Medical Dead-ends and Learning to Identify High-Risk States and Treatments
Authors: Mehdi Fatemi, Taylor W. Killian, Jayakumar Subramanian, Marzyeh Ghassemi
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then train three independent deep neural models for automated state construction, dead-end discovery and confirmation. Our empirical results discover that dead-ends exist in real clinical data among septic patients, and further reveal gaps between secure treatments and those that were administered. We validate De D in a carefully constructed toy domain, and then evaluate real health records of septic patients in an intensive care unit (ICU) setting [22]. |
| Researcher Affiliation | Collaboration | Mehdi Fatemi Microsoft Research mehdi.fatemi@microsoft.com Taylor W. Killian University of Toronto, Vector Institute twkillian@cs.toronto.edu Jayakumar Subramanian Media and Data Science Research, Adobe India jayakumar.subramanian@gmail.com Marzyeh Ghassemi Massachusetts Institute of Technology mghassem@mit.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and pretrained models to replicate the analysis (including figures) presented in this paper is located at https://github.com/microsoft/med-deadend. |
| Open Datasets | Yes | We use De D to identify medical dead-ends in a cohort of septic patients drawn from the MIMIC (Medical Information Mart for Intensive Care) III dataset (v1.4) [22, 48]. The MIMIC-III databases (DOI: 10.1038/sdata.2016.35) that support the findings of this study are publicly available through Physionet website: https://mimic.physionet.org, which facilitates reproducibility of the presented results. |
| Dataset Splits | Yes | All models are trained with 75% of the patient cohort (14,179 survivors, 1,509 nonsurvivors), validated with 5% (890 survivors, 90 nonsurvivors), and we report all results on the remaining held out 20% (2,660 survivors, 282 nonsurvivors). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | Thus, a stratified minibatch of size 64 is constructed of 62 samples from the main data, augmented with 2 samples from this additional buffer, all selected uniformly. This same minibatch structure is used for training each of the three networks. For the training details see Appendix A4 and A5. |