HBDA-An-Online-Question-Answering-System-for-Medical-Questions

System description

The system can be divided into two parts. The first part is the knowledge graph question answering and the second part is if the knowledge graph cannot find the answer of the user inputted question, the second part will help to retrieve the top k most related answers by computing the medical question similarity from a medical question answer pair dataset.

Here are some useful reference links.

Knowledge graph establishment refer the following link

https://github.com/liuhuanyong/QASystemOnMedicalKG

Google English words pre-train model: GoogleNews-vectors-negative300.bin.gz

The pre-train model will be used to load word embedding before training the BiLSTM+Attention model and HBDA model.

https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing

BERT

https://github.com/google-research/bert

BiLSTM+Attention

https://zhuanlan.zhihu.com/p/31638132

https://github.com/likejazz/Siamese-LSTM

https://github.com/LuJunru/Sentences_Pair_Similarity_Calculation_Siamese_LSTM

AttentionLayer is referred from the following link

https://github.com/uhauha2929/examples/blob/master/Hierarchical%20Attention%20Networks%20.ipynb

Dataset

Medical knowledge dataset collected from the following medical website

https://www.medicinenet.com/medterms-medical-dictionary/article.htm

https://www.nhsinform.scot/illnesses-and-conditions/a-to-z

Model train_dev_test dataset are filter out the medical related questions from Quora question pair dataset.

https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs

Medical question and answer pair dataset is referred from the following link.

https://github.com/LasseRegin/medical-question-answer-data

Model eval-accuracy comparison

The total number of medical related data from Quora dataset is nearly 70000, but we randomly pick the 10000 as the (train/dev/test) dataset.

The number distribution of train: dev: test = 6:2:2

Model	Average Eval_accuracy by three times	Range of change
BERT baseline model	0.7686	(-0.0073, +0.0057)
HDBA model	0.8146	(-0.0082, +0.0098)
Bi-LSTM + Attention model	0.8043	(-0.0103, +0.0062)

The scale of knowledge graph about 700 diseases. For each disease, there exists symptom, accompany_disease, prevent_way, cure_way and totally 6 entities.

System architecture

Web GUI manager

Siamese Hierarchical BiLSTM Word Attention Manhattan Distance model

Reference papers

Hierarchical attention networks for document classification (https://www.aclweb.org/anthology/N16-1174)

Siamese Recurrent Architectures for Learning Sentence Similarity (https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPaper/12195)

Disease symptom entity extraction & User intention recognition

Knowledge graph answer selection

Question_answer_pair_answer_selection

Paper link

HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention

Citation

@inproceedings{bao2020hhh,
  title={HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention},
  author={Bao, Qiming and Ni, Lin and Liu, Jiamou},
  booktitle={Proceedings of the Australasian Computer Science Week Multiconference},
  pages={1--10},
  year={2020}
}

Acknowledgement

This research was supported by summer scholarship funding from the Precision Driven Health research partnership.

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