Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier
Authors: Joseph Futoma, Sanjay Hariharan, Katherine Heller
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In a large cohort of heterogeneous inpatient encounters at our university health system we find that it outperforms several baselines at predicting sepsis, and yields 19.4% and 55.5% improved areas under the Receiver Operating Characteristic and Precision Recall curves as compared to the NEWS score currently used by our hospital. |
| Researcher Affiliation | Academia | 1Dept. of Statistical Science, Duke University, Durham NC, USA. Correspondence to: Joseph Futoma <jdf38@duke.edu>. |
| Pseudocode | Yes | Algorithm 1 Lanczos Method to approximate Σ1/2ξ |
| Open Source Code | Yes | 1https://github.com/jfutoma/MGP-RNN |
| Open Datasets | No | Our dataset consists of 49,312 inpatient admissions from our university health system spanning 18 months, extracted directly from our EHR. |
| Dataset Splits | Yes | We train our method to 80% of the full dataset, setting aside 10% as a validation set to select hyperparameters and a final 10% for testing. |
| Hardware Specification | Yes | On a server with 63GB RAM and 12 Intel Xeon E5-2680 2.50GHz CPUs |
| Software Dependencies | No | We implemented our methods in Tensorflow1. No specific version number for TensorFlow or other software dependencies is provided. |
| Experiment Setup | Yes | We train all models using stochastic gradient descent with the ADAM optimizer (Kingma & Ba, 2015) using minibatches of 100 encounters at a time and a learning rate of 0.001. |