with imprecise, incorrect and incomplete
data in addition to being time aware.
Methods: We aimed to build time-specific stacked
ensembles and a non-specific XGBoost learner to predict
sepsis 6 hours prior to the sepsis onset. The models were
trained on a triple split of 40,336 ICU stays taken from the
training sets of the 2019 PhysioNet/CinC Challenge. Data
was cleaned and features were built based on rolling windows including several clinical scores and criteria, such as
shock index, qSOFA, SOFA, SIRS, NEWS, cNEWS. Model
performance was evaluated using task-specific utility functions. Furthermore, variable importance was assessed.
Results and conclusion: Although no official score
was obtained in the Challenge as team Sepsis2G, we found
normalized utility score of 0.394 for our non-specific XGBoost model on a held out subset of the training data. The
threshold selection was displaced in time-specific metalearners leading to an inferior performance. Most important variables included the assumed presence of ventilation, white blood cell count, partial thromboplastin time,
blood urea nitrogen and rolling quantiles of the temperature. Partial SOFA-scores, cNEWS, and the shock index
showed major importance in the ICU admission phase.
Original languageEnglish
Number of pages4
Publication statusPublished - 30-Dec-2019

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