Summary of Early Prediction Of Onset Of Sepsis in Clinical Setting, by Fahim Mohammad et al.
Early prediction of onset of sepsis in Clinical Setting
by Fahim Mohammad, Lakshmi Arunachalam, Samanway Sadhu, Boudewijn Aasman, Shweta Garg, Adil Ahmed, Silvie Colman, Meena Arunachalam, Sudhir Kulkarni, Parsa Mirhaji
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study proposes using Machine Learning models to predict early sepsis onset using deidentified clinical data from Montefiore Medical Center. A supervised learning approach was employed, training an XGBoost model on 80% of the train dataset and evaluating it on the remaining 20%. The model was validated on prospective unseen data. To assess performance at the individual patient level and timeliness, a normalized utility score was used, as outlined in the PhysioNet Sepsis Challenge paper. Metrics such as F1 Score, Sensitivity, Specificity, and Flag Rate were devised. The model achieved a normalized utility score of 0.494 on test data and 0.378 on prospective data at threshold 0.3, with F1 scores of 80.8% and 67.1%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses Machine Learning to predict when someone will get sepsis, a serious illness. They use medical records from one hospital in New York City to train a computer model. The model is then tested on new data that it didn’t see before. The researchers used a special way of measuring the model’s accuracy and found that it was good at predicting when someone would get sepsis. |
Keywords
* Artificial intelligence * F1 score * Machine learning * Supervised * Xgboost
