Contextual question answering for the health domain

Studies have shown that natural language interfaces such as question answering and conversational systems allow information to be accessed and understood more easily by users who are unfamiliar with the nuances of the delivery mechanisms (e.g., keyword-based search engines) or have limited literacy in certain domains (e.g., unable to comprehend health-related content due to terminology barrier). In particular, the increasing use of the web for health information prompts us to reexamine our existing delivery mechanisms. We present enquireMe, which is a contextual question answering system that provides lay users with the ability to obtain responses about a wide range of health topics by vaguely expressing at the start and gradually refining their information needs over the course of an interaction session using natural language. enquireMe allows the users to engage in 'conversations' about their health concerns, a process that can be therapeutic in itself. The system uses community-driven question-answer pairs from the web together with a decay model to deliver the top scoring answers as responses to the users' unrestricted inputs. We evaluated enquireMe using benchmark data from WebMD and TREC to assess the accuracy of system-generated answers. Despite the absence of complex knowledge acquisition and deep language processing, enquireMe is comparable to the state-of-the-art question answering systems such as START as well as those interactive systems from TREC

An ontology for clinical questions about the contents of patient notes

AbstractObjectiveMany studies have been completed on question classification in the open domain, however only limited work focuses on the medical domain. As well, to the best of our knowledge, most of these medical question classifications were designed for literature based question and answering systems. This paper focuses on a new direction, which is to design a novel question processing and classification model for answering clinical questions applied to electronic patient notes.MethodsThere are four main steps in the work. Firstly, a relatively large set of clinical questions was collected from staff in an Intensive Care Unit. Then, a clinical question taxonomy was designed for question and answering purposes. Subsequently an annotation guideline was created and used to annotate the question set. Finally, a multilayer classification model was built to classify the clinical questions.ResultsThrough the initial classification experiments, we realized that the general features cannot contribute to high performance of a minimum classifier (a small data set with multiple classes). Thus, an automatic knowledge discovery and knowledge reuse process was designed to boost the performance by extracting and expanding the specific features of the questions. In the evaluation, the results show around 90% accuracy can be achieved in the answerable subclass classification and generic question templates classification. On the other hand, the machine learning method does not perform well at identifying the category of unanswerable questions, due to the asymmetric distribution.ConclusionsIn this paper, a comprehensive study on clinical questions has been completed. A major outcome of this work is the multilayer classification model. It serves as a major component of a patient records based clinical question and answering system as our studies continue. As well, the question collections can be reused by the research community to improve the efficiency of their own question and answering systems

Improving Retrieval of Information from the Internet

To improve the quality of the search result returned by the internet which makes users have to look through a huge amount of links for the real answers, we utilized the high quality links Google produces and the Information Retrieval technology to implement a Question Answering (QA) system. This system analyzes and downloads the text contents from the relevant web pages Google searches based on the users\u27 questions to build a dynamic knowledge collection; retrieves the relevant passages from the collection and sends the ranked passages back. The users can further refine their questions in the query refinement step for the better answers. A novel search strategy was designed to detect the semantic connections between the question and the documents. This answer retrieval also involves the TF-IDF algorithm and Vector Space Model for the document indexing. We have modified the original Cosine Coefficient Similarity Measurement to rank the candidate answers