Predicting Sepsis with Analytics - RCG Global Services and Boston College Collaboration
RCG Global Services and Boston College (BC) recently hosted an analytics competition, challenging BC students to predict sepsis using panel data. The competition featured a large dataset: 1 million observations from 28,000 patients for training, and 454,000 observations from 6,000 patients for testing. With only 2% of cases labeled sepsis-positive, participants tackled severe class imbalance.
The Challenge and Evaluation
The primary success metric was the F1-score, which balances precision and recall. Judges also assessed the overall presentation, innovation in modeling, clarity of communication, and understanding of the problem through a detailed evaluation rubric.
Winning Solution
The top team’s solution, based on CRISP-DM methodology, excelled in understanding and innovation. Key steps included:
- Preprocessing: Data cleaning, imputation, feature engineering, train-validation splits, and oversampling.
- Model Testing: Evaluating logistic regression, tree models, neural networks, RNNs, LSTMs, and GRUs. An RNN achieved the best F1-score.
- Analysis: Thoughtful predictions and insights.
Despite not achieving the highest F1-score (“0.21”), their approach, communication, and insights set them apart.
Results and Impact
RCG awarded $3,000 in prizes to the top teams, providing students with hands-on experience in tackling healthcare challenges. The competition reinforced the importance of analytics in transforming healthcare outcomes, aligning with RCG's mission to foster data-driven innovation.