Early prediction of onset of sepsis in Clinical Setting
Fahim Mohammad Lakshmi Arunachalam Samanway Sadhu Boudewijn Aasman Shweta Garg Adil Ahmed Silvie Colman Meena Arunachalam Sudhir Kulkarni Parsa Mirhaji
This study proposes the use of Machine Learning models to predict the early
onset of sepsis using deidentified clinical data from Montefiore Medical Center
in Bronx, NY, USA. A supervised learning approach was adopted, wherein an
XGBoost model was trained utilizing 80\% of the train dataset, encompassing 107
features (including the original and derived features). Subsequently, the model
was evaluated on the remaining 20\% of the test data. The model was validated
on prospective data that was entirely unseen during the training phase. To
assess the model's performance at the individual patient level and timeliness
of the prediction, a normalized utility score was employed, a widely recognized
scoring methodology for sepsis detection, as outlined in the PhysioNet Sepsis
Challenge paper. Metrics such as F1 Score, Sensitivity, Specificity, and Flag
Rate were also devised. The model achieved a normalized utility score of 0.494
on test data and 0.378 on prospective data at threshold 0.3. The F1 scores were
80.8\% and 67.1\% respectively for the test data and the prospective data for
the same threshold, highlighting its potential to be integrated into clinical
decision-making processes effectively. These results bear testament to the
model's robust predictive capabilities and its potential to substantially
impact clinical decision-making processes.