Computing in cardiology · 2019 · 11 citations · 11 references
ComputingData ScienceHealth InformaticsMachine LearningPredictive AnalyticsSepsisMissing Data ComputationMissing Data HandlingMissing Data ImputationMissing Data MethodsPredictive Health ModelingSepsis PrognosisData ImputationEarly Sepsis DetectionHandling Missing Clinical DataMissingness MechanismsPredictive Modeling Of SepsisSepsis Prediction ModelsSepsis PredictorsXgboost
Aims: Physicians have to routinely make crucial decisions about patients' health in the ICU. Sepsis affects about 35% of ICU patients, killing approximately 25% of the afflicted. In this paper, we aim to predict the occurrence of sepsis early by studying the missingness of physiological variables and using it with the overall trends in data. Methods: We chose XGBoost as our base model and tried several variations by changing hyperparameters, window sizes and imputation methods. To further improve the model, we used masking vectors to represent the missingness of features in the dataset. Additional modifications include shifting the Sepsis Label to earlier time steps and tuning the classification probability threshold to further improve the model's performance.
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Anand Kumar, Daniel Roberts, Kenneth E. Wood et al. · Critical Care Medicine · 2006 · 6.1K citations
Veterinary Medicine, Infectious Diseases, Antimicrobial Pharmacotherapy +13
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho et al. · Scientific Reports · 2018 · 2.1K citations · Full text