Query: Health Question Answering

title: On the Role of Question Summarization and Information Source Restriction in Consumer Health Question Answering.
abstract: Despite the recent developments in commercial Question Answering (QA) systems, medical QA remains a challenging task. In this paper, we study the factors behind the complexity of consumer health questions and potential improvement tracks. In particular, we study the impact of information source quality and question conciseness through three experiments. First, an evaluation of a QA method based on a Question Answer collection created from trusted NIH resources, which outperformed the best results of the medical LiveQA challenge with an average score of 0.711. Then, an evaluation of the same approach using paraphrases and summaries of the test questions, which achieved an average score of 1.125. Our results provide an empirical evidence supporting the key role of summarization and reliable information sources in building efficient CHQA systems. The latter finding on restricting information sources is particularly intriguing as it contradicts the popular tendency ofrelying on big data for medical QA.

title: Resource and Response Type Classification for Consumer Health Question Answering
abstract: Health question answering systems often depend on the initial step of question type classification. Practitioners face several modeling choices for this component alone. We evaluate the effectiveness of different modeling choices in both the embeddings and architectural hyper parameters of the classifier. In the process, we achieve improved performance over previous methods, achieving a new best 5 fold accuracy of 85.3% on the GARD dataset. The contribution of this work is to evaluate the performance of sentence classification methods on the task of consumer health question type classification and to contribute a dataset of 2,882 medical questions annotated for question type.

title: Corpus development for Indonesian consumer health question answering system
abstract: Web based question answering services facilitate users to seek more personalized health related information. However, the users sometimes have to wait for a while until their questions to be answered. Automatic question answering research can assist the system to generate or retrieve the answer to users. Our work was a pioneering study on consumer health question answering for Bahasa Indonesia. We built a corpus of 86,731 consumer health questions, collected from 5 different websites. As part of annotation, we classify the sub topics for each question, which corresponds to medical specialization. Question sub topic classification is completed by two complementary approaches: dictionary based and machine learning based.

title: CMU LiveMedQA at TREC 2017 LiveQA: A Consumer Health Question Answering System
abstract: In this paper, we present LiveMedQA, a question answering system that is optimized for consumer health question. On top of the general QA system pipeline, we introduce several new features that aim to exploit domain specific knowledge and entity structures for better performance. This includes a question type/focus analyzer based on deep text classification model, a tree based knowledge graph for answer generation and a complementary structure aware searcher for answer retrieval. LiveMedQA system is evaluated in the TREC 2017 LiveQA medical subtask, where it received an average score of 0.356 on a 3 point scale. Evaluation results revealed 3 substantial drawbacks in current LiveMedQA system, based on which we provide a detailed discussion and propose a few solutions that constitute the main focus of our subsequent work.

title: A Knowledge Based Health Question Answering System
abstract: With the quickly increasing of the Question Answering (QA) corpus, the health QA systems provide a convenient way for patients to provide instant service, and the effectiveness of the answer is a very important and challenging problem to be solved. Therefore, this paper proposes a solution based on medical knowledge base. In the process of generating answers, we utilize the entity set provided by medical knowledge base to calculate the correlation between answers and questions, at the same time we make use of the entities provided by relationships in the knowledge base but not appearing in the answers. Experiment conducted on a real data set in our HealthQA system shows that our method can effectively improve the relevance and accuracy of answer matching by using the medical knowledge base.

title: An open architecture for messaging based consumer health question answering
abstract: We are investigating question answering systems for different applications within consumer health, including health promotion and health information attainment. Our work focuses on the interaction environment afforded by text and multimedia (SMS and MMS) messaging because of the growing popularity of text messaging and because of the prevalence of devices that support it. The development of content for messaging may also better serve populations with diverse cognitive competencies, as the shorter blocks of text that are required for messaging may be easier for readers to process, especially if they have deficits in memory or vision. One of the challenges of this work is to provide an architecture that will allow our partners in public health and medicine to review or contribute to the domain content and that will allow us to use that content to drive a dialog engine, with little or no manual intervention. In this paper, we describe how we have adapted the TuTalk dialog system to create a new messaging based question answering system to promote healthy birth outcomes among low income, expectant women in Milwaukee.

