Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning
Authors: A. Tuan Nguyen, Hyewon Jeong, Eunho Yang, Sung Ju Hwang9081-9091
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate our Temporal Probabilistic Asymmetric Multi-Task Learning (TP-AMTL) model on four clinical risk prediction datasets against multiple baselines, which our model significantly outperforms without any sign of negative transfer. The results show that our model obtains significant improvements over strong multi-task learning baselines with no negative transfer on any of the tasks (Table 2). |
| Researcher Affiliation | Collaboration | 1 School of Computing, Korea Advanced Institute of Science and Technology, 2 AI Graduate School, Korea Advanced Institute of Science and Technology 3 Aitrics, 4 Department of Computer Science, University of Oxford |
| Pseudocode | No | The paper describes its proposed methods in detail using mathematical equations and textual explanations but does not provide a formal pseudocode block or an algorithm label. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We compile a dataset out of the MIMIC III dataset (Johnson et al. 2016)... Physio Net (Citi and Barbieri 2012)... We use a variant (Ude M 2014) of the MNIST dataset (Le Cun and Cortes 2010)... |
| Dataset Splits | Yes | After pre-processing, approximately 2000 data points with a sufficient amount of features were selected, which was randomly split to approximately 1000/500/500 for training/validation/test. ... We use a random split of 2800/400/800 for training/validation/test. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware specifications (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper refers to various deep learning models and architectures (e.g., LSTM, Transformer, RETAIN), but it does not specify version numbers for any software dependencies or programming languages used. |
| Experiment Setup | No | The paper states: 'Please see the supplementary file for descriptions of the baselines, experimental details, and the hyper-parameters used.' This indicates that detailed experimental setup information, including hyperparameters, is deferred to supplementary materials rather than being explicitly provided in the main text. |