# A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches
## 2 Medical Question Answering Systems
### Representative Medical QASs
The user can choose the field of research in which he/she wants to ask a question, such as websites accredited by HON or all the websites, irrespective of accreditation. Figure 4 demonstrates a detailed process, which the HONQA system follows to answer questions asked by users. (4) EAGLi  
EAGLi is the biomedical question-answering and information-retrieval interface of the EAGL project [19]. This system uses MEDLINE's abstracts as an information source. EAGLi can answer definition questions with the help of Medical Subject Headings. However, EAGLi is very slow, and cannot support high-level traffic. The system answer questions by displaying a list of possible answers alongside the confidence level of answers. EAGLi interface is easy and clear, and provides the possibility of either using the PubMed search or a special search engine while the user asks a question. (5) MiPACQ  
The Multi-source Integrated Platform for Answering Clinical Questions (MiPACQ) is a QA pipeline incorporating a diversity of IR and NLP systems into an extensible QAS [52]. It uses a human-annotated evaluation dataset based on the Medpedia health and medical encyclopedia. As illustrated in Figure 5, the system can receive questions from clinicians using a web-based interface, and then semantic annotation is used to process questions. The IR system is then applied to retrieve candidate answer paragraphs; after the re-ranking system re-orders the paragraph, the results are finally presented to the user [33]. Figure 4: The architecture of HONQA [33]. (6) CHiQA  
The Consumer Health Question Answering System (CHiQA) is an application system, which can find the health-related questions for the consumers. CHiQA used a hybrid answer retrieval strategy, which combines a free text search with a structured search based on the focus and type of information. Therefore, this system can obtain good results [37]. The CHiQA system is made of a back end and a responsive web interface. The back end comprises a preprocessing module, a question-understanding module, two complementary answer-retrieval modules, and an answer-generation module. Figure 6 exhibits a generalized architecture of CHiQA and gives more details of different QA tasks such as query formulation, query classification, answer ranking, and answer extraction. Figure 5: The architecture of MiPACQ, where ClearTK is a framework used to develop NLP and machine learning components [52]. Figure 6: CHiQA’s system architecture [37]. (7) CLINIQA  
The CLINIcal Question Answering system (CLINIQA) is an automatic clinical question answering system, developed to answer the questions medical practitioners face in their daily work [54]. The system is made of four major sections: question classification, query formulation, answer extraction, and answer ranking (see Figure 7). CLINIQA system analyses semantically medical documents and questions with the help of the Unified Medical Language System (UMLS). Besides this, the system makes use of the machine learning algorithms to identify the question focus, classify documents, and select the answer. Once a clinical question is given to the system, CLINIQA retrieves highly reliable answers from existing medical research documents. The PUBMED abstracts are also used to extract and locate the most relevant answers. A comparison of the representative medical QASs introduced above is listed in Table 1.  
In addition, other biomedical QAS have been studied, which are all publicly accessible online. For example, [PERSON], et al. [55] created a biomedical QAS based on Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT).