Change Matters: Medication Change Prediction with Recurrent Residual Networks
Authors: Chaoqi Yang, Cao Xiao, Lucas Glass, Jimeng Sun
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated MICRON on real inpatient and outpatient datasets. MICRON achieves 3.5% and 7.8% relative improvements over the best baseline in F1 score, respectively. We evaluate MICRON against several baselines in both inpatient and outpatient datasets. |
| Researcher Affiliation | Collaboration | Chaoqi Yang1 , Cao Xiao2 , Lucas Glass2 and Jimeng Sun1 1Department of Computer Science, University of Illinois Urbana Champaign 2Analytics Center of Excellence, IQVIA |
| Pseudocode | No | The paper describes the model architecture and algorithms using mathematical equations and descriptive text, but it does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Codes and baselines can be found here1. 1https://github.com/ycq091044/MICRON |
| Open Datasets | Yes | We consider a benchmark inpatient dataset: MIMIC-III [Johnson et al., 2016] |
| Dataset Splits | No | The paper mentions using a 'validation set' for threshold selection ('Specifically, we load the pre-trained MICRON on the validation set'), but it does not provide specific details about the size or percentages of the train/validation/test splits. |
| Hardware Specification | No | The paper mentions training time and model size, but it does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper describes the software architecture of MICRON and mentions using Sigmoid functions and neural networks, but it does not specify any particular software libraries, frameworks, or their version numbers (e.g., PyTorch 1.x, TensorFlow 2.x). |
| Experiment Setup | Yes | The loss functions are combined by weighted sum, Ltotal = λ1L(t) rec + λ2L(t) ddi + λ3 γL(t) bce + (1 γ)L(t 1) bce + λ4 γL(t) multi + (1 γ)L(t 1) multi , where λi, i = 1, 2, 3, 4, are different weights for four types of loss functions, and γ is introduced to balance two consecutive visits. During the training, one batch contains all visits of one patient, and the loss is back-propagated after each batch. In the paper, we treat the weights as hyperparameters. |