# A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches
## 2 Medical Question Answering Systems
### Representative Medical QASs  
Several authors proposed different approaches to medical QASs. For example, [PERSON], et al. [49] developed an end-to-end biomedical QAS named SemBioNLQA, which consists of question classification, document retrieval, passage retrieval and answer extraction modules. The SemBioNLQA system takes input questions in a natural language format, then generates short and precise answers, as well as summarizing the results. [PERSON], et al. [50] presented a biomedical QAS that provides answers to multiple-choice questions while reading a given document. In their study, Question Answering for Machine Reading Evaluation (QA4 MRE) was used as a dataset with a focus on Alzheimer's disease. Several other medical QAS have been discussed in [33]. Some of the main MQA systems will be introduced in detail as follows, based on what was discussed in [33] and other MQA systems that they did not mention. (1) AskHermes  
AskHermes is an online MQA system designed to help healthcare providers find answers to health-related questions in order to take care of their patients. The goal of this system is to achieve a robust semantic analysis of complex clinical questions, resulting in question-focused summaries as answers [51]. Queries in natural language are allowed without the need for many representations. Furthermore, this system can answer complex questions with the help of structured domain-specific ontologies. The authors in [33] investigated the AskHermes tasks and categorized them into five modules: data sources and preprocessing, question analysis, document processing, passage retrieval, and summarization and answer presentation, as shown in Figure 3. In the data sources and preprocessing modules, the medical literature and articles are collected and indexed. The collected data are then preprocessed to retain the semantic content. In the question analysis module, questions are classified into several topics for making easy information retrieval. The binary classifier (yes, no) is used to avoid a question being assigned to multiple topics. In the document retrieval module, a designed probabilistic BM25 model has been analytically adjusted for retrieval. The final module of summarization and answer presentation is divided into two sub-sections: the first is topical clustering, ranking, and hierarchical answer presentation; the second is redundancy removal based on the longest common substring. (2) MedQA  
MedQA is an MQA system proposed to learn answering questions in clinical medicine using knowledge in a large-scale document collection [52]. The primary purpose of MedQA is to answer real-world questions with large-scale reading comprehension, which read individual documents and integrate information across several documents in a timely manner. MedQA's architecture has the following modules: question classification, query generalization, document retrieval, answer extraction, and text summarization. Question classification categorizes the posed question into a question type. After identifying the question type, the query generalization module evaluates the question to extract noun phrases as query terms. The document retrieval module uses query terms to retrieve documents from the locally indexed MEDLINE collection or the Web documents [53]. After retrieving the same document, which contains the answer,the answer extraction module  
Figure 3: AskHermes’s system architecture [51]. detects sentences that provide answers to questions. Finally, in the text summarization module, redundant sentences are removed, then the summary is presented to the user. (3) HONQA  
HONQA is a medical question-answering system designed by the Health On the Net Foundation (HON) in two different versions: English and French [33]. This system uses two corpora to answer health-related questions. These health-related questions were extracted from health experts' discussions on the internet and in the forums of health professionals.