Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning
Authors: Hao-Cheng Kao, Kai-Fu Tang, Edward Chang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies on a simulated dataset show that our proposed model drastically improves disease prediction accuracy by a significant margin (for top-1 prediction, the improvement margin is 10% for 50 common diseases1 and 5% when expanding to 100 diseases). |
| Researcher Affiliation | Industry | Hao-Cheng Kao HTC Research & Healthcare haocheng kao@htc.com Kai-Fu Tang HTC Research & Healthcare tkevin tang@htc.com Edward Y. Chang HTC Research & Healthcare edward chang@htc.com |
| Pseudocode | Yes | Algorithm 1: Training Master Model |
| Open Source Code | No | The paper refers to the use of 'Deep Q Open AI Platform (Zou et al. 2017)' and 'Deep Q Tricorder (Chang et al. 2017)' but does not provide any explicit statement or link to the source code for the methodology described in this paper. |
| Open Datasets | Yes | To evaluate our algorithm, we generated simulated data based on Sym CAT s symptom-disease database (AHEAD Research Inc 2017) composed of 801 diseases. |
| Dataset Splits | No | The paper describes generating training data and sampling test data ('We sampled 10,000 simulated patients for each disease in the testing dataset for each task') but does not specify a separate validation dataset split (e.g., by percentage or count). |
| Hardware Specification | No | The paper mentions using 'Deep Q Open AI Platform' for training management but does not specify any hardware details like GPU/CPU models, memory, or specific computing environments used for the experiments. |
| Software Dependencies | No | The paper mentions using 'Deep Q Open AI Platform' and concepts like 'DQN', but does not provide specific version numbers for any software dependencies, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | In all tasks, we used ten million mini-batches, each consisting of 128 samples for training. |