title: Research Design: Understanding Semantic Relationships in Health Question Answering Behavior in Social Context
abstract: This poster introduces a research design focusing on understanding the semantic relationships in socially generated health information in social Q&A. A total of 164,279 questions and 413,900 answers posted during 2013 will be used for text mining and content analysis in this study. This poster explains the process of using the mixed methods for identifying the semantic relationships between major concepts in the questions and the answers.

title: Consumer health information and question answering: helping consumers find answers to their health related information needs
abstract: Consumers increasingly turn to the internet in search of health related information; and they want their questions answered with short and precise passages, rather than needing to analyze lists of relevant documents returned by search engines and reading each document to find an answer. We aim to answer consumer health questions with information from reliable sources.  We combine knowledge based, traditional machine and deep learning approaches to understand consumers' questions and select the best answers from consumer oriented sources. We evaluate the end to end system and its components on simple questions generated in a pilot development of MedlinePlus Alexa skill, as well as the short and long real life questions submitted to the National Library of Medicine by consumers.  Our system achieves 78.7% mean average precision and 87.9% mean reciprocal rank on simple Alexa questions, and 44.5% mean average precision and 51.6% mean reciprocal rank on real life questions submitted by National Library of Medicine consumers.  The ensemble of deep learning, domain knowledge, and traditional approaches recognizes question type and focus well in the simple questions, but it leaves room for improvement on the real life consumers' questions. Information retrieval approaches alone are sufficient for finding answers to simple Alexa questions. Answering real life questions, however, benefits from a combination of information retrieval and inference approaches.  A pilot practical implementation of research needed to help consumers find reliable answers to their health related questions demonstrates that for most questions the reliable answers exist and can be found automatically with acceptable accuracy.

title: CO Search: COVID 19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization
abstract: The COVID 19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID 19 and SARS CoV 2 related publications across scientific disciplines. As of May 2020, 128,000 coronavirus related publications have been collected through the COVID 19 Open Research Dataset Challenge. Here we present CO Search, a retriever ranker semantic search engine designed to handle complex queries over the COVID 19 literature, potentially aiding overburdened health workers in finding scientific answers during a time of crisis. The retriever is built from a Siamese BERT encoder that is linearly composed with a TF IDF vectorizer, and reciprocal rank fused with a BM25 vectorizer. The ranker is composed of a multi hop question answering module, that together with a multi paragraph abstractive summarizer adjust retriever scores. To account for the domain specific and relatively limited dataset, we generate a bipartite graph of document paragraphs and citations, creating 1.3 million (citation title, paragraph) tuples for training the encoder. We evaluate our system on the data of the TREC COVID information retrieval challenge. CO Search obtains top performance on the datasets of the first and second rounds, across several key metrics: normalized discounted cumulative gain, precision, mean average precision, and binary preference.

title: Predicting the quality of online health expert question answering services with temporal features in a deep learning framework
abstract: Currently, online health expert question answering (HQA) services are the most popular services for health consumers to search online health information due to their convenience and the cost effectiveness. However, one main challenge of these services is that the quality of the answers posted by physicians varies over a large spectrum. Physicians may not have sufficient time to answer the questions or just want to post advertisements in the answers. To determine high quality answers, we can apply classification models on them. However, it is critical to include the corresponding features in the classification models. To further enhance the model performance, we propose a set of novel temporal features based on the characteristics of HQA services. Moreover, we propose a deep learning framework, referred to as a collaborative decision convolutional neural network (CDCNN) to learn the semantic non linear features embedded in the data. By exploiting the learned non linear semantic features, we can apply the factorization machines (FM) to obtain the quality score of an answer. Finally, we conduct extensive experiments to show the advantages of the proposed framework.