Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning
Authors: Chang Lu, Chandan Reddy, Ping Wang, Yue Ning
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate that our proposed training strategies effectively enhance the performance of existing ICD coding methods. |
| Researcher Affiliation | Academia | Department of Computer Science, Stevens Institute of Technology Department of Computer Science, Virginia Tech |
| Pseudocode | Yes | Algorithm 1: Section Title Extraction |
| Open Source Code | Yes | The code of the proposed DF-IAPF method and training strategies can be found at: https://github.com/Lu Chang-CS/semi-structured-icd-coding. |
| Open Datasets | Yes | The MIMIC-III [11] dataset is a popular publicly available EHR dataset |
| Dataset Splits | Yes | The detailed training/dev/test dataset statistics for each task are listed in Table 1. |
| Hardware Specification | Yes | All programs are executed using a machine with Python 3.9.3, CUDA 11.7, an Intel i9-11900K CPU, 64GB memory, and an NVIDIA RTX 3090 GPU. |
| Software Dependencies | Yes | All programs are executed using a machine with Python 3.9.3, CUDA 11.7, an Intel i9-11900K CPU, 64GB memory, and an NVIDIA RTX 3090 GPU. |
| Experiment Setup | Yes | For contrastive pre-training, the batch size is 16, the learning rate is 5 10 4, the optimizer is Adam W, and the epoch number is 20. For the masked section training, we set γ to 0.2 for MIMIC-full prediction and 0.3 for MIMIC-50/MIMIC-rare-50 prediction. |