Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning
Authors: Chang Lu, Chandan Reddy, Ping Wang, Yue Ning
NeurIPS 2023 | Venue PDF | 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. |