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..
Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning
Authors: Hao-Cheng Kao, Kai-Fu Tang, Edward Chang
AAAI 2018 | Venue PDF | 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 EMAIL Kai-Fu Tang HTC Research & Healthcare tkevin EMAIL Edward Y. Chang HTC Research & Healthcare edward EMAIL |
| 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. |