Extracting Topical Phrases from Clinical Documents
Authors: Yulan He
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on patients discharge summaries show that the proposed approach outperforms the state-of-the-art topical phrase extraction model on both perplexity and topic coherence measure and finds more interpretable topics. |
| Researcher Affiliation | Academia | Yulan He School of Engineering and Applied Science Aston University, UK y.he@cantab.net |
| Pseudocode | No | The paper includes a plate diagram (Figure 2) and describes the generative process textually, but no explicit pseudocode or algorithm block is provided. |
| Open Source Code | No | The paper mentions using an 'off-the-shelf tool called Med Tagger' and provides its URL (http://www.ohnlp.org/index.php/Med Tagger), but there is no explicit statement or link for the open-source code of the authors' proposed method (TPM). |
| Open Datasets | Yes | We use the clinical record data released as part of the i2b2 Natural Language Processing Challenges for Clinical Records (Uzuner et al. 2010). |
| Dataset Splits | No | The paper mentions using '10% of the data as a held-out set' for testing, but does not specify a separate validation set split or detailed split percentages/counts for all data partitions. |
| Hardware Specification | No | No specific hardware details (such as GPU or CPU models, or memory specifications) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions software like MALLET and Med Tagger, but does not provide specific version numbers for these or any other key software dependencies needed for replication. |
| Experiment Setup | Yes | We train TPM with a maximum of 1,000 Gibbs sampling iterations and stop if the total log-likelihood converges. We optimise all the hyperparameters including α and an 1, bn 1 for different context length n in HPYP every 50 iterations. |