Abstract
Symptom diagnosis is a challenging yet profound problem in natural language processing. Most previous research focus on investigating the standard electronic medical records for symptom diagnosis, while the dialogues between doctors and patients that contain more rich information are not well studied. In this paper, we first construct a dialogue symptom diagnosis dataset based on an online medical forum with a large amount of dialogues between patients and doctors. Then, we provide some benchmark models on this dataset to boost the research of dialogue symptom diagnosis. In order to further enhance the performance of symptom diagnosis over dialogues, we propose a global attention mechanism to capture more symptom related information, and build a symptom graph to model the associations between symptoms rather than treating each symptom independently. Experimental results show that both the global attention and symptom graph are effective to boost dialogue symptom diagnosis. In particular, our proposed model achieves the state-of-the-art performance on the constructed dataset.
Highlights
With the widespread use of electronic health records (EHRs) in medical treatment, symptom diagnosis based on EHRs have received a lot of attention in the natural language processing (NLP) research community (Linder et al, 2007; Shivade et al, 2013)
We build a symptom graph to model the associations between symptoms, which further helps improve the precision of symptom inference
The results show that our proposed model with global attention significantly outperforms the bidirectional long shortterm memory network (Bi-LSTM) CRFinference model for symptom inference across all the categories
Summary
With the widespread use of electronic health records (EHRs) in medical treatment, symptom diagnosis based on EHRs have received a lot of attention in the natural language processing (NLP) research community (Linder et al, 2007; Shivade et al, 2013). Previous work on EHRs achieved great success in determining the diagnosis of clinical depression (Trinh et al, 2011), identifying community-acquired pneumonia (DeLisle et al, 2013), improving medication reconciliation (Persell et al, 2018) and infection detection (Tou et al, 2018). Patient: My kid has been born for 50 days. He has a cough, Patient: with phlegm.
